On the other hand, having all that critical data in one place can be an attractive target for hackers, who continuously probe defenses looking for weaknesses, and the penalties for data breaches can be enormous. IT security teams need a system that allows for security to differentiate between different categories of data to isolate and secure it against misuse.

Data lakes provide the current solution to maximizing data availability and protection. For large enterprises, their data managers and data security teams can choose from many different data lake vendors to suit their needs.

However, while anyone can create a data lake, not everyone will have the resources to achieve scale, extract value, and protect their resources on their own. Fortunately, vendors offer robust tools that permit smaller teams to obtain the benefits of a data lake without requiring the same resources to manage them.

See the Top Data Lake Solutions

What are Data Lakes?

Data lakes create a single repository for an organization’s raw data. Data feeds bring in data from databases, SaaS platforms, web crawlers, and even edge devices such as security cameras or industrial heat pumps.

Similar to a giant hard drive, data lakes also can incorporate folder structures and apply security to specific folders to limit access, read/write privileges, and deletion privileges to users and applications. However, unlike a hard drive, data lakes should be able to grow in size forever and never require a deletion of data because of space restrictions.

Data lakes support all data types, scale automatically, and support a wide range of analytics, from built-in features to external tools supported by APIs. Analytic tools can perform metadata or content searches or categorize data without changing the underlying data itself.

Self-service Data Lake Tools

Technically, if a company can fit all of its data onto a single hard drive, that is the equivalent of a data lake. However, most organizations have astronomically more data than that, and large enterprises need huge repositories.

Some organizations create their own data lakes in their own data centers. This endeavor requires much more investment in:

• Capital expense: buildings, hardware, software, access control systems
• Operational expense: electrical power, cooling systems, high-capacity internet/network connections, maintenance and repair costs
• Labor expense: IT and IT security employees to maintain the hardware, physical security

Vendors in this category provide tools needed for a team to create their own data lake. Organizations choosing these options will need to supply more time, expenses, and expertise to build, integrate, and secure their data lakes.

Apache: Hadoop & Spark

The Apache open-source projects provide the basis for many cloud computing tools. To create a data lake, an organization could combine Hadoop and Spark to create the base infrastructure and then consider related projects or third-party tools in the ecosystem to build out capabilities.

Apache Hadoop provides scalable distributed processing of large data sets with unstructured or structured data content. Hadoop provides the storage solution and basic search and analysis tools for data.

Apache Spark provides a scalable open-source engine that batches data, streams data, performs SQL analytics, trains machine learning algorithms, and performs exploratory data analysis (EDA) on huge data sets. Apache Spark provides deep analysis tools for more sophisticated examinations of the data than available in the basic Hadoop deployment.

Hewlett Packard Enterprise (HPE) GreenLake

The HPE GreenLake service provides pre-integrated hardware and software that can be deployed in internal data centers or in colocation facilities. HPE handles the heavy lifting for the deployment and charges clients based upon their usage.

HPE will monitor usage and scale the deployment of the Hadoop data lake based upon need and provide support for design and deployment of other applications. This service turbo-charges a typical internal-deployment of Hadoop by outsourcing some of the labor and expertise to HPE.

Cloud Data Lake Tools

Cloud data lake tools provide the infrastructure and the basic tools needed to provide a turn-key data lake. Customers use built-in tools to attach data feeds, storage, security, and APIs to access and explore the data.

After selecting options, some software packages will already be integrated into the data lake upon launch. When a customer selects a cloud option, it will immediately be ready to intake data and will not need to wait for shipping, hardware installation, software installation, etc.

However, in an attempt to maximize the customizability of the data lake, these tools tend to push more responsibility to the customer. Connecting data feeds, external data analytics, or applying security will be more manual a process than compared with full-service solutions.

Some data lake vendors provide data lakehouse tools to attach to the data lake and provide an interface for data analysis and transfer. There may also be other add-on tools available that provide the features available in full-service solutions.

Customers can choose either the bare-bones data lake and then do more heavy lifting or pay extra for features that create the more full-service version. These vendors also tend not to encourage multi-cloud development and focus on driving more business towards their own cloud platforms.

Amazon Web Services (AWS) Data Lake

AWS provides enormous options for cloud infrastructure. Their data lake offering provides an automatically-configured collection of core AWS services to store and process raw data.

Incorporated tools permit users or apps to analyze, govern, search, share, tag, and transform subsets of data internally or with external users. Federated templates integrate with Microsoft Active Directory to incorporate existing data segregation rules already deployed internally within a company.

Google Cloud

Google offers data lake solutions that can house an entire data lake or simply help process a data lake workload from an external source (typically internal data centers). Google Cloud claims that moving from an on-premises Hadoop deployment to a Google Cloud-hosted deployment can lower costs by 54%.

Google offers its own BigQuery analytics that captures data in real-time using a streaming ingestion feature. Google supports Apache Spark and Hadoop migration, integrated data science and analytics, and cost management tools.

Microsoft Azure

Microsoft’s Azure Data Lake solution deploys Apache Spark and Apache Hadoop as fully-managed cloud offerings as well as other analytic clusters such as Hive, Storm, and Kafka. Azure data lake includes Microsoft solutions for enterprise-grade security, auditing, and support.

Azure Data Lake integrates easily with other Microsoft products or existing IT infrastructure and is fully scalable. Customers can define and launch a data lake very quickly and use their familiarity with other Microsoft products to intuitively navigate through options.

See the Top Big Data Storage Tools

Full-service Data Lake Tools

Full-service data lake vendors add layers of security, user-friendly GUIs, and constrain some features in favor of ease-of-use. These vendors may provide additional analysis features built into their offerings to provide additional value.

Some companies cannot or strategically choose not to store all of their data with a single cloud provider. Other data managers may simply want a flexible platform or might be trying to stitch together data resources from acquired subsidiaries that used different cloud vendors.

Most of the vendors in this category do not offer data hosting and act as agnostic data managers and promote using multi-cloud data lakes. However, some of these vendors offer their own cloud solutions and offer a fully integrated full-service offering that can access multiple clouds or transition the data to their fully-controlled platform.

Cloudera Cloud Platform

Cloudera’s Data Platform provides a unifying software to ingest and manage a data lake potentially spread across public and private cloud resources. Cloudera optimizes workloads based on analytics and machine learning as well as provides integrated interfaces to secure and govern platform data and metadata with integrated interfaces.

Cohesity

Cohesity’s Helios platform offers a unified platform that provides data lake and analysis capabilities. The platform may be licensed as a SaaS solution, as software for self-hosted data lakes, or for partner-managed data lakes.

Databricks

Databricks provides data lake house and data lake solutions built on open source technology with integrated security and data governance. Customers can explore data, build models collaboratively, and access preconfigured ML environments. Databricks works across multiple cloud vendors and manages the data repositories through a consolidated interface.

Domo

Domo provides a platform that enables a full range of data lake solutions from storage to application development. Domo augments existing data lakes or customers can host data on the Domo cloud.

IBM

IBM cloud-based data lakes can be deployed on any cloud and builds governance, integration, and virtualization into the core principles of their solution. IBM data lakes can access IBM’s pioneering Watson AI for analysis as well as access many other IBM tools for queries, scalability, and more.

Oracle

Oracle’s Big Data Service deploys a private version of Cloudera’s cloud platform and integration with their own Data Lakehouse solution and the Oracle cloud platform. Oracle builds on their mastery of database technology to provide strong tools for data queries, data management, security, governance, and AI development.

Snowflake

Snowflake provides a full service data lake solution that can integrate storage and computing solutions from AWS, Microsoft, or Google. Data managers do not need to know how to set up, maintain, or support servers and networks and therefore can use Snowflake without previously establishing any cloud databases.

Also read: Snowflake vs. Databricks: Big Data Platform Comparison

Choosing a Data Lake Strategy and Architecture

Data analytics continues to rise in importance as companies find more uses for wider varieties of data. Data lakes provide an option to store, manage, and analyze all data sources for an organization even as they try to figure out what is important and useful.

This article provides an overview of different strategies to deploy data lakes and different technologies available. The list of vendors is not comprehensive and new competitors are constantly entering the market.

Don’t start by selecting a vendor. First start with an understanding of company resources available to support a data lake.

If the available resources are small, the company will likely need to pursue a full-service option over an in-house data center. However, many other important characteristics play a role in determining the optimal vendor, such as:

• Business use case
• AI compatibility
• Searchability
• Compatibility with data lakehouse or other data searching tools
• Security
• Data governance

Once established, data lakes can be moved, but this could be a very expensive proposition since most data lakes will be enormous. Organizations should take their time and try test runs on a smaller scale before they commit fully to a single vendor or platform.

Read next: 10 Top Data Companies

The post Data Lake Strategy Options: From Self-Service to Full-Service appeared first on IT Business Edge.

Enough ink has been spilled on the topic to fill libraries, but this article is meant to distill some of the key arguments, viewpoints, and general information related to facial recognition systems and the impacts they can have on our privacy today.

What Are Facial Recognition Systems?

The actual technology behind FRS and who develops them can be complicated. It’s best to have a basic idea of how these systems work before diving into the ethical and privacy-related concerns related to using them.

How Do Facial Recognition Systems Work?

On a basic level, facial recognition systems operate on a three-step process. First, the hardware, such as a security camera or smartphone, records a photo or video of a person.

That photo or video is then fed into an AI program, which then maps and analyzes the geometry of a person’s face, such as the distance between eyes or the contours of the face. The AI also identifies specific facial landmarks, like forehead, eye sockets, eyes, or lips.

Finally, all these landmarks and measurements come together to create a digital signature which the AI compares against its database of digital signatures to see if there is a match or to verify someone’s identity. That digital signature is then stored on the database for future reference.

Read More At: The Pros and Cons of Enlisting AI for Cybersecurity

Use Cases of Facial Recognition Systems

A technology like facial recognition is broadly applicable to a number of different industries. Two of the most obvious are law enforcement and security. 

With facial recognition software, law enforcement agencies can track suspects and offenders unfortunate enough to be caught on camera, while security firms can utilize it as part of their access control measures, checking people’s faces as easily as they check people’s ID cards or badges.

Access control in general is the most common use case for facial recognition so far. It generally relies on a smaller database (i.e. the people allowed inside a specific building), meaning the AI is less likely to hit a false positive or a similar error. Plus, it’s such a broad use case that almost any industry imaginable could find a reason to implement the technology.

Facial recognition is also a hot topic in the education field, especially in the U.S. where vendors pitch facial recognition surveillance systems as a potential solution to the school shootings that plague the country more than any other. It has additional uses in virtual classroom platforms as a way to track student activity and other metrics.

In healthcare, facial recognition can theoretically be combined with emergent tech like emotion recognition for improved patient insights, such as being able to detect pain or monitor their health status. It can also be used during the check-in process as a no-contact alternative to traditional check-in procedures.

The world of banking saw an increase in facial recognition adoption during the COVID-19 pandemic, as financial institutions looked for new ways to safely verify customers’ identities.

Some workplaces already use facial recognition as part of their clock-in-clock-out procedures. It’s also seen as a way to monitor employee productivity and activity, preventing folks from “sleeping on the job,” as it were. 

Companies like HireVue were developing software using facial recognition that can determine the hireability of prospective employees. However, it discontinued the facial analysis portion of its software in 2021. In a statement, the firm cited public concerns over AI and a growing devaluation of visual components to the software’s effectiveness.

Retailers who sell age-restricted products, such as bars or grocery stores with liquor licenses, could use facial recognition to better prevent underaged customers from buying these products.

Who Develops Facial Recognition Systems?

The people developing FRS are many of the same usual suspects who push other areas of tech research forward. As always, academics are some of the primary contributors to facial recognition innovation. The field was started in academia in the 1950s by researchers like Woody Bledsoe.

In a modern day example, The Chinese University of Hong Kong created the GaussianFace algorithm in 2014, which its researchers reported had surpassed human levels of facial recognition. The algorithm scored 98.52% accuracy compared to the 97.53% accuracy of human performance.

In the corporate world, tech giants like Google, Facebook, Microsoft, IBM, and Amazon have been just some of the names leading the charge.

Google’s facial recognition is utilized in its Photos app, which infamously mislabeled a picture of software engineer Jacky Alciné and his friend, both of whom are black, as “gorillas” in 2015. To combat this, the company simply blocked “gorilla” and similar categories like “chimpanzee” and “monkey” on Photos.

Amazon was even selling its facial recognition system, Rekognition, to law enforcement agencies until 2020, when they banned the use of the software by police. The ban is still in effect as of this writing.

Facebook used facial recognition technology on its social media platform for much of the platform’s lifespan. However, the company shuttered the software in late 2021 as “part of a company-wide move to limit the use of facial recognition in [its] products.”

Additionally, there are firms who specialize in facial recognition software like Kairos, Clearview AI, and Face First who are contributing their knowledge and expertise to the field.

Read More At: The Value of Emotion Recognition Technology

Is This a Problem?

To answer the question of “should we be worried about facial recognition systems,” it will be best to look at some of the arguments that proponents and opponents of facial recognition commonly use.

Why Use Facial Recognition?

The most common argument in favor of facial recognition software is that it provides more security for everyone involved. In enterprise use cases, employers can better manage access control, while lowering the chance of employees becoming victims of identity theft.

Law enforcement officials say the use of FRS can aid their investigative abilities to make sure they catch perpetrators quickly and more accurately. It can also be used to track victims of human trafficking, as well as individuals who might not be able to communicate such as people with dementia. This, in theory, could reduce the number of police-caused deaths in cases involving these individuals.

Human trafficking and sex-related crimes are an oft-spoken refrain from proponents of this technology in law enforcement. Vermont, the state with the strictest bans on facial recognition, peeled back their ban slightly to allow for its use in investigating child sex crimes.

For banks, facial recognition could reduce the likelihood and frequency of fraud. With biometric data like what facial recognition requires, criminals can’t simply steal a password or a PIN and gain full access to your entire life savings. This would go a long way in stopping a crime for which the FTC received 2.8 million reports from consumers in 2021 alone.

Finally, some proponents say, the technology is so accurate now that the worries over false positives and negatives should barely be a concern. According to a 2022 report by the National Institute of Standards and Technology, top facial recognition algorithms can have a success rate of over 99%, depending on the circumstances.

With accuracy that good and use cases that strong, facial recognition might just be one of the fairest and most effective technologies we can use in education, business, and law enforcement, right? Not so fast, say the technology’s critics.

Why Ban Facial Recognition Technology?

First, the accuracy of these systems isn’t the primary concern for many critics of FRS. Whether the technology is accurate or not is inessential. 

While Academia is where much research on facial recognition is conducted, it is also where many of the concerns and criticisms are raised regarding the technology’s use in areas of life such as education or law enforcement. 

Northeastern University Professor of Law and Computer Science Woodrow Hartzog is one of the most outspoken critics of the technology. In a 2018 article Hartzog said, “The mere existence of facial recognition systems, which are often invisible, harms civil liberties, because people will act differently if they suspect they’re being surveilled.”

The concerns over privacy are numerous. As AI ethics researcher Rosalie A. Waelen put it in a 2022 piece for AI & Ethics, “[FRS] is expected to become omnipresent and able to infer a wide variety of information about a person.” The information it is meant to infer is not necessarily information an individual is willing to disclose.

Facial recognition technology has demonstrated difficulties identifying individuals of diverse races, ethnicities, genders, and age. This, when used by law enforcement, can potentially lead to false arrests, imprisonments, and other issues.

As a matter of fact, it already has. In Detroit, Robert Williams, a black man, was incorrectly identified by facial recognition software as a watch thief and falsely arrested in 2020. After being detained for 30 hours, he was released due to insufficient evidence after it became clear that the photographed suspect and Williams were not the same person.

This wasn’t the only time this happened in Detroit either. Michael Oliver was wrongly picked by facial recognition software as the one who threw a teacher’s cell phone and broke it.

A similar case happened to Nijeer Parks in late 2019 in New Jersey. Parks was detained for 10 days for allegedly shoplifting candy and trying to hit police with a car. Facial recognition falsely identified him as the perpetrator, despite Parks being 30 miles away from the incident at the time. 

There is also, in critics’ minds, an inherently dehumanizing element to facial recognition software and the way it analyzes the individual. Recall the aforementioned incident wherein Google Photos mislabeled Jacky Alciné and his friend as “gorillas.” It didn’t even recognize them as human. Given Google’s response to the situation was to remove “gorilla” and similar categories, it arguably still doesn’t.

Finally, there comes the issue of what would happen if the technology was 100% accurate. The dehumanizing element doesn’t just go away if Photos can suddenly determine that a person of color is, in fact, a person of color. 

The way these machines see us is fundamentally different from the way we see each other because the machines’ way of seeing goes only one way.  As Andrea Brighenti said, facial recognition software “leads to a qualitatively different way of seeing … .[the subject is] not even fully human. Inherent in the one way gaze is a kind of dehumanization of the observed.”

In order to get an AI to recognize human faces, you have to teach it what a human is, which can, in some cases, cause it to take certain human characteristics outside of its dataset and define them as decidedly “inhuman.”

That said, making facial recognition technology more accurate for detecting people of color only really serves to make law enforcement and business-related surveillance better. This means that, as researchers Nikki Stevens and Os Keyes noted in their 2021 paper for academic journal Cultural Studies, “efforts to increase representation are merely efforts to increase the ability of commercial entities to exploit, track and control people of colour.”

Final Thoughts

Ultimately, how much one worries about facial recognition technology comes down to a matter of trust. How much trust does a person place in the police or Amazon or any random individual who gets their hands on this software and the power it provides that they will only use it “for the right reasons”?

This technology provides institutions with power, and when thinking about giving power to an organization or an institution, one of the first things to consider is the potential for abuse of that power. For facial recognition, specifically for law enforcement, that potential is quite large.

In an interview for this article, Frederic Lederer, William & Mary Law School Chancellor Professor and Director of the Center for Legal & Court Technology, shared his perspective on the potential abuses facial recognition systems could facilitate in the U.S. legal system:

“Let’s imagine we run information through a facial recognition system, and it spits out 20 [possible suspects], and we had classified those possible individuals in probability terms. We know for a fact that the system is inaccurate and even under its best circumstances could still be dead wrong.

If what happens now is that the police use this as a mechanism for focusing on people and conducting proper investigation, I recognize the privacy objections, but it does seem to me to be a fairly reasonable use.

The problem is that police officers, law enforcement folks, are human beings. They are highly stressed and overworked human beings. And what little I know of reality in the field suggests that there is a large tendency to dump all but the one with the highest probability, and let’s go out and arrest him.”

Professor Lederer believes this is a dangerous idea, however:

“…since at minimum the way the system operates, it may be effectively impossible for the person to avoid what happens in the system until and unless… there is ultimately a conviction.”

Lederer explains that the Bill of Rights guarantees individuals a right to a “speedy trial.” However, court interpretations have borne out that arrested individuals will spend at least a year in prison before the courts even think about a speedy trial.

Add to that plea bargaining:

“…Now, and I don’t have the numbers, it is not uncommon for an individual in jail pending trial to be offered the following deal: ‘plead guilty, and we’ll see you’re sentenced to the time you’ve already been [in jail] in pre-trial, and you can walk home tomorrow.’ It takes an awful lot of guts for an individual to say ‘No, I’m innocent, and I’m going to stay here as long as is necessary.’

So if, in fact, we arrest the wrong person, unless there is painfully obvious evidence that the person is not the right person, we are quite likely to have individuals who are going to serve long periods of time pending trial, and a fair number of them may well plead guilty just to get out of the process.

So when you start thinking about facial recognition error, you can’t look at it in isolation. You have to ask: ‘How will real people deal with this information and to what extent does this correlate with everything else that happens?’ And at that point, there’s some really good concerns.”

As Lederer pointed out, these abuses already happen in the system, but facial recognition systems could exacerbate these abuses and even increase them. They can perpetuate pre-existing biases and systemic failings, and even if their potential benefits are enticing, the potential harm is too present and real to ignore.

Of the viable use cases of facial recognition that have been explored, the closest thing to a “safe” use case is ID verification. However, there are plenty of equally effective ID verification methods, some of which use biometrics like fingerprints.

In reality, there might not be any “safe” use case for facial recognition technology. Any advancements in the field will inevitably aid surveillance and control functions that have been core to the technology from its very beginning.

For now, Lederer said he hasn’t come to any firm conclusions as to whether the technology should be banned. But he and privacy advocates like Hartzog will continue to watch how it’s used.

Read Next: What’s Next for Ethical AI?

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Whether it’s called hybrid transaction and analytics processing (HTAP), hybrid operational/analytics processing (HOAP), translytics, or continuous intelligence, what’s being described is nearly synonymous with operational analytics.

Regardless of the name, operational analytics is a business strategy of leveraging real-time information to enhance or automate decision making. It’s an attempt to replace the traditional model of forming corporate decisions around quarterly or annual reports with making responsive pivots off of data as it’s processed in the present. It’s basically turning business intelligence and analytics insights into action at the application and systems level so users can put those insights to work.

Also read: The State of ITOps: Digital Transformation, Technical Debt and Budgets

How Does Operational Analytics Work?

The key to the success of operational analytics is the timeliness and freshness of data.

Fresh data comes into an enterprise through a variety of means, whether it be analytics data gathered from mobile apps, self-submitted customer feedback forms, documentation built on a collaboration platform, or customer data entered into customer relationship management (CRM) software.

As it streams in, different enterprise departments will share data more fluidly, finding value in innovative ways. For instance, the customer support desk may cross correlate its service tickets against customer sales records and prioritize service based upon how valuable the customer is. Or product and CRM data could be combined to better target sales and marketing efforts.

Operational Analytics in Practice

Power suppliers

In some ways, the practice of operational analytics can be said to have originated in the energy sector, which processes huge volumes of analytics and responds almost instantaneously, often with the benefit of artificial intelligence (AI).

Electricity suppliers are in a constant struggle to provide a balanced load across the energy grid, adjusting output as needed for both industrial and residential consumers.

Power consumption is gauged by the second, and as the demand goes up power plants burn hotter, boil more water, produce more steam, spin the turbines faster, and output greater amounts of electricity.

It’s a monumentally complex process that takes in data gathered across thousands of miles of infrastructure and automatically makes adjustments down to the second because even a momentary lapse in energy production is consequential.

Video game developers

Video game developers are also using operational analytics to an increasing degree, particularly as they debut actively developed products through early access programs like those on Valve’s Steam platform.

Some developers gather extensive data on player tendencies and preferences, what encounters or levels give players the most difficulty or the greatest ease, average play times, how many players actually finish the game, bugs encountered, crashes, freezes, and much more. This data is harnessed throughout the development cycle to make fixes, tweaks, buffs to weak mechanics, nerfs to overpowered ones, and so on.

This application of operational analytics has proven most valuable in competitive games, where achieving the optimal balance between characters or teams is a never-ending battle.

Online retailers

Online retailers have become one of the biggest and most controversial adopters of operational analytics strategies. Many retailers monitor every aspect of their customers’ behavior, serving product recommendations and ads tailored to their customers’ preferences.

These dynamic recommendations are powered by machine learning (ML)-enabled AI recommendation engines. Furthermore, even prices can be dynamic, fluctuating based on the geolocation of the customer’s IP address and potentially reflecting the in-store prices near the customer.

At the scale of a company like Amazon or Walmart, operational analytics is a necessity when it comes to inventory management as well. These companies have warehouses, distribution centers, and even trucking companies dispersed throughout all of North America.

Each day they process millions of orders, and the concentration of these orders creates the expectation that more product will need to be warehoused, more trucks will need to be supplied, and more staff will be required to pick and pack each order at the corresponding points of greatest demand.

The integration of real-time data across these organizations, even as their facilities span the continent, enable such companies to meet their rigorous supply chain demands and automatically trigger resupply orders from their partners if a shortage is anticipated.

Pitfalls of Automation

A few years ago, Amazon fully embraced its mastery of customer data, harnessing those daily analytics to produce a same-day-delivery service that would serve communities with the highest density of Amazon customers.

The expected result would bolster customer satisfaction and increase revenues while customers saved themselves a trip to the store because their intended grocery would arrive at their doorstep later that same afternoon anyway. Only through the power of data harvesting and artificial intelligence could such a strategy be successful.

However, Amazon’s AI produced an efficient same-day-delivery service map that prioritized wealthy neighborhoods and glaringly excluded poor ones. As a result, the company received backlash for what was seen as a discriminatory service map, and Amazon quietly reconsidered its approach.

Thoughtful Implementations

Operational analytics can produce highly valuable returns for a company, but it’s important to exercise human judgment and foresight before executing what might seem like a profitable idea. Actions that customers might find irritating or even offensive have a downside that might not show up in a strict data analysis.

Read next: Leveraging Conversational AI to Improve ITOps

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Who is a Business Technologist?

A business technologist is an IT (information technology) professional with a combination of broad general knowledge of technology along with soft skills and business skills. Most importantly, they have a deep understanding of how to integrate technology into strategies that align with business outcomes and drive competitive advantage.

The basic concept behind a business technologist is a hybridized talent of sorts. You need to be skilled at both technology and business, and those skills need to work in tandem with one another. At their core, some business technologists are citizen developers—employees who don’t just consume technology but can also create it. While not all business technologists are citizen developers, all citizen developers are business technologists. These professionals have technical skills as well as industry-specific knowledge; they not only understand how to develop tools but know what to develop for particular business needs using low-code development tools or APIs (application programming interfaces).

Also read: Bringing Data Democratization to Your Business

What Does a Business Technologist Do?

An experienced business technologist can help move an enterprise forward by increasing collaboration among departments and streamlining workflow, all while reducing costs by making technology more efficient and scalable. Here are some functions of a business technologist.

Monitoring industry trends and developments 

A business technologist may not spend all day keeping up with industry trends and developments. However, they regularly monitor publications related to their field of expertise to stay abreast of any changes in relevant laws, regulations, case law, scientific developments, and more that might affect potential business operations. 

Understanding how technology impacts different industries and sectors

Understanding key differences between industries, sectors, and enterprises allows them to shape technology proposals to their specific needs and ensure full alignment with business strategy. 

Envisioning the future of technology for their company and redefining business processes

A business technologist envisions new ways to use technology in their enterprise and create systems that can improve efficiency and effectiveness over time. They also redefine business processes, so they are able to incorporate new technologies or leverage existing ones in novel ways to make certain activities more streamlined and efficient. 

Advising top management on potential technology investments 

Since they understand how best to apply new technologies to different business functions, they can advise top management about whether it makes sense for them to invest in certain products or services at different points along their lifecycle. 

Why They Are Needed

The proliferation of powerful technologies has led to a culture shift in how technology is leveraged within organizations. This has been dubbed the age of democratized technology, where a once-complicated developer skill set is becoming accessible to all. It’s now possible for citizen developers (regular businesspeople) to download powerful applications and frameworks, then utilize these tools to create customized solutions that meet their needs without needing technical assistance from IT professionals. This cultural shift has created an increased need for those with access to business and technology knowledge (business technologists) to integrate technology into effective business processes. 

Also read: Best Data Analytics Tools for Analyzing & Presenting Data

How Enterprises Can Benefit from Having Business Technologists

Business technologists can improve productivity, efficiency, and decision-making capabilities by unifying enterprise systems. Not only do business technologists have a holistic view of how technology supports an organization’s goals, but they also keep up with technological innovations to anticipate needs for future business operations.

A business technologist focuses on applying technology as a key enabler for delivering better customer outcomes, reducing costs, increasing efficiencies, and improving employee satisfaction within a business environment. This value proposition is what differentiates business technologists from other professionals who work in IT. 

What Skills Do Business Technologists Need?

Business technologists must have the following qualities:

• Big-picture outlook: They have the ability to see how technology can be applied across an entire business in order to optimize efficiency, from marketing and sales to client and partner management, product development, and delivery.
• Data cruncher: They are someone who really understands numbers, crunching raw data into valuable information about customers and markets for both strategic decisions and tactical campaigns.
• People person: A business technologist has a strong sense of what motivates others within a business environment and helps ensure smooth execution.
• Ideation: They have the ability to bring creativity to projects through brainstorming sessions and idea creation; they foster creativity among team members with inspiring leadership abilities.
• Long-term vision: They can predict which technologies will become outdated, so they don’t waste resources developing them now.
• Knowledge of technology and digital media: A business technologist should understand how both core systems work as well as each major application solution that runs on top of those core systems (e.g., customer relationship management, enterprise resource planning, marketing automation, accounting solutions, etc.). Business technologists should be able to identify what a business problem is or could be and formulate a solution on how that could be solved by better integrating all these applications together or leveraging more streamlined solutions which meet most, if not all, requirements already available in the marketplace.
• Understanding of process flow and methodologies: A business technologist should know about how complex processes operate in businesses across all industries from finance to human resources, marketing, research & development, etc. 

The Future of Business Tech in an Enterprise

The pace of change in business has accelerated to such a degree that business technologists will become an indispensable resource in order for organizations to keep up with new developments and technology trends. This is true whether you’re an entrepreneur starting out or a seasoned CEO trying to navigate through a shifting economy.

The reality is that over time, almost every industry will be transformed by digital disruption as entire marketplaces appear and evolve so quickly that their lifespan becomes nearly nonexistent. In order to stay ahead of these challenges—and harness them—business leaders need skilled technologists who can partner with them to stay abreast of changes and pivot when necessary.

Today, businesses need someone whose expertise cuts across different platforms, such as cloud computing, enterprise social networks, app development, data analytics, and more. More importantly, they must have experience strategizing with senior leadership, showing how each piece of emerging technology can make an impact in an enterprise. Every enterprise needs a digital game plan today if they want to compete tomorrow—and having a business technologist can help lead your enterprise there.

Read next: Top 10 In-Demand IT Certifications 2021

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Unfortunately, the slightest distraction for an employee can tank an otherwise successful day. According to a study by the Mopria Alliance, the top three distractions are personal communications (online chats, texts, and calls), checking email or surfing the web, and unplanned conversations with work colleagues. These three gobble up to 23.6 hours per employee weekly with grave ramifications on productivity and the company’s bottom line.

What is Employee Monitoring Software?

Employee monitoring software is a tool that monitors employee computer activity to provide reports that are used for decision-making.

It is one of the most effective ways for managers to measure employee productivity, particularly in home and remote working environments. In addition, it allows supervisors to monitor employee activity on company-owned devices.

Also read: Labor Shortage: Is AI the Silver Bullet?

How Does Employee Monitoring Software Work?

A standard employee monitoring software will capture an employee’s desktop screen, desired user activity, and employee keystrokes. It also logs the employee’s browser history.

Good employee monitoring software tools go a step further and analyze the data by executing algorithms and help find out if there are any areas of improvement in an employee’s productivity. Managers can select from a range of reports to inform their decision-making.

Below is a summary of the benefits of employee monitoring software:

• Reduce workplace distractions and increase employee productivity and efficiency: An employee monitoring software helps act as a deterrent. When employees know their computer activity is being monitored, they actively avoid distractions. Some software tools also alert staff when their screen or mouse/mouse pad has been idle for a specific time. Such reminders help ensure the team remains productive and efficient.
• Feedback on employee performance: During employee performance reviews, employees can show their managers their daily activity reports to prove they used the time productively and any distractions were necessary for work.
• Gain a complete understanding of employee activity: When managers review an employee’s report, they can see both sides of the story—employee performance and how they spend their time at work.
• Enforce company policies: Employers are responsible for enforcing corporate employee policy guidelines on how staff should use computers during working hours. And if a worker fails to abide by those rules, employers must verify that transgressions occurred or were likely to have happened.
• Improve security: Employee monitoring software also serves as the first line of defense against data leaks and employee fraud as well as employee cyber threats.

Best Employee Monitoring Software 

We360.ai

We360.ai is a cloud-based employee monitoring program that allows you to get insights into your workers’ working environment. Whether they’re at home, in the office, or anywhere else in the world, it gives you the ability to turn data from all areas of your organization into useful information and practical solutions, so you can enhance how people work.

With over 5000 enterprise users worldwide, We360.ai’s solution is configurable in minutes to provide instant visibility into how employees are engaging with their company. It’s an ideal solution for HR professionals and C-suite executives wanting to scale their operations rapidly.

Key Features

• Stunning Dashboards: You no longer have to waste time consolidating productivity data or performing manual analysis. It delivers beautiful dashboards with intuitive graphs and charts based on employees’ activities, which you may view in a few minutes without needing any coding knowledge.
• Automated Attendance: Turn employee downtime into productive time by ensuring your team is present and accountable every minute of the day.
• Productivity Measurements: Track employee productivity per project or employee. You can view productivity data on various levels, such as mouse clicks, mouse movement, and keyboard clicks.
• Screenshots: Capture employee activity and screenshots at fixed intervals to validate their work.
• Reports: Generate detailed daily, weekly, monthly, or even yearly reports with a few clicks.
• Application Usage: Track employee activity per application and across productive and non-productive applications. Internal research by the developer shows that tracking application usage leads to a 63% increase in team productivity.
• Real-time Analytics: Analyze employee activity in real time, such as a live version of the employee’s screen, and combine with powerful analytics to generate actionable insights.
• Admin Mobile Application: The Admin mobile app on Android or IOS platforms gives you real-time visibility of employee activity and employee data from anywhere at any time.
• Domain Blocking: To enhance employee security, the developer offers an enterprise feature that allows administrators to block or unblock categories of external domains, such as social media, games, shopping portals, and other distracting websites.
• Project and Task Management: With We360.ai, you no longer need to use other platforms to manage and track projects. Easy-to-use task management is integrated with employee activity and productivity data, so you can assess how much time is spent on tasks and which employee spends the most time on each project.

Pros

• Easy to use and configurable within minutes
• It runs on MAC OS 10.11 and above, most Linux OS versions, and Windows 7 or later
• 24/7 remote support
• A dedicated account manager
• It comes with a free trial

Cons

• Lacks auto report scheduling

Pricing

We360.ai has a single pricing edition. Customers pay $3 per user per month for annual billing or $3.99 per user per month for monthly billing. The trial is a full-feature trial for seven days and allows up to five users.

Time Doctor

The developer bills its tool as “the cure for time-wasting habits.” With detailed analytics of where time is spent on the workday, Time Doctor lives up to its billing by providing employee-time analysis covering everything from web to computer usage. It is a comprehensive employee monitoring solution for managers who want complete insight into their team’s productivity.

Key Features

• Time Tracking and Employee Monitoring: You can track employee time and productivity by day, week, or month. It’s easy to see employee activity from a list of projects and tags.
• Screen Recording: Capture employee activity with the employee’s actual screens. This feature, which can be configured at fixed-time intervals or manually triggered, allows you to see exactly what employees are doing on the job.
• Activity Reports: Receive employee activity reports based on employee activity across all applications, employee screen recordings, and employee screenshots.
• Distraction Alerts: With Time Doctor computer monitoring software, employees get pop-up notifications when off track, such as being on Facebook for too long or their keyboard sitting idle beyond a set time threshold.
• Project Management and Budgeting: Easily track employee activity across projects to ensure productivity and financial goals are met.
• Customization Options: Flexibility is a key advantage of employee monitoring software. Many of Time Doctor’s options can be enabled and disabled as needed.
• Offline Tracking: Even when they go offline, Time Doctor employee monitoring software automatically tracks employee activities and builds a report that syncs the next time they get online.
• Integrations and API: Integrates with 64 third-party applications such as Asana, Basecamp, GitHub, Jira, QuickBooks, Salesforce, Slack, Teamwork, Trello, and more. 

Pros

• Quick setup for both the admin and users
• Compatible with multiple devices and operating systems, such as Windows, Mac, Linux, Android, iOS, and Chrome OS, and comes with a mobile app for Android and iPhone
• Comes with a free trial

Cons

• Lacks 24/7 support over calls
• Lacks a dedicated account manager
• Lacks in-depth screenshot insights

Pricing

There are three editions, Basic, Standard, and Premium, with pricing dependent on the number of users. For example, for 50 users, Basic Edition users pay $350 per month, while Standard users pay $490 per month, and Premium users pay $990 per month. In addition, custom quotes are available for more than 50 users. All plans come with a free 14-day full-feature trial.

Workpuls

Workpuls is a workforce analytics and productivity solution that allows your organization to function more effectively with actionable data insights. Learn how your team works best and boost productivity with employee productivity monitoring, automatic time tracking, remote team management, and more.

Workpuls’ insights assist you in developing more efficient processes, improving workflows, and balancing workloads, so your team can deliver their best work—all within a simple, lightweight platform.

Workpuls runs in the cloud or on-premises. It secures your data and makes compliance easier. Moreover, it scales with your ambitions whether you have 10 or 10,000 devices.

Key Features

• Automated Time and Attendance: Replace manual timesheets with automated time and attendance that is accurate to the second for a more efficient operation.
• Real-time Productivity Insights: Instantly gain insights on employee productivity with real-time dashboards, reports, and alerts.
• Time Tracking: Employees can track their own time with the employee app; managers can also use employee tracking time to keep everyone accountable throughout the day.
• Automated Time Mapping: Time mapping is a process of exploring and unlocking new value from your team’s everyday efforts by recording and analyzing how it’s spent.

Pros

• Simple and quick installation for both admins and users
• Multi-certificate security and dual data encryption
• Comes with a free trial
• Compatible with Windows and macOS devices

Cons

• No support for the Linux operating system
• No dedicated account manager
• Lacks 24/7 support over calls

Pricing

Workpuls has four pricing tiers:

1. Employee Monitoring: $6.40 per user per month billed annually
2. Time Tracking: $8 per user per month billed annually
3. Automatic Time Mapping: $12 per user per month billed annually
4. Enterprise: Custom quote

All plans come with a full-feature 14-day trial.

ActivTrak

ActivTrak helps businesses convert their latent productivity potential into reality. The world’s leading workforce analytics and productivity management software offers sophisticated insights that enable people to perform at their best, streamline operations, and maximize technology. In addition, ActivTrak’s Workforce Productivity Lab is a global center for ground-breaking research and expertise in human resources that helps organizations embrace and embody the future of work.

Key Features

• Productivity Reports: Get real-time insights into employee productivity with a customizable dashboard.
• Team Summaries: Track employee activity on a group basis and gain valuable insights on team efficiency, employee performance, and potential areas for improvement.
• Application and Website Usage: Get instant visibility over your entire digital business footprint to spot anomalies in web usage, apps used, and employee behavior patterns.
• Workload Management: Monitor your employee workloads and optimize employee productivity, ensuring employees have the right amount of work to do with a customizable employee activity meter.
• Productivity Coaching: You’ll have access to education and assistance for developing a culture that encourages employees to achieve ambitious objectives.
• Integrations: Integrates with familiar tools that power businesses such as Salesforce, Teams, Slack, Zoom, Google Workspace, Jira, Asana, Monday, and many more.
• Alarms and Website Blocking: Configure employee blocking on sensitive projects or configure website alarms that trigger based on a combination of conditions.
• Activity Classification: Track employee behavior patterns productive or unproductive activity classifications. You can also create distinct categories.
• Data Privacy Controls: Without losing any productivity insights, safeguard privacy and confidentiality while gaining employees’ trust.

Pros

• Simple setup for both admins and users
• Supports macOS 10.14 and above and Windows 8.1 and above
• Comes with a free trial

Cons

• Lacks Linux support
• Does not come with a dedicated account manager
• Lacks 24/7 support over calls
• Lacks a project management feature

Pricing

ActivTrak has four pricing editions based on the feature set:

1. Free: This plan allows an admin to add up to three users. It has limited functionality and features and only 3GB of storage.
2. Advanced: $9 per user per month billed annually.
3. Premium: $15 per user per month billed annually.
4. Enterprise: Custom quote.

Upon signup to the free plan, users get a free 14-day upgrade to the Premium version.

Hubstaff

Hubstaff is an employee monitoring program that offers business owners complete visibility into their workforce’s computer usage and more than 30 different integrations for third-party applications like Slack, Asana, Github, Jira, PayPal, and more.

The tool offers streamlined time tracking, team and project management on a nimble desktop, web, and mobile app. Admins can track work in real time with screen capture technology, geofences, and GPS tracking. You can also view reports, send out invoices, and pay your team from within the platform. It is available for Mac, Windows, Linux, Chromebook and Chrome extension, Android, and iOS.

Key Features

• Time Tracking: Hubstaff offers intelligent, streamlined time tracking. Track labor hours, set restrictions, and receive detailed timesheets to assess and approve all with a single tool. You can track your team’s time using a web app, desktop app, Android, or iOS device.
• Productivity: With Hubstaff, you can automate the management of remote teams. Proof-of-work tools boost efficiency and build trust. Customizable screenshots let you see the progress of your project as it happens. View what applications and URLs your team used. In addition, you can get productivity stats based on mouse and keyboard use. Virtual achievement badges can be awarded for efficiency and morale, while you can eliminate idle time with a single click.
• Field Service GPS-based Features: GPS Geofences automatically track employee movements around a given area, ensuring they can’t be logged in at job sites you define.
• Integrations: Use the Hubstaff Platform to build employee monitoring solutions with more than 30 integrations or APIs to be used across your business workflow.
• Workforce Management: Ensure you have the right staff for the job by scaling down workforces when necessary. The program’s workforce management capabilities offer flexible employee allocations based on employee availability, location, skillset, tools used, projects assigned, and time spent in an activity category.

Pros

• Comes with a free trial
• Offers employee monitoring features for a single employee for free
• It comes with a desktop, web app, and mobile apps that you can use on laptops, PCs, or phones
• Integrates with over 30 third-party applications
• 24/7 support over calls
• Flexible employee allocations based on employee availability, location, skillset, tools used, projects assigned, and time spent in an activity category

Cons

• Lacks a dedicated account manager
• Lacks in-depth screenshot insights

Pricing

Hubstaff has three pricing categories, each with three to four pricing tiers depending on the number of users required:

Paid plans come with a free 14-day trial.

Choosing Employee & Computer Monitoring Software

Choosing employee and computer monitoring software requires businesses to do their homework and decide which platform is right for their needs. For example, you may need an all-inclusive package or something more specific like time-tracking applications only. Once you know what features work best for your business, make sure they’re included in the pricing structure before deciding which one is right for your team.

Read next: 6 Ways Your Business Can Benefit from DataOps

The post Best Employee & Computer Monitoring Software 2022 appeared first on IT Business Edge.

For employers, the job shortage poses major problems. With the holiday season, companies are looking to ramp up their employment to meet the rising demand. For example, Walmart and Amazon are planning on hiring 150,000 employees.

To deal with this, employers are hiking wages and benefits as well as offering more flexibility with the work arrangements, such as with providing options for remote work.

However, even these strategies may not be enough. As a result, employers are investing more in automation technologies like artificial intelligence (AI). They can streamline processes, improve outcomes, and allow for lower costs. But of course, there are inherent risks as well, including issues of privacy. 

So, let’s take a look.

Why the Job Shortage?

A key reason for the job shortage is that employees are quitting their jobs. The phenomenon is often referred to as the “great resignation.” In September of 2021, the quit rate hit 3%. While this may sound small, it is actually an all-time record. About 4.4 million people decided to leave their jobs. 

What would lead to such a high quit rate? There are many factors. If anything, the high quit rate means that many employees actually have confidence. They believe it will not be tough to find another job.

But there are other drivers, including high costs of childcare, which was exacerbated by the COVID-19 pandemic; changing of attitudes toward the work-life balance; and rethinking career options or even retiring early.

“The pandemic has really opened up peoples’ eyes to what matters in life and what they want out of a job,” said Hari Kolam, CEO of Findem. “They’re looking for more than a paycheck. 

“They want to cut back on their work hours, to avoid hour-long commutes, and to spend more time at home with their families.”

Also read: Data Management with AI: Making Big Data Manageable

What Does the Job Shortage Mean?

The impact of the job shortage is certainly far reaching. There are already signs that it is contributing to problems with the global supply chain. Then again, there remains a severe shortage of truck drivers.

The result is that inflation is rising quickly, reaching 6.2% in October. This was the highest level since the 1980s. The University of Michigan Consumer Sentiment Index points out that about a quarter of consumers have reduced their living standards.

In a recent Senate hearing, Federal Reserve Chairman Jerome Powel indicated he was considering reigning in the money supply. While this should temper inflation, it could also slow the economy. This uncertainty was reflected in the Dow Jones Industrials and S&P 500, which both dipped after Powel’s comments. 

How AI Can Help

In response to the COVID-19 pandemic, companies have scrambled to invest in AI technologies to better manage the hiring process. This has spanned the sourcing, screening, and onboarding of job candidates.

As should be no surprise, tech companies have been at the leading edge. Since they are often data-driven, it has been much easier to adopt AI. The result is that companies like Uber, DoorDash, and Lyft have been able to hire candidates within hours.

But non-tech companies are also getting more aggressive. Just look at Southwest Airlines. It generally takes the company 35 to 45 days to make a job offer. This is simply too long. In fact, by the time the offer is made, the candidate may have already accepted a position with another company.

This is why Southwest has rolled out AI software like chatbots to reduce the time to get to an offer. The company also uses technology that optimizes the language in their job postings to improve the matches and conversions.

Training is another important factor. It’s not uncommon for there to be churn because of deficits in skills. This is why there should be educational programs that are personalized—which AI can be a big help with. There are myriad systems which use sophisticated diagnostics and role-based approaches that can help in the onboarding process.

Finally, AI technology may lead to significant improvements in productivity, which may mean less of a need to hire new people. Consider the customer service industry, which has about 1.7 million open jobs on LinkedIn. 

“AI is having a significant impact on customer service because anywhere between 40-80% of customer queries are repetitive,” said Puneet Mehta, founder and CEO of Netomi. “With AI-powered virtual assistants, companies can automatically resolve these and let human agents solely focus on more complex queries. 

“This decreases resolution time and increases team elasticity while allowing companies to scale support operations up and down as ticket volume fluctuates.”

While all this is great, AI should not be an automatic response either. Sometimes simple technologies may be the best. For example, one of the most time-consuming parts of hiring is scheduling an interview. To deal with this, you can use a self-calendaring app.

Also read: How AI and Risk Management Can Work Together

The Issues with AI

The HR department has traditionally seen underinvestment in IT. Instead, the focus has often been on areas like finance, marketing, and sales. 

But with the job shortages, HR is certainly getting more attention; however, there are some issues, such as with privacy. Will the AI be unfair or discriminatory? Do employees want their personal information to be part of an algorithm?

Interestingly enough, Amazon became a source of controversy because of this. In 2018, the company terminated its development of software that used AI to screen résumés. For the most part, it selected mostly males even though the software did not use gender as a variable. All in all, it showed the problems when using résumés as a data source since they often have gender-specific language or experiences.

The Future of AI and HR

Even with the issues, AI is likely to be more and more important with hiring. Companies will have no choice but to look for ways to automate their process. But there will need to be guardrails, such as using responsible AI and explainability. 

“AI can hire at a scale that’s just impossible for humans, even a large team of humans, to replicate,” said Kolam. “Humans can’t help but get tired, whereas machines don’t. 

“Most recruiters can only reach out to a maximum of 40 candidates in a given day, whereas there’s feasibly no limit with AI. There’s just no match for the speed and efficiency that AI brings to that part of the hiring process.”

Read next: Edge AI: The Future of Artificial Intelligence and Edge Computing

The post Labor Shortage: Is AI the Silver Bullet? appeared first on IT Business Edge.

While it’s generally understood that the Americans with Disabilities Act (ADA) protects individuals with disabilities from discrimination, it’s less clear how this applies to the digital accessibility of your organization.

Understanding the Need for Digital Accessibility

The CDC defines a disability as “any condition of the body or mind (impairment) that makes it more difficult for the person with the condition to do certain activities (activity limitation) and interact with the world around them (participation restrictions).” These impairments, activity limitations, and participation restrictions can range from mild to severe. They can be temporary or permanent. They can also be incredibly diverse and difficult to accommodate.

So why does your organization need to be concerned with understanding and accommodating disability? Is it worth dedicating resources to improving digital accessibility?

As of 2018, the CDC released a functional study indicating that 85.3 million Americans identify as having a disability. With an estimated $1.28 trillion dollars in annual disposable income, this isn’t a demographic your organization can afford to ignore. 

The disability market includes friends and family

Accommodating the needs of people with disabilities also offers the opportunity for your organization to reach their friends and family members. The CDC estimates these additional 149 million Americans have an estimated $7.1 trillion dollars in annual disposable income.

By respecting the individual needs of all users, socially conscious consumers and advocates will take notice and become brand loyalists. 

Employees have disabilities too

Hiring an employee with disabilities while failing to provide that worker with the tools needed to perform their job is a discriminatory practice. Be sure all accessibility assessments review the technologies required by your team for time reporting, payroll information, meeting and event scheduling platforms, sales systems, etc.

Search engines love accessibility features

The same metadata devoured by screen readers is also a significant factor in search engine rankings and improved click-through-rates. Alt text describing images and descriptive link anchor text also provide context and validation to search engine crawlers.

Also read: The Five Barriers to Digital Innovation

Creating a Digital Accessibility Strategy

Your digital accessibility responsibility is to provide an equal and equivalent experience for users of all abilities, much like a ramp offers wheelchair users access to buildings with stairs.

While there are many approaches to improving digital accessibility, and one size doesn’t fit all, it’s best to begin by identifying the functionality of your application. Your organization’s ability to improve digital accessibility relies on clearly defining how any person access or benefits from a site, system, or application.

A successful digital accessibility strategy will eliminate all barriers for disabled users. 

Supplement Digital Accessibility with Inclusive Behaviors

It may not be possible (or it may take time) to replace all of the software solutions employed by your organization with fully accessible alternatives. Consider bridging accessibility gaps with inclusive behaviors and simple practices.

As an example, a few small changes can significantly improve interactions with clients, customers, or employees, in virtual meetings or during support calls:

• Identify yourself before you speak, when the interaction isn’t simply one-on-one. Do not assume that just because your camera is on, that the person you are speaking to is able to see you clearly.
• If possible, enable captions for the platform you are using.
• Avoid using culture-specific idioms.
• Replace acronyms and abbreviations with complete words.
• Describe and review on-screen images.
• Appoint a moderator for meetings with larger populations. 

Also read: Q&A with Skedulo: How AI is Shaping Software Development

Curb Cut Effect: Accessibility Benefits Everyone

I’ll bet that the last time you pushed a heavy cart full of groceries to your car, took your child for a walk in a stroller, or backed your vehicle out of a driveway, the sloping ramp down from the sidewalk to the road made life easier. Initially designed for wheelchairs and power chairs, these curb cuts are a great example of how designing for disabilities can actually make things better for everyone.

Let’s take it a few steps further:

• Utilize the closed captioning on a television in a loud room?
• Use Siri to set a reminder or send a message when you need to be hands-free?
• Turn up the contrast on your smartphone to make the screen easier to read?
• Choose an elevator instead of the stairs? 

While it may be tempting for your organization to be satisfied when technology solutions meet the needs of most people, increasing accessibility is an opportunity to make things better for all people.

Also read: Are Apple AirTags a Valuable IT Asset Management Tool?

The post Why Should Your Organization Care About Digital Accessibility? appeared first on IT Business Edge.

Machine learning (ML), the key component of AI technology, has become powerful to the level of displaying superhuman capabilities on most human tasks. However, this superhuman performance comes with higher complexity in the AI and ML models, turning them into a “black box,” a decision-making model too complex to be understood by humans.

Today, ML models are deployed to replace human decision-making in areas ranging from driving cars to the prevention of crimes to advertising. They are also employed in decision-making in investments, loan approvals, and hiring employees. The decisions made by these black box systems have the potential to influence business decisions and impact many lives. Thus, they come with severe ramifications.

What is Explainable AI (XAI)?

The dire need to make the decision-making process of the algorithms understandable to the stakeholders has become a significant part of every business. It can also help to gain their trust and confidence in an enterprise’s AI decision-making process.

This demand for transparency in the decision-making process of the AI and ML models resulted in an increased interest in Explainable Artificial Intelligence (XAI). XAI is a field of technology that deals with the development of methods that explain and help users interpret ML models. In simpler terms, Explainable AI is an AI model built to provide an easily understandable explanation of how and why an AI system has made a specific decision.

Today, every enterprise should place top priority on a clear understanding of the inner functions of their AI system. It helps them face the persistent challenges posed by bias, accuracy, and many more problems associated with AI systems.

Also read: The Struggles & Solutions to Bias in AI

The Significance of Explainable AI in Businesses

Explainable AI has greater potential and strategic value to drive various businesses. Some of the benefits, include:

Accelerate AI adoption

As the complex black box decision-making process becomes easily understandable by everyone, it can build the trust and confidence of stakeholders in the ML models. This, in turn, increases the adoption rate of AI systems across various industries providing a competitive advantage to various enterprises.

Provide accountability

Explainable AI lets business leaders easily understand the behavior of AI systems and potential risks associated with them. It makes the leaders confident to accept the accountability for the AI systems in their business. It can also help to garner sponsorship for future AI projects. Greater support for AI from major stakeholders and executives can put an enterprise in a better position to foster innovation and transformation.

Provide valuable insights on business strategies

Explainable AI can bring valuable insights into key business metrics such as sales, customer behavior patterns, and employee turnover among others. These insights on valuable data help evolve business goals and improve the decision-making and strategy planning of various enterprises.

Ensures ethics and regulatory compliance

Some enterprises are compelled to adopt Explainable AI due to the new regulatory compliance requirements. Others face growing pressure from customers, regulators, and industry watchdogs to ensure their AI practices align with ethical norms and publicly acceptable limits. The implementation of Explainable AI can safeguard vulnerable consumers, ensure data privacy, strengthen the ethical norms of businesses, and prevent both bias and loss of brand reputation.

How to Implement Explainable AI?

Here are the five guidelines to effectively implement Explainable AI in your enterprise. It can also be taken together as a roadmap with some major milestones that can guide an enterprise to deal with the limitations and risks associated with XAI.

Diversification of XAI objectives

It is ML engineers who currently develop Explainable AI technology by placing the priority on both the needs of them and their company. But all these should be within the framework of legal regulations and industry policies and standards.

Diversification with a broader array of XAI objectives requires both greater awareness of the objectives and a shift in the motive to accomplish them. In order to motivate this shift, it is critical to include the needs of stakeholders, users, and communities in the standards and policy guidelines of Explainable AI.

XAI case studies are excellent tools that can help entrepreneurs and developers alike understand and develop more holistic Explainable AI strategies. In addition, there are a wide variety of guidance documents, recommendations, and frameworks that can give a walkthrough along the key solutions to support XAI that are useful to different stakeholders.

Put XAI metrics in place

Several attempts have been made to assess the explanation of AI, but most of them are either expensive or focus on a smaller part of a “good explanation” and fail to bring light to other dimensions. Holistic measurement of effectiveness requires the combination of a comprehensive overview of XAI approaches, a review of the various types of opacity, and the development of standardized metrics. Besides, to assess explanations of AI, the specific contexts, norms, and needs in each case, with both quantitative and qualitative measures, should be used. This will help businesses hold themselves accountable and deploy AI successfully.

Also read: Data Management with AI: Making Big Data Manageable

Bring Down Risks

XAI comes with the elements of risk. Explanations may be misleading, deceptive, or maybe exploited by cybercriminals. They can also pose data privacy risks since they can take out information about the XAI model or training data. Competitors can easily replicate proprietary XAI models or use the models for further research.

Every enterprise should implement practical methods for both documenting and mitigating these risks. These practical methods must be a part of the XAI standards and policy guidelines. At times, in the case of business decisions with higher stakes, it is always better to avoid the need for deep learning models and Explainable AI technology.

The prioritization of user needs

Until now, the development of XAI has primarily served the interests of AI engineers and businesses. It has helped debug and improve AI systems but failed to let users oversee and understand its intricacies.

Every enterprise should prioritize user needs for profitable growth and to build trust in users. Some of the key considerations include clarifying the context of an explanation to users, communicating the unpredictability associated with model predictions, and enabling user interaction with the XAI explanation. Businesses can also consider incorporating valuable ideas from the theory of risk communication.

Explainable AI isn’t enough

While useful, simply possessing a better understanding of how a biased AI model arrived at a result will do little to attain trust in users. Trust can only be built alongside testing, evaluation, and accountability measures that should go the extra mile to expose and mitigate known problems. For instance, the 2017 Loomis v. The State of Wisconsin case revealed that the racial bias of a criminal risk assessment algorithm not only violated due process but also highlighted the gaps in accountability.

Independent auditing and updated policies and standards, among other accountability measures, will also be needed to promote lasting trust in users.

Read next: Leveraging Conversational AI to Improve ITOps

The post What Does Explainable AI Mean for Your Business? appeared first on IT Business Edge.

As we’ve moved from in-person to remote events, the effectiveness of a scripted keynote has been significantly reduced because, with the distractions we all have at home, an executive reading a script doesn’t seem to hold the audience’s attention. To keep the audience interested, you need more.

Dell went from a traditional keynote to a panel format that was well moderated by Dell’s SVP of Corporate Affairs, Jennifer “JJ” Davis, who is a legend at the company. On her panel were Dell’s CEO Michael Dell, Chairman and Co-COO Jeff Clarke, Co-COO Chuck Whitten, and Allison Dew, Dell’s CMO. 

Let’s talk about why a panel works better than a scripted monolithic keynote for an online event.  

Also read: Dell Makes Strong Push Towards Autonomous Operations

Building Trust

Dew spoke to this during her interaction on the panel. As CMO, she gets that the impression and effectiveness of any executive team is a mix of company priorities and company personality. While the priorities, at least the stated ones, can be stated in a monolithic executive keynote, that format doesn’t convey the company’s personality.  That personality is more easily conveyed when you see the executives interact.  Do they like each other? Do they get along? Is the executive team looking for ways to undercut their peers, or do they have each others’ backs?

For someone considering working with a company, you want to see if its management is dysfunctional. That will directly relate to how well they can execute the plans and priorities they articulate. A few years back, I was at an event held by a Dell competitor. At that event, two critical executive team members demonstrated their hatred for each other, which predicted the firm’s later massive and more obvious inability to execute. I would argue it is at least as necessary to know the odds a vendor can execute their plans as it is to know those plans in the first place because, if you use them and can’t execute, their dysfunction will become your problem.  

When I watched the executives’ body language, I could tell that they liked and supported each other, which helped me trust what Michael Dell and his executives said and made me believe that infighting was unlikely to derail their plans. A well-moderated panel like this one can establish a natural empathy and depth in the executive team and better allow you to see if interpersonal problems in the firm might become your problems later.

Also read: Facebook’s Unique Ownership Structure Might Be its Downfall

Creating Interest

When you are at a venue listening to a speaker with full audio/video support, it can hold your interest if it is well-staged, scripted, and performed. At home, much of what is typically staged — product demos, slides, and videos — don’t seem to be done as well because you need a full-on TV studio to make all of that work, and most companies don’t have that. Microsoft often does far better in this regard because they have funded a full studio and thus can better professionally produce a show or event.  

The panel approach,  if done well, provides interaction that is more interesting to watch. Television networks use panels to discuss news items on news shows because they work better when conveying a complex topic like Dell Apex, Dell’s big “everything as a service” push.  The variety and interaction between the speakers tend to hold interest better. The speakers are better positioned to speak up to fill information gaps or better explain a topic.  

The key to doing this, however, is having the panel well moderated. A poorly moderated panel can be worse than a monolithic speaker because it often devolves into a linear progression of poorly linked talks with little preparation or rehearsal. Ultimately, what made the Dell panel work was Davis’s preparation and her execution as moderator. In addition, she was the best prepared on the stage, which tended to pull up the performance of the other panelists, further improving the overall quality of the effort.  

Notable Observations

I’ve noticed during vendor events the main focus is more about surviving the event itself.  However, events are supposed to convey information, drive sales, and improve a company’s image. Executives often change slides until the last minute, resulting in unforced errors by the support staff and speakers, all of which reflect poorly on the image and perceived quality of the company.  

A well-moderated panel can mitigate this because you don’t need as much rehearsal. The moderator owns the flow and can be brought in as a professional. And, from my perspective, a panel better conveys the company’s personality than a monolithic keynote.  

Here’s an additional observation. Michael Dell opened with his new book Play Nice But Win, which is about the birth of Dell and the management practices that turned Dell Technologies into the powerhouse it is today.  The book provided a solid foundation to who Michael Dell and his namesake company are and what the company’s lasting priorities will be in a believable context. 

Read next: The Overlooked and Undervalued Importance of Marketing

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TIBCO, during TIBCO NOW 2021 online conference, also revealed it has expanded its existing portfolio to data analytics and integration platforms to include a TIBCO Spotfire Mods framework that makes it easier to create visualizations for its Spotfire analytics tool. 

There is also now a Spotfire Data Functions capability that extends the Mods framework that integrates Spotfire with other data sources. TIBCO is also promising to integrate Spotfire with TIBCO ModelOps, a no-code tool it provides for building models that infuse processes with artificial intelligence (AI) capabilities.

Meanwhile, the TIBCO WebFOCUS business intelligence (BI) tool can now be deployed as a containerized application. The company is also making available an instance of TIBCO WebFocus available as a managed service through which it manages the BI application on behalf of customers. There are also now enhanced authoring and assembly capabilities such as filtering, styling, reporting, and app development that end users can employ to augment data preparation, content creation, and collaboration.

Adding Data Sources

TIBCO is also adding support for additional data sources such as Spofire, Apache Spark, and AutoML frameworks along with integration with TIBCO Data Virtualization to the TIBCO Data Science platform in addition to adding a Dynamic Learning capability to the TIBCO Streaming platform that enable real-time analytics.

The TIBCO Data Virtualization platform, now part of the TIBCO Data Quality (DQ) series of offerings, has been updated to add a search capability to its catalog along with making available low-code tools to data engineers. TIBCO EBX master data management platform, part of the same family of tools, now also supports low-code scripting, simplified configuration, smart match and merge of data processes, and integration with additional application programming interfaces (APIs). 

The company has also updated TIBCO Cloud to add lifecycle management capabilities for APIs enabled by its Mashery platform, support for messages sent from platforms such as Apache Kafka and Apache Pulsar, and additional automated workflows.

Finally, TIBCO has added additional projects to a TIBCO LABS initiative through which it collaboratively develops intellectual property with partners and customers. TIBCO Cloud Discover is a process mining tool that will become part of TIBCO Cloud, while TIBCO Cloud Composer is a rapid application development platform. There’s also TIBCO LABS Gallery, a portal for accessing these projects.

Also read: Top Data Science Tools 2021

Driving Digital Transformation

The overall goal is to make it easier for organizations to drive digital transformation initiatives using platforms that are provided by a single vendor, says TIBCO COO Matt Quinn. Rather than having to navigate multiple silos, Quinn notes that as digital transformation continues to gain momentum there will be more convergence across disparate IT platforms that today are typically acquired and managed in isolation from one another.

At present, that level of complexity tends to provide larger organizations with a strategic advantage because they have the expertise required to master all the platforms needed to digitally transform a process, adds Quinn. However, as more technologies start to converge it should become easier for small companies to take advantage of the same capabilities, he notes. “There is still a digital divide,” says Quinn.

It may be a while before that digital divide is bridged. However, once it is, many larger companies may soon find themselves competing with smaller agile companies that can employ the same rich mix of platforms to compete more aggressively than ever.

Read next: Best Data Governance Tools & Software of 2021

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