The metaverse has been around for some time as a kind of urban legend, perhaps aptly described in the 2011 science fantasy book Ready Player One. That was until some of the biggest names in tech started investing heavily in related technologies, including virtual and augmented reality (VR/AR), Internet of Things (IoT), and artificial intelligence (AI).

Today AI is one of the most exciting technology fields to work on. Zuckerberg said the metaverse is something he’s wanted to work on since even before the conception of Facebook. And the company’s Meta AI research lab is on the cutting edge of both AI and the metaverse.

The new direction was met with some trepidation. The social media site Facebook, which retains its name, is already infamous for its black-box algorithms, which are going to grow more and more complex under the Meta AI efforts.

While this news is undoubtedly exciting for enthusiasts and futurists, what are the practical real-world implications beyond the metaverse hype?

See the Top Artificial Intelligence (AI) Software

How Far Along is Meta AI?

Despite the initial excitement, Meta AI is years away from reaching its ultimate goal of having a fully operational metaverse. Meta AI is Meta’s longest scheduled project, perhaps as big in development time as it is in ambition. But that doesn’t mean it can be ignored, as it has many partially developed applications.

Meta is presently leading the AI race, with many of its applications in direct competition with giants across different categories. Meta is working on a voice interface like Apple’s Siri or Google Voice Assistant. Meta is also competing with VR gaming consoles such as Hololens with the acquisition of Oculus.

Additionally, some of Meta’s projects include an AI-based image tagging and image search algorithm that was shown to beat the FBI’s image recognition ability at the IEEE Computer Vision Conference. Meta AI can also generate text predictions in messages like Google assistant through DeepText.

Meta AI Controversies

Facebook has been guilty of a number of ethical failings over the years and has been called out for prioritizing profit over safety by Frances Haugen, a former product manager at the company. On more than one occasion, it has been reported that this prioritization has led to the spread of misinformation and hate speech, which tends to engage more people, and the company has been accused of ignoring the negative effects of social media on teens and other groups.

A huge chunk of Meta revenue comes from Facebook ads. However, the ads are only valuable if the content you find on the site is engaging. Reports suggest that Facebook algorithms unilaterally favor engagement and, as a result, might promote misinformation and hate speech.

In response to these claims, Meta announced several changes in June 2022, including:

• The Responsible AI organization is set to join the Social Impact team.
• The AI for Product teams working to protect users on any of the Meta-owned platforms will move to the product engineering team and focus on improving recommendations and making content more relevant, including ads and commerce services.
• The Meta AI research team, FAIR, will join the Reality Labs Research to serve as foundation and support, with a mission to drive breakthroughs in AI through “research excellence, open science, and broad collaboration.”

The Latest Developments in Meta AI

Meta has made the news multiple times since its rechristening, and not all of it has been troublesome. Just this May, Meta announced that it has created a massive new language model that will be freely available to researchers around the globe.

In a move that is not typical of a for-profit organization, Meta surprised its followers, saying that the idea was “democratizing access to large-scale language models, to maintain the integrity and prevent misuse.”

This will be the first time a fully trained language model of this size and scope will be accessible to researchers. The move has been well-received by critics of privately owned and funded research on AI models.

Meta has been full of surprises this year, releasing its AI Research SuperCluster (RSC), the world’s fastest AI supercomputers, earlier this year. The supercomputer will accelerate AI research and help Meta build the metaverse. New announcements from the AI effort come frequently, the latest an AI chatbot.

Also read: Python for Machine Learning: A Tutorial

What Areas Will Meta AI Influence?

What influence might Meta AI have on the evolving virtual world of the metaverse?

Marketing

Your meta life could involve digital clothing and world-building, and marketers would have to consider it while strategizing on sales through the metaverse. Game developers and marketers are no strangers to in-game marketing and have displayed custom skins, virtual locations, in-game advertisements, and promotional items. The metaverse would be no different.

Culture

The metaverse brings together an alternate online subculture pushed forward by gamers, buyers, and the brands serving them.

In December 2019, GTA V allowed players to dress up as protesters in Hong Kong. Gamers put on black clothes, with yellow hard hats and gas masks to start a riot in-game. Chinese players reciprocated by dressing up as the police and fighting back.

The GTA incident was a unique phenomenon, showing unexpected ways people might use the metaverse. People from different cultures can unite as groups driven by a shared sentiment, creating a unique cultural phenomenon.

Economy

The metaverse is the advent of a shared, virtual economy. People might behave differently in the metaverse than in real life, creating alternate spending patterns.

Whenever the metaverse rolls towards global adoption, people from different economies will be on a shared platform. The metaverse might facilitate in-game trade. Brands might turn into pseudo-super-powers. Only time will tell how a metaverse might affect what we know about economics.

Why We Need to Take Meta AI Seriously

If it is not Meta AI, it could be any other AI project, but the virtual world platform race is on. What makes Meta unique is its application-oriented approach. Meta has the benefit of being the parent company to the world’s most prominent content churning machine, which allows Meta to leverage user-generated data to build its AI.

However, there don’t seem to be any signs of significant disruption, and growth appears to be moving slowly and steadily. So when someone says Meta AI is a big deal, it is so in scope but perhaps more evolutionary than revolutionary. Only time will tell how world-changing it will be as we wait for more updates from the social media – and now AI – giant.

Read next: 10 Top Data Companies

The post How Revolutionary Are Meta’s AI Efforts? appeared first on IT Business Edge.

The Alphabet CEO once said, “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything.”

A November 2020 study by Gartner predicted a near-20% growth rate for managed services like Vertex AI. Gartner said that as enterprises invest more in mobility and remote collaboration technologies and infrastructure, growth in the public cloud industry will be sustained through 2024.

Vertex AI replaces legacy services like AI Platform Training and Prediction, AI Platform Data Labeling, AutoML Natural Language, AutoML Vision, AutoML Video, AutoML Tables, and Deep Learning Containers. Let’s take a look at how the platform has fared and what’s changed over the last year.

Also read: Top Artificial Intelligence (AI) Software

What Is Google Vertex AI?

Google Vertex AI is a cloud-based third-party machine learning (ML) platform for deploying and maintaining artificial intelligence (AI) models. The machine learning operations (MLOps) platform blends automated machine learning (AutoML) and AI Platform into a unified application programming interface (API), client library, and user interface (UI).

Previously, data scientists had to run millions of datasets to train algorithms. But the Vertex technology stack does the heavy lifting now. It has the computing power to solve complex problems and easily do billions of iterations. Vertex also comes up with the best algorithms for specific needs.

Vertex AI uses a standard ML workflow consisting of stages like data collection, data preparation, training, evaluation, deployment, and prediction. Although Vertex AI has many features, we’ll look at some of its key features here.

• Whole ML Workflow Under a Unified UI Umbrella: Vertex AI comes with a unified UI and API for every Google Cloud service based on AI.
• Integrates With Common Open-Source Frameworks: Vertex AI blends easily with commonly used open-source frameworks like PyTorch and TensorFlow and supports other ML tools through custom containers.
• Access to Pretrained APIs for Different Datasets: Vertex AI makes it easy to integrate video, images, translation, and natural language processing (NLP) with existing applications. It empowers people with minimal expertise and effort to train ML models to meet their business needs.
• End-to-End Data and AI Integration: Vertex AI Workbench enables Vertex AI to integrate natively with Dataproc, Dataflow, and BigQuery. As a result, users can either develop or run ML models in BigQuery or export data from BigQuery and execute ML models from Vertex AI Workbench.

Also read: The Future of Natural Language Processing is Bright

What’s Included in the Latest Update?

Google understands research is the only way to become an AI-first organization. Many of Google’s product offerings initially started as internal research projects. DeepMind’s AlphaFold project led to running protein prediction models in Vertex AI.

Similarly, researching neural networks provided the groundwork for Vertex AI NAS, which allows data science teams to train models with lower latency and power requirements. Therefore, empathy plays a significant role when AI use cases are considered. Some of the latest offerings within Vertex AI from Google include:

Reduction Server

According to Google, the AI training Reduction Server is an advanced technology that optimizes the latency and bandwidth of multisystem distributed training, which is a way of diversifying ML training across multiple machines, GPUs (graphics processing units), CPUs (central processing units), or custom chips. As a result, it reduces time and uses fewer resources to complete the training.

Tabular Workflows

This feature aims to customize the ML model creation process. Tabular Workflows let the users decide which parts of the workflow they want AutoML technology to handle and which side they like to engineer themselves.

Vertex AI lets elements of Tabular Workflow be integrated into existing pipelines. Google also added the latest managed algorithms, including advanced research models like TabNet, advanced algorithms for feature selection, model distillation, and many more functions.

Serverless Apache Spark

Vertex AI has been integrated with serverless Apache Spark, a unified open-source yet large-scale data analytics engine. Vertex AI users can easily engage in a serverless Spark session for interactive code development.

The partnership of Google and Neo4j enables Vertex users to analyze data features in Neo4j’s platform and then deploy ML models with Vertex. Similarly, the collaboration between Labelbox and Google made it possible to access Labelbox’s data-labeling services for various datasets—images and text among the few—from the Vertex dashboard.

Example-based Explanations

When data turns into mislabelled data, Example-based Explanations offer a better solution. The new feature of Vertex leverages Example-based Explanations to diagnose and solve data issues.

Problem-Solving With Vertex AI

Google claims that Vertex AI requires 80% fewer lines of coding than other platforms to train AI/ML models with custom libraries, and its custom tools support advanced ML coding. Vertex AI’s MLOps tools eliminate the complexity of self-service model maintenance, streamlining ML pipeline operations and Vertex Feature Store to serve, share, and use advanced ML features.

Data scientists with no formal AI/ML training can use Vertex AI, as it offers tools to manage data, create prototypes, experiment, and deploy ML models. It also allows them to interpret and monitor the AI/ML models in production.

A year after the launch of Vertex, Google is aligning itself toward real-world applications. The company’s mission is solving human problems, as showcased at Google I/O. This likely means that its efforts will be directed toward finding a transformative way of doing things through AI.

Read next: Top Data Lake Solutions for 2022

The post What’s New With Google Vertex AI? appeared first on IT Business Edge.

In addition, beginning with a strategy in mind with the multicloud approach toward data management can go a long way in optimizing data management and minimizing risks.

Also read: Top 7 Data Management Trends to Watch in 2022

Multicloud Environments for Enterprise Functionality

If you find it challenging to match the different cloud platforms against the various needs of your organization, you might appreciate how difficult hosting can get.

Fortunately, developing a multicloud infrastructure can help you avoid haphazardly appending data clouds into a complex architecture that is difficult to maintain and manage. Using different clouds in such a way can be beneficial in several ways.

1. Flexibility

A common problem IT managers face is that one cloud service provider might be perfect for a portion of the organization’s functionality. In contrast, another service might be better suited for other applications. For example, a proprietary cloud would be perfect for hosting proprietary apps but might not be cost-efficient for storing public records. In a case like this, multicloud allows you to use an appropriate cloud suited to a particular area.

2. Proximity

When you are operating globally, you might have to make additional efforts to manage compliance with the local data sovereignty laws. In this case, you could host a part of your workload with regional cloud providers. It will have the additional benefit of better speeds for the end user.

3. Shadow IT

Multicloud is a straightforward approach to consolidating shadow IT architectures. Shadow IT has become common today, partly because of the ease of use offered by cloud services. However, it is a kind of data silo that causes redundancies and security issues and ultimately slows you down.

4. Failover

Multicloud ensures business continuity by offering backup that can scale with your enterprise and host data and workflows. This would mean getting back up in no time and without any data loss in the case of an outage.

In disaster recovery, you would typically restart the service. However, there might be a chance that it doesn’t work. In that case, if you have a secondary cloud, you can deploy the service there and reduce recovery time.

Also read: AWS vs Azure vs Google vs Alibaba: Compare Top Cloud Providers

Multicloud and Data Management

Multicloud presents a unique architecture, with specific requirements for data migration and storage. Having a multicloud approach will require data management strategies specifically designed for it. However, it must be noted that having a new data management strategy offers several advantages of its own, such as:

• If the demand for the workload increases beyond capacity, also known as cloud-burst, a secondary cloud can provide the additional space while the primary cloud deals with the regular traffic.
• Applications typically deploy all service instances to every available location. However, with multicloud, you can selectively deploy data based on the hardware availability.
• Multicloud abolished the trend of proprietary formats, which made users dependent on a particular cloud solution, and made it possible for users to be independent of the cloud service it uses.
• Multicloud favors analytics and artificial intelligence (AI) operations. AI and machine learning can be used to filter through data for metrics that help you improve operations and predict issues.
• Ultimately, multicloud allows a holistic model for data management by reducing architecture complexity. Though there will be serious changes in on-premises architecture, the result will be better optimized.

How Multicloud Helps with Data Management Best Practices

Data management best practices are equally applicable to multicloud as they would be to any other approach, and they must be planned and executed with due diligence.

1. Having a plan

Having multiple environments needs properly designed and documented ways to manage the data generated. Simply having the same old strategy and applying it across platforms does not work and will limit full functionality, such as collaboration. A good strategy will account for ongoing changes and be open to adoption.

2. Addressing complexity

A multicloud environment would have multiple locations on-premises and across clouds, leading to more complexity. In the end, your architecture should work seamlessly between them, which requires a good data strategy.

3. Compliance and data sovereignty

While data compliance requirements can be complicated, they get even trickier with the new complex architecture. Cloud backups solve the issue for you to an extent. Use proper resources for data mobility and consistency to ease up your workload.

Challenges for Data Management

A successful multicloud strategy addresses the following challenges that anyone operating over the cloud will inevitably face at some point.

Security concerns

Having multiple clouds means moving and managing data across different channels and, inevitably, more access points. Unfortunately, more access points make your database more vulnerable to security threats, so security must be built in every step of the way.

Data governance

Globally, regulations such as GDPR and CCPA require users and providers to share the accountability for privacy breaches. A multicloud strategy would increase the data governance requirements due to having more clouds, which in turn increases the liability for your organization.

Visibility

Having applications running on different clouds would lead to issues with the invisibility of the cloud storage landscape. It would further demand more tools and processes to function, which can sometimes be difficult to manage.

Data migration

There is still a lack of cloud-native tools to migrate data between providers. You are likely to require third-party migration tools, meaning extra licensing costs.

Also read: Cloud Security Best Practices for 2022

Adopting Best Data Management Practices

The requirement of data management practices remains the same, but their complexity varies with multicloud. Multicloud not only supports data management best practices but in some cases mandates them. Data management on multicloud can be tricky, but it has many benefits if done right.

To make full use of multicloud, and create a strategy capable of addressing the varying complexities of the new system, never try to replicate the same methodologies across the whole enterprise. This can limit your return on investment (ROI) and lead to poor performance, as it would further aggravate the possibility of having data silos and lead to a lack of visibility across cloud storage environments. Have a plan can help you reach data synergies, leading to cost optimization.

You need the right data management strategy, from planning and migrating data to running day-to-day operations. A good strategy will include consideration of data protection and security, visibility, and governance. In the end, you want to get the best features of all the cloud options that you choose and seamless data management so your IT department can focus on innovation instead of maintenance.

Read next: Unifying Data Management with Data Fabrics

The post Multicloud Strategies for Data Management appeared first on IT Business Edge.

Similarly, it wasn’t too long ago that health records were kept on physical shelves in thick folders. But today they’re in the form of Electronic Health Records (EHRs), and patients can easily access them via online platforms or Internet of Things (IoT) devices.

While this easy accessibility and abundance of data benefits patients, it’s even more useful for cybercriminals. It has been recently reported that nearly 90% of healthcare institutions faced a data breach in the past two years. According to Statista, the average cost of a healthcare data breach is over $9 million.

Also read: Top Cybersecurity Companies & Service Providers

Why is Healthcare the No. 1 Target of Cyber Criminals?

Today, healthcare information is even more valuable than financial data. Therefore, the exposure of an individual’s healthcare data is a critical privacy risk and has far-reaching personal consequences.

In case of a healthcare data breach, the patient or an individual might experience embarrassment due to health conditions or personal issues, and the breached data might be used for illegal activities like blackmailing, identity theft, and fraud.

Unfortunately, because of a number of cybersecurity weaknesses, breaching healthcare data can be a relatively simple job for hackers.

6 Cybersecurity Challenges of the Healthcare Industry

As new technology and compliance regulations arrive on the scene, every industry faces new cybersecurity threats to personal data. Unfortunately for healthcare, there are many reasons why it’s become the Number One target of cybercriminals. Here we look into the six significant healthcare cybersecurity challenges and solutions in today’s digital age.

Phishing

Recent research shows that phishing is the most common cybercrime in the healthcare industry. In a typical phishing attack, users are tricked into disclosing passwords or other relevant personal information. Emails are the most common platform for this cybercrime. For example, a hacker sends an email to a healthcare employee stating that their password is no longer valid and sends a link to reset their password. If the employee is not knowledgeable about phishing or lacks proper training, he may follow the link and reset his password – this is all a hacker needs to put a healthcare institution at risk.

Also read: Best Cybersecurity Training & Courses for Employees

The IoT challenge

The healthcare industry has quickly adopted IoT devices and conducted massive IoT innovations over the past decade. But unfortunately, cybersecurity innovations lag behind IoT innovations and adoption. Although positives have been seen from IoT adoption in the healthcare industry, cybersecurity issues are rising.

Hackers take advantage of IoT providers’ rush to roll out devices without considering the cybersecurity implications. Therefore, with numerous IoT devices circulating in the market and health organizations, hackers easily exploit their vulnerabilities.

Also read: Best IoT Device Management Platforms & Software

Distributed denial-of-service

Hackers devise distributed denial-of-service (DDoS) attacks to flood a business organizations’ network with internet traffic to the point where the business ceases to operate normally. DDoS attacks are usually carried out along with malware or ransomware attacks (which will be discussed later). In sophisticated DDoS attacks, hackers fill a network with massive volumes of data from millions of hacked computers.

Therefore, DDoS attacks are hazardous to healthcare providers who need access to a faster network to provide efficient patient care, including email communication, filling prescriptions, and accessing and retrieving health records.

See also: 5 Best Practices for Mitigating DDoS Attacks

Ransomware attacks

A ransomware attack is a sort of malware attack devised by a cybercriminal to infect systems, devices, and files to gain a ransom from the victim. Most common ransomware attacks come as requests to click on a malicious link, view a malware ad (malvertising), or respond to phishing emails.

Ransomware slows down or ceases business operations until a ransom has been paid to the hacker. Untrained employees may fall into these traps, and it can cost a health organization lots of time and money. A health organization could have used this time and money to invest in new technology or improve patient care standards.

Also read: How to Prevent & Respond to Ransomware

Data breaches

Protected Health Information (PHI) contains personal data, including Social Security numbers, contact information, test results, diagnoses, and prescriptions. There is indeed an active black market for PHI.

So hackers are interested in PHI because an individual’s health and diagnosis history cannot be simply deleted or hidden like credit card numbers. Once hackers obtain this information, they can use it to get loans, medication, insurance claims, or set up credit lines—everything under fake identities.

The Health Insurance Portability and Accountability Act (HIPAA) states that healthcare organizations must practice adequate data security measures in collecting and distributing PHI. But in reality, most organizations fail to update protocols, implement security measures, and adequately staff their IT departments.

Unauthorized disclosure

The unauthorized access or disclosure of PHI is equally dangerous and damaging as a ransomware attack. PHI exposure results from the intentional and accidental negligence of providers and employees.

The South Florida Community Care Network’s case is a real-world example of unauthorized disclosure. In September 2021, the organization announced that a former employee had been emailing internal documents containing PHI to their personal email inbox for several months.

While some of these instances arise from malicious intent, in most cases, these incidents stem from negligence or a lack of proper cybersecurity measures.

Tackling Healthcare Cybersecurity Challenges

Knowledge is power in the digital Information Age. Proper knowledge also plays a significant role in tackling cybersecurity challenges. Let’s look at some of the ways a healthcare organization can improve its cybersecurity efforts to ensure proper management and protection of sensitive data.

Create a cybersecurity culture

It pays well to build a cybersecurity culture into the structure of a health organization. Activities to create this culture include continuous ongoing cybersecurity training and educational programs for each employee that emphasize their role in protecting PHI.

The protection of devices

Since healthcare organizations are undergoing digital transformation and becoming more tech-savvy, their dependence on smartphones, tablets, and other IoT devices has risen. Therefore, these organizations must follow cybersecurity measures like data encryption to ensure data security.

Install antivirus application

Antivirus software enhances network and data security; however, these applications should be constantly updated. Constant updating is essential for a health organization’s protection against ever-changing cyber threats.

A zero-trust policy is the best policy

A health organization shouldn’t make the PHI readily available to anyone. Instead, always exercise control over the network access to PHI under a zero-trust policy. This policy grants access to PHI only to those who view and use it within the limits of their daily work schedules.

See the Top Zero Trust Security Solutions & Software

Maintain strong passwords

This may sound silly but creating and regularly updating strong passwords plays a vital role in an organization’s cybersecurity. A typical strong password is 12 to 14 characters long and should be a combination of numbers, symbols, and upper case and lower-case letters. Not only that, employees must understand the relevance of setting up strong passwords and the difference between strong and weak passwords.

Strong Cybersecurity in Healthcare Demands Expertise

In precisely the same way a health organization cleans up a human health system and helps build strong immunity, several third-party healthcare cybersecurity solutions can help your health organization in various ways. Although you can implement cybersecurity measures, it would be challenging to maintain strong cybersecurity without external yet additional support in a constantly evolving cyber threat landscape.

In addition, an external healthcare solution also improves your organization’s cyber health as it continuously monitors third-party vendor and IoT platforms, safeguards PHI, and remains in compliance with the evolving regulatory standards of the healthcare industry.

See the Best Managed Security Service Providers (MSSPs)

The post Healthcare Cybersecurity: The Challenges of Protecting Patient Data appeared first on IT Business Edge.

However, data migration is a complicated and often expensive process. You will need the right approach to migrating data without error, including well thought-out strategies and appropriate tools.

Also read: Best Cloud Migration Vendors & Services

What is Data Migration?

Data migration refers to the process of transferring data from one storage system to another. It begins with data selection and preparation, during which extraction and transformation takes place. Following this step, permanent data is moved from the old storage system and loaded onto an appropriate data store. Then, the data migration ends with decommissioning the old storage system.

Data migration typically falls into one of two categories:

• Cloud Migration: Data or applications are migrated from a physical storage system to the cloud or between two cloud environments.
• Data Center Migration: Data is migrated from one on-premises data center to another for upgrading or relocation.

After decide where you’re going to migrate, next you need to determine what you need to migrate:

• Storage Migration: Data is moved from one physical storage solution to another.
• Database Migration: Structured, or database managed, data is moved using a database management system.
• Application Migration: Data is migrated from one computing environment to another to support a change in application software.
• Business Process Migration: Business applications and data related to business processes and metrics are migrated.

Why Do You Need Data Migration?

Organizations opt to upgrade their storage systems and consequentially migrate data for several reasons that ultimately help them gain a competitive advantage. Database migration helps companies overcome storage limitations and can facilitate better data management features and processing speed. On the other hand, storage migration is chiefly focused on upgrading to support new technology.

Other scenarios where you might find the need for data migration include:

• You want to upgrade to a new infrastructure to make up for size constraints.
• You want to optimize the overhead costs of running a data center.
• You need to merge new data following an acquisition.
• You need to relocate your data center.
• You want to implement a disaster recovery solution.
• You want to move an application to the cloud, for reasons ranging from ease of maintenance and access to cost

Strategies and Precursors to Data Migration

Strategizing in advance will help you save on costs and prevent downtime to ensure business continuity. It is essential to consider your limitations and understand the overall scope of your data migration project. There are two key factors that you need to consider before launching a data migration project, namely the size and time.

• Data Size: Most datasets are too big to be simply uploaded to the cloud and will need to be shipped on physical devices. This is primarily because of speed and cost constraints. You can send data below 10TB through standard drives, while larger data in the petabyte range will need specialized devices meant for data migration.
• Time Constraints: Bandwidth, network speed and limitations, and dataset size are key considerations when calculating how much time a data migration will take. If data needs to be shipped on physical devices, that time should also be taken into account.

After considering data size and time constraints, you can formulate your project budget and timeline. You also need to decide on the tools and framework for database migration. This will give you an overview of the entire process of data migration.

In addition, you will also need to decide on the migration approach, i.e., to pick between the big-bang approach and doing it in one go or the trickle approach–where you migrate in phases with both systems operating side-by-side.

Also read: 5 Cloud Migration Strategies

Key Steps to Data Migration

Data migration is one of the most critical projects your company will undertake, requiring careful efforts at every step. The reason behind the complexity is that you do not want to compromise data quality, as data-driven businesses will suffer errors in core operations otherwise.

After planning, there are roughly five more stages to data migration:

1. Data preparation involves some key actions targeted at making the data suitable for the migration. Beginning with auditing, an automated process is run to analyze data quality and inform you about inconsistencies, duplicate entries, or poor health. Next, you back up files and establish access levels.
2. Data mapping involves matching the data field between the source and the new destination.
3. Execution is where data is extracted, processed, and loaded to the destination.
4. Testing is ideally a continuous process in data migration, especially when you are migrating data in phases. Once the entire migration process is complete, you need to run another iteration of automated testing, fix the issues, and proceed to go live.
5. Auditing the data again once it is live is necessary to ensure successful completion. You should also run timely audits and monitor the system’s health.

Tools of Migration

There are numerous tools that can assist you through the migration process. And many cloud providers offer their own set of tools. Other tools, including several free and open-source applications such as Data Loader by Salesforce, are also available. Like the migration types, the migration tools can be self-scripted, on-premises, and cloud-based. Other major tools include Amazon’s AWS Data Pipeline, IBM Informix, and Microsoft Azure CosmosDB.

Also read: Successful Cloud Migration with Automated Discovery Tools

Challenges in Data Migration

Data migration is inherently complex, and there are likely going to be several challenges when carrying out this project in your organization.

• Failing to include concerned parties might disrupt your business activities and the data migration process in general. Keep them updated on a weekly basis about the progress.
• Lack of data governance or clarity about who has access to the data in the source system can create confusion and hamper data quality. A clearly defined data governance framework is essential to overcome this challenge.
• A generic and unproven migration method might do more harm than good. Always look for a reliable testimonial-backed service provider, and pick an experienced team.
• Insufficient skills and inadequate tools can both lead to unexpected delays and cost you valuable time. Give it due to research and ensure that the team assigned with data migration is sufficiently trained and has all the necessary tools.
• Planning is indispensable. It might not be sufficient by itself to guarantee successful migration, but it is necessary.

Ready to Migrate Your Data?

While data migration might not sound too daunting in theory, it is a complex process with many variables that must be figured out beforehand. Therefore, you’ll need a specialized team to execute and monitor the data migration process and treat it like a major project.

You can also take advantage of several premium and open-source applications to help you with your data migration. Like the migration types, migration tools can be self-scripted, on-premises, and cloud-based, giving you plenty of flexibility to proceed with your data migration in a way that’s best for your company.

Although it is a major undertaking, you can proceed without hesitation once you have given it due thought.

Read next: Top 7 Data Management Trends to Watch in 2022

The post Strategies for Successful Data Migration appeared first on IT Business Edge.

Consequently, the data analytics market is surging, and now tops $200 billion in annual spending, according to IDC analysts.

Similarly, a rising trend is also seen in the data analytics job market. The U.S. Bureau of Labor Statistics predicts a strong growth of over 30% in data science positions by 2030. Moreover, according to Gartner, nearly every business (up to 90%) is estimated to value information as a critical asset and data analytics as an essential competitive edge.

Several factors are fueling this exponential growth in the data management arena. Here we look at the top seven trends that determine the data management market in 2022 and beyond, as enterprises strive to meet every data-centric demand for competitive edge.

Also read: Best Big Data Tools & Software for Analytics 2022

Top Data Management Trends in 2022

1. Intercloud and multi-cloud technologies

More and more data and applications are moving to the cloud, and this data migration requires business leaders to implement complex data management strategies and technologies. Some include managing data within the same cloud ecosystem, handling different cloud services, or using an on-premises data management system.

In fact, a 2021 IDC survey found that nearly 82% of businesses currently use or plan to use multiple clouds within the next 12 months.

Multi-cloud technology allows a data management service to operate on more than one cloud ecosystem. On the other hand, intercloud technology lets data management systems to seamlessly collaborate using different cloud services running on diverse cloud ecosystems.

As such, multi-cloud and intercloud data management are becoming more crucial to support diverse data management strategies.

Also read: Successful Cloud Migration with Automated Discovery Tools

2. Artificial intelligence

The COVID-19 pandemic and remote work culture have significantly changed the way enterprises all over the globe collect and analyze data, creating a new data-driven business culture. As a result, this new data-driven business culture fuels investments in analytics based on artificial intelligence (AI).

AI, machine learning (ML), and automation are game-changers for every business all over the globe. These technologies augment human capabilities in data analytics and help create better business value. For example, AI can help increase sales by predicting market demand and keeping an appropriate supply of products at warehouses.

Also read: Top Artificial Intelligence (AI) Software

3. AnalyticsOps

AnalyticsOps is the only way to manage the highly complex AI and other advanced data analysis approaches. Simply put, AnalyticsOps is an information technology (IT) framework that monitors the automation of analytics across a business organization.

It comprises a series of steps, integrated processes, and technologies that helps an enterprise successfully deliver business value from AI-based advanced analytics models. As a result, AnalyticsOps frameworks eliminate silos and speed up a time to value by collating data science, IT engineering, and the business.

4. Data fabric

As volumes and data types continue to increase as businesses migrate to the cloud, seamlessly weaving together a network’s data is necessary to make a company more efficient and profitable.

Data fabric is a cloud-based architecture that uses a data storage ecosystem in theory and practice. It offers large sets of tools, granting centralized access to data from multiple sources. This single view of data can be used across the network.

Data fabric system offers several benefits, such as eliminating data silos, enabling hybrid cloud, simplifying data management, reducing data disparity, and augmenting scalability.

5. Blockchain technology

Bitcoin introduced Blockchain technology, also known as Distributed Ledger Technology (DLT). It helps enterprises keep more secure transaction records, audit trails, and create assets. DLT, along with blockchain technology, stores data in a decentralized way devoid of alteration but with improved authenticity and accuracy.

In simpler terms, DLT and blockchain technology are all about creating a decentralized network beyond the conventional centralized networks and systems, which rely on a third-party authority. As a result, these technologies have far-reaching consequences on different industries and sectors and their data management strategies.

Also read: Potential Use Cases of Blockchain Technology for Cybersecurity

6. Edge computing

The edge computing market is expanding at nearly a 20% compound annual growth rate (CAGR) every year. It is also estimated to grow from $36.5 billion in 2021 to $87.3 billion in 2026. As computing power moves to the edge—that is, smartphones and Internet of Things (IoT) devices—technologies such as data analytics are more likely to reside at the edge.

Therefore, edge computing brings speed, agility, and flexibility by supporting real-time data analytics. In addition, it also provides autonomy for IoT devices.

Moreover, the data analytics potential of edge computing is so vast that Gartner predicts that 50% of the data analytics job will be done on the data created, managed, and analyzed at the edge by 2023.

See also: Edge AI: The Future of Artificial Intelligence and Edge Computing

7. The transition from big data to small and wide data

AI, data fabric, and composable analytics enable businesses to collect and analyze the combination of micro and macro data and structured and unstructured data, applying techniques that derive valuable insights.

Composable data analytics combine and utilize several analytics techniques from multiple data sources. As a result, it helps enterprises make more effective and intelligent decisions.

In addition, tools like composable data analytics provide greater agility than traditional approaches and tools. They also let organizations utilize reusable and swappable modules that can be deployed anywhere, including containers.

Enterprises are more likely to continue leveraging and harnessing their capability to access big, small, and broad data sources in the coming years. According to a Gartner study, by 2025, 70% of enterprises will shift their focus from big data to small and wide data—the data derived from a wide array of sources. It gives more space for comprehensive analytics and intelligent decision-making.

Prioritize Data Management for Effective Decision Making

Managing data efficiently in a complex, data-driven digital world empowers the successful operations of every organization across all industries all over the globe. The digital world is cluttered with heavy chunks of data. However, if your enterprise has access to efficient data management and analytics, it opens the door to seize more opportunities, raise more questions, and solve more problems.

Since almost all enterprises collect data today, it makes sense to manage it well to provide better insights. Moreover, the need for real-time data analysis will also rise with expanding volume, variety, and velocity of data. And those trends will put enterprises under tremendous pressure to make efficient data management their highest priority.

In a data-driven world, only the businesses that successfully derive actionable insights by harnessing core data management technologies can innovate faster, devise better strategies, and manage change more effectively.

Read next: Top Data Quality Tools & Software

The post Top 7 Data Management Trends to Watch in 2022 appeared first on IT Business Edge.

In automation and interconnectivity, high-speed wireless communication plays a significant role, as it acts like a bridge between seamless yet scalable connectivity and machines, sensors, and users. It also connects the Internet of Things (IoT), robots, drones, and automated guided vehicles (AGVs). Another benefit comes in the form of eliminating cables from devices with limited mobility.

The expansion of Industry 4.0 has hastened the demand for faster and more secure connectivity. The fifth-generation cellular network (5G) offers the stability and speed to connect all these devices and then fetch and analyze the data.

5G for Industrial Applications, a new study by ABI Research, predicts widespread adoption of 5G technology in the manufacturing sector by 2028. The study also reveals that the manufacturing industry alone will generate 25% of the total revenue in the 5G global market.

Advantages of 5G in Industrial Automation

Faster and reliable digital connectivity

5G, the successor to the fourth-generation cellular network (4G), enables faster data transfer over the internet. Not only does 5G enhance the digital connectivity of users but also the connectivity of sensors and other IoT devices. 5G offers data transfer rates at 20Gbps with a low latency of one millisecond or, in other words, without any delay.

More than anything else, 5G is nearly as reliable as wired connectivity and makes it possible to conduct critical communications in real time. This sets the groundwork for a reliable, faster, and secure operation of applications and devices. Moreover, 5G offers new opportunities where other wireless technologies like Wi-Fi will not be sufficient.

Competitive edge

Currently, the number of IoT devices connected to the internet is many times greater than the number of actual human netizens. Recent research predicts that IoT devices worldwide will cross 31 billion units by 2025, a sharp rise from roughly 14 billion units in 2021. This additional number of connected devices is mainly used in industrial applications and automation.

Several IoT companies are investing a great amount on 5G technology research, as they realize it can revolutionize the automated and connected smart factories of the future. Thus, adopting 5G technology is crucial in gaining a competitive edge in today’s market. However, if a business fails to adopt it early enough, it will be left behind in the market.

Also read: Best Enterprise 5G Network Providers 2022

Secured, enhanced, and flexible production

In a smart factory with 5G networking, only walls, ceiling, and floor will be the immovable components. Every other part will be scalable, portable, and easily reconfigurable. 5G networking creates a wireless yet high-performing infrastructure that enables efficient communication between machinery, people, and facilities.

Moreover, 5G technology allows the implementation of new industrial manufacturing concepts. It also has the potential to streamline gadgets and workforces in the field of industrial production and logistics.

Guaranteed data sovereignty

With the advent of 5G technology, industrial production lines got the first-time opportunity to set up, operate, and tailor local networks precisely for industrial applications. Additionally, it allows users to bring every relevant security aspect under their control. In this way, businesses can reduce cybersecurity and enterprise risks by guaranteeing data sovereignty.

More straightforward conversion to 5G technology

It is true that 5G technology accelerates data transmission speed among IoT devices and gives an extra boost to Industry 4.0. Everything from logistics to production lines benefits from faster, real-time data transmission.

But all of this is only possible if the business installs 5G-enabled devices and networks in the first place. As a result, several globally leading IT companies have begun helping businesses implement 5G technology in industrial automation from scratch.

Practical Use Cases of 5G Technology in Industrial Automation

Here are five practical 5G technology use cases and the companies that are currently pioneering these innovative approaches.

Industrial process automation

Smart factories powered by 5G technology automates monotonous, labor-intensive, and dangerous tasks. This brings down human errors and the risk of fatal accidents and, at the same time, provides the workforce with more time to concentrate on critical tasks.

MTU Aero Engines, a German aircraft engine manufacturer, experimented with 5G technology and made their operations more efficient. The innovations include testing their applications on blade integrated disks (blisk), a high-tech jet engine component, and reducing the manufacturing time of blisks by 75% using a smart factory.

Remote monitoring

In smart factories, production lines can be monitored and controlled remotely without the need for workers or operators on the factory floor. With its high-speed data transmission and lower latency, 5G technology makes real-time remote monitoring easy.

To take a real-world example, Siemens installed its first real-time remote monitoring system for Factory Acceptance Tests (FAT) in one of its factories in Mexico.

Robotics

Industrial manufacturers have been employing robots for quite a long time, but the scene has totally changed with the advent of 5G technology.

Robots, designed to work alongside humans, are used mainly to move goods from one location to another. Previously, these robots were connected using a wired network, but 5G technology eliminated the wired network system and allowed faster and more efficient robotics.

KT Corp, South Korea’s leading telecom company partnered with Hyundai Engineering & Construction to develop 5G network infrastructure at construction sites. Their partnership has an objective to develop construction and automation technology. Along with that, they plan to deploy robots over the 5G infrastructure to boost productivity and efficient monitoring at construction sites.

Predictive maintenance

A recent Wall Street Journal report states that unpredictable downtimes cost more than $50 billion each year for industrial manufacturers. HIROTEC, a globally leading automobile parts manufacturer, deployed an IoT cloud platform and edge analytics to get real-time visibility into the efficiency of its business operations.

HIROTEC deployed all these industrial automation initiatives over a 5G network as a way to leverage machine learning (ML) to predict and prevent downtime and mishaps. The result led to reduced downtime and accidents as well as the elimination of manual inspections.

3D printing

3D printing, also known as additive manufacturing, has already made ripples in industrial manufacturing, particularly for spare parts and construction. The higher bandwidth and lower latency of 5G technology are revolutionizing the arena of construction.

For instance, Hadrian X, an autonomous bricklaying robot and the first of its kind in the world, does its job effortlessly, as it can quickly process massive amounts of data transferred over a 5G network.

When 3D printing is synchronized with the speed and low latency of 5G technology, a four-bedroom house can be printed 95% percent faster and up to 90% cheaper. So, now there are no limits to the imaginations of the construction companies and architects. They are now free to design and construct buildings beyond traditional design-to-cost limitations.

5G Technology: The Central Nervous System of Industry 4.0

The emergence of 5G technology will transform how Industry 4.0 produces and distributes goods and services. The key features of 5G technology, such as lower latency, higher reliability, and increased speed, support emerging technologies and their innovative approaches and applications in smart factories.

CNBC reports that by 2023, smart factories, mainly because of their efficiency and cost-effectiveness, will contribute more than $2 trillion to the global economy.

Read next: 5G and AI: Ushering in New Tech Innovation

The post 5G and Industrial Automation: Practical Use Cases appeared first on IT Business Edge.

A sustainable business looks forward to a broader horizon that includes present and future generations. Additionally, it devises innovative business strategies to bring positive social and environmental impact while accelerating business performance.

With 5G and other data-driven technologies, organizations can begin moving toward both environmental and enterprise sustainability.

The Rise of Sustainability Efforts

Today, preserving nature and fighting environmental issues like atmospheric pollution and climate change has become a business imperative rather than a corporate social responsibility (CSR) activity.

There is no doubt that risks and opportunities associated with environmental issues pose challenges to the strategies and operations of all enterprises in all shapes and sizes. But on the positive side, the ecological sustainability strategy on a broader scale will define an enterprise’s prospects in the current competitive marketplace.

Environmental challenges are plenty, and it stands among the top five risks for a business. So, these days investors and entrepreneurs can’t shy away from implementing sustainability ideas and strategies in their organizations.

In 2020, Blackrock, the largest fund manager globally, declared sustainability as their new standard for investing, and it embarked on a mission to make their customers and employees more environmentally conscious.

In addition, 80% of respondents in a recent IBM research study personally favor environmental sustainability, while 60% are ready to change their consumer behavior to bring down environmental impact.

However, while it is easy to talk about environmental sustainability efforts, putting them into practice is much more difficult.

Digital Transformation and Sustainability

Luckily, today’s digital transformation (DX) initiatives by most enterprises worldwide will make a huge difference in environmental sustainability. This is because there are several innovative technologies like the fifth generation of cellular networks (5G), artificial intelligence (AI), Internet of Things (IoT), cloud, and blockchain that drive DX. 

These technologies accelerate sustainability in three ways:

• They utilize data to attain new insights and reach new solutions to existing problems.
• They transform business practices and operations, creating a sustainable enterprise.
• They create a new enterprise governance model forging public, private, and non-profit collaboration with environmental sustainability.

In short, since all these technologies are data-driven by nature, DX brings greater transparency and insights into business operations.

It transforms the way enterprises, investors, consumers, and governments buy, sell, produce, consume, transform, and operate businesses. This transition can even positively influence how economies function all over the globe and, in turn, bring improved environmental sustainability.

Also read: Top Digital Transformation Companies & Services 2022

Ways 5G Influences Environmental and Enterprise Sustainability

Energy efficiency

It is proven that fourth-generation cellular network (4G) stations consume more energy than the 5G base stations. Telecommunications giant Huawei reports that nearly half of the energy consumption of 4G base stations is used to cool down the transmission equipment.

Another recent research reveals that 5G technology brings down carbon emissions by nearly 80% and reduces operating costs by almost a third.

Streamlined water management

With just 3% of the water resources being consumable and just two-thirds of it being accessible makes water a valuable resource. Moreover, the lack of streamlined water management can lead us to face water shortages by 2025.

The World Bank found that the agricultural sector consumes an average of 70% of the world’s freshwater per year. But unfortunately, farmers worldwide use obsolete irrigation systems that use water inefficiently and impact climate change.

IoT devices paired with 5G technology helps transfer, monitor, and analyze agricultural data like soil moisture levels, pesticide levels, weather conditions, and other valuable information at record speeds. In addition, 5G grants farmers access to multiple technologies like GPS systems, chlorophyll sensors, and sprayer control to manage crops and water resources more efficiently.

The technologies leveraging 5G help farmers and the field of agriculture streamline water management. And, it can also do the same in cities.

5G technology set the ground for large-scale IoT sensor deployments that streamline water management. These inexpensive IoT devices detect dangerous chemicals, manage leaks in the water supply, alert people of possible health hazards, early flood warnings, and transform the agriculture industry.

Enhanced traffic management

As per a recent WHO report, the environmental pollution from vehicles kills over 3 million people globally every year. With 5G technologies, we can monitor traffic operations and devise more effective and scalable traffic designs to reduce carbon emissions.

Another 5G-enabled innovation that reduces carbon emissions would be driverless, or connected, cars. These fully automated cars connected over a 5G network, with streamlined cruise control and automated driving features, can improve energy efficiency up to 20–30%.

Energy efficient smart buildings

The heating, lighting, cooling, and other operations of buildings use up to 40% of the energy consumed globally. Smart buildings connected with 5G networks using automation will save energy, bring down carbon emissions, and fight climate change.

IoT sensors over a 5G network automatically turn lights off when not needed and adjust the lighting per the natural lighting. As a result, it reduces energy consumption by up to 70%.  

Beyond lighting, automated temperature control will also reduce energy utilization. Sensors connected to a high-speed 5G network monitor temperature levels, ventilation, and air conditioning automatically adjust the temperature.

A real-life example of a smart building is the Empire State Building in New York City. It has implemented sensors and meters that measure each tenant’s energy usage, letting them optimize energy utilization. These automated meters and sensors bring down energy costs by nearly 40% and cut the building’s carbon emissions by more than 100 thousand tonnes within a year.

Challenges of Implementing 5G for Sustainability

While 5G technologies offer several benefits to environmental and enterprise sustainability, there are still a few challenges for the effective implementation of the technology in our daily lives. 

Information technology (IT) is currently accountable for roughly 4% of global electricity consumption and nearly 2% of global carbon emissions, according to the 2020 report of the Information Technology and Innovation Foundation. And it has been anticipated that the rollout of 5G technology and devices can increase the current rate of IT energy consumption and carbon emissions.

Another 5G challenge is associated with the increased electronic waste, or e-waste, generation with the increased adoption of 5G devices and networks all over the globe. However, solutions like decarbonization, implementing more efficient cooling systems, recycling, and network sharing can overcome these challenges.

It is doubtless that 5G technology has the capability to reduce global greenhouse gas emissions to a more significant extent and to efficiently streamline business operations at any level.

From a futuristic point of view, every business leader must take responsibility for their enterprise’s environmental impact by embracing 5G technology as a positive change. The businesses that leverage 5G technology to bring sustainability will definitely become trendsetters in this age of digital transformation.

Read next: Best Enterprise 5G Network Providers 2022

The post The Role of 5G in the Sustainability Fight appeared first on IT Business Edge.

It features a flexible use of resources that can be scaled based on real-time requirements. As a result, it is favored by DevOps thanks to its quicker development lifecycle.

How Does Serverless Computing Work?

Serverless computing provides provisioning, scheduling, scaling, and other back-end cloud infrastructure and operations tasks to the cloud provider. As a result, developers get more time to develop front-end applications and business logic. This eases up on the workload of your teams and ensures their maximum focus is on innovation.

Though technically, it does use servers, it is called serverless computing because the servers, in this case, are hosted by a third-party service provider, making them seemingly non-existent to the customer, who is not responsible for managing them. This is an essential step towards NoOps (no operations).

Every major cloud service provider offers a serverless platform, including Microsoft Azure, Google Cloud, and Amazon Web Services. Serverless is also at the core of cloud-native application development.

Also read: Is Serverless Computing Ready to Go Mainstream?

How are Serverless Platforms Improving DevOps?

The serverless model is better suited for certain customers than the IaaS and SaaS models, which demand a fixed monthly or yearly price. Sometimes developers do not need to use the entire capacity offered by their cloud solution.

In such cases, serverless computing provides a fine-grain, pay-as-you-go model, so you only pay for the resources consumed for the life of the called function. This can lead to significant reductions in the projected cost, allowing for greater savings over other cloud models for many workloads.

However, serverless models can only be considered an evolving technology at best. Considering it as a universal solution for development and operations problems, this model can lead to certain drawbacks.

That being said, IT professionals have reported using serverless for a large array of applications, including customer relationship management (CRM), finance, and business intelligence.

Popular Applications of Serverless Computing

Many major cloud service providers offer serverless platforms to users who can finally enjoy a NoOps state, including Amazon, Google, and Microsoft. Serverless platforms by Alibaba, IBM, and Oracle, among others, are soon to follow. At the same time, open-source projects such as OpenFaaS (function as a service) and Kubeless are bringing serverless technologies to on-premises architecture. 

Get support for microservice architecture

One key usage of serverless computing is its support for microservice architectures, which enable the creation of small services with a singular job that can use APIs to connect to one another.

Serverless is uniquely suited for this model, which needs to run code that supports automatic scaling. Plus, the pricing model functions in such a way that you aren’t charged when no operations are running as opposed to PaaS or containers.

Also read: Securing Your Microservices Architecture: The Top 3 Best Practices

Work with different file types

Serverless works perfectly well with files in most formats such as video, image, text, or audio. You can carry out various functions such as data transformation and cleansing. In addition, text processing, such as PDF processing, sound manipulation like audio normalization, or video and image processing is also possible.

Compute parallel tasks

Any parallel task presents an excellent example for serverless runtime, and each parallel task triggers one action. Such tasks may include searching and sorting objects stored on the cloud, such as web scraping or map operations. Further, you can perform complex tasks like business process automation or hyperparameter tuning.

Robust foundation for streaming applications

Using FaaS, it is possible to build a steady foundation for the real-time creation of data pipelines and streaming apps. It is compatible with all kinds of data streams, including log data for IoT and other applications, validation, cleansing, enrichment, and transformation.

Test service continuity

You can set up FaaS, such as AWS Lambda functions, to make API calls to your services, much like API calls made by users. You can even create a mock flow of traffic to the services in production using FaaS.

These are good practices to test your service continuity periodically. Any failures that you might encounter are visible in your monitoring tool, so you are aware of failures or any performance drops.

Serverless pipelines for continuous deployment

You can use serverless to improve the CI/CD (continuous integration and continuous delivery) process and automate the entire process, from merging pull requests to deploying in production. And since FaaS functions are cost-efficient and easy to set up, DevOps engineers can focus on other parts of the infrastructure and further reduce costs.

Also read: Effectively Using Low-Code/No-Code in the Developer Cycle

Advantages of Using DevOps with Serverless Computing

Serverless computing has the potential to transform IT operations. By extending its property of levying charges based on function calls, developers can enjoy several applications. Some of the other major benefits of serverless computing include:

• Infinite scalability: Serverless computing allows you to scale functions horizontally and elastically based on the user traffic.
• NoOps: Infrastructure management in serverless computing is completely outsourced, so your in-house teams only need to deal with the operational tasks.
• No idle time costs: Legacy cloud computing models charge you per hour for running virtual machines. With a serverless computing model, you only need to pay for the execution duration and the number of functions executed.

Drawbacks of Serverless Computing

Serverless computing has enabled a range of operations, and organizations can run many different applications on it. However, it might not be the best choice for specific applications, which leads to a few possible disadvantages, including:

• Stable or predictable workloads: Serverless offers the most cost-effective model for unpredictable workloads. However, steady workloads with predictable performance requirements do not need this feature. Instead, traditional systems suit it better as they are much simpler and can be cheaper than serverless in such cases.
• Cold starts: Serverless architectures are optimized for scaling up and down to zero, but not long-running processes, meaning they might be starting up from zero for a new request. This might cause a noticeable startup latency, which might not be acceptable to specific users.
• Monitoring and debugging: A serverless architecture aggravates the complexity of the already challenging operational tasks. For example, debugging tools are typically not updated to meet the requirements of serverless computing.

The Future of Serverless Computing in DevOps

Serverless computing works uniquely well with DevOps, opening a vast array of applications faster at lower cost and complexity of architecture. Developers rely on it for its various functions which offer several unique features.

The concept of serverless computing is constantly evolving to solve more and more development and operational challenges. Though there are certain challenges that need addressing, the tools and strategies in serverless will eventually adapt to serve DevOps better. Today, most major cloud service providers are betting on serverless, and one can expect better-optimized solutions in the future.

Read next: Best DevOps Monitoring Tools for 2022

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When it comes to establishing a smooth and influential change process, change management is a project management approach designed for that very purpose. This article defines change management, discusses its benefits for an organization’s success, and serves as a step-by-step guide to adopting an effective change management strategy.

What is Change Management?

Change management is a structured approach toward change. Change often requires a certain amount of cooperation between every level and individual entity within organizations. Enacting change management helps employees achieve a common goal and promote the advancement of an entire company. 

If a company does not have a plan for implementing, monitoring, and reporting on the direction of change, the chances of failure are greater. Harnessing change through methodical means facilitates smoother transitions with an increased success rate. 

There are three common types of organizational change: 

• Developmental change: This type of change refers to developing or enhancing an existing process in the organization. This includes improving existing skills, processes, practices, performance standards, or conditions. 
• Transformational change: Transformational change is a more drastic type of change. It often involves doing away with or bringing about extreme change in work processes.
• Transitional change: Transitional change happens when the company emerges into a new state of working, approach, or management. The clearest example of transitional change is when a company is merged or taken over by another company. 

Also read: Has Remote Work Really Benefited Enterprises?

Benefits of Change Management

• Change management provides a clear framework for everyone to follow. This framework is based on the best practices for change and aimed at the best results, making the process of change less complicated and more scalable. 
• The implementation of change management lessens the chance of risks and disruption. And, with a clear sense of direction and a roadmap to follow, employees can chart inconsistencies and rectify them almost immediately. 
• Change management reduces the amount of time required to implement change by eliminating unnecessary hindrances and setting coherence between departments when it comes to achieving the same goal. This allows businesses to respond faster to customer demands.
• A change management strategy helps align change with business strategy. A constructive strategy should always consider the organization’s long-term goals, allowing businesses to make headway toward their ultimate goal while executing small changes. 
• Through strategizing, it becomes easier to assess the overall differences brought by change. Moreover, measuring the impact of one change initiative helps better identify faults and needs, which helps shape the next strategy to work even better. 
• Planning what to change also brings people together to work collaboratively toward the same goal. This boosts employee morale and gives everyone a sense of purpose.
• With better response time, higher success rates, and enhanced employee morale, organizations gain an edge over their competitors. These qualities are just a supplement brought in by a robust change management strategy other than its main objective.
• Change management helps businesses stay true to their budget. With predetermined areas of change and fewer chances of setbacks and downtimes, change management makes change more affordable.

Key Steps in Change Management

Define change

While businesses may be eager to replace an ongoing process with a better-suited one, it is necessary to determine whether changing it is required. Will the alternatives meet expectations for desired results? Or will the anticipated results align with the organizational goals?

These questions need to be addressed at all management levels. Once the need for change has been specified, the next step is to compare the current scenario with the outcomes of the change along with any other situation with better outcomes.

Build engagement

Ideally, any change management program should start with educating the workforce about the change and how it will affect operations. Business leaders or influential managers must share the effect of the change along with the challenges and benefits of implementation with everyone within the same work environment.

When more people understand the need for change and its broader implication, higher engagement reduces the chances of failure. Hence, change leaders must perform a readiness assessment in the initial stages to determine the organization’s readiness to embrace change.

Strategizing in a top-down manner

The strategizing stage is all about assembling the right change management team. This should include major stakeholders, management, departments, and individuals. With a top-down involvement approach, major concerns can be addressed more thoroughly.

Moreover, with different inputs from everyone, the change drivers can record more scenarios and devise a workable plan for everyone’s benefit. Such an approach makes it possible to address the concerns of stakeholders without compromising the success of the plan.

Also read: Why Business Technologists are Becoming Indispensable

Achieve goals in parts

Change management is more than knowing in what direction the change is taking the organization. It is to steer the organization’s collective efforts and adapt at every stage of change-making. 

A major advantage of achieving change step-by-step is that the change drivers can alter the plan for greater benefits and save time by eradicating the need to go back to the previous step in case of a miscalculation. In addition, establishing key performance indicators (KPIs) and setting milestones can help gauge the progress more effectively.

Analyze, rectify, proceed

Quantifying the gaps and resistance working against the original plan can help make the plan more fruitful. The plan that the organization may have initially started with may not seem suitable to adhere to at some point in time.

This is why it is necessary to alter or rectify the plan in accordance with the desired outcome. If the organization fails to stop and reflect at crucial moments, the plan may not bring the preferred outcome, or it may even go wrong.

Reinforce change-oriented values

As an organization gains familiarity with the practice of change management, it can implement change without experiencing any lapses and expedite the process. For this to happen, executives must constantly engage employees to accept change and feel motivated to implement change.

Employees need to get acquainted with goal assessment and acceptance toward outcomes, aiming for higher success rates, rewards, consequences, and such variables. In addition, business leaders should make an effort to ingrain the change program into the company culture and create a change-friendly environment.

Start With Small Steps

In an ever-changing market, change management is a vital methodology for the long-term success of businesses of all sizes. An organization that will condition itself to implement change will shine on top of its industry due to its ability to quickly adopt new technologies, respond to crises, and leverage previously untapped skills.

Before planning a large-scale change program, try exploring a small pilot project. This will help your organization discover the benefits of this initiative, encourage adoption by attracting participants, and better identify threats when jumping into it.

Read next: Best Decision Making Tools & Software

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