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.

First, its chip business is second to none. Broadcom semiconductors can be found in everything from your Raspberry Pi and mobile phone to your data center’s high-end storage and networking devices, plus sensor, automotive, LED and many other cutting-edge applications.

And for the last few years, the company has been diversifying into higher-margin enterprise software. CEO Hock Tan saw enterprise software as an important growth market for the company, so they bought CA’s IT management business and Symantec’s enterprise security business – two prescient moves, particularly in security – and now it looks like Broadcom may be acquiring VMware.

Acquiring VMware would give the company a strong position in critical enterprise cloud computing and virtualization markets on top of all those other important markets. The growing breadth of the company’s enterprise offerings has us wondering if we’re looking at the emergence of the new IBM.

See the Top Cloud Providers & Companies for 2022

A Rising Tech Star

IBM’s annual revenues have been falling for more than a decade, from a peak of $106.9 billion in 2011 to around $60 billion now. About $20 billion of that decline came from spinning off its services unit as Kyndryl, but much of the drop has come as Big Blue has been repositioning itself for a cloud and AI world.

Over that same time period, Broadcom’s sales have soared from about $2 billion in 2011 to around $30 billion now. If VMware’s revenues are added to that mix, Broadcom would have sales of more than $40 billion, roughly where Oracle is today.

Wall Street has been valuing Broadcom as an emerging player with a great deal of potential. Among Forbes 2000 tech companies, Broadcom is 19th in revenue. But more importantly, it’s 9th in market capitalization – investors are assigning it a value well above its current level of business (see chart below; Forbes data from earlier this month). Interestingly, just about every company with a higher market value on that list gets the bulk of its revenues from consumers.

Broadcom is as ambitious as it is well positioned, so it’s reasonable to assume that the current trend will continue. The company in the past has tried unsuccessfully to acquire analytics giant SAS and fellow mobile chip maker Qualcomm. An analytics acquisition still makes sense, as nothing is hotter in enterprises than data analytics. But a VMware acquisition would potentially be an even better fit; the two are complementary in data center and cloud operations, so there would be little overlap.

Expect Broadcom to continue to rise even after the anticipated VMware deal. Privately, some employees have said they wouldn’t be surprised to see Broadcom acquire Intel someday. Neither would we.

Read next: 8 Top Data Startups

The post Broadcom: The New IBM? appeared first on IT Business Edge.

In the past few years, data has exploded in size and complexity. For example, the amount of data created, captured, copied, and consumed worldwide will hit 181 zettabytes by 2025, up from only two zettabytes in 2010.

This fact has made it difficult for businesses to promptly gather, analyze, and act on data. However, DataOps (data operations) is a software framework that was created to address this very problem.

What is DataOps?

Introduced by IBM’s Lenny Liebmann in June 2014, DataOps is a collection of best practices, techniques, processes, and solutions that applies integrated, process-oriented, and agile software engineering methods to automate, enhance quality, speed, and collaboration while encouraging a culture of continuous improvement in the field of data analytics.

DataOps began as a collection of best practices but has since grown into a novel and autonomous data analytics method. It considers the interrelatedness of the data analytics team and IT operations throughout the data lifecycle, from preparation to reporting.

Also read: 6 Ways Your Business Can Benefit from DataOps

What is the Purpose of DataOps?

DataOps aims to enable data analysts and engineers to work together more effectively to achieve better data-driven decision-making. The ultimate goal of DataOps is to make data analytics more agile, efficient, and collaborative.

To do this, there are three main pillars of DataOps:

• Automation: Automating data processes allows for faster turnaround times and fewer errors.
• Quality: Improving data quality through better governance and standardized processes leads to improved decision-making.
• Collaboration: Effective team collaboration leads to a more data-driven culture and better decision-making.

DataOps Framework

The DataOps framework is composed of four main phases:

• Data preparation involves data cleansing, data transformation, and data enrichment, which is crucial because it ensures the data is ready for analysis.
• Data ingestion handles data collection and storage. Engineers must collect data from various sources before it can be processed and analyzed.
• Data processing is the process of data transformation and data modeling to transform raw data into usable information.
• Data analysis and reporting helps businesses make better decisions by analyzing data to generate insights into trends, patterns, and relationships and reporting the results.

DataOps tools operate as command centers for DataOps. These solutions manage people, processes, and technology to provide a reliable data pipeline to customers.

In addition, these tools are primarily used by analytics and data teams across different functional areas and multiple verticals to unify all data-related development and operation processes within an enterprise.

When choosing a DataOps tool or software, businesses should consider the following features:

• Collaboration between data providers and consumers can guarantee data fluidity.
• It can act as an end-to-end solution by combining different data management practices within a single platform.
• It can automate end-to-end data workflows across the data integration lifecycle.
• Dashboard and visualization tools are available to help stakeholders analyze and collaborate on data.
• It can be deployed in any cloud environment.

Also read: How to Turn Your Business Data into Stories that Sell

5 Best DataOps Tools and Software

The following are five of the best DataOps tools and software.

Census

Census is the leading platform for operational analytics with reverse ETL (extract, transform, load), offering a single, trusted location to bring your warehouse data into your daily applications.

It sits on top of your existing warehouse and connects the data from all of your go-to-market tools, allowing everyone in your company to act on good information without requiring any custom scripts or favors from IT.

Over 50 million users receive personalized marketing thanks to Census clients’ performance improvements, including a 10x sales productivity increase due to a support time reduction of up to 98%.

In addition, many modern organizations choose Census for its security, performance, and dependability.

Key Features

• Work With Your Existing Warehouse: Because Census operates on top of your current warehouse, you can retain all your data in one location without the need to migrate to another database.
• No-Code Business Models: With the simple interface, you can build data models without writing code, allowing you to focus on your business instead of worrying about data engineering.
• Works at Scale: Census is built to handle data warehouses with billions of rows and hundreds of columns.
• Build Once, Reuse Everywhere: After you create a data model, you can use it in any tool connected to your warehouse. This means that you can build models once and use them in multiple places without having to recreate them.
• No CSV Files and Python Scripts: There is no need to export data to CSV files or write Python scripts. Census has a simple interface that allows you to build data models to integrate into sales and marketing tools without writing code.
• Fast Sync With Incremental Batch Updates: Census synchronizes data in real time, so you can always have the most up-to-date data. Incremental updates mean that you never have to wait for a complete data refresh.
• Multiple Integrations: Census integrates with all of the leading sales, marketing, collaboration, and communications tools you already use. These include Salesforce, Slack, Marketo, Google Sheets, Snowflake, MySQL, and more.

Pros

• It is easy to set up and sync a data pipeline.
• Census offers responsive and helpful support.
• The solution reduces engineering time to create a sync from your data warehouse to third-party services.

Cons

• Many integrations are still in active development and are buggy to use.

Pricing

Census has three pricing tiers:

• Free: This tier only includes 10 destination fields but is ideal for testing the tool’s features.
• Growth: At $300 per month, Growth includes 40 destination fields as well as a free trial.
• Business: At $800 per month, Business includes 100 destination fields and a free demo.
• Platform: This is a custom solution for enterprises that would like more than 100 destination fields, multiple connections, and other bespoke features.

Mozart Data

Mozart Data is a simple out-of-the-box data stack that can help you consolidate, arrange, and get your data ready for analysis without requiring any technical expertise.

With only a few clicks, SQL commands, and a couple of hours, you can make your unstructured, siloed, and cluttered data of any size and complexity analysis-ready. In addition, Mozart Data provides a web-based interface for data scientists to work with data in various formats, including CSV, JSON, and SQL.

Moreover, Mozart Data is easy to set up and use. It integrates with various data sources, including Amazon SNS, Apache Kafka, MongoDB, and Cassandra. In addition, Mozart Data provides a flexible data modeling layer that allows data scientists to work with data in various ways.

Key Features

• Over 300 Connectors: Mozart Data has over 300 data connectors that make it easy to get data from various data sources into Mozart Data without hiring a data engineer. You can also add custom connectors.
• No Coding or Arcane Syntax: With Mozart Data, there is no need to learn any coding or arcane syntax. All you need to do is point and click to get your data into the platform.
• One-Click Transform Scheduling and Snapshotting: Mozart Data allows you to schedule data transformations with a single click. You can also snapshot your data to roll back to a previous version if needed.
• Sync Your Favorite Business Intelligence (BI) Tools: Mozart Data integrates with most leading BI tools, including Tableau, Looker, and Power BI.

Pros

• The solution is easy to use and requires little technical expertise.
• It offers a wide variety of data connectors, including custom connectors.
• Users can schedule data transformations with a single click.
• Mozart Data has straightforward integrations with popular vendors such as Salesforce, Stripe, Postgres, and Amplitude.
• A Google Sheets sync is available.
• Mozart Data provides good customer support.

Cons

• Non-native integrations require some custom SQL work.
• The SQL editor is a bit clunky.

Pricing

Mozart data has three pricing tiers starting at $1,000 per month plus a $1,000 setup fee. All plans come with a free 14-day trial.

Databricks Lakehouse Platform

Databricks Lakehouse Platform is a comprehensive data management platform that unifies data warehousing and artificial intelligence (AI) use cases on a single platform via a web-based interface, command-line interface, and an SDK (software development kit).

It includes five modules: Delta Lake, Data Engineering, Machine Learning, Data Science, and SQL Analytics. Further, the Data Engineering module enables data scientists, data engineers, and business analysts to collaborate on data projects in a single workspace.

The platform also automates the process of creating and maintaining pipelines and executing ETL operations directly on a data lake, allowing data engineers to focus on quality and reliability to produce valuable insights.

Key Features

• Streamlined Data Ingestion: When new files arrive, they are handled incrementally within regular or continuous jobs. You may process new files in scheduled or ongoing processes without keeping track of state information. With no requirement for listing new files in a directory, you can track them efficiently (with the option to scale to billions of files) without listing them in a directory. Databricks infers and evolves the schema from source data as it loads into the Delta Lake.
• Automated Data Transformation and Processing: Databricks provides an end-to-end solution for data preparation, including data quality checking, cleansing, and enrichment.
• Build Reliability and Quality Into Your Data Pipelines: With Databricks, you can easily monitor your data pipelines to identify issues early on and set up alerts to notify you immediately when there is a problem. In addition, the platform allows you to version-control your pipelines, so you can roll back to a previous version if necessary.
• Efficiently Orchestrate Pipelines: With the Databricks Workflow, you can easily orchestrate and schedule data pipelines. In addition, Workflow makes it easy to chain together multiple jobs to create a data pipeline.
• Seamless Collaborations: When data has been ingested and processed, data engineers may unlock its value by allowing every employee in the company to access and collaborate on data in real time. Data engineers can use this tool to view and analyze data. In addition, they can share datasets, forecasts, models, and notebooks while also ensuring a single consistent source of truth to ensure consistency and reliability across all workloads.

Pros

• Databricks Lakehouse Platform is easy to use and set up.
• It is a unified data management platform that includes data warehousing, ETL, and machine learning.
• End-to-end data preparation with data quality checking, cleansing, and enrichment is available.
• It is built on open source and open standards, which improves flexibility.
• The platform offers good customer support.

Cons

• The pricing structure is complex.

Pricing

Databricks Lakehouse Platform costs vary depending on your compute usage, cloud service provider, and geographical location. However, if you use your own cloud, you get a 14-day free trial from Databricks, and a lightweight free trial is also available through Databricks.

Datafold

As a data observability platform, Datafold helps businesses prevent data catastrophes. It has the unique capacity to detect, evaluate, and investigate data quality concerns before they impact productivity.

Datafold offers the ability to monitor data in real time to identify issues quickly and prevent them from becoming data catastrophes. It combines machine learning with AI to provide analytics with real-time insights, allowing data scientists to make top-quality predictions from large amounts of data.

Key Features

• One-Click Regression Testing for ETL: You can go from 0–100% test coverage of your data pipelines in a few hours. With automated regression testing across billions of rows, you can also see the impact of each code change.
• Data flow Visibility Across all Pipelines and BI Reports: Datafold makes it easy to see how data flows through your entire organization. By tracking data lineage, you can quickly identify issues and fix them before they cause problems downstream.
• SQL Query Conversion: With Datafold’s query conversion feature, you can take any SQL query and turn it into a data quality alert. This way, you can proactively monitor your data for issues and prevent them from becoming problems.
• Data Discovery: Datafold’s data discovery feature helps you understand your data to draw insights from it more easily. You can explore datasets, visualize data flows, and find hidden patterns with a few clicks.
• Multiple Integrations: Datafold integrates with all major data warehouses and frameworks such as Airflow, Databricks, dbt, Google Big Query, Snowflake, Amazon Redshift, and more.

Pros

• Datafold offers simple and intuitive UI and navigation with powerful features.
• The platform allows deep exploration of how tables and data assets relate.
• The visualizations are easy to understand.
• Data quality monitoring is flexible.
• Customer support is responsive.

Cons

• The integrations they support are relatively limited.
• The basic alerts functionality could benefit from more granular controls and destinations.

Pricing

Datafold offers two product tiers, Cloud and Enterprise, with pricing dependent on your data stack and integration complexity. Those interested in Datafold will need to book a call to obtain pricing information.

dbt

dbt is a transformation workflow that allows organizations to deploy analytics code in a short time frame via software engineering best practices such as modularity, portability, CI/CD (continuous integration and continuous delivery), and documentation.

dbt Core is an open-source command-line tool allowing anyone with a working knowledge of SQL to create high-quality data pipelines.

Key Features

• Simple SQL SELECT Statements: dbt uses simple SQL SELECT statements to define data models, which makes it easy for data analysts and data engineers to get started with dbt without learning a new language.
• Pre-Packaged and Custom Testing: dbt comes with pre-packaged tests for data quality, duplication, validity, and more. Additionally, users can create their own custom tests.
• In-App Scheduling, Logging, and Alerting: dbt has an inbuilt scheduler you can use to schedule data pipelines. Additionally, dbt automatically logs all data pipeline runs and generates alerts if there are any issues.
• Version Control and CI/CD: dbt integrates with Git to easily version and deploy data pipelines using CI/CD tools such as Jenkins and CircleCI.
• Multiple Adapters: It connects to and executes SQL against your database, warehouse, platform, or query engine by using a dedicated adapter for each technology. Most adapters are open source and free to use, just like dbt.

Pros

• dbt offers simple SQL syntax.
• Pre-packaged tests and alerts are available.
• The platform integrates with Git for easy deployment.

Cons

• The command-line tool can be challenging for data analysts who are not familiar with SQL.

Pricing

dbt offers three pricing plans:

• Developer: This is a free plan available for a single seat.
• Team: $50 per developer seat per month plus 50 read-only seats. This plan includes a 14-day free trial.
• Enterprise: Custom pricing based on the required features. Prospective customers can request a free demo.

Choosing DataOps Tools

Choosing a DataOps tool depends on your needs and preferences. But, as with anything else in technology, it’s essential to do your research and take advantage of free demos and trials before settling on something.

With plenty of great DataOps tools available on the market today, you’re sure to find one that fits your team’s needs and your budget.

Read next: Top Data Quality Tools & Software 2022

The post Top DataOps Tools 2022 appeared first on IT Business Edge.

What is Network Access Control?

The best way to understand network access control is to think about an office block and its security. An office building typically has doors, floor levels, lifts, and various offices at each level. Access to each level or company office is restricted to company employees, while guests usually have designated areas. There are also access restrictions for specific staff within each organization’s office. Enforcement is done using various methods such as biometric access controls, smart cards, password-locked doors, or physical methods such as security guards.

Network access control works similarly. Substitute the office building for a corporate network. Network access restrictions are enforced by limiting access to certain areas of the network based on user identity, device security, and other network policies.

NAC software is a network security technology that limits access to a private network until the user or device has been authenticated and meets predefined security policies.

Also read: Understanding the Zero Trust Approach to Network Security

How to Choose a NAC Solution?

When looking for a NAC solution, there are several features you need to consider. The most important are:

• Ecosystem Compatibility and Integration: You must ensure that the NAC solution you choose is compatible with the other security solutions you have in place. The NAC solution must integrate well into your existing environment to avoid conflicts or disruptions.
• Agent-based or Agentless: Another critical consideration is whether you want an agent-based or agentless solution. Agent-based solutions require installing a small piece of software on each device that needs to be monitored. Agentless solutions don’t require any software to be installed and can be more efficient in large environments. However, agentless solutions can be more difficult to troubleshoot if something goes wrong.
• Ease of Use for Administrators: The NAC solution should be easy to use for administrators. The solution must be intuitive and have a user-friendly GUI. If the solution is difficult to navigate, administrators may not use it correctly or at all.
• Device Limits: You also need to decide how many devices or endpoints you want the NAC solution to monitor. Some solutions can only monitor a certain number of devices, while others have no limit. This will also have a pricing implication.
• Temporary Guest Access: Guest access is becoming an increasingly important feature for companies. Employees often need to bring their devices into the office or give guests temporary access to company resources. The best NAC solutions will have a way to easily and securely give guests temporary access to the network.
• Regulatory Compliance: Depending on your industry, you may need to comply with certain regulatory requirements. Make sure the NAC solution you choose is compliant with any relevant regulations.
• How Well the Solution Scales as Your Company Grows: As a company grows, its IT needs will also grow. Make sure the NAC solution you choose can scale along with your company. Otherwise, you’ll need to replace it as your company grows, which can be costly and disruptive.
• Value-added Services: Some NAC solutions come with value-added services such as vulnerability management or intrusion detection. These can be helpful, your overall cost of acquisition for IT security services.

Also read: Top Infrastructure Monitoring Tools 2022

5 Best Network Access Control (NAC) Solutions

We reviewed the various network access control solutions on the market. Below are the top five vendors in this field based on our analysis and evaluation.

Twingate

Twingate is a remote access solution for private applications, data, and environments on-premise or in the cloud. It replaces outdated business VPNs that were not designed to handle a world where “work from anywhere” and cloud-based assets are increasingly common.

Twingate’s cutting-edge zero-trust network security strategy boosts security while retaining simplicity.

Key Features

• Zero-trust network: Twingate’s zero-trust network security strategy is based on the principle that a network should not trust users and devices until authenticated. The network is segmented into different security zones, and each user is only given access to the resources they need.
• Software-only solution: Twingate is a software-only solution, which means no hardware is required. This makes it easy to deploy and can be used with existing infrastructure without requiring changes.
• Least privilege access at the application level: Users are only given the minimum amount of access they need to perform their job. This reduces the risk of data breaches and unauthorized access.
• Centralized admin console: The Twingate admin console is web-based and is accessible anywhere. It manages users, applications, and devices.
• Effortless scaling: Twingate can be easily scaled as your company grows. There is no need to add hardware, segment networks, or make changes to your existing infrastructure.
• Easy client agent setup: The Twingate client agent can be installed by users without IT support. This makes it easy to deploy and reduces the burden on IT staff.
• Split tunneling: Split tunneling allows users to access local and remote resources simultaneously. This reduces network congestion and improves performance.

Pros

• Uses a zero-trust approach to network access.
• Intuitive and easy to use.
• Simple documentation.
• Quick setup.

Cons

• Lacks a GUI client for Linux.

Pricing

Twingate has three pricing tiers as follows:

F5 BIG-IP Access Policy Manager

F5 BIG-IP Access Policy Manager manages global access to users’ networks, cloud providers, applications, and API endpoints. F5 BIG-IP APM unifies authentication for remote clients and devices, distributed networks, virtual environments, and web access.

F5 BIG-IP supports modern and legacy authentication and authorization protocols and procedures. When applications cannot use modern authentication and authorization standards such as SAML or OAuth with OIDC, BIG-IP APM converts user credentials into the proper authentication standard required by the application.

Key Features

• Identity-aware proxy (IAP): The identity-aware proxy (IAP) is a key feature of F5 BIG-IP that deploys the Zero Trust model. It inspects all traffic to and from the protected application, regardless of location. This provides granular visibility and control of user activity.
• Identity federation, MFA, and SSO: Identity federation allows companies to manage access to multiple applications with a single identity provider. F5 BIG-IP supports multi-factor authentication (MFA) and single sign-on (SSO). This feature provides an additional layer of security for remote and mobile users.
• Secure remote and mobile access: F5 BIG-IP provides secure remote and mobile access to company applications and data. SSL VPN in conjunction with a secure and adaptive per-app VPN unifies remote access identities.
• Secure and managed web access: The tool provides a secure web gateway to protect against malicious activity. It uses a web app proxy to centralize authentication, authorization, and endpoint inspection.
• API protection: F5 BIG-IP provides secure authentication for REST APIs, integrating OpenAPI files. 
• Offload and simplify authentication: For a smooth and secure user experience across all apps, it uses SAML, OAuth, and OIDC.
• Dynamic split tunneling: F5 BIG-IP offers dynamic split tunneling, allowing users to access both local and remote resources simultaneously. This reduces network congestion and improves performance.
• Central management and deployment: The tool provides a central management console for easy deployment of policies across all applications.
• Performance and scalability: F5 BIG-IP supports up to 1 million access sessions on a single BIG-IP device and up to 2 million on a VIPRION chassis.

Pros

• Centralized management.
• Easy to troubleshoot.
• Secure remote and mobile access.
• API protection.
• Dynamic split tunneling.

Cons

• Logs can be complicated to read.

Pricing

The company does not publish pricing information but provides a free demo and free trial. Contact the company for custom pricing in all business models including subscription, Enterprise License Agreements (ELAs), perpetual licenses, and public cloud marketplace.

Cisco ISE (Identity Services Engine)

Cisco is an internationally acclaimed cybersecurity leader. Its ISE is a specialized network access control product that increases security and reduces the risk of data breaches.

Cisco ISE uses the 802.11X standard to authenticate and authorize devices on a network. It also uses posture assessment to ensure that each endpoint meets certain security criteria before being granted access.

Cisco ISE supports a wide range of devices, including Windows, Mac, Linux, and Android. It also supports various authentication methods, including Active Directory, LDAP, RADIUS, TACACS+, and XTACACS+.

Key Features

• Software-defined network segmentation: This feature extends zero trust and reduces the attack surface. In addition, it limits the spread of ransomware in the event of a breach and allows admins to rapidly contain the threat.
• Policy creation and management: Cisco ISE allows administrators to create granular access policies based on user identity or device posture. Admins can apply these policies to any network resource, including wired, wireless, and VPN networks.
• Guest access: The tool provides a secure guest portal that allows guests to access the internet without compromising the security of the corporate network. In addition, admins can customize the guest portal to match the company’s branding.
• Reporting and analytics: Cisco ISE provides comprehensive reports on all activity across the network. These reports can be used to identify security threats, assess compliance, and troubleshoot network issues.
• Device profiling: It uses device profiling to create a database of authorized devices. This feature allows administrators to quickly and easily grant or deny access to specific devices.
• Integration: Cisco ISE integrates with a wide range of other Cisco products, including the Catalyst series switches, the ASA firewalls, and the Cloud Services Router.

Pros

• Wide range of authentication methods.
• Comprehensive reporting and analytics.
• Device profiling.
• Integration with other Cisco products.

Cons

• The UI presents a steep learning curve.

Pricing

Cisco does not publish pricing information. Most customers contact Cisco partners to purchase Cisco ISE.

FortiNAC

The FortiNAC product line consists of hardware and virtual machines. A Control and an Application server are required for each FortiNAC deployment. If your installation needs more capacity than a single server can provide, you may stack servers to gain additional capacity. There is no maximum number of concurrent ports.

It can be deployed on-premises, in the cloud, or as a hybrid solution.

Key Features

• Agentless scanning: FortiNAC uses agentless scanning to detect and assess devices. This feature eliminates the need to install software on every device and allows you to scan devices not connected to the network.
• 17 profiling methods: FortiNAC uses 17 methods to profile devices and determine their identity.
• Simplified onboarding: FortiNAC provides a simplified, automated onboarding process for a large number of users, endpoints, and guests.
• Micro-segmentation: FortiNAC allows you to create micro-segments that segment devices into specific zones. This feature reduces the risk of a breach spreading throughout the network.
• Extensive multi-vendor support: You can manage and interact with network devices (switches, wireless access points, firewalls, clients) from over 150 vendors using FortiNAC.
• Scalability: The FortiNAC architecture is ideal for scale across multiple locations.

Pros

• Easy to implement and manage.
• Good customer support.
• Complete device visibility.
• Simple onboarding.
• Extensive multi-vendor support.

Cons

• Limited third-party native integration.

Pricing

Customers can get pricing information by requesting a quote. You can also sign up for a free demo or start a free trial.

Aruba ClearPass Access Control and Policy Management

Aruba is a Hewlett Packard (HP) company. Clearpass uses policies and granular security controls—such as how and where the connected traffic can navigate throughout the network— to ensure that authorized access is given to users in both wired and wireless business networks.

Key Features

• Agentless policy control and automated response: ClearPass uses agentless policy control and automated response to detect and assess devices. The Aruba ClearPass Policy Manager allows you to put in place real-time policies for users and devices connecting and what they can access.
• AI-based insights, automated workflows, and continuous monitoring: ClearPass has built-in artificial intelligence (AI) that provides insights, automated workflows, and continuous monitoring. This helps you to quickly identify issues and automate the response.
• Dynamically enforced access privileges: ClearPass gives you the ability to dynamically enforce access privileges for authorized users, devices, and applications. You can also create custom policies that fit your specific needs.
• Secured access for guests, corporate devices, and BYOD: Aruba ClearPass provides secure access for guests, corporate devices, and Bring Your Own Device (BYOD). It uses role-based access control to give you granular control over what users can do on the network.
• Scale and resilience: The ClearPass platform is designed to scale and be resilient. It can handle large volumes of traffic and has a high availability architecture.

Pros

• Uses AI-based insights.
• Highly scalable and excellent for large enterprises.
• Integrates with more than 170 IT management solutions.
• Supports multiple authentication protocols. 

Cons

• Some customers have found support to be hit-or-miss.

Pricing

Aruba does not publish pricing information. Pricing models include subscription and perpetual licenses. You can also try out a fully interactive demo.

Getting Started with a NAC Solution

Choosing the right Network Access Control (NAC) solution can be overwhelming. There are many different options on the market, and each one has its own set of unique features. The best way to find the right NAC solution for your business is to consider your specific needs and compare solutions that fit those needs.

Read next: Evolving Digital Transformation Implementation with Hybrid Architectures

The post Best Network Access Control 2022: NAC Solutions appeared first on IT Business Edge.

A few years ago, most online traffic was headed towards sites with static information. But now, more than half of current traffic accesses software-as-a-service (SaaS) and cloud applications that contain crucial data. This paradigm shift in network traffic caused a network reversal, diverting network traffic from on-premises data security measures directly to the cloud.

Today, it is common sense that a business organization can’t simply trust the authentication of remote workers working out of their company LAN, using devices and networks their company can’t trust.  

According to recent research, one in four companies using public cloud services is prone to data theft. The same study also reveals 83% of enterprises store sensitive information in the cloud, and one in five of them has to fight sophisticated attacks against their public cloud infrastructure.

Today, as 97% of businesses organizations worldwide use cloud computing services, a deeper evaluation of cloud computing security and the development of an efficient data protection strategy should be their priorities.

What is Zero Trust?

Zero trust was first coined in 2010 by John Kindervag, an analyst at Forrester Research and a thought leader following the motto, “never trust, always verify.” His ground-breaking idea was based on the assumption that risk is always present inside and outside the network. Kindervag believes that “trust,” as a human emotion, brings vulnerability and exploitation in a digital ecosystem.

The traditional perimeter security strategies using firewalls and other network-based security tools to protect valuable digital resources like user data and intellectual property are no longer sufficient in an age of digital transformation and cloud computing.

Furthermore, zero trust is an information technology (IT) security framework that authenticates, authorizes, and continuously verifies users inside or outside an organization’s network for security configuration and posture before granting access to its applications and data.

Zero trust addresses the modern business challenges, including securing remote workers, hybrid cloud ecosystems, and warding off ransomware threats. It can also accommodate the growing data processing, management, and security demands.

The 2021 Cost of a Data Breach Report states that enterprises that have not deployed a zero trust architecture had to spend an average of $5 million to recover from data breach attempts. And those who implemented zero trust saw those costs decrease by nearly $2 million. Even the business organizations in early stages of zero trust deployment displayed almost $660,000 less burden.

Also read: Emerging Cybersecurity Trends in 2022 and Beyond

The Benefits of Zero Trust

Enhanced security

The improved security posture of a zero trust architecture is partly because of using advanced cybersecurity tools and platforms such as identity and access management (IAM), multi-factor authentication (MFA), and extended detection and response (XDR).

As per an ESG Research Report, around 43% of North American enterprises experienced improved security operations center (SOC) efficiency after implementing a zero trust security model.

The simplification of IT security architecture

Adopting an advanced security infrastructure like zero trust simplifies an organization’s IT security architecture, as the cybersecurity teams can efficiently respond to security reports and remain proactive in securing the organization’s IT environment.

Improved user experience

Simplification of the IT architecture by applying either the Secure Access Service Edge (SASE) architectural model or through secure web gateways, like zero trust network access (ZTNA) or a cloud access security broker (CASB), improves user experience.

Secure remote work ecosystem and cloud adoption

The usage of public cloud services is on the rise among business organizations. A zero trust infrastructure can ensure and continually verify the legitimacy of everything trying to connect to an organization’s network, data, applications, and resources.

Also read: Securing Work-From-Home Networks to Safeguard Your Business

The Challenges of Zero Trust

Zero trust needs a strong identity system

Identity systems, often a part of an IAM tool, authenticate a user or device and prove the entity’s legitimacy to other security tools in the IT infrastructure. Unfortunately, the probability of attacks towards identity systems is always higher.

The cybersecurity risks still remains in a zero trust model

Although termed as zero trust, an organization should trust a few users and non-users who access its data, applications, and resources for smoother business operations. But sometimes that trust can be broken.

Delay and complications in implementing zero trust

ZTNA, a network-based security system, is a popular technology that supports zero trust. But the truth is that a network is only a part of an enterprise’s IT ecosystem and resources. Enterprises should also consider the security of their applications, data, and other resources. Hence, the scope of zero trust is more extensive; it takes years to get implemented and can often run into complications.

The Implementation of Zero Trust Architecture

You can use a five-step model for implementing and maintaining zero trust. Through this procedure, you can understand your implementation process and your next step.

1. Mark the protect surface

The attack surface continuously expands in today’s cyber threat landscape, making it difficult to define, shrink, or defend. However, with zero trust, it is always better to define your protect surface rather than focusing on the larger attack surface.

The protect surface consists of the crucial data, applications, assets, and services (DAAS) considered the most valuable resources of your company. Once defined, you can easily control the protect surface, creating a micro-perimeter with precise, understandable, and limited policy statements. 

2. Map transaction flows

The protection of the network should be determined by the way traffic moves across it. Therefore, it’s crucial to gain contextual insight into the interdependencies of your DAAS. Documenting the movement of specific resources assists you in correctly placing controls and provides valuable information to ensure the controls protect your data rather than hinders your business operations.

3. Design a zero trust network

Zero trust networks don’t have a single, universal design; hence they can be completely customized. But the infrastructure should be constructed around the protect surface. After defining the protect surface and mapping transaction flows, you can design a zero trust infrastructure, beginning with a next-generation firewall.  

4. Devise zero trust policies

Once the network is designed, you can devise zero trust policies using the Kipling Method by asking who, what, when, where, why, and how questions to check out which resources should access others.

5. Maintain and monitor the network

This ultimate step consists of reviewing all internal and external logs all the way down to Layer 7, focusing on zero trust’s operational aspects. Since zero trust is a repetitive process, monitoring and logging all traffic will provide valuable insights into improving the network over time.

Zero Trust Will Evolve to Meet Data Security Requirements

A zero trust strategy can offer a feasible IT security framework for mitigating the complete spectrum of cybersecurity risks by introducing a proactive verification model for every attempt to access data and resources by any user, application, or device.

Zero trust is a framework that can genuinely bestow the level of security needed in today’s digital world. However, it should continue to adapt to meet the world’s changing digital requirements. Similar to how the concept of cloud has evolved since its innovation, zero trust will also do the same eventually.

Read next: Top Zero Trust Security Solutions & Software

The post The Need for Data Protection is Evolving Zero Trust Frameworks appeared first on IT Business Edge.

This new tech in AI determines the original pattern entered in the input to generate creative, authentic pieces that showcase the training data features. The MIT Technology Review stated Generate AI is a promising advancement in artificial intelligence.

Generative AI offers better quality results through self-learning from all datasets. It also reduces the challenges linked with a particular project, trains ML (machine learning) algorithms to avoid partiality, and allows bots to understand abstract concepts.

Gartner mentioned Generative AI in its lists of major trends of 2022 and highlighted that enterprises could use this innovative technology in two ways:

• Enhancing current innovative workflows together with humans: Developing artifacts to aid better creative tasks performed by humans. For instance, game designers can utilize generative AI to create dungeons, highlighting what they prefer and don’t prefer about the content created in terms like “somewhat like this” or “little less like that.”
• Functioning as an artifact production unit: Generative AI can produce creative pieces in any quantity with little human involvement (apart from shaping the parameters of what they want to create). It only requires setting the context, and the results will be generated independently.

Benefits of Generative AI

• Protection of your identity: The avatars produced by generative AI offer security to those who don’t wish to reveal their identities during interview sessions or work.
• Robotics control: Generative AI strengthens ML models, makes them less partial, and realizes more abstract concepts in imitating the real world.
• Healthcare: The technology has simple and easy detection of probable malice and develops efficient treatments against it. For instance, Generative Adversarial Networks (GANs) can calculate several angles of an X-ray picture to show the possibility of tumor expansion.

Also read: What’s Next for Ethical AI?

Challenges of Generative AI

• Safety: It has been observed that malicious people use generative AI for scamming purposes.
• Highly estimated abilities: Generative AI algorithms need considerable training data to perform tasks like creating art; however, the images created are not wholly new. Instead, these models only mix and match what they know in the best possible ways.
• Unpredictable outcomes: In some models of generative AI, it is simple to handle their behavior, but sometimes, they may yield erroneous or unexpected results.
• Data Security: With the technology relying on data, sectors like healthcare and defense may face privacy concerns when leveraging generative AI applications.

Is Generative AI Just Supervised Training?

Generative AI is a semi-supervised training framework. This learning methodology involves manually marked training information for supervised training and unmarked data for unsupervised training methods. Here, unmarked data is used to develop models that can predict more than the marked training by enhancing the data quality.

Some of the key advantages of GANs, a semi-supervised framework of generative AI against supervised learning, are:

• Overfitting: Generative AI models have lesser parameters, so it may be tougher to overfit. Also, generative models need a high quantity of data because of the training procedure, making them sturdier to obstructions.
• Human partiality: Human marks are not as evident as in the supervised learning methodology in generative AI modeling. The learning works on the data properties that permit the exclusion of bogus correlations.
• Model partiality: Generative models don’t generate results the same as the training data. Hence, the shape and texture problem disappears.

Also read: What Does Explainable AI Mean for Your Business?

Applications of Generative AI

AI-generative NFTs

With sales of non-fungible tokens (NFTs) reaching $25 billion in 2021, the sector is currently one of the most lucrative markets in the crypto world. Art NFT, in particular, is creating a major impact.

While the most popular art NFTs are cartoons and memes, a new kind of NFT trend is emerging that leverages the power of AI and human imagination. Coined as AI-Generative Art, these non-fungible tokens use GANs to produce machine-based art images.

Art AI is one such example of an art gallery that showcases AI-generated paintings. It released a tool that transforms text into art and helps the creators sell their art pieces on NFT. Metascapes, on the other hand, combine images to generate a new photograph. It uses two learning models, and the output gets better every time. These art pieces are placed on sale online.

Identity security

Generative AI allows people to maintain privacy using avatars instead of images. In addition, it can also help companies opt for impartial recruitment practices and research to present unbiased results.

Image processing

AI is used in extraordinary ways to process low-resolution images and develop more precise, clearer, and detailed pictures. For example, Google published a blog post to let the world know they have created two models to turn low-resolution images into high-resolution images.

The upscale examples include photography of a woman from 64 x 64 input to 1024 x 1024 output. The process helps restore old images and movies and upscale them to 4K and more. It also helps to transform black and white movies into color.

Healthcare

Generative AI better identifies an ailment to help patients receive impactful treatment even during the early stages.

Audio synthesis

With Generative AI, it is possible to create voices that resemble humans. The computer-generated voice is helpful to develop video voiceovers, audible clips, and narrations for companies and individuals.

Design

Many businesses now use generative AI to create more advanced designs. For instance, Jacobs, an engineering company, used generative design algorithms to design a life-support backpack for NASA’s new spacesuits.

Client segmentation

AI allows users to acknowledge and differentiate target groups for promotional campaigns. It learns from the available data to estimate the response of a target group to advertisements and marketing campaigns.

Generative AI also helps develop customer relationships using data and gives marketing teams the power to enhance their upselling or cross-selling strategies.

Sentiment analysis

ML involves using text, pictures, and voice evaluation to grasp people’s emotions. For example, AI algorithms can learn from web activity and user data to interpret customers’ opinions towards a company and its products or services.

Detecting fraud

Several businesses already use automated fraud-detection practices that leverage the power of AI. These practices have helped them locate malicious and suspicious actions quickly and with superior accuracy. AI is now detecting illegal transactions through preset algorithms and rules and is making the detection of theft identification easier.

Trend evaluation

ML and artificial learning technology are helpful to predict the future. These technologies aid in providing valuable insights on the trends beyond conventional calculative analysis.

Software development

Generative AI has also influenced the software development sector by automating manual coding. Rather than coding the software completely, the IT professionals now have the flexibility to quickly develop a solution by explaining the AI model about what they are looking for.

For instance, a model-based tool GENIO can enhance a developer’s productivity multifold compared to a manual coder. The tool helps citizen developers, or non-coders, develop applications specific to their requirements and business processes and reduces their dependency on the IT department.

The Road Ahead for Generative AI Looks Promising

While generative AI is becoming a boon today for image production, restoration of movies, and 3D environment creation, the technology will soon have a significant impact on several other industry verticals. By empowering machines to do more than just replace manual labor and take on creative tasks, we will likely see a broader range of use cases and adoption of generative AI across different sectors.

Read next: Top Artificial Intelligence (AI) Software 2022

The post What is Generative AI? appeared first on IT Business Edge.

RPA tools are used across all industries. In 2016, the global RPA spend was 300 million USD. That number is expected to reach 7.2 billion USD by the end of the year in 2022 and skyrocket to 10.4 billion USD by 2023.

The use of these tools has proved very useful in increasing productivity while reducing operational costs significantly. As RPA continues to infiltrate businesses across industries globally, there are two big players that companies typically turn to when trying to implement RPA solutions—UiPath and Automation Anywhere.

Both of these companies offer solutions for different industry verticals as well as specific functionalities. In order to choose between UiPath and Automation Anywhere, you need to know how both platforms compare against each other.

Benefits of RPA Tools

Some benefits of RPA tools include:

• Increased productivity: Automation gives rise to increased productivity by doing tasks quicker than humans. The process gets sped up and takes less time than manually doing tasks would take.
• Customers get better services: Businesses with efficient automation processes help their customers get better service. They achieve higher degrees of accuracy and can perform faster without compromising accuracy or quality. 
• Better employee efficiency: With effective use of RPA software, employees gain confidence as well as improve their overall efficiency while working on repetitive tasks.
• Improved operational agility: With technology like RPA becoming mainstream, businesses have a greater ability to respond rapidly to changes.
• Increased security levels: As there are no chances for human error since machines make fewer mistakes while performing tasks, there is also a minimal chance for confidential data leaks.

UiPath vs. Automation Anywhere

Here is how these solutions stack up against each other:

UiPath

UiPath is a cloud-based RPA platform that allows businesses to automate repetitive and non-differentiated tasks across their information technology (IT) environment. It automates business processes by recording and replicating user actions digitally through robotic software agents that mimic user behavior. The UiPath automation platform provides capabilities for scheduling, monitoring, debugging, versioning, and auditing.

Key Features of UiPath

• Drag-and-Drop Workflow Builder: Users can build an automation process in UiPath using a drag action to drop processes into the graphical user interface (GUI) of UiPath Studio and visually create them without writing code. 
• Record and Playback: UiPath offers several types of recording options, including basic recording for automating single processes to develop each activity’s complete selector; desktop recording for multiple actions and application development; web recording, commonly used for viewing and recording web page activities; and Citrix recording, which is often used for recording pictures and virtualized environment automation. 
• Process Deployment to Clients: UiPath can be delivered via an on-premises or cloud architecture, which makes it easier for business users to work with.  
• Pre-Defined Activity Sets: UiPath users are able to automate a wide range of tasks, from simple workflows to more complex integration projects. 
• Intelligent Scheduling of Process Automation: The scheduling module enables the orchestrator to run tasks based on priority. It prioritizes each task based on deadline, arranging work queues and assigning each task the appropriate priority.

UiPath Primary Components

The UiPath automation platform consists of three main components. They are:

• UiPath Studio: UiPath Studio is used for creating automation by writing code lines with visual drag-and-drop actions that don’t require the user to know how to code. All that is needed from users unfamiliar with coding is for them to drag operations onto different screens and link them together into procedures, or workflows.

• UiPath Robot: This component executes predefined workflows designed by UiPath Studio. With each execution, it can react differently based on received user inputs. 

• UiPath Orchestrator: The Orchestrator works like a hub to orchestrate, execute, and monitor all robots’ activities, either triggered automatically by scheduled events or manually upon request. The Orchestrator is a web-based application centralized hub for managing and maintaining all software bots.

Pricing

UiPath provides a variety of price options to fit the needs of different types of customers. UiPath Unattended automation costs $1,380 per month, while a subscription to the automation team and an Automation Developer costs $1,930 and $420 per month, respectively.

UiPath also offers an enterprise-grade solution that can be tailored to your company’s needs. For more information about this plan, contact their sales team.

Also read: Blue Prism vs UiPath: RPA Tool Comparison

Automation Anywhere

Automation Anywhere is an RPA platform that helps businesses automate repetitive manual tasks without human intervention. Through a GUI, businesses can create and manage processes without writing code.

Key Features of Automation Anywhere

• Recorder: Users may use recorders to record, save, and run tests in automation anywhere. The platform offers three kinds of recorders. Smart Recorder records activity such as object cloning, Screen Recorder catches on-screen actions, and Web Recorder captures online data extraction.
• Screen Scraping: This functionality enables users to extract both structured (tables) and unstructured data spread across web pages.
• Predictive Operational Analytics: Automation Anywhere RPA analytics gives users insight into a bot’s efficiency and performance level.
• Workload Management: Workload Management supports the human prioritization of high-value tasks inside the automated queue management architecture.
• Task Editor: Users can create tasks by dragging and dropping components from the toolbox. The Task Editor allows the user to change, break down, and even enhance the recorded tasks.

Automation Anywhere Primary Components

• Bot Creator: It acts as a development environment. Developers use a drag-and-drop interface to design rule-based automation, which is then submitted to the Control Room and, if suitable, deployed.
• Control Room: This is basically the command and control center for all of your RPA robots. Robots may be launched, paused, terminated, or scheduled from the Control Room. Credentials and audit logs may be kept here as well.
• Bot Runner: This component operates robots on dedicated machines and has a similar appearance to the Bot Builder component, but its major function is to operate robots. The end-to-end status of the Bot Runner’s execution is transmitted back to the Control Room.

Pricing

The Automation Anywhere website does not provide pricing information. They do, however, offer a 30-day free trial and can provide detailed quotes tailored to your needs if you contact the sales team.

Features Comparison

Uipath vs. Automation Anywhere: Which Tool is Right for You?

When it comes to enterprise-level business automation software, UiPath and Automation Anywhere are two of the most widely used RPA platforms. Both tools have their advantages and are quite proficient in their capabilities. For this reason, it is necessary to evaluate the two platforms side-by-side in terms of functionality, cost, integrations with your enterprise’s existing systems, and other factors.

Reviewers considered UiPath to be easy to use, set up, and manage to help them better meet their business’s requirements.

Before selecting a tool, it is recommended that you request a demo from both providers before making a buying decision to evaluate which solution best fulfills your company use cases, gives the best return on investment, and provides the best value to your enterprise.

Read next: Top RPA Tools 2022: Robotic Process Automation Software

The post UiPath vs Automation Anywhere: RPA Tool Comparison appeared first on IT Business Edge.

Homomorphic encryption enables users to edit data without decrypting it, meaning the broader dataset is kept private even as it is being written. The technology may not be an ideal solution for everyone, but it does have significant promise for companies looking to protect huge troves of private data.

How Homomorphic Encryption Works

Homomorphic encryption was proposed in 2009 by a graduate student, who described his concept through an analogy of a jewelry store owner.

Alice, the owner, has a lockbox with expensive gems to which she alone has the key. When Alice wants new jewelry made from the gems, her employees wear special gloves that allow them to reach into the closed box and craft the jewelry using the gems without being able to pull them out of the box. When their work is done, Alice uses her key to open the box and withdraw the finished product.

In a conventional encryption model, data must be downloaded from its cloud location, decrypted, read or edited, re-encrypted, and then reuploaded. As files expand into the gigabyte or petabyte scale, these tasks can become increasingly burdensome, and they expose the greater dataset to wandering eyes.

By contrast, data that is encrypted homomorphically can have limited operations performed on it while it’s still on the server, no decryption necessary. Then, the final encrypted product is sent to the user, who uses their key to decrypt the message. This is similar to end-to-end encryption, only the receiver can access the decrypted message.

Also read: Data Security: Tokenization vs. Encryption

Use Cases for Homomorphic Encryption

AI-driven healthcare analytics have come a long way in recent years, with AI being able to predict disease and other health risks from large sets of medical data.

Today, services like 23 and Me allow customers to hand over sensitive medical information for genetic testing and ancestry information. But these companies have been hit with accusations of selling this personal information or providing it to third parties such as the government, without customer knowledge or consent.

If that data was protected through homomorphic encryption, the company would still be able to process the data and return its results to the customer, but at all times that information would be completely useless until it is decrypted by the customer, keeping his or her information entirely confidential.

Within the last two years, Microsoft, Google, and many other of the largest names in tech have been investing in developing the technology, even freely offering their open-source implementations.

In the case of Google, the company may be pursuing the technology as a means of complying with privacy regulations such as the European GDPR. With homomorphic encryption, Google could continue to build an ad profile, based on large volumes of personal data that it collects through various means, and compile it into an encrypted database with limited usage or applications that only the end user might experience.

For instance, a user may search Google for restaurants near them. The query would hit the homomorphic black box, privately process the user’s preferences and location, and return tailored results.

Types of Homomorphic Encryption

There are three common iterations of this technology, and one size does not fit all.

• Partially homomorphic encryption (PHE): Allows for very narrow interaction with data, limited to a single mathematical function at a time
• Somewhat homomorphic encryption (SHE): Perform up to two operations at a time
• Fully homomorphic encryption (FHE): Several types of operations can be performed simultaneously, and an unlimited number of times. While most desirable, FHE incurs significant hits to system performance.

The Limitations of Homomorphic Encryption

Homomorphic encryption has yet to see widespread adoption. However, it’s not uncommon for encryption protocols to spend a decade in development.

There are community standards that need to be established. Public confidence that the technology is safe, secure, solid, and not exploitable needs to be reached. APIs need to be implemented. And lastly, perhaps the biggest hurdle for homomorphic encryption is that the technology needs to perform well.

No one wants to adopt a more secure protocol only to discover that system performance has taken a massive hit. From an end-user standpoint, that will feel more like a massive setback than a step forward. While the protocol has become massively more efficient since its inception in 2009, it still lags behind today’s conventional encryption methods, particularly as users move from PHE to SHE to FHE.

While the computational overhead is too large for many businesses that don’t need the added security, homomorphic encryption may yet become the go-to standard for sensitive industries like finance and healthcare.

Read next: Best Encryption Software & Tools

The post Enabling Data Security with Homomorphic Encryption appeared first on IT Business Edge.

Data Mining Tools Overview

• What is Data Mining?
• What are Data Mining Tools?
• Top Data Mining Tools
  • RapidMiner
  • Oracle Data Miner
  • Sisense
  • Alteryx APA
  • SAS Data Mining
  • Teradata
  • Dundas BI
  • H2O
• Data Mining Leads to Actionable Insights

What is Data Mining?

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Data mining is the process of pulling information from large datasets in order to find patterns or trends that can inform future decisions. Organizations can also use it to highlight anomalies and attempt to identify the root cause of issues. How does data mining work? Typically, it uses artificial intelligence (AI), machine learning (ML), and statistical models to identify relevant information. Data mining is a big part of business intelligence, which helps companies cut costs, improve relationships with their customers, and increase revenue.

What are Data Mining Tools?

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Data mining tools are software solutions that use AI and ML to pull and analyze data, highlight trends, and provide actionable insights for businesses. The software can refine information from both structured and unstructured datasets, so organizations can make predictions and understand relationships between different parts of their business. Data mining tools allow businesses to address questions that would take too much time to answer if they had to analyze data by hand.

Also Read: 6 Ways Your Business Can Benefit from DataOps

Top Data Mining Tools

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The following data mining tools all have good user reviews and healthy feature sets.

RapidMiner

RapidMiner offers automated data mining and modeling tools with AI and ML to provide clear visualizations and predictive analytics. The drag-and-drop interface makes it easier for analysts to create predictive models, and the library includes over 1,500 pre-built algorithms, meaning there’s a model for nearly any use case. There are also pre-built templates for common scenarios, including fraud detection and maintenance, to lower the time analysts have to spend building the models. RapidMiner can connect to any data source, or users can import data from Excel. Interested parties must request pricing from RapidMiner; it’s not available on the website.

Key Features

Oracle Data Miner

Oracle Data Miner is an extension of the Oracle SQL Developer that helps analysts quickly build a variety of machine learning models, apply them to new data, and compare the models for actionable insights. It offers a drag-and-drop editor, allowing both data scientists and regular users to get answers to their data-related questions. The workflow API makes it easier to deploy the model throughout the business, embedding analytics into the applications where analysts are already working. Pricing is not clearly available on the website, so businesses will have to contact Oracle for more information.

Key Features

Sisense

Sisense is data analytics software that allows users to embed analytics into the platforms they already work in, putting the information in the same place they’re making decisions. Additionally, businesses can white label the embedded analytics, so they can also push them out to their customers. With live data connections, businesses can get real-time insights and a strong self-service platform. With code-first, low-code, and no-code options available, analysts of any skill level can get their data questions answered and build helpful models. Plus, the AI allows analysts to type in a question, and then it guides them through the investigation. Pricing is not available on the website.

Key Features:

Alteryx APA

Alteryx APA offers automated analytics with machine learning across the entire process, including mining, modeling, and visualization. There are over 80 natively-integrated data sources that users can pull from, including Oracle, Amazon, and Salesforce, or they can use APIs to connect to others. Analysts can also add maps to their visualizations to highlight geographic trends. Alteryx offers step-by-step guides to help analysts of any skill level build models without coding. However, expert data analysts can also use R-based models. Pricing information is not available on the website.

Key Features

SAS Data Mining

SAS Data Mining helps organizations answer complex questions with analytics through automated modeling and a collaborative platform. With natural language generation, the platform can create a post-project summary, detailing important trends, outliers, and insights. Then, users can add notes to the report to make communication and collaboration easier. SAS Data Mining supports a variety of coding options, so analysts can create or adjust algorithms in their language of choice. Data scientists can also combine structured and unstructured data in models to get as much information as possible. Pricing is not available on the SAS website.

Key Features

Teradata

Teradata is a data mining tool built for organizations using multi-cloud deployments, providing access to all databases, data lakes, and external SaaS applications. No-code options allow users from any business department to get answers to their questions to make more informed decisions. Organizations can deploy Teradata on any of the major public cloud platforms, including AWS, Azure, and Google, as well as in private clouds or on-premises. Teradata doesn’t charge upfront costs, instead offering a pay-as-you-go model. A pricing calculator is available on the website to help users estimate their costs.

Key Features

Dundas BI

Dundas BI is a data analytics platform that offers real-time insights and visually-appealing reports and dashboards. It can consolidate data from any source with open APIs, ensuring that users have all the information they need to create effective models. Users can create content that’s easy to understand with minimal input from IT. Interactive dashboards allow analysts to edit models to see how different variables would impact the business. Dundas BI offers a lot of out-of-the-box functionality without requiring add-ons or upgrades. Pricing information is not available on the website.

Key Features

H2O

H2O is an AI cloud built for data mining to improve the insights businesses get from their data and their decision making. Automated machine learning solves complex problems while providing results in an easy-to-understand format. Analysts can train and deploy the AI in any environment, and there are several different modeling types that they can choose from. Real-time data analysis provides accurate predictions and fast insights to help businesses make quicker decisions and improve their scalability. H2O can be deployed with either hybrid or fully managed options. The platform is open-source and free to use, but businesses can pay for enterprise support and management.

Key Features

Data Mining Leads to Actionable Insights

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Companies that use data mining software get faster access to important information and actionable insights that can improve their decision-making process. Each day, businesses take in so much data that it would be impossible to sort through manually. They need data mining tools that include AI to run what-if scenarios and get accurate forecasts. Businesses looking for the best data mining software for their business should take advantage of free trials and read user reviews to determine which one will work best for their team.

Read Next: Attention CIOs: Many Will Fail the Data Science Game

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Every bottleneck in that system has an affixed dollar cost. Data that is improperly analyzed for use results in essentially a waste product, and as datasets grow, it has become a more burdensome task to extract the appropriate, most valuable information to funnel downstream.

In recognition of this challenge, a handful of companies have sought to automate stopping points along the data pipeline, a process called Robotic Data Automation, or RDA.

Data Wrangling

Enterprise datasets aren’t just growing, in many cases they’re also becoming real-time. These sets are embodied in a variety of formats and spread across a company’s sprawling IT infrastructure—including on-premises servers, off-premises clouds, and along the edge. 

They require collection, cleanup, validation, extraction, metadata enrichment—an extensive series of steps just to get the data prepped for its intended use. Every step can be time-intensive, and failure at any step can result in invalid outputs. 

RDA aims to automate many of these processes using low-code bots that perform simple, repetitive tasks, with linkages to more complex artificial intelligence (AI) tools, such as IBM Watson, OpenAI, GPT-3, or hundreds of other bots, to execute natural-language processing (NLP) tasks when necessary.

Effectively, a simple machine is designed to cobble together disparate elements, calling on more sophisticated machines when they’re needed, in order to compile raw data into something usable. If executed correctly, automation can help enterprises realize the value of information far more quickly.

RDA tools can also help break up the existing paradigm of data handling, whereby AIOps vendors offer limited, pre-defined sets of tools for customers to interact with their data. These tool sets have limited linkages with other tools, narrower scopes of use cases, and more restrictive data formatting outputs.

Companies like CloudFabrix, Snowflake, and Dremio claim their RDA tools liberate customers from these constraints and include other benefits, such as synthetic data generation; on-the-fly data integrity checks; native AI and machine learning (ML) bots; inline data mapping; and data masking, redaction, and encryption.

Other use cases for RDA tools include:

• Anomaly Detection: Pulling data from a monitoring tool, comparing historical CPU usage data for a node, then using regression to construct a model that can be sent as an attachment
• Ticket Clustering: Compiling tickets from a company’s ticket management software, clustering them together, and then pushing the output into a new dataset for visualization on a dashboard of choice
• Change Detection: Examine virtual machines (VMs) and make comparisons against current states to detect unplanned changes

RDA vs. RPA

Many will be familiar with robotic process automation, or RPA. The older concept carries similarities with RDA in that both aim to simplify common tasks through the use of low-code bots. Where they diverge is that RPA is intended for simplifying common user tasks and workflows, whereas RDA is aimed squarely at the data pipeline.

Although, both RDA and RPA simply mean using simple bots to save time on time-consuming, menial tasks, though with different contexts.

A common example of RPA is a bot empowered with ML capabilities for form completion. The bot monitors how a human repeatedly fills a form until the RPA is trained on the appropriate manner in which the form is to be completed. This type of machine learning is similar to how cellphones can generate predictive text suggestions based on their users’ conversational habits and vocabulary.

Once trained, the bot can take command of form completion, along with other aspects such as submitting the form to its expected targets. While this can expedite the process in the long run, RPA systems can take months to train before their advantages come to fruition.

Also read: Top RPA Tools 2021: Robotic Process Automation Software

RDA’s Long Term Value

There’s always going to be value in automating time-intensive tasks and freeing up human labor for jobs that are more cognitively demanding. As one bottleneck is opened, another will come to take its place. However, the success of these systems like RDA or RPA hinges on their implementations.

Naturally, the tools need to be designed properly to interact with their intended datasets, but enterprises also have a responsibility to properly integrate new tools with their existing data pipelines. AI-driven tools and automation softwares are still in their infancy, still finding new niches to serve, and still being refined in terms of how they deliver service. How RDA shakes up data pipelines is a story yet to be told.

Read next: 6 Ways Your Business Can Benefit from DataOps

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