Enabling universal access to data can create opportunities to generate new revenue and drive operational efficiencies throughout an organization. Even more importantly, data democratization, as it’s known, is crucial to business transformation. For that reason, vendors have made a lot of promises about enabling data democratization—and not all have panned out. For instance, various vendors have touted data for the masses through self-service analytics for many years. The objective has been to make information accessible to non-technical users without requiring IT involvement. Vendors have focused their efforts on shielding users from underlying data complexities, making analytics tools easier to use, and expanding reach to users in any location throughout the world via the cloud.

However, even with simplified access to data, organizations still haven’t made the progress they would like to when it comes to democratizing data. While it has become more common for non-technical users to access data on their own, for the most part, they can only do so in certain situations. Barriers still stand in the way, making it difficult for users to access all the data they need for decision-making.

Here are the four top barriers to data democratization that organizations must overcome in 2022 to adopt new data platform approaches to help reduce cost and complexity.

1. Users Can’t Access Data in Silos

Organizations typically store data for analytics and decision-making in a centralized data warehouse or similar repository optimized for analytics. But that’s only a subset of all the data that might be useful. Much of it remains sequestered in disparate data silos that most users cannot access. To run the analytics they want and gain insights to inform new programs and processes, users need access to transactional databases, IoT databases, data lakes, streaming data, and more—data that may be spread across multiple data centers and multiple clouds. Several use cases come to mind, including automated personalized e-commerce offers, supply chain optimization, real-time quotes for insurance, credit approval and portfolio management.

2. Today’s Semantic Layers Aren’t Enough

A semantic layer is a business representation of data that helps users access data without IT assistance. Although semantic layers are great at shielding users from the underlying complexities of data, they are designed to represent the data in only one database at a time. Today’s users need a semantic layer that is more ubiquitous to connect to and interact with multiple data sources across multiple locations. As Gartner puts it, users need frictionless access to data—from any source located on-premises and in the cloud.

Data fabrics and data meshes are emerging data architecture designs that can make data more accessible, available, discoverable, and interoperable than a singularly-focused semantic layer can. A data fabric acts as a distributed semantic layer connecting multiple sources of data across multiple locations. A data mesh goes a step further, treating data as a product that is owned by teams who best understand the data and its uses.

3. Lack of Shared Services

Indirectly impacting data democratization is a lack of shared services. The absence of such services means that too much time and resources are spent on separate efforts to manage, maintain, and secure data, which leaves less time to focus on enabling data access and delivering business value to end users. Plus, inconsistencies in security, controls, upgrades, patches, and more—across multiple deployments—often result in time-consuming and costly consequences.

4. Weak Tool Support

The purpose of and value delivered by different types of analytical tools vary greatly, so different users—including data engineers, data scientists, business analysts, and business users—need different tools. Many data warehouse vendors, though, fail to provide flexible analytic and development tool integration, which limits the utility of the tools to users and limits the variety of use cases that a data warehouse can serve.

How to Progress Data Democratization Efforts

To overcome these data democratization challenges, organizations must ensure that business-critical systems can analyze, transact, and connect at their very best using the right tool for the right job. As we head into 2022, now is the time to consider if your data democratization platform is exceeding your expectations and fulfilling your business needs. Actian is leading the way with our data platform approach. The data platform must bring together a wide range of data processing and analytic capabilities that focus on easier access to data and less management overhead. As organizations tackle these challenges, they will be able to generate new revenue and drive operational efficiencies to truly transform their business.

This article was originally published on vmBlog.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

What’s an Edge Data Fabric?

A data fabric is a data architecture, management practices, and policies to deliver a set of data services that span all these domains and endpoints. Data fabrics provide that framework. They essentially serve as both the translator and the plumbing for data in all its forms, wherever it sits and wherever it needs to go, regardless of whether the data consumer is a human or machine.

Data fabrics aren’t brand new, but they are suddenly getting a lot of attention in IT these days as companies move to multi-cloud and the edge. That’s because organizations desperately need a framework to manage it – to move it, secure it, prepare it, govern it, and integrate it into IT systems.

Data fabrics got their start back in the mid-2000s when computing started to spread from data centers into the cloud. They became more popular as organizations embraced hybrid clouds, and today data fabrics are helping to reduce complexities involving data streams moving to and from the network’s edge. But the goalposts have moved, the network’s edge is now the IoT, collectively labeled “the edge.”

What’s different is where the data will emanate from and how fluid it will be. In other words, mobile and IoT – the edge – will drive data creation. Further, the processing and analysis will happen at various points from on the device, at the gateways, and across the cloud. Perhaps a better term would be Fluid Distributed Data instead of Big Data?

Regardless, more data ultimately translates to more viable business opportunities – particularly given that this new data is generated at the point of action from humans and machines. To take full advantage of the growing amounts of data available to them, enterprises need a way to manage it more efficiently across platforms, from the edge to the cloud and back. They need to process, store, and optimize different types of data that come from different sources with different levels of cleanliness and validity so they can connect it to internal applications and apply business process logic, increasingly aided by artificial intelligence and machine learning models.

It’s a big challenge. One solution enterprises are pursuing now is the adoption of a data fabric. And, as data volumes continue to grow at the network’s edge, that solution will evolve further into what will more commonly be referred to as an edge data fabric.

How Data Fabric Applies to the Edge

Edge computing provides a unique set of challenges for data being generated and processed outside the network core. The devices themselves operating at the edge are getting more complex. Smart devices like networked PLCs manage solenoids that, in turn, control process flows in a chemical plant, pressure sensors that determine the weight and active RFID tags to determine the location of a cargo container. The vast majority of the processing used to take place in the data center, but that has shifted to the point where a larger portion of the processing takes place in the cloud. In both cases, the processing happens on one side of a gateway. The data center was fixed, not virtual, but the cloud is fluid. If you consider the definition of cloud, you can see why a data fabric would be needed in it. Cloud is about fluidity and removing locality, but, like the data center, it’s about processing data associated with applications. We may not care where the Salesforce cloud or Oracle cloud or any other cloud is actually located but we do care that my data must transit between various clouds and persist in each of them for use in different operations.

Because of all that complexity, organizations have to determine which pieces of the processing are done at which level. There’s an application for each, and for each application there’s a manipulation. And for each manipulation, there’s processing of data and memory management.

The point of a data fabric is to handle all the complexity. Spark, for example, would be a key element of a data fabric in the cloud, as it quickly has become the easiest way to support streaming data between various cloud platforms from different vendors. The edge is quickly becoming a new cloud, leveraging the same cloud technologies and standards in combination with new, edge-specific networks such as 5G and WLAN 6. And, like the core cloud, there are richer, more intelligent applications running on each device, on gateways, and at what would have been the equivalent of data center running in a coat closet on the factory floor, in an airplane, on a cargo ship and so forth. It stands to reason you will need an analogous edge data fabric to the one that is solidifying in the core cloud.

Edge Data Fabric’s Common Elements

To handle the growing number of data requirements edge devices pose, an edge data fabric has to perform several important functions. It has to be able to:

• Access to many different interfaces: http, mttp, radio networks, manufacturing networks.
• Run on multiple operating environments: Most importantly POSIX compliant.
• Work with key protocols and APIs: Including more recent ones with REST API.
• Provide JDBC/ODBC database connectivity: For legacy applications and a quick and dirty connection between databases.
• Handle streaming data: Through standards such as Spark and Kafka.

Conclusion

Data fabric is not a single product, platform, or set of services and neither is edge data fabric. Edge data fabric is an extension of data fabric but, given the differences in resources and requirements at the edge, sufficient change to what is necessary to manage edge data is required. In the next blog we’ll discuss why edge data fabric matters and why now.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.