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Actian Blog / Data Democratization: Promise vs Reality

Data Democratization: Promise vs Reality

Data Democratization

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 in order 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. There are several use cases that 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 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

As the Director of Product Marketing at Actian, Teresa Wingfield focuses on hybrid cloud data solutions. Prior to joining Actian, Teresa managed cloud and security product marketing at industry leaders such as Cisco, VMware, and McAfee. She was also Datameer’s first Vice President of Marketing where she led all marketing functions for the company’s big data analytics solution built on Hadoop. Before this, Teresa was VP of Research at Giga Information Group, acquired by Forrester, providing strategic advisory services for data warehousing and analytics. Teresa holds graduate degrees in management from MIT’s Sloan School and software engineering from Harvard University.