GigaOm Radar for Data Governancev2.01

1. Summary

Data governance refers to the curation and protection of an organization’s data. In recent years, two market factors have reshaped data governance into a facet of data management predominantly focused on access control.

The first is the need to reduce time to insight—and action—from data processes. End users need the latest, most relevant data possible to capitalize on modern business opportunities like cross-selling, upselling, and personalized marketing. Latency in these applications of business intelligence (BI), analytics, and transactional systems is rapidly decreasing, requiring data for organizations to satisfy customers and stay competitive. Simply getting that data into the hands of the business has been a prime concern.

At the same time, customer expectations are higher than ever. Consumers expect more from organizations than prompt, tailored service based on their previous interactions and purchase history. They also expect a degree of control, if not ownership, of their data, which has accelerated the movement toward data privacy and regulatory compliance. Each passing data breach is simply another reason for organizations to demonstrate to customers that they can control data access in a reliable, trustworthy, traceable manner. Breaches have consequences, including lawsuits, financial penalties, loss of reputation, and customer churn. With such ramifications, it’s incumbent upon organizations to master data access in terms of both data availability and data protection.

Data governance—and access control governance in particular (which, increasingly, is what the term data governance has come to mean today)—is tasked with securing this dual mandate. Contemporary governance solutions must provide flexible, nuanced, secure access to data in such a way that it is auditable and dependable for all stakeholders—including business users and customers.

A general observation of the vendor offerings reviewed in this report reveals there’s a three-pronged method to succeeding in this undertaking:

• The first is data discovery to locate sensitive data that requires specific controls. Most solutions use a range of techniques that automate this vital aspect of data governance, which frequently includes artificial intelligence (AI).
• The next step is to classify data according to various governance constructs, such as taxonomies, subject area models, business glossaries, and metadata. There are multiple approaches to automating this requisite.
• Finally, governance solutions implement specific controls for accessing data, which may include anything from segmentation to pervasive encryption. The variation in those controls and how customers can enable them is a critical point of distinction for vendors.

Within this framework, there are a number of other criteria organizations must consider when scaling these and other data governance processes to meet their needs. For example, architecture—both that of the governance solution and that of the organization using it—is another important consideration for selecting a solution to deliver optimal value to users. It’s also essential that organizations scrutinize the data types that need governance, as not all solutions offer the same capabilities for structured, unstructured, and semi-structured data.

This GigaOm Radar report highlights key data governance vendors and equips IT decision-makers with the information needed to select the best fit for their business and use case requirements. In the corresponding GigaOm report “Key Criteria for Evaluating Data Governance Solutions,” we describe in more detail the key features and metrics that are used to evaluate vendors in this market.