This content is part of the Buyer's Guide: How to determine which data governance tool best meets your needs

Buyer's Guide

Browse Sections

Examining the top data governance tools on the market

Expert John Ladley examines the leading data governance software products, comparing and contrasting their features to help you determine which tool will best meet your needs.

The list of tools that can enhance the productivity of a data governance program is growing. In addition to existing, established vendors, new players are entering the governance software market. This makes it even more complicated to determine which vendors and data governance tools you should bring in to help enhance the efficiency and sustainability of your data governance program.

There's a readiness aspect to buying data governance tools; before you make a purchasing decision, you need to determine what you will do with the software and where the business value will come from. Once you've reached that point, you're ready to evaluate products. But which tools should you consider to meet your particular governance needs?

To assist your data governance program in narrowing down the choices, let's examine the leading tools in the market from 10 vendors, applying the usage scenarios and product categories from our previous articles.

Evaluating data governance tools by use case

Showing value with a data governance tool usually means meeting the challenges of a business scenario, so let's look at how governance vendors support the following three basic use cases:

Highly regulated industries. Financial services and healthcare organizations (especially in the U.S.) are representative of these types of data governance use cases. This use case requires software that offers a full range of data governance functionality, including tools that support consistency in meaning, tracking of data lineage, workflow and day-to-day administration of high-visibility data governance processes.

Tools offering business glossary functionality for maintaining common data definitions in this area include Alation Data Catalog, Adaptive Metadata Manager, Collibra Data Governance Center, Data3Sixty Data Collaboration Suite, Diaku Axon, IBM InfoSphere Information Governance Catalog and SAS Data Governance. In some cases, there are industry- or compliance-specific aspects within these offerings; for example, IBM, Collibra and Diaku focus on the financial area, and Information Builders Omni-Gen offers products for healthcare providers and insurers.

In addition, the specific glossary functionality that's offered differs between vendors. For example, for some vendors, discovery and collection of glossary entries is key, whereas for others, managing synonyms and other complexity around semantics is important. These tools also vary on how they scan and import metadata, with tools such as Adaptive Metadata Manager featuring established metadata gathering and others -- such as products from Alation, Collibra and Data3Sixty -- supporting more interactive discovery methods.

The data lineage requirement for this use case is met to varying degrees by tools from Adaptive, Diaku, Informatica Master Data Management and SAS. The functionality they provide ranges from aiding in lineage discovery to actually extracting lineage information from data sets, though some of the tools merely allow you to record it. A great deal of how you support lineage is dependent on your particular application's architecture (e.g., if you're using a vendor product versus an in-house developed application), infrastructure and database management system technologies, so make sure you vet this capability thoroughly if required.

The workflow and program management functionality is again met in different ways by products from Adaptive and Collibra, as well as SAP's Master Data Governance software. Each of these vendors has a different approach to administration processes. Collibra started with workflow in mind, so it's deeply embedded. As you might expect, SAP's governance tool is primarily (but not entirely) tied to its ERP suite. The Adaptive workflow feature is recent. Workflow can be culture- and organization-specific, so examine that feature carefully to make sure the product you're considering will address your needs.

An urgent need to consolidate a significant data domain. Immediately behind regulatory-driven data governance programs are ones driven by master data management (MDM) and "golden copy" efforts. Here, the offerings broaden substantially, as the embedded repository and data quality aspects of MDM tools are combined with data governance functionality. Vendors such as SAP blend data quality, business alignment, and value and policy management. SAS also blends data governance, data quality and policy management alongside MDM capabilities. Information Builders mixes data quality and remediation along with MDM management.

It's imperative you try a data governance tool with your own data and use cases before you proceed with a purchase.

Note that many data governance tools are pointed at a specific information management use case such as big data or MDM, while others are more general. Your use case is essential to determining what you need.

Usefulness and accessibility of data. Big data analytics and business intelligence present use cases where data governance is deployed to make data movement efficient, data dimensions more reliable, and information more secure and relevant. The data governance tools in this area are, for the most part, the same as those noted above for the other use cases. They're just deployed differently.

Informatica Master Data Management specifically provides big data support. Data usefulness is enhanced through reference data management, and several of the data governance tools specifically address this topic, including Collibra.

Many of the tools connect to, or offer basic data quality features. For example, SAP incorporates broad-based data quality functionality in its Master Data Governance product. As mentioned above, Information Builders' Omni-Gen also addresses remediation and rules, and Informatica provides options to connect to its data quality functionality.

Other considerations for evaluating data governance tools

Ultimately, data governance represents upgrading how you gather and handle your data. In turn, this means data governance tools need to behave well within your business model and technology portfolio.

Although the vendors offer a wide range of licensing and deployment options -- from on site to software as a service, web services and traditional client server -- a strong consideration is being able to try a product via a subscription before bringing it on site. Even stronger is the technical infrastructure you're operating. For example, while many vendors offer web service options, some don't work as well in one web service environment as they do in another. Many vendors offer various connectivity options, but their approaches to connecting to other file structures vary widely. All of the options can add up to a much larger number than might have been originally mentioned as a starting price. It's extremely important that the tool fits into your particular infrastructure.

As with many other types of software tools, it's imperative you try a data governance tool with your own data and use cases before you proceed with a purchase. Many times, one of these products will work well with a certain combination of client environment and requirements. Then the same product will fall flat in another scenario.

In addition, governance tools often will work better with other data management products from the same vendor. However, it isn't uncommon to find that one of the tools from a particular vendor has an inappropriate user interface for your needs or that a competing tool from another vendor works better and is more compelling. Remember that the users of these tools will lean heavily toward data stewards in business units and other non-IT roles. Vendor loyalty won't be as strong with them as in other areas of data management.

A key aspect to tool deployment is where it will live within your infrastructure -- will it be offered as a web service or be distributed via on-premises servers or in the cloud? Frankly, some of the available data governance offerings could be more flexible in their deployments, so if your organization is headed solidly toward service-oriented architecture, you will need to make that a key differentiator. Many of the vendors offer cloud-based licensing, and this can be a great way to start using a tool without significant internal technology disruption.

Remember that, over time, you will likely be meeting your data governance tool needs using more than one vendor. In this case, you must verify that multiple vendor products will work together. While all tools pretty much offer the traditional, generic access options (i.e., SQL), many of them -- Adaptive, Collibra, IBM InfoSphere Information Governance Catalog and SAS -- offer various levels of sophistication and interconnectivity options. This is a key area to verify through a thorough shakedown or proof-of-concept project. The nature of your infrastructure and technology stack will heavily influence interoperability across multiple vendor offerings.

Some of the tools' suites are more suited for larger organizations or organizations with heavy investments in the same vendor. It goes without saying that legacy software players such as IBM, Informatica, Information Builders, SAP and SAS will generally make the initial selection list in larger organizations. But the data governance tool market is also specialized enough that you can't dismiss the other players. Adaptive, Alation, Collibra, Data3Sixty and Daiku all present feature, price and deployment options that can be well-suited to many organizations.

The market for data governance software can be confusing, but, hopefully, this information has helped you understand the benefits data governance tools can provide your organization as well as how to differentiate between the specific tools examined here.

Next Steps

Self-service BI, big data pose challenges to data governance process

The data steward role shouldn't be viewed as the data police

Strategies to help you get started with big data governance

Dig Deeper on Data governance

Business Analytics
Content Management