data governance too capabilities

Data Governance Tools: Capabilities to Look For

By Sharad Varshney, Posted September 4, 2019 In Data Governance

Organizations are heavily investing in a data governance initiative to help ensure their data delivers business value. A successful data governance initiative depends on careful planning and the right people, including business executives and data stewards.

However, using the appropriate technology and tools is also extremely important for effective and sustainable data governance. Companies sometimes get overwhelmed with the variety of solutions in the market. However, the process of buying a data governance tool can be simplified once you understand your needs. Then one can decide which features and functionality are required.

Capabilities of a Versatile Data Governance Tool

The tool should be versatile and should have the following features: 

Integrated Data Catalog

If a data governance tool has a built-in integrated data catalog, it makes data discovery a smooth process for users.  

Data Discovery is crucial for BI and Analytics. Most data engineers and data scientists spend 20% of their time in finding the data for their specific business problem. This process can take weeks or even months to get to the relevant data. 

The tool must make data discovery smooth. Data engineers/scientists should have a Google/Amazon type of solution where they can search for the data and understand it as well.

Data Quality Assessment and Maintenance

 Data quality is another significant driving force in most activities of data governance. 

For enterprises to attain better data quality, some software solutions are – data mining tools, data editors, data differencing utilities, data link tools, workflow, project management system, and version control.

Data cleansing or data scrubbing is also a part of the data quality initiative. It correlates, identifies, and removes the duplicate occurrences of the same data points. 

A System of Reward and Attribution

Every business leader wants to own the data as it holds tremendous value. Most times, application owners, by default, become data owners because they are the only ones that have access and knowledge about it. Some organizations make CDO (Chief Data Officer) the owner of the data. However, his/her office most likely does not have adequate information about the data – this becomes another problem. Most people know this as corporate politics. Some organizations want to define ownership at the functional level, such as the VP of sales owning customer’s data while the CPO owns a supplier’s data. 

Ownership is about providing access to the data to other business units so that they can also benefit from it. 

Let’s take a hypothetical scenario of the banking industry. There are two data-warehouses which aggregate the customer’s transactions and the risk & customer insights team. Now let’s assume a third business unit (e.g., compliance) is also looking for the aggregate customer’s transactions. Now in the real corporate world. Both business units would want to divert third BU to the leading data source, but it would create an unnecessary workload on their data-warehouse. 

The solution should offer a mechanism and process to manage the ownership of the data and should also reward owners for better quality control and processing of their data. 

Now let’s assume that there is a price tag for the aggregate customer’s transaction. It is coming from the compliance team’s budget. In this scenario, both business units would be willing to go the extra mile to provide access to their customer’s aggregate transaction. It is one of the easiest ways to curb corporate politics into real data sharing culture. It ultimately creates more value to the entire company. 

Data Stewardship

Data stewardship is about managing the data quality in terms of accessibility, accuracy, completeness, consistency, and updating. 

Teams of stewards are typically formed to carry out data security and usage policies as determined through organization data governance initiatives. In a more simplified way, they are established to protect data governance implementation. Some of the team members may include business analysts, database administrators, and business personnel that are familiar with some specific areas of data within the enterprise.

Business Glossary

A business glossary is an essential aspect of data governance, yet many overlook it. When it comes to running a business, everyone needs to understand what’s going on from top to bottom of the sales finance.

How can this be possible when, in many cases, the marketing or IT unit speak a different language? Alternatively, in the case of acquisition and mergers, where there is no uniformity? These situations are where the importance of a business glossary sets in.

A business glossary helps to solve these problems by creating a common vocabulary across an entire organization. It additionally ensures the consistency of these terms by synthesizing all of the information of the organization’s data assets through an array of data dictionaries. It then rearranges it into a more understandable and straightforward format.

To create a useful business glossary, organizations should implement a data governance solution that can connect data quality, data lineage, and data definitions.

 To have a common language across the business is an essential component of data governance.

Automated Data Lineage

Data lineage is about understanding how and where the data has originated and its processing logic and destination. It gives visibility and also helps in tracing errors back to the root cause in a typical BI process. The data lineage is vital to create trust in the data. 

Usually, we depict lineage in graphical format, so any person with data acumen can easily understand. 

The solution should not only show the lineage graphically but should be able to build the lineage automatically. As creating the data lineage manually is still a time-consuming process. Some of the techniques used to build automatically are:

  1. Code parsing
  2. ETL tool parsing, (Like parse the XML in Informatica )
  3. SQL log parsing
  4. AI 

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