The objectives of this article are:

• To help the business keep compliant with government regulations, which are moving targets for many companies.
• Helping businesses democratize data governance and data management, strengthening a company’s ability to ensure its systems are safe and secure.
• To educate the business on the benefit of an effective data governance strategy to an organization.

The importance of data governance cannot be underestimated in today’s complex IT world. A data governance tool should capture and understand data through discovery, profiling, benchmarking, validation, and cleansing. An effective data governance strategy provides so many crucial benefits to an organization, including providing an increase in data security, a reduction of data leakage, as well as helping create a common understanding of data. This can help the business keep compliant with government regulations, which are moving targets for many businesses. Advanced analytics, machine learning are helping democratize data governance and data management, strengthening a company’s ability to ensure its systems are safe and secure.

The cloud data integration leader Talend believes that “Data governance is a requirement in today’s fast-moving and highly competitive enterprise environment.” “Data governance is not only about control and data protection; it is also about enablement and crowdsourcing insights,” says Talend. Companies now have to capture enormous amounts of data from a multitude of sources, including structured, unstructured, and semi-structured data, which all have to be corralled, metadata-tagged, cleansed, and optimized before it can be used in models or visualization dashborads. Data is more important than ever before, which means data governance is needed more than ever.

An effective data governance strategy provides so many crucial benefits to an organization, including providing an increase in data security as well as a reduction of data leakage. It also helps a company create a common understanding of its data. This can help the business keep compliant with government regulations, which are moving targets for many businesses. A 360-degree view of each customer can also help customer understanding, which increases the opportunity for personalization marketing, the only type of marketing businesses should be doing right now. Data governance should be about minimizing data risk while maximizing use. Counter-intuitively, strong data governance makes data more, not less, accessible.

A data governance tool should be able to capture and understand data through discovery, profiling, benchmarking, validation, and cleansing. For example, data elements can be checked for accuracy and corrected when necessary, as can all kinds of personal data. Once this data is captured, a data governance tool can actively review it, then monitor it going forward. The data governance tool should also enable the people who know the data best to help with its stewardship.

Data cataloging tools can help schedule the data discovery processes that crawls an EDW and examines the data so that it can be understood, documented, and actioned. Relationships can be drawn between datasets and these can be connected to a data dictionary or a business glossary. The benefit for data owners is they get an overview of their data and can take appropriate action on it. For the data consumers, they get visibility into how their data is being used.

Data profiling is the process of looking at the six facets of data quality — accuracy, completeness, consistency, timeliness, uniqueness, and validity. Data profiling analyzes the data’s structure, its catalogs, schemas, and tables, then stores a description of its metadata in the system. A “trust index” can be created from this profiling, which can be tracked on a regular and automated basis. Whenever the data moves beyond the set bounds of the trust index, alerts can be sent to necessary parties.

“Advanced analytics and machine learning help democratize data governance and data management because they make things much simpler,” argues Talend. “They improve developers’ productivity and empower non-data experts to work with data as well by suggesting next best actions, guiding users through their data journey,” says Talend. The ability to cross-reference video, images, and human language data against a company’s CRM, social media, and transactional data systems will have enormous security, CRM, and marketing implications going forward.

One typical machine learning use case is data error resolution and record matching. Machine learning can fully automate the deduplication of records process, turning a low value and time-consuming task into an automated process that can be scaled up to handle millions of records at once.

Failing to establish strict data privacy controls can leave a company exposed to massive corporate financial risk, severe regulatory fines, and a damaged brand reputation, which might be worse than the regulatory penalties as trust is hard to regain once lost. Data governance and data cataloging technologies can help with this. Once data elements have been defined with a Personal Identification Information (PII), data sets that relate to them can automatically be spotted and masked, if necessary.

In the past, disciplines like data masking were sparingly used, but with the explosion of data privacy scandals and the proliferation of regulations, a much more aggressive approach to data masking is needed now. With important customer or business information masked, production-quality data can be shared across an organization for analysis and business intelligence, without the threat of exposing important personal or even personnel information.

The explosion of data is making data governance an integral part of a business’s IT operations. It is no longer something that can be ignored. Hackers are getting bolder and more effective. Governments are adding privacy legislation and clamping down on offenders who get hacked. The enormous amount of data that has to be captured, corralled, metadata-tagged, cleansed, and optimized before it can be effectively used by a company should motivate them to get their data house in order as quickly as they can.

Unquestionably, data is more important than ever before, which means data governance is more necessary than ever. If the idea of getting one’s data house in order isn’t enough for CIOs, then they should recognize that losses in the tens or even hundreds of millions of dollars due to hacks are getting more and more common. Regulatory bodies are losing patience for companies that allow their customer’s private information to be compromised. Companies that don’t take data governance seriously may learn a very painful lesson about its necessity should they get hacked.

How Cognitive BI can help?

Data Quality impacts our business in many ways, from regulatory reporting, management reporting, and operations to how we serve our customers. The experts at Cognitive BI, Inc can do an assessment of your data management maturity using a structured evaluation approach. Contact us to discuss.