What is Data Governance ?

Data governance is the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of data and information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of data and information in enabling an organization to achieve its goals.

Data governance is one of the least visible aspects of Web analytics, it’s easily one of the most impactful. Companies that get it right generate “Ferrari” benefits – significant savings of time and money, plus greatly improved confidence in their data. In other words, data governance is actually a “must have.” Without it, an organization’s entire data strategy and online marketing approach may rest on a shaky foundation.

In particular, data governance is not:

• Tactical management
• Technology & IT Department alone.

Why Data Governance is important?

The word “governance” determines quality because that is the fundamental aim of data governance. “Why should we look at data quality?” For Example, If you are doing any online purchase and due to lack of data quality the purchased goods is dispatched to wrong address or you will receive goods that don’t match with the proper description and so on. However, there are other more subtle effects of poor data quality – i.e. missing the opportunity to upsell to a customer because you can’t accurately identify the product categories that they purchased, not being able to negotiate purchasing discounts because the supplier is duplicated so many times that we can’t say what our total spend is, losing web sales because your inaccurate sizing data makes you look bad on comparison sites.

It’s likely that there are already some people in the organization who are checking data quality as part of their regular job. For example the accountants are probably ensuring that postings are made to the correct ledger codes, your accounts payable department is ensuring that invoices are sent and matching payments are received. Much of your operational data is already part of an active management process but to a large extent their interest is in quantities and values. The areas that get less quality checking are the reference data (or master data) that drive many of your business processes. Data Governance aims to put in place formal management responsibilities for the quality of this data.

One of the changes in attitude that is driven by data governance is to move away from a reactive approach to quality into a more proactive approach. Often poor data quality is only found when a business process fails – when a delivery can’t be made or when your IT system stops working – and there are few instances where that is the best way to find problems. It is also common when disasters occur through poor data quality that nobody can be found to take responsibility! Data Governance ensures that somebody is clearly responsible – not just for fixing the disasters but also for reducing the likelihood of one occurring.

Below are the few points which briefly determine why Data Governance is required:

A. You will get consistent, reliable and repeatable data. A central role of data governance is to ensure that metrics are defined consistently across the organization. So when managers or analysts talk about “conversion rates” or “unique visitors,” everyone else knows precisely what they’re talking about. Without clearly documented standards around metrics, decisions may be made around false assumptions. Obviously, communication and reporting suffer in such situations, an especially important consideration in environments with multiple analytics tools.

B. Analysis and reporting issues are most often data governance problems. Many organizations are quick to blame their tools or technology when there is confusion about the meaning of Web analytics data or lack of clarity in reports. Typically, the tools and reports have not been configured to clarify what various metrics mean, how they align to specific goals, or where they may vary from data provided by different systems. Companies end up ripping and replacing perfectly good systems before doing the necessary governance work to ensure they work properly.

C. To Enhance the efficiency of process. If your team is spending a lot of time checking and rechecking your reports, it can be quite inefficient. When a report generated conflicts with another report, it may bring some doubt to the validity of all reports. There is likely a data quality issue is behind it. The problem manifests itself as a huge time-suck on monthly and quarterly closes. Data champions must point to this inefficiency in order to put in place a solid data management strategy.
D. It saves money. Having a firm grip on how you define “page views” or other core metrics can help you when you’re negotiating to buy or sell advertising. You won’t be low-balled because an external organization (like an ad network) claims your traffic is too low or theirs is too high. And you’ll be able to probe the metrics used by others to price their own inventory and determine if their information is credible and accurate. Even if you’re not in the advertising market, data governance can save you money in other ways. For example, it may help you avoid the premature and costly “flipping” of systems.

How to achieve Data Governance?

Data governance is not just a collection of ad-hoc data quality projects, but the development and integration of a set of rules - policies, guidelines, and standards - for managing the corporation's data. It is implemented by a data governance management team of information technology and business associates who are unified by a common goal to ensure that:

• data is what it is supposed to be (Data Quality)
• data is in the correct context (Data Integrity)
• data and its associated metadata are accessible (Data Usability)

There are several tools which are already there in market to achieve Data Governance such as “Information Analyzer”. When we say “How to achieve Data Governance” it never means that we are going to use any tools for that or any overall separate process we need to follow to achieve Data Governance, We just want to ensure that the Data which is going to get used/ observed by our processing system is in proper format and deliver the exact quality for which it meant as well as we are making sure that our data is ready for audit purpose and its satisfy all the respective protocols. Achieving data governance is very important when we are dealing with critical data of banking Industries or any financial sector.