All companies face the need to measure and analyze their business performance. The bigger the organization, the bigger the problem, with various operating companies, business units and departments all having their own priorities and ways of doing things. That includes their data, which makes a strong enterprise data governance strategy a must.
For example, say you want to answer a simple question like, "Who are my most profitable customers?" To do so, you need to be able to gather data from around the enterprise on customers, the products they buy and the costs involved in marketing and selling to them. Just figuring out how much revenue is associated with a given Customer may be no trivial task. If it's a complex multinational company, you need to be sure your sales teams have correctly identified that operations they have invoiced are part of the broader entity. It's easy enough to figure out that Shell UK is part of Royal Dutch Shell, but what about Pennzoil or Jiffy Lube? They're also Shell subsidiaries.
The data to support calculations of profitability is scattered among different applications. Even if you have a standard ERP system, is there just one instance of it? What about hierarchies of classifications for Customer, product and sales data? Do you have just one perfectly consistent set of data classification hierarchies globally? Even if you miraculously do, what happens when you acquire another company, which has its own applications and hierarchies?
How data governance can help companies The reality is that almost all large companies have data silos that contain inconsistent data and classifications. It requires a major effort to align data collection, creation and classification across organizational boundaries, and thus, data governance has become one of the core elements of an overall data management strategy. Data governance is a set of processes to actively manage and control data and how it's used in an organization. Here are the key benefits that can be expected from a successful data governance program.
If you have well-governed data and the ability to do business analytics with it, you can improve operational efficiency in many areas. A rule of thumb is that 20% of your customers provide 80% of your profits; clearly measuring that enables you to better target your marketing and sales investments. Understanding product profitability enables you to weed out underperforming product lines and invest in ones that show promise. Analysis of business processes can reveal opportunities for improvement -- but only if the data underlying those processes is reliable.
Despite significant IT investments, maintaining good data quality remains an intractable problem. A study published in 2019 by software and services provider Experian Data Quality found that 95% of organizations feel the impact of poor data quality and that, on average, 29% of all customer data is viewed as "suspect." The effects of data quality issues can be profound, which is why data quality improvement is a key part of the remit of a data governance program.
