3 Reasons Why Data Management Should Be Strategic Rather Than Tactical

Global Business Communication

During the 18th International Conference on Petroleum Data Integration, Information and Data Management (commonly known as the “PNEC” conference) on May 20-22, 2014, there was a session in the first day dedicated to the topic of professional data management. During the panel discussion, an attendee asked the following question: Why do we even need a professional role dedicated to managing data, since data service is a supportive role to various operations?

Trudy Curtis, CEO of PPDM Association, answered this by emphasizing that data management (especially data content management) should not be viewed as a tactical function, but a strategic one, needing lots of focus and planning to help businesses truly benefit from the potential of strong, quality data.

Many businesses indeed do not view data management as a strategic function. Below, I will give three reasons why data management deserves to be a key strategic function within any modern digital business.

Data is the blood of modern business workflow

When considering business processes, or workflows, of a business, many would consider them comprising mainly operation procedures. For many of these workflows, the role of data is supportive (i.e. they are the inputs and artifacts of the workflow, but the data itself would not alter how the workflow is run). Enter the digital age, though, and you suddenly have important workflows that cannot run without putting data management into a much more strategic, proactive role.

Suppose that an upstream company manages leases where, upon a certain level of production from the wells of the land, division of interest changes. For example, when the company produces X barrels of crude oil, a particular division of interest doubles (while proportionally shrinking interests of other entities). The data about the leases and the data of production accounting are stored in separate places. In this case, two challenges would occur:

  • If the production level data is not accurate (data quality issue), it may trigger the change of division of interest at the wrong level, or not trigger at the right level. This will bring losses to the company, and/or damage relationships with customers.
  • If the production level data is not accessible from the lease management department (data accessibility), then the whole workflow completely relies on someone at the accounting department to notify the land department in order to make the necessary change. Not only is this cumbersome, but the probability of missing the change notice is very high.

As you can see, today’s workflows are increasingly dependent upon data quality, accessibility, governance, etc. to ensure the execution quality of the process. To minimize negative impact due to data issues, data management needs to be done at a strategic level, so that it can plan forward and ensure that all processes in the company are well supported by the needed data. If there is no plan, when you need it, it will not be there.

Unplanned data cannot give meaningful information

One wave that the industry is catching on is Business Intelligence (BI). By utilizing data integration, dashboard, data warehouse, etc., it provides a powerful platform to generate useful information, helping the business line to make better decisions. There is, though, not enough discussion about the underlying requirement: data quality.

Simply put, the data needs to be a certain quality to support BI objectives. One common challenge is that in order to do a useful rollup of a certain dataset, there will be certain required data often not captured. BI projects rely on well-captured data to be successful and useful; if the data has not been captured, a BI project will not miraculously fix this problem.

As the saying goes: “garbage in, garbage out.” BI projects also rely on data that is in good quality, with accurate and precise data to do correct rollups, so that it can provide adequate and realistic information. In fact, the most costly portion of many BI projects is data cleansing, which is required to make the projects successful.

If the data has already been managed strategically, ensuring certain quality, governance and availability, projects and operations that rely on this data will be much more cost efficient and successful.

Data maturity needs to grow with the business itself

Many people talk about data growth in terms of volume. Data volume is certainly a key factor, but it would be unwise to overlook the fact that data maturity needs to grow with the business itself as well. It will not magically catch up with the business, and ignoring it in the business roadmap can lead to negative impacts.

Realistically speaking, setting up a mature data model and strategy is costly and time-consuming. For small businesses, they need quick wins to maintain positive cash flow; therefore, most small businesses could not afford high data maturity, and “getting the job done” is what they focus on.

As the organization grows, though, the data has to become more mature with the organization. Since the business requirement will expand, or even become different, when the organization grows, the original data model, quality, governance, etc. will not be able to support the growing operations.

Projects to improve data quality, set up governance, ensure accessibility, etc. are expensive and time-consuming, therefore these data improvement projects need to be planned ahead, in accordance with the organization’s roadmap.

Moving forward

Just like IT, data management used to be viewed under the spotlight of a supportive, tactical function. However, in this new digital age, data management deserves better management. Align data management with your company strategy roadmap, and your organization will have a head start to quality data, ensuring operation efficiency and cost savings in the long run.