Big Data Analytics
Data Consistency Is Key: Why A Data Management Policy Matters
When it comes to business intelligence (BI) applications, data availability typically isn’t an issue. Whether it is the data our business applications generate, or the availability of data from other sources such as partners and distributors, or even the brave new world of social media, data is everywhere. In fact, the volume of data continues to grow by unprecedented volumes each year. Yet both the quality and usability of that data are critical to the success and acceptance of any company’s business intelligence (BI) strategy. Data inconsistency results in misinformation and incorrect decisions.
As the car mechanic in the 1970’s Fram Oil Filter commercial would say, “You can pay me now or pay me later.” The same is true with data quality; either you can address the issue of data quality at its source or pay the price to correct the issue later. When left uncorrected, inaccurate or misaligned data has the potential to become such a burden that it might render the BI system unusable.
From a company perspective, there needs to be a data management policy in place which addresses all sources of data—both internal and external—that is required by the BI repository. There are many names for this process, including Master Data Management, Data Hygiene and Data Governance, to name a few. Each data management policy must have unique requirements, activities and complexity, and all must address two basic items:
- What is the process? What are the minimum requirements for the data? What is the timing, and how much lead time is required? Are approvals required as part of the process? Is data quality achieved while minimizing the impact on business operations?
- Who is the owner of the data? Who is responsible for setting up new customer accounts or new products? Is it the customer service department? Is it the product manager or inventory manager for new products? The owner could be a combination of individuals, depending on the attributes of the data in question. Regardless of what area takes on the ownership, it is this person’s responsibility to ensure that the data and its associated attributes meet company requirements.
Data Management Policies Over Time
Like other operational processes, a data management policy is not static, and must be reviewed and updated on a periodic basis. As the business changes, so might the data requirements, changing the policies you currently have in place. New product lines, new channels of distribution, sales force reorganizations and the creation of new business divisions (due to acquisitions and mergers) are some of the activities that force a review of established data management policies. For data rationalization of common customers from third-party businesses or acquisitions, consider maintaining the source value in your business intelligence software application to easily track back to the originating source system.
As changes are made to master data through the data management process, consider the data that exists in the BI data repository itself. In most cases the historical data will need to be re-aligned to coincide with the new requirements. In some instances, you might choose to also maintain a historical view. For example, align the data based on the current sales organization, but also maintain the original sales region, territory and representative hierarchy.
Data Policies in Practice: Getting Personal
Our own customers take advantage of the BI software that we provide to help with the data management process. And as part of this, they can also schedule “Actions” to email information to data owners where key attributes for customer and product dimensions are missing, incomplete or inaccurate.
Establishing and maintaining a data management policy to help ensure data consistency in your BI solution is well worth the time and energy expended upfront, compared to continually compensating for inconsistencies in the data during analysis.
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