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The Data Life Cycle

By Pradeep Raju, CMA, and Barry Nathan, CMA
July 1, 2018
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It takes time, resources, and commitment to assure data takes the right journey through the enterprise.

 

Data is an essential part of any enterprise. An enterprise that’s agile and innovative requires an understanding of data as it flows through the organization, interacts within various departments, and transforms itself. Though an international standard for the data life cycle doesn’t exist, the following phases, in order, are identified as typical during data life cycle management:

 

  1. Capture. Business is surrounded by data, but an enterprise needs to capture it in order to make use of it. Data capture occurs in three major distinct ways:

 

  • Data Entry. Manual or automated entry of data into the data warehouse to create new data values.
  • Data Acquisition. Acquiring or transferring data from an already existing data source or data warehouse.
  • Connected Devices. Internet of Things (IoT) has and will continue to transform the way data is captured by making it real time and continuous as devices listen to and interact with the environment and each other. These devices capture and transmit the data so it can be stored.

 

Data capture is such an important phase to create a truly data-centric enterprise that companies spend a commensurate amount of time on this phase (see Figure 1).

 

 

  1. Qualify. Have you ever wondered why month-/year-end close processes are prone to errors or why reconciliations take such a long time? Inaccurate or incomplete data may lead to major problems later in the data life cycle. These problems may include critical business processes being held up, bad decision making, or final reports running afoul of compliance because of erroneous data values. In this phase, data is assessed for its quality and completeness using a set of predefined rules.

 

The Capture and Qualify phases are traditionally seen as under the purview of the IT team, which sets up the system architecture. But management accountants, with their knowledge of accounting processes and the way data will be utilized in later phases, have the ability and responsibility to envision the framework of the system in partnership with IT.

 

  1. Transform. The advent of Big Data has led to enterprises being able to capture a seemingly infinite amount of data. Couple this situation with IoT, and soon you can be drowning in data. Thus, enterprises have to transform, synthesize, and simplify the data so it can be utilized by functional departments. This phase is commonly called “analytical modeling” in the financial world. A certain level of functional expertise is required at this phase as data from different sources is linked together to find the intrinsic value hidden beneath.

 

  1. Utilize. The final aim of data is to help enterprises make good business decisions, and, in some cases, data itself is the final product or service of the enterprise. Either way, the true value of data is unlocked in this phase, and the previous efforts made in data capture, qualification, and transformation finally bear fruit.

 

Management accountants act as business partners during the Transform and Utilize phases. As business partners, we need to translate the data values into business stories that help enterprise leadership understand the magnitude of their decisions and their long-term impact.

 

  1. Report. This phase relates to external reporting. Internal management reporting for decision making is realized during the Utilize phase in the data life cycle. External reporting could involve quarterly/yearly financial reports, financial data sent to other vendors for bids, and other compliance reports.

 

Reporting of data is a key phase of the data life cycle that is ripe for automation. Since rules, definitions, and requirements for these reports either rarely change or have slight changes year after year, automated processes designed by management accountants help to create and publish these reports in a more efficient manner.

 

  1. Archive. This is the beginning of the end for the data that the enterprise has spent a considerable amount of time and resources on to unlock its value. Data archiving is the transfer of data from an active stage to a passive stage so that it can be retrieved and reutilized as needed.

 

  1. Purge. The final phase of the data life cycle is the removal of the data (and any copies) from the enterprise. It occurs in the data archive and is sometimes accompanied by a communication both inside and outside the enterprise.

 

Financial compliance rules within an enterprise or those imposed by regulatory bodies normally drive the Archive and Purge phases. And management accountants act as custodians to ensure that these compliance rules are followed within the enterprise for financial data.

 

DATA GOVERNANCE

 

Before we end our discussion on the data life cycle, we need to mention the overarching rules and framework that define it within an enterprise.

 

Data governance helps an enterprise administer the data as it flows through the various phases of the data life cycle. During the Capture phase, enterprises need to identify the capture points for the data and define the data that will be captured. As data enters the Qualify phase, the rules of data governance act as a check to ensure that inaccurate data is identified, assessed for completeness, and secured.

 

At the Transform and Utilize phase, focus shifts toward adherence to transformation rules and the legal utilization of the data according to regulatory standards for decision-making purposes. As the Reporting phase is all about showcasing data to external parties, data governance lists the steps to take when inaccurate data is reported outside the enterprise.

 

Archiving data relies on a set of rules that define what occurs, as well as when and how. And in the Purge phase, it’s critical to set a purge schedule for the data as per the retention period requirements. List the steps to ensure that data and all its copies have been purged from the enterprise.

 

Although on paper this process appears simple, it takes time, resources, and commitment to have a data program that helps your enterprise. But remember: Not all data needs to go through every single phase of the data life cycle. Hence it’s imperative that enterprises create a business case on why a particular set of data is required and then identify the gaps within the data life cycle where investments need to be made. As management accountants, data resource management is part of our responsibility to build that business case to help the enterprise manage the data through its different phases.

 

Pradeep Raju, CMA, is Finance manager at Genentech and a member of IMA’s Portland Chapter. He can be reached at raju_pradeep@outlook.com.  
  Barry Nathan, CMA, is the fiscal manager for a state government IT department. He also has served as an interim deputy CIO, responsible for a security department. You can reach him at barryn@prodigy.net.
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