“Four things come not back: The spoken word, The sped arrow, The time passed, The neglected opportunity.”
The above is one of my favorite proverbs. But I would add a fifth – “Disposed data”.
With all of the talk about big data, one would think that the more data retained and stored the better. But as any good analyst knows, there is a lot of noise out there in the data world – and not all data can or should be stored and/or archived. The trick is having the foresight to determine what data contains business value and should be stored.
This week at IBM’s Analytics Solution Center in Washington, DC, Servando Verala, John Masters, and Bob Nonnenkamp discussed Modernizing Records Management and Improving Information Economics. They were primarily referring to policies for retaining and disposing of data.
Let’s start by putting this into a little context:
- 90 percent of the information in the world was created within the last two years.
- There will be a 44 fold increase in the amount of information that will exist in the world by 2020.
It has been said that data is the new oil. But if this is the case, for it to be useful, it needs to be refined. And similar to oil, it is not quantity that necessarily matters, it is usable, refined, quality that powers our world. Records management provides some guidance in helping organizations determine what enterprise data to keep and what and when to archive and/or dispose. It was pointed out that most organizations have disposal policies for paper records, but not for electronic records.
There are four types of enterprise data as it relates to retention:
- Business Value: Data that should be maintained due to its inherent business value that provides business insights that provides strategic or operational business value.
- Legal Value: Data that provides value with respect to legal departments in defending organizations against potential legal action.
- Regulatory Value: Data that is required by regulatory authorities to demonstrate compliance.
- Data Debris: Data that is simply artifacts and provides no value today or in the future.
The legal and regulatory data should be fairly straightforward to identify. That there are various models, such as the Electronic Discovery Reference Model and the Compliance, governance, and Oversight Counsel that provide these guidelines.
Embedded within this discussion at the ASC is the fine line between data that provides business value and data debris, of which to be disposed. Determining the difference between data that provides current or potential business value and data debris is found in the intersection of business strategy and information management. My colleagues and I have developed a methodology to help determine how to embed Big Data and Advanced Analytics into organization strategy to maximize value. This helps to determine which data to keep and which data to delete, therefore helping to provide additional insights into the records management practices. The process of our Big Data and Strategic Analytics Maturity Roadmap include:
- As-is: Current comprehensive inventory of current data, reports, and analytics to be fed into the maturity model, including an inventory of the organization’s technology-related resources available. By mapping the organization’s as-is state to industry best practices, an organization can estimate their maturity on the Big Data and Strategic Analytics Maturity Model.
- Business Analytics Strategy: Often overlooked is the inclusion of the organization’s business strategy. The organization’s business strategy provides input as a part of the as-is state and can be combined and baselined through a SWOT analysis.
- To-be: Secondly, based on the results of the SWOT analysis and resources available to the organization, the to-be vision is developed, as it relates to enhancing current data, reporting, and business analytics. Included in this is the definition of how this vision will fortify the attainment of the business strategy and the technology-related resources required to achieve this vision?
- Big Data and Strategic Analytics Requirements: From the to-be scenario, organizations should be able to develop specific reporting and analytics requirements as they relate to achieving the business analytics strategy.
- Roadmap: Finally, the above to-be scenario can be benchmarked relative to best practices to determine how to utilize best practices to develop a roadmap, to achieve that vision a reasonable period of time to target attainment.
To be clear, ancient philosophers did not intend for us to store and archive all data. But I think that they would agree that since “disposed data come not back”. A well thought-out Big Data Strategy combined with smart electronic records management practices enables organizations to avoid disposing of valuable data that may be critical to achieving the organization’s potential.