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Information governance and big data offer organizations the ability to have unprecedented visibility into their operations and use these insights to drive revenue and outsmart the competition.
However, the quality of the data being used is of utmost importance. The old saying "garbage in, garbage out" applies very well here.
Big data (and the mining of it for business use) continues to show results for companies. However, this big data is useless if it is not being properly governed and managed. For a business to effectively use big data insights, they must first have complete visibility of all that data.
Information governance expert Jeffrey Ritter makes an important note about what data is being fed into a company's big data analytics technology: "The engines are performing crunching on the numbers, but they can only crunch numbers that filter in correctly -- the data has to qualify, it has to meet the rules that are useful for the engine to compare to find the patterns, and if the data does not qualify, it is embargoed -- it is quarantined or it is rejected."
This white paper from IDC gives some examples of businesses using big data:
Unstructured data accounts for about 90% of an organization's information. Unfortunately, most companies are unprepared to manage this data throughout its lifecycle.
By setting up proper information governance and the rules around it, the data can be used to actually drive new revenue by uncovering new business intelligence instead of becoming ROT.
As discussed, most data is unstructured within a typical organization. This is the root of the problems faced when attempting to retrieve the proper information. The first step is working with the department directors to establish which types of documents are critical and declared as records.
Once that has been established, these content types will need to be included in the overall file plan and the proper metadata must be included when these records are being declared. This not only eliminates confusion, it is the engine that allows an automated software like RecordLion to correctly apply policies to records.
Traditional information governance programs are designed to be risk-averse by deleting data that is redundant, obsolete or trivial (ROT) or simply completed its lifecycle. This is all done defensibly and in line with federal, state, local and industry regulations.
On the other hand, big data analytics relies on, well, big amounts of data to work. This is where the records management and analytics teams must work together to find a balance that will prevent risk while maximizing value.
Overall, solid information governance equals better big data insights. By using quality data, organizations ensure their insights are accurate and efficient.