It is impossible not to know; the current amount of data and expected growth is overwhelming. More and more companies are acknowledging this, facing the problem / opportunity head on. In order to prevent the effect of data overload versus information underload, Big Data and Master Data Management are such hot topics at the moment.
Information underload is the effect where companies riks; - having no overview on hidden gems within their available data - losing the ability to be compliant to for instance access management or supervisory legislation - losing the overview of what data is correct, accurate, complete and relevant within processes - having such poor data quality that the effect 'garbage in, garbage out' emerges. In order to keep in control of these issues, many companies turn validly to Analytics or Business Intelligence solutions.
While BI offers a solution for data questions in the current times, it would be interesting to consider the data maintanance for the long run. Having data in large (Big data) volumes, requires mutual connectivity between different data warehouses etc. as well as compliant access management (security) and maintaining accuracy and relevancy of data. In his blog Leon Smiers stated; "Data is flowing through processes, but what data do we need? It sounds like a contradiction (..) but the answer is ‘As less as possible….’"
While the last decade, we've tried to gather 'As much as possible' it is an interesting point of view. Using this as a different angle for data management it is possible to do some scenarioplanning for datamanagement. Questions that are valid to answer are: Can I still be in control of this ever growing data volume? Can data in different environments and of different formats (i.e. unstructured and structured) connect? How to prevent 'garbage in, garbage out'principle How to prevent digital sustainability? How are internal processes and departments effected?
Acquering and storing less data is not a the holy grail for data control. But it offers an interesting starting point to look at data in a new way which will lead to further maturing company data management. Some first steps: - Try to get used to the concept, it is different to what has been done in the last decade - Involve business and IT - Check data standards such as ISO 15489, DOD, DMA-DMBOK or Master Data Management for input - Plan scenarios on what will happen, risks and benefits.
I'm sure it will bring interesting insights!