Tech mahindra
Tech mahindra

The Data Metaphor – Birth and Death of Data

Posted by: On May 05, 2014 02:08 PM

Working in Data Services day in and day out, and talking to many customers and stakeholders about their ‘ambitions’ has struck me with a new metaphor that we can liken Data to. Its simple – The lifecycle of Data seems a lot like that of human beings! Let me elaborate.

Much like humans, most Data is born among much fanfare and noise. Let’s take customer data for example. The moment a new customer approaches your POS kiosk and ‘converts’, is the moment the stars align and your ‘new addition’ to the family is born. Like the doctors, nurses and the administrators, the agents are busy ensuring they enter all the ‘required’ fields, the applications are busy ensuring all the ‘rules’ are met and the workflows are busy raising their own exceptions. After the initial hype dies down, you are allowed to take your child (data) home to its database. And then the real fun begins.

Guests come and go and everyone wants to hold your new born – it does not matter if the child is sleeping, sick or simply uncooperative. They want to process it, analyze it and squeeze the value out of it. As countless repetitions of this cycle happen, there comes a time for your data to ‘grow up’. You need to send it out to the school, then onto college and then finally to a ‘productive’ life. So on your data goes then, in its journey to be processed, to be changed and to be transformed. Many transactions, many interactions, many queries later, it becomes ready for the big league - The league of the Warehouse.

After an emotional, albeit brief, farewell, your data moves into the warehouse. But life does not stop there. In fact, this is where it actually begins. Irrespective of the type of data, there is a tool waiting to query it and analyze it. “How useful is the data?” “How much revenue has it generated?” “Where did it generate the revenue from?” “Does it have the capability to generate more revenue?” And then a day comes when this data is no longer giving the right answers. It’s time to retire the data. That brings us to the next milestone where your data is quietly moved to the archival system, ready to be whipped out anytime for all the ‘regulatory’ and ‘historical’ requirements. And of course finally, the time comes when the data is so useless that the organization can’t even find a small space to store it and so a final farewell.

Now, for all the milestones that data has passed by, let’s take a moment and reflect upon what makes some data successful (read useful) while some other not so much. What makes one human being successful while another not?

When the data was born, was it adequately looked after? Did your applications really have all the rules that need to be checked for your data to be complete, concise, valid, accurate, relevant…? Before your data moved into the big league, was the health of your data considered? Was it audited, profiled, analyzed, standardized and corrected to enable it to perform at its best?

Even after it moves into the warehouse, did you ensure your data is kept up to date, non-duplicated, and clean? Did you ensure that the data that feeds into your costly BI and Analytics investments are indeed worthy of such systems and the analysis they do? Are you sure the data that you ‘analyze’ is healthy and the true representation of ground realities? Did you ensure seamless transformations and migrations when they were needed?

Whatever potential a human being may possess, unless that potential is nurtured by his surrounding ecosystem, his/her well-being periodically assessed and assisted with and he is provided with all the support he may require (directly or indirectly), more often than not, that person will fail to fully realize that potential. It’s the same with your data. Why then would you treat it any differently? If you want your data to generate value, you need to first set it up to success.

So are you sure you are doing that to your data? Let’s explore more about the data journey in the forthcoming blogs; meanwhile I look forward to seeing your views in the comments below.

Tags: Thought Leadership, Data Management Services
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