The data rush

The data rush

Companies can gain the maximum benefit out of data by investing in information filtering solutions to store and keep what is useful from a business perspective and leave out the rest  


In one of my previous organisations, I was chasing a very well-known retail organisation to get some projects, when one of the key people in the leadership team told me that he understood how valuable data is and he would share it with us only if we paid them money in return. This comment left me stunned for some time because here I was, trying to tell him how, by using the data, he could better understand his customer’s buying behaviour, help them buy more of what they would like to buy, and thereby, increase his store sales, thus requiring a fee for our services, and there he was, asking me for money in return! Then, I realised in hindsight, that those were the days leading up to the frenzy around the Facebook IPO and the media was awash with articles on how having data on consumers was what made Facebook so valuable. No wonder this gentleman was thinking that he was sitting on a goldmine (that was precisely the word that he used) and he would sell the rights of mining only to the highest bidder.

The reason I am highlighting this story is because, the world of business is now talking about data-driven decision making and every CXO worth his penny understands the need to build a data-driven organisation. However, even today, most people in various parts of their organisations do not know what data needs to be collected because they do not know how it is going to be used to build strategies.

The reason for this new data explosion and consequently the “need to collect” all possible data is because of a couple of fundamental reasons such as huge amount of online or offline data being collected and cheaper costs of storage. In 1990, the cost of storing 1GB of data was about US $ 9000, while in 2010 the cost of storing 1 GB of data was around US $0.08. That is a sheer 1, 00,000 times fall in cost. While Moore’s law states that computer speed doubles every 18 months, the ‘storage law’ states that storage capacity doubles every nine months. Thus, the speed at which data can be processed and the capacity to store that data is increasing at a phenomenal pace.

When you don’t know what data you need, collect everything that comes your way This maybe a good strategy in the shorter-term, when you are setting up your data collection systems and processes, but it may not be as good from a long-term perspective. In the longer run, organisations that collect zillions of data will struggle to get a single view of it because, the data from each system will tell a different story. Thus, they will end up spending millions of dollars, additionally, in creating complex data warehouses, which business users of data may not be able to understand and hence use. The biggest tragedy of this mad data rush is that business users that could hugely benefit by mining this data often have to wait for months, before their internal IT teams or external IT service providers pull out data that are relevant and usable for their purpose.How to pick and choose relevant data I will provide a very simple example that I often use when I explain the phenomenon of data usability to folks who do not use it as much. Consider a simple information field in your huge data repository called the time stamp. The time stamp stores information about any event that takes place for which data will be collected. Thus, the time stamp field in a credit card transaction database will record the time at which a credit card was swiped, but will hardly be of any use to a person in the collections team, because they just follow-up for late payments from defaulting card users. Instead, one that will be useful to them will be the time since last payment, which will record payments made to the credit card provider, by the users. This data will help the members of the collections team categorise card users into good, bad and write-off categories and accordingly make calls to the card users for recovering dues, keeping the tone of their collections call as good, bad and ugly.

Thus, as companies continue their mad data rush and invest tons of money in more sophisticated IT systems, they need to also invest in a second layer of information filtering solutions to store and keep what is useful from a business perspective. Data is like a raw, uncut diamond- one needs to sharpen it painstakingly and turn it into a gem.

Snehamoy Mukherjee is a part of the Strategic Leadership Team at Axtria, a New Jersey-based Analytics firm, where he is responsible for business development, solution development, delivery leadership and strategy formulation. Prior to this, he used to head the analytics practice at Technopak Advisors and has over a decade of experience in the analytics industry having worked in multiple domains like retail consulting, FMCG/CPG, insurance and market research.

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