It’s a fascinating time to be in the data management business. Pundits are using terms like “oil” and “soil” to describe the business value of data. The Economist magazine in a special report on managing information published in 2010 featured a quote from a computer expert describing the current times as the “industrial revolution of data”. The same report stated “data as becoming the new raw material of business: an economic input almost on a par with capital and labor”. To say data is important to running a business is an understatement, looks like storing, managing and analyzing the data could well be difference between success and failure.
Pundits agree that across all the data available in an organization, 20% is structured and 80% unstructured. Structured data refers to the human generated data like orders, leads, support calls etc. which is generally well stored, managed and analyzed. Unstructured data refers to machine generated data like RFID sensors, web logs, application logs, click streams etc. which is loosely stored and infrequently analyzed to drive business decisions. This is not to say that the unstructured data is not analyzed at all, it is generally used to drive technological decisions like improving applications and systems performance. So, in essence, organizations are using only 20% of their data to run the business. Imagine running a business where 80% of your labor or capital is not utilized…how terribly unproductive, yet leading organizations around the world are doing just that day in and day out.
Lot of businesses takes comfort in fact that they are really not data oriented business. Conventional wisdom states that structured and unstructured data is definitely relevant to online e-commerce businesses like Google, Facebook, eBay etc. but the unstructured data is of not much use to traditional businesses like manufacturing, utilities, consumer goods etc. Nothing could be further from the truth.
So, how can a manufacturing business like automobile manufacturer benefit from analyzing both structured and unstructured data? Automobile buying just like any big ticket item purchase involves the traditional buying steps like need recognisition, information search, and alternative evaluation and purchase decision. The buyers spend a lot of time on the manufacturers or 3rd party information provider websites generating unstructured data around making selections, gaining information and comparing alternatives. This user behavioral data can be constantly analyzed and combined with the structured “compare vehicle” section of the websites to make the comparative selection dynamic and based on user behavior vs. a static list. Similarly, the attitudinal data generated by the customers around a vehicle’s features can be used as an input to improve the vehicle design process.
Another example can be around achieving balance between mass customization and mass production with a service like NIKEid. NIKEiD is a service provided by Nike allowing customers to personalize and design their own Nike merchandise. NIKEiD offers online services as well as physical studios in different countries around the world. Mass customization provides personalization but without mass production the cost and lead time is prohibitive. NIKEid can use the unstructured user generated design data to identify the top selling merchandise and sell them as innovatively designed, semi-mass produced items at lower cost, with less lead time and at higher volumes generating better profits. E.g. NIKEiD’s unstructured personalization and design data can be used to identify major trends like demand for “shoes with a smaller carbon footprint” or “green shoes” and can be used to launch a new mass produced product line.
These are just a few examples on how data can be used as a strategic asset to drive profitable business performance.
In closing, as Rollin Ford, CIO of Wal-mart says “ Every day I wake up and ask, how do I flow, manage and analyze data better?”, data is your most strategic asset which could well be way underutilized. So, it’s time to ask the tough questions and start defining a comprehensive data management strategy, one that includes people, processes and technology and addresses all of your corporate data both structured and unstructured.