Understanding Small Data Before Exploring Big Data
Among all the furor surrounding big data in the 21st century, we often tend to disregard small data. Small data ideally form the basis of a corporate’s big data, but over time, it has slowly drowned in the wave of big data. The intention of this aide-mémoire on small data is to question whether organizations are aware of the structures and architecture of their data and most importantly, how to use it. There is a lot of information that is hidden among data and it is important to be able to decipher and cull out intelligence that is relevant to an organization and can be used to understand their customers and competitors.
My belief is that there is a lot that an organization needs to achieve before it pursues projects where they start looking for insights from the outside world. Big-data is a great deliberation topic and it gets people enthusiastic and thinking about what to do with social media and other external data. However, the big-data hype should not distract an organization from the more immediate issue of how to monetize the data the organization currently has.
I would leave you with 5 key areas an organization can work on to ensure that their internal data is being optimally utilized and value is realized.
1. Make data part of the Business Strategy – Any organization wanting to realize value from their data, has to start viewing data as an asset. It has to provide data the same treatment that is provided to other organizational assets like finance, people and technology/infrastructure. The starting point for this should be to draw a linkage of data to all strategic initiatives on the balanced scorecard and bringing out a dependency score to know the impact, data would have on the success of these projects or programs.
2. Who owns the data?–If you own a house or a car, you may have a special interest or concern about these possessions. Anything that can go wrong with these possessions impacts you in a big way. Similarly, every organization needs to identify senior business leaders who own data and the health of the data has to be made their responsibility. Ironically, in most of the organizations, the accountability of data rests with IT. This does not augur well for an organization keen on optimizing value from their data. It is a recent phenomenon (3-5 years) that organizations in the US and European region have started having dedicated Information Management teams to help drive the data agenda within an organization reporting directly to the senior management. These teams help organizations manage data in a more strategic manner and drive processes which help in achieving high-quality data leading to accurate and usable insights.
3. Put an enterprise level continuous data improvement process in place – Poor data quality is an age-old challenge and despite this, there are very few companies who have taken a proactive approach to arrest this issue. The mantra for getting the data right is to give a view of what the world of high-quality data looks like, what the business benefits are, and then show them the direction to make it happen.
4. Putting a Context to Enterprise data – An organization has many processes and every process leads to the generation of data. An organization’s capability to marry the relevant data coming from processes across the organization helps to arrive at an aggregate data which is more dramatic and powerful than a dataset viewed in a silo. Adding more contexts to data elements leads to a complementary change in the understanding of the meaning of data. Contextual computing can help organizations drive enhanced customer experience, cross-sell, and detect frauds.
5. Get your Customer Data right – Organizations striving customer intimacy need to get their customer data right. The starting point would be to revisit the customer contact processes which capture these data points and carry out a root cause to understand the reasons for the poor quality data. More often than not, it happens to be a culture and awareness issue and no conscious effort is being made to capture the critical customer data points. Training, incentive programs, and communication can help you set this right. Technology can help in a big way with tools (MDM) which help you achieve a 360-degree view of your customers. Poor customer data management will lead to poor customer experience which further leads to attrition and failed acquisition/cross-sell campaigns.
The way an organization approaches the treatment and nurturing of data as an asset determines the success of all pursuits wherein data acts as fuel.