Getting Small Data Right before Pursuing Big Data
Most of the conferences and seminars I attend, talk about Big Data and Analytics and other futuristic and related subjects like Internet of Things, Blockchain and Machine learning. These subjects are all very relevant and are most definitely the future and there is no wrangling about that. I meet executives from various organisations who have huge plans to pursue big data analytics and are very excited to embark on these projects.
My immediate question to most of them has always been “How are they doing on small data?” The intention of asking this question is to really understand whether an organisation has got its internal data architecture right and that the firm has been able to realise value from all the data it captures. Does the organisation, with the data that it possesses have all possible knowledge about their customers and competitors? My belief is that there is a lot that an organisations needs to achieve before it pursues projects where they start looking for insights from the outer 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 organisation from the more immediate issue of how to monetize the data an organisation currently has.
I would leave you with five 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 organisation wanting to realise value from their data, have to start viewing data as an asset. They have to provide Data, the same treatment as 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 have a special interest and concern about these possessions. Anything going 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 organization manage data in a more strategic manner and drive processes which help in achieving high quality data leading to accurate and
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 generation of data. An organization’s capability to marry the relevant data coming from processes across the organization helps arriving at an aggregate data which is more dramatic and powerful than a data set viewed in silo. Adding more contexts to data element leads to a complementary change to the understanding of meaning of the data. Contextual computing can help organizations drive enhanced customer experience, cross sell and fraud detection.
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 leading to attrition and failed acquisition/cross sell campaigns.
An organisations approach to how they nurture and treat data as an asset would determine the success of all initiatives where data acts as the raw material or fuel.