23 Jan 20 Leveraging Data: Mindset comes before technology

Given the incessant noise around big data and analytics, businesses are scrambling to board the train. And a lot of money is being spent on data collection and technology. The impression is that AI and machine learning algorithms willLeveraging Data Mindset comes before technology magically crunch the data and solve all our problems. Sadly, this is like trying to catch a tiger by the tail. Before worrying about data collection and technology, we need to think about the business problems we’re trying to solve.

Companies often collect data because they collect it! And the connection with decision making is usually tenuous. This is not new, and has nothing to do with technology – it’s more about mindset.

Let me illustrate with a few examples:

  • Last year, a client asked us to design and run a customer satisfaction We discovered that multiple surveys were conducted by different departments, with differing objectives. They had lots of historical data – and kept collecting more. Data was stored in multiple disaggregated documents. Analysis was sporadic, ad-hoc, and limited to a single dataset at a time. When we asked for a comprehensive list of data sets and samples – they could not even find all of them!
  • In another instance, we were developing a “competitive intelligence framework” for a large corporate. Multiple business units (Finance, Sales, Strategy, Marketing, etc.) were collecting the same information – competitor news, commodity prices, customer news, industry trends and the like. Little of this was shared, and colleagues were unaware of valuable information or insight sitting in the next cubicle. Less than a third of the data collected was actually used in any analysis!
  • All companies today use CRMs, but most are unable to do even rudimentary win-loss analysis. This is largely because of the poor quality of data – incomplete or incorrect information, and missing data fields that are critical for analysis. In the end, it becomes too cumbersome to clean up – so there’s never any analysis possible.

Do these sound familiar?

Companies have always collected a lot of data, and this is only growing. Yet the answer may not lie in getting fresh data – but in first figuring out how best to use what you already have. And this has nothing to do with technology!

Ideally, organizations must drill down from business objectives to define their key intelligence topics (KITs) and key intelligence questions (KIQs). Ultimately, the answers to the questions must aid decision-making – i.e. be actionable. The KIQ process is non-trivial and requires the inputs and time of decision makers. Only after this, are we in a position to think about what data or information is needed, where we will get it from, and how. At this point, we can audit existing data sets and assess what’s useful, and how we might fill the gaps.

And then finally, we come to technology! Or – how do we process or analyze the data? While AI might help, not all businesses are dealing in “big data”, and most B2B businesses have relatively small customer data sets. A lot can be done with fairly basic tools like Excel or SPSS. But again, technology is not the problem – it’s the mindset!

Do we think about what needs to be collected? Do we understand how this will be analyzed? Do we know how this will feed into decision making?

This thought process is independent of technology – some businesses are culturally attuned to leveraging insights from data, while others are not – and this pre-dates analytics and big data.

Arun Jethmalani
Arun Jethmalani

Arun is one of the founders of ValueNotes. Apart from trying to build a high-quality research business, he has spent the last 27 years researching, analyzing, and dissecting companies and industries. He has worked with clients of all shapes and sizes, from all parts of the world – in providing them insights that make a difference to their business.
Prior to ValueNotes, he was an equity analyst/advisor, and wrote extensively on investing – including a column titled “Value for Money” which ran for 10 years in the Sunday edition of the Economic Times. To this day, he remains an avid “value” investor.
He has also been published in several other publications, and is a regular speaker at events related to technology, investing, competitive intelligence, business process management, Internet, etc. See: Valuenotes Events
He has been instrumental in developing a community of research and intelligence professionals in India, and is the founder and current chairman of the SCIP (India) Chapter. Arun holds a B Tech from IIT, Bombay and an MS from Duke University, NC, USA. LinkedIn Profile

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