Data for an organisation is akin to blood in the human body. You can't live without it. Data is the reason why organisations work and analysing it tells you about the health of your business. As various business functions have got digitised over the last decade with the use of automation, apps, software and digital devices, there is a data explosion in every organisation. Thus making data a challenge to interpret. Further, many large and mid-sized organisations still don't employ data technicians nor make them report to business heads to interpret this data deluge.
Like a single blood test can only highlight issues with few organs, you need to run many tests to know more about the body. The same is true for data. But you also need an expert (doctor) to interpret the data and relate it to your business, industry, competition and efficiency. Else, data just creates sporadic alarms which may have no direct connect with the real business issues.
An alarming fact was discussed at the World Economic Forum 2019 by Bill Thomas, Global Chairman, KPMG, "CEOs and the companies they lead are investing and spending more money on data-driven insights than ever before. Yet, nearly 70 per cent of the CEOs we spoke to admitted they have relied on their own intuition over data-driven insights to make strategic decisions in the past three years."
The question which arises is, why data interpretation is not resulting in the right insights for business decisions. The problem will continue as long as data collection progresses faster than the ability of organisations to analyse it.
Any insights generated by data serves two purposes. One, they tell the CEO something which he already knows, so obviously that's not an insight nor is it helpful. The second is, data throws up new insights which may challenge some of the fundamentals on which the business is based. Which again becomes a challenge to explain as businesses have in the past been run with perceptions, assumptions and emotions. The belief on both these data outputs is low if it can't logically link to insights to business issues or situations.
How can data-based insights be made more acceptable?
• If it's a known insight, it needs to be used to validate and quantify the issues at hand so that knowing the extent of the issues can make the solution easier.
• If it's a new insight then the same needs to be linked to the problem at hand. Also, the insight has to be supported by analytics on all the different cause or effect parameters it (the insight) is being influenced by, thereby establishing a stronger support.
• Most importantly, data interpretation and analysis have to be finally done with the involvement of the business heads or functional heads as the cause and effect of any issue is best understood by them. This becomes one more addition to the business heads already overflowing responsibilities, for which he needs to find time. As before reporting can be automated, it requires to be proven and covering all the relevant key metrics of evaluation.
What are the challenges in adoption of data-based insights?
Many mid-sized and large local organisations have, over the last 5-10 years, moved from being purely promoter-led to being at least partly professional. The operational team being professional generates data and insights and presents the same to the promoters for decisions. As many of these organisations are still run by promoters, they prefer going by their own perceptions of business than follow the data insight. It takes time for promoters to trust data-based insights as they feel they have a better control and knowledge of their business. Data is a new variable in many businesses, which need to prove itself, but that can only happen with human intervention.
A simple example being syndicated industry data on market shares is available from many agencies, but many promoter-led challenger organisations don't buy this data. One reason being cost and the other is that they don't know how helpful this data can be made to improve their business. Further, promoters trust their instincts and experience more than the data which may sometimes not be very accurate. Due to the limited ability to analyse and create practical insights, such data doesn't find buyers.
Knowledge and capability to generate insights:
There is a gap today in organisations understanding of data analysis techniques. Most of the data is now a deluge as acceptance of any new tools for analysis takes time. Many business heads may not be accustomed to this new analysis techniques or aware of how to manage the tools and insights thereof. While the data scientist knows the analysis, he may not know the right combination of factors he needs to analyse to ladder up to the right insights. There is a need for better training for business/functional heads to understand the tools and techniques which can be used for data analysis. Many of these tools can highlight business inefficiencies, provided we know how to use the data to our advantage.
Accuracy of data:
This is of prime importance as data collection is new, at times the data is not being recorded rightly and/or not being stratified as per the needs of the organisation. This leads to inaccurate analysis, thus creating low trust on the data and its analysis. As new devices or software are being introduced to record data, it takes time to get the process right. Well, there are a few who will always try to beat the system for their gains and record data inaccurately. This reduces trust on the data and its insights are then not accepted.
It is a major factor both in generating accurate data as well as analysing it. Not using the right tools which cost, or not being able to interpret or analyse data with the competent people who cost will defeat the purpose of collecting data. If we have invested in data collection then not analysing it well or using it for making decisions is a waste.
People are social and emotional - they react more to stories than facts or data. While data has no emotions, it can be made to tell a story which weaves in the perceptions, knowledge, culture and current issues to increase acceptability. Data-based insights need to be woven into a story of success for it to be accepted.
(Vineet Trakroo is a brand consultant who runs a strategy, marketing and sales consultancy called Evolution Strategy Advisors.)
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