Opinion – We are at risk of falling into the Solow Paradox.
“You can see the computer age everywhere but in productivity statistics” – Robert Solow, New York Review of Books, 1987. Only – replace the computer above with Analytics and we have the 2020 iteration of the Solow Paradox.
These days Analytics & Data are buzz words which have taken over business reports and included in strategy discussions in most e-meeting rooms. After all, “Data-driven organizations are 23x more likely to acquire customers, 6x as likely to retain customers, and 19x as likely to be profitable as a result,” according to McKinsey Global Institute, 2019. Given these statistics, only a fool wouldn’t incorporate data and analytics as well as their synonyms into the ways of working, right?
But at the same time, often one finds it difficult to crystalize an answer when asked
o What is this analytics you are going to do?
o What is this data you refer to?
o What is our business going to gain by doing this?
These are just some of the questions senior executives also find themselves asking with some going as far as quoting, “Sometimes I feel we are doing analytics for the sake of doing analytics. We need to have more clarity on what business value we are trying to create,” in an anonymous survey conducted by McKinsey, (Advanced Analytics Insights, McKinsey, 2018).
This has become ever more evident in this new fast-tracked digitized revolution we are currently experiencing because of the global pandemic. The collective business economies of the world were tipped on their axis, and some are still trying to get their bearings right. Overnight, business value propositions became obsolete, new ones seemingly appeared out of the air, supply chains and consumers were forced into digitizing their purchases through the internet. Customer needs became wants – where finding something that was as easy as going down to a grocery store became a timeslot war.
With the sheer abundance of the data collected and made available for businesses to analyse and power business decisions (GDPR Compliant, of course), we should be able to overcome this recession at the same velocity that COVID-19 impacted it. Also, get all the business functions in sync like Karnaugh’s heat engine. But this isn’t true, businesses are still finding their bearings in this “Post COVID World”. Why is that?
Please allow me to share my thoughts on how we can resolve this 2020 iteration of the Solow Paradox.
FIRST, THE WHO?
Identify and prioritize the business functions which would be most impacted by Data & Analytics. Generally, across industries, the Sales & Marketing functions benefit the most followed by R&D and supply chain.
NEXT, COMES THE WHY?
Identify the end objective as a business you want data to achieve. This will help to guide your data analytics strategy and align on the clear objectives to be achieved. Keep in mind that this might be a collection of smaller objectives for each business function. For instance, being Business objective – Using data analytics to improve sales might translate into mini-objectives such as:
- Product Team might use data analytics to improve their product designs
- Sales & Marketing might be able to improve the personalisation of the marketing and advertising efforts in targeting
- Supply Chain – might use the data to create a cost-effective supply chain for the product teams.
Ergo, all these are leading up to a singular objective of ultimately increasing sales.
THE FINAL STEP – THE WHAT?
Data & Analytics can impact and affect core business functions, but some organizations are currently only applying Data & Analytics in an ad-hoc, staggered approach. This approach and the siloed structure of business functions lead to each one doing their own thing — a body who wants to move, but each limb is moving in a separate direction, in other words lacking a brain.
Curate a Data & Analytics strategy for your business which informs & guides each of your business functions with the same underlying data set, so that everyone is referring to the same base (as applicable) rather than disjointed and staggered data sets and voila – paradox resolved.
Only then will there be true synchronicity between business & data analytics.
I know, I know. It’s easier said than done. But isn’t knowing half the battle? Give it a thought & let’s discuss! Would love to hear your views.