Better, not bigger, by MediaCom’s Burt Reynolds

Big data is not always the answer to digital transformation challenges – it’s what you do with the data that counts, writes MediaCom’s Burt Reynolds.

By Burt Reynolds, regional lead – data, technology and consulting, MediaCom

MIT Sloan’s George Westerman, the author of Leading Digital, wrote: “When digital transformation is done right, it’s like a caterpillar turning into a butterfly. But, when done wrong, all you have is a really fast caterpillar.”

If you are still reading this thought piece, then at some point over past the two years you will have definitely googled the phrase ‘digital transformation’.

While we can all appreciate that there has been a lot of talk and walk in this space, it is easy to get lost in parlance and not appreciate the systemic changes and challenges that we are witnessing, in what is remarkable, record time.

This leads in nicely to the fact that by 2023 digitally transformed organisations across the world will contribute about $53.3 trillion to the economy, according to Statista. That’s more than half the world’s nominal GDP.

In our consulting and greenfield projects, we have seen several underlying themes (or opportunity areas, if you will) that have become cornerstones in pivoting from ‘still figuring it out’ to ‘seeing incremental value’.

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In that recipe of agility and success are two ingredients that are not just significant as change drivers for brilliant digital design, but fundamental to drive momentum.

We call this formula C2D2. That is, empowerment through the right calibration of culture, and enablement through data dexterity. Too often the first hurdle in transformation is when we begin the process with a search for leaders who can envision the roadmap or iteration and execute it to fruition.

The barrier here is that effort is spent in revolutionising top-down. This is akin to asking your five-year-old, who has just taken off the training wheels from his bicycle, to go in-line rollerblading. Both sports rely greatly on body balance, but to generalise is the first fallacy.

An extreme example? Yes. But that is the underlying sentiment across most departments unless you are IT. There is the fear of automation, which will inherently have an impact on headcounts and potential growth prospects.

And then there is the human scepticism towards change; this reticence gets accentuated by the organisation’s domain and legacy and whether it encourages cross-cultural thinking.

There is no easy answer to orbit around this troika, but we have seen that our change management delivery framework, when deployed to its full intent, has helped allay these fears and resultantly made transformation less of an internal friction.

Here are the core components that lead into what we call the ACID framework: a strengths-mapping audit that investigates your value-chain through ‘now’, ‘near’ and ‘far’ lenses; criteria to identify change agents within the value-streams, and then bucket them on a spectrum, while forming sub-committees to drive specific agendas; identify micro-gains and cross-functional synergistic opportunities for these change agents; and deploy in a step-phase manner (show success before commencing on the next milestone, which in itself should be an expanded scope to the previous one).

Applying this to your transformation workflow, should help navigate your talent pool from where you are to your next leapfrog, while addressing tension points. Have you realised that the literature on data tends to turn most marketing practitioners bipolar? And this is even though it has been part of our lexicon for more than two decades now.

Let’s go back to basics – evolution in technology has accelerated the volume, veracity and velocity of data and, reciprocally, data has helped define and refine the next big thing in tech.

The problem we have today is not the capacity to hold this data together in an operational space (we have the AWSs and Azures of the world to thank for this), but separating the rice from the chaff. Why? Because the core principle of data since the 1980s has been simply GIGO – garbage in, garbage out.

Instead of reeling in the past, let us take a leaf from how successful tech start-ups (who, no contest, are the ones ahead on the change curve) have dealt with this. Generally speaking, they go through several phases of product evolution before that app gets updated on your mobile device.

And all throughout those wireframes, proof-of-concept, DEV and beta versions, there is only one consistent guardrail to how they see their product or service change their industry. It is called minimum viable data – in simple terms, clear, signposted data that is usable and testable without spending significant time and effort on cleansing, manipulation, migration and maintenance of the data that is not core to functioning.

I am not suggesting that there is no place for valuable insights in big data (especially when you have finessed a level of automation in collection and flow), but to make digital transformation a data-first and informed journey, speed and efficiency is the mantra.