DataIQ Transform - Keeping transformations on track with the right leadership

David Reed, knowledge and strategy director, DataIQ

Transformations are like building a domino run - you spend a long time putting all the pieces in place, building anticipation of when you get to push the first piece and start the chain going. Only some of what you need to have in place is not ready and leaves a gap, while others fall over early. It can be frustrating and by the time it is finally ready, you have forgotten why you wanted to do it in the first place.

DataIQ Transform provided some unique insights into how organisations try to move faster and get their dominos to fall as they want them to. From Sainsbury’s looking at how square metre-level farm management impacts on the food customers can find on its shelves, through Sky applying machine learning to transform call volumes to its customer services centres, new analytical approaches are helping businesses to run leaner, improve their customer satisfaction and be more profitable.

It’s why Zurich and Ordnance Survey created data offices and why RBS has been building out its artificial intelligence capabilities. But just as those organisations noted in their presentations, so the 100-plus delegates revealed in Slido polls run during the day. All of them were from end-user organisations representing blue-chip brands and major users of data and analytics. As Figure 1 reveals, at 67% of companies, the dominos are still being put in place, although 16% have their finger hovering over the master chip waiting to push it.

 

Unlike seeing and hearing rows of plastic crashing down, transformations are less obvious in their progression. Which is why having clear metrics, especially a benchmark of the before state against which to measure the after state is important. 

Yet in the pursuit of digital transformation and datafication of their businesses, just 13% say they have this full set of metrics and clear benchmarks (see Figure 2). Instead, it is much more common among four in ten to measure some aspects, while leaving others with no tracking of their impact on the business. 

If having visibility on at least some dimensions of a transformation is to be applauded, hoping to reach a general outcome - which three in ten stated - is surely too vague to deliver proof of the benefit of the project. But at least this view is strategic - the 13% who just use ad-hoc measures will find answering any questions they get from stakeholders or the board harder to answer.

One explanation for this gap around metrics could be that what the transformation is aiming to achieve gets changed along the way. New issues are identified which were not in scope at the start, while obstacles arise that take resources away. Measures may therefore no longer be relevant or useful.

Seven out of ten (69%) organisations report that new obstacles continue to arise during their projects (see Figure 3). This is a strong argument for adopting agile working - an approach used by RBS to ensure its AI projects align with changing business needs, for example - in order to avoid embarking on a lengthy transformation that fails because it is not fit-for-purpose by the time it concludes.

As if to underline the problem of trying to measure a project while it is still in motion, 58% of companies say a key challenge to their transformation is changing objectives. But while some new challenges are inevitable, there are also underlying fundamentals which any transformation needs to consider. Prime among these are data quality and data governance - eight out of ten organisations named these as challenges they have encountered, suggesting that they are universal issues (see Figure 4).

For half of organisations, it is executive buy-in that is getting in the way of transformation - it is hard to change a business without top down support. Equally, it is difficult to do without the necessary skills in place (45%). Even where these are present, the organisation has to want to move to evidence-based decision-making, yet one third (35%) say analytics adoption is a significant challenge.

While no major project can be guaranteed to be a success, there are factors that will improve its chances. Most notable of these is winning executive buy-in, according to 83%, the very thing that half say they lack. A parallel factor is building a good relationship with the lines of business (51%). If this happens, then the chances of analytics being adopted will rise because the function is developing solutions to problems that really matter to the company.

Well-managed, well-led and well-planned transformations are also critical, as seen in the score given to these factors by around two-thirds of respondents (see Figure 5). By contrast, technology and data are less significant in driving success - unless you are one of those businesses that experiences a problem in these domains, of course.

As the results of our poll show, digital transformation and datafication are challenging projects that require a lot of focus, resources and energy. Typically, they have long lead times before reaching a conclusion, but interim milestones and agile adaptation to changing requirements are critical. It can sometimes seem like putting an endless number of dominos on end without the satisfaction of getting them to tip. But with patience and determination, it is possible to get everything falling in the right sequence.

 

Knowledge and strategy director, DataIQ
David is developing the framework for soft skills and career development among data and analytics practitioners. He continues to be editor-in-chief and research director for DataIQ.