Digital & Data Transformation: Failure vs. AI-Driven Success

There is no doubt the global health crisis accelerated digital transformation, which was already fundamental to business change yet tricky to get right. And given the size, scope, and speed of change even in pre-pandemic digital transformation, it’s not hard to see why failure and mistakes are more common than they ought to be.

The Drive to Digital Is Gathering Pace

Digital transformation was afoot long before COVID arrived but, according to McKinsey, the pandemic sped up the adoption of digital technologies by several years, driven by the need to ‘go remote’ almost overnight. According to the survey, businesses could scarcely remain operational without setting up the required infrastructure and reliable online communication channels — all of which was achieved in 11 days on average. In pre-pandemic times, executives suggested that a transition of such scale would have taken up to a year.

While the impact of the health crisis may be dissipating in major economies, the drive to digital is only gathering pace. IDC reported that worldwide spending on digital transformation will be nearly $2 trillion in 2022 and will account for more than half of all Information Communication Technology (ICT) investment by 2024, according to IDC FutureScape. But throwing money at the problem isn't a silver bullet. Although businesses typically understand the benefits, getting it right isn’t easy. A staggering 70% of digital transformation projects fall short of their goals, per a Boston Consulting Group study. 

So, where are businesses going wrong and, more importantly, how can they get digital transformation right?

Failures Often Tie Back to Data

Unfortunately, failures often tie back to data: too much of the wrong data, in the wrong places, of insufficient quality to support the intended transformation, and with no way to change any of this. In short, unless digital transformation drives a parallel transformation in the way data is used to create value, the same problems will simply be recreated in digital form. This will immediately damage employee confidence in the wider transformation and weaken business buy-in, both commonly cited reasons for the failure of transformation initiatives.

For example, digital transformation often relies on poor data quality in disparate systems which degrades over time. A one-off data cleanse will solve this once, enough to start digital transformation efforts but, without building better quality data as an outcome of the digital transformation, data existing in a silo has been replaced by digital transformation happening in a silo. If this happens in too many parts of a digital transformation it will never “lift off” — so the job of digital and data leaders is to scale data science efforts, so the business actually gets an exponential amount of value from the exponential quantity of data it is amassing. 

Data-Driven Success 

Nearly a decade ago, a large pharmaceutical company decided to invest in data and analytics. When the company started on its journey, it looked for what data sources were available, what teams needed to garner from the data, and then it started stitching together those data sources. 

A lot of the work the company had was pulling together what data was available and where and identifying the needs and skills at its disposal in order to build out new data and analytics capabilities. Clear goals were set to benchmark the state of affairs before, during, and after the transformation. Data and analytics project goals were communicated clearly, and there was global alignment across business and technical teams. 

Today, the company has over 3,000 different data projects running concurrently, hundreds of thousands of datasets, and nearly 1,000 direct contributors to the data process. These people, coders and visual users alike, are using the tools best suited for them to untap new insights and reach new levels of productivity.

How Everyday AI Helps Escape Misalignment Between Digital and Data Transformation

Preventing these data-driven failures of digital transformation requires understanding exactly what the data problems are, and how solving them will change the business, not just improve the data. And the best people to do this are the business owners of the processes being transformed, not the technical owners of data who are one step removed from those transformative business outcomes! 

Realising this means introducing Everyday AI and empowering more individuals throughout the business to be engaged in the digital transformation process, working with their own data to accelerate and safeguard the transformation. Doing so breaks down barriers, silos, and gaps formed by data, because talented data teams can work better with the knowledgeable stakeholders who are willing to collaborate. The result is faster ideation towards more meaningful business objectives.  

But this too requires planning and careful consideration. Embedding data and analytics across the enterprise successfully depends on enabling people to easily access data relevant to them, and work with it to deliver business outcomes faster or more reliably. Ideally, digital transformation creates more relevant data and makes it more accessible while generating opportunities for the people empowered and upskilled by data initiatives to continually improve their processes.

Go Faster for Greater ROI

Successful digital transformation is not about launching apps, services, or buying technology but creating conditions for permanently greater efficiency and continuous innovation. No single use case for data and AI will support this, only a permanent change in how data contributes to digital transformation. This means better connecting processes, systems, and people, in new and imaginative ways. 

As their competitive environment evolves, businesses cannot afford to take digital and data transformation lightly anymore. Waiting too long to get their data right before digital transformation begins can lead to years and millions of pounds wasted, since data can only be judged in context, against specific use cases. Likewise, digital transformation efforts that do not enable a parallel data transformation represent a missed opportunity: just the “surface layer” changes but not the underlying ways of working with the data that digital solutions generate.