Every CEO has been through or has planned more than one “digital transformation” program. The term can mean anything from end-to-end value chain reinvention to rip and replace infrastructure, rethinking core business processes, re-platforming ecommerce, or customer experience technologies, or bringing artificial intelligence (AI) tools to the organization. But at the end of the day, all these tools run on data. The entire experience is either comprised of or supported by data.
However, recent studies by firms such as New Vantage Partners and Deloitte indicate that realizing business value from data remains a challenge. New Vantage’s research showed that “77% of respondents say that ‘business adoption’ of big data and AI initiatives continues to represent a challenge for their organizations.” Deloitte found that “63% do not believe their companies are analytics-driven and 67% say they are not comfortable accessing or using data from their tools and resources”.
Digital transformations are data transformations. In our many years of experience working with C-level execs at Fortune 500 organizations, we have found that only the rare, in-the-weeds executives truly get what this means for their tens or hundreds of millions of dollars in investments in digital transformations.
The bottom line is that if the data isn’t right, your program will fail. Obvious, right? Then why is this so frequently the challenge? It is because the scope and scale of the problem is hard to grasp as are the true costs, complexity and timeline for solving it.
Several years ago, as I have written about in my book, The AI Powered Enterprise, a large manufacturer was spending $25 million on a digital transformation. Key to this was a new website and new B2B ecommerce capabilities. The company’s business relied on “design win” contracts that had been in place for more than 20 years. These were large programs in defense and civilian aerospace and other long lifecycle products, as well as new innovative microelectronics constantly being churned out by both startups and established tech giants.
While their long-established relationships kept them on the radar of their largest manufacturing clients, the company was invisible to new customers because key product data was missing online. The overall user experience was abysmal. Existing customers continued to do business despite the organization’s inability to serve them digitally, but new customers faced an uphill battle. When the data is missing online, the products are invisible to searchers. If people cannot find your products, they cannot buy them. The digital transformation program was launched to remediate the online deficiencies.
The company hired a large digital agency and a global systems integrator to take on the job. One task was to organize the enormously complex product catalog with hundreds of thousands of products. The integrator provided a proposal for $150,000 to carry out this task. Included in this proposal was a line item for capturing photos and putting them into a hierarchy. This approach represented a fundamental lack of understanding regarding the nature of the problem and the scope of a solution. While it may have sounded appropriate to a lay person, it was ill-conceived and neglected the route cause issues. Product photography is expensive, and in this case, it had almost nothing to do with the data sourcing and workflow issues that were significant challenges, and which would undermine any other approach or solution. The proposal vastly under-scoped the problem and demonstrated a lack of understanding of how to develop a long-term cost-effective solution. Luckily, one of the people on the project had a Ph.D. in library science and had also been through one of my workshops at an academic conference several years earlier. He recognized the inadequacy of the approach they were using, and called our firm in to assist.
We developed a two-year program that included professional services required to design the correct architecture, fix the problems with data, and get the organizational processes in place (including those around governance, KPI’s and data quality) were around $4 million. (A far cry from the original $150,000). The $25 million program was a success, and the transformation led to a multibillion dollar increase in the market capitalization of the business. Had the program proceeded without the investments in data and related processes, it would have failed. But what caused that original decision? On the surface, the global integrator looked competent, but leadership could not judge whether the approach was correct or not. They were a trusted partner with relationships at the C-level. Had it not been for the library scientist, the program may well have been on a disastrous track.
Getting the foundation right is not sexy or fun. But it is key. No digital transformation will succeed in the long term without a strong foundation of quality data and the related supporting processes. It cannot be an afterthought It must be owned by the business and applied in a way that considers the end-to-end value chain. The value chain is a data chain. Some believe that AI will save the day by fixing the data automatically. AI can help, but is not the answer by itself. Executives must take a hard look at their data competencies and maturity, and make meaningful investments in data. Data is what the digital world is comprised of. It is an asset of inestimable potential value and should be treated as such.
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