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In 2016, Dell Technologies commissioned the first Digital Transformation Index (DT Index) study to assess the digital maturity of enterprises around the world. Since then, we have ordered a survey every two years to track the digital maturity of the business.
Sam Grocott is Senior Vice President of Marketing, Dell Technologies Business Unit.…
Our third part of the DT Index, launched in 2020 (the year of the pandemic), showed that “data overload / inability to extract information from data” was the third largest obstacle to transformation, compared to 11th in 2016. This is a huge leap from bottom to top in the ranking of obstacles to digital transformation.
These results point to a curious paradox: data can be the number one obstacle to business transformation. in the same time be their greatest asset. To learn more about why this paradox exists and where businesses need help most, we commissioned a study with Forrester Consulting to go deeper.
The final study, based on a survey of 4,036 senior executives who are responsible for data strategy in their companies, titled: “Identifying the data problems facing companies around the world.”, is available for reading now.
Quite frankly, the study confirms our concerns: In this decade, data has become both a burden and an advantage for many enterprises, depending on how data-ready the business can be.
While Forrester uncovers several data paradoxes that plague businesses today, I have identified three major contradictions.
1. The paradox of perception
Two-thirds of respondents would say their business is data-driven and say that “data is the lifeblood of their organization.” But only 21% believe they treat data as capital and prioritize its use throughout business today.
Clearly there is a disconnection here. To provide some clarity, Forrester created an objective measure of enterprise data availability (see figure).
The results showed that 88% of businesses have not yet improved their technology and data processing processes and / or their data culture and skills. In fact, only 12% of businesses are identified as “data champions”: companies that are active in both areas (technology / process and culture / skills).
2. The paradox “they want more than they can stand”.
The study also shows that businesses need more data, but they have too much data to process right now: 70% say they collect data faster than they can analyze and use, but 67% say they constantly need more. data than their current capabilities allow. …
While this is a paradox, it is not so surprising when looking at the study as a whole, for example, the proportion of companies that have yet to provide data protection at the board level and that are resorting to an IT strategy that cannot scale (i.e. bolts on more data lakes).
The implications of this paradox are deep and far-reaching. Six out of 10 companies struggle with data silos; 64% of respondents complain that they have such a surplus of data that they cannot meet security and compliance requirements, and 61% say their teams are already overwhelmed with the data they have.
3. The paradox of “seeing without doing”.
While the economy has suffered during the pandemic, the on-demand services sector has expanded rapidly, sparking a new wave of data-driven businesses where data is anywhere, who pay for what they use and only use what they need. , which is determined by the data they generate. and analyze.
While these businesses are emerging and doing well, their numbers are still relatively small. Only 20% of enterprises have migrated most of their applications and infrastructure to model as a service, although more than 6 out of 10 believe that model as a service will enable companies to become more flexible, scalable and provide services. applications without complexity.
Making a breakthrough together
The study is sobering, but there is hope on the horizon. Companies are looking to redefine their multi-cloud data processing strategies by moving to a data-as-a-service model and automating data processing with machine learning.
Of course, they still have a lot to do to prepare the pumps for the rapid dissemination of data. However, there is a way forward if you first modernize your IT infrastructure so that they can work with data where it is, at the edge. This includes bringing enterprise infrastructure and applications closer to where data is collected, analyzed and processed, while avoiding data sprawl by maintaining a consistent cross-cloud model.
Second, by optimizing data pipelines so that data can flow freely and securely while supplemented with AI / ML; and third, by developing software to deliver the personalized, integrated experience that customers crave.
The staggering volume, variety and speed of data transfer may seem overwhelming, but with the right technologies, processes, and culture, companies can tame the data beast, innovate, and create new value
