In a decade of data, data can be both an advantage and a burden


In 2016, Dell Technologies commissioned our first Digital Transformation Index (DT Index) study to assess the digital maturity of businesses around the world. Since then, we commission a survey every two years to track the digital maturity of businesses.

Sam Grokot, Senior Vice President, Business Unit Marketing, Dell Technologies.

Our third part of the DT Index, launched in 2020 (the year of the pandemic), showed that “data overload / inability to retrieve data from data” became the third-level barrier to conversion from 11th place in 2016. – this is a huge leap from the bottom to the top of the ranking barriers to digital transformation.

These findings point to a curious paradox – the data could potentially be an obstacle for businesses in the transformation so far too was their greatest asset. To learn more about why this paradox exists and where businesses need the most help, we commissioned a study from Forrester Consulting to delve deeper.

The final study, based on a survey of 4,036 senior decision makers responsible for their companies’ data strategy, is titled, “Disclosure of Data Problems Affecting Business Around the World.”,, available to read now.

Honestly, the study confirms our concern: in this decade, data has become a burden for many businesses, and an advantage — which depends on how ready the data is for the business.

Although Forrester identifies several data paradoxes that hinder today’s business, for me, three serious contradictions have emerged.

1. The paradox of perception

Two-thirds of respondents would say that their business is data-driven, and state that “data is the lifeblood of their organization”. But only 21% say they view data as capital today and prefer to use it throughout the business.

Obviously, there is a shutdown here. To bring some clarity, Forrester has created an objective indicator of readiness for these enterprises (see Figure).

The results showed that 88% of enterprises have not yet advanced in technology, data transfer processes, or data culture and skills. In fact, only 12% of enterprises are defined as data champions: companies that are actively involved in both areas (technology / process and culture / skills).

2. The paradox of “wanting more than they can handle”

The survey 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 they provide their current capabilities.

While this is a paradox, it’s not all that surprising when you look at the study holistically, such as the proportion of companies that don’t yet provide data protection at the boardroom level and return to an IT strategy that can’t scale (i.e. .bolt on other data lakes).

The consequences of this paradox are profound and far-reaching. Six out of 10 businesses are struggling with data warehouses; 64% of respondents complain that they have so much data that they cannot meet security and compliance requirements, and 61% say their teams are already overwhelmed by the data they have.

3. The paradox of “seeing without doing”

While economies were hit during the pandemic, the on-demand sector has expanded rapidly, igniting a new wave of businesses primarily with data that transmits data anywhere, paying for what they use, and using only what they need. which is determined by the data they generate and analyze.

Although these businesses are emerging and operating very well, their numbers are relatively small. Only 20% of businesses have moved most of their applications and infrastructure to a service model — although more than 6 out of 10 believe that the model as a service will allow firms to be more agile, large-scale, and secure applications without complexity.

Achieve a breakthrough together

The study is sober, but there is hope on the horizon. Companies are seeking to rethink their data transfer strategies in a multi-cloud environment by moving to a data model as a service and automating data transfer processes through machine learning.

Of course, they need to do a lot to refuel the pumps to distribute the data. However, there is a way forward, first, by upgrading their IT infrastructure so that they can retrieve data where it lives, on the edge. This involves bringing enterprise infrastructure and applications closer to where data needs to be captured, analyzed, and responded to, while avoiding data dissemination while maintaining a consistent multi-block operating model.

Second, by optimizing data pipelines so that data can move freely and securely when expanding AI / ML; third, by developing software to provide the individual, integrated experience that customers crave.

The staggering volume, variety and speed of data transfer may seem insurmountable, but with the right technologies, processes and culture, businesses can tame the data beast, innovate and create new value.

To learn more about the study, visit

This content was prepared by Dell Technologies. This is not written by the MIT Technology Review.


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