In 2016, Dell Technologies commissioned our first Digital Transformation Index (DT Index) survey to assess the digital maturity of businesses around the world. Since then, we have commissioned a biennial survey to track the digital maturity of the business.
Our third installment from DT indexlaunched in 2020 (the year of the pandemic), revealed that “data congestion / inability to extract information from data” is the third highest barrier to transformation from 11th place in 2016. This is a huge leap from the bottom to approach the top of the barrier to digital transformation.
These findings point to a curious paradox – data has the potential to become the number one barrier to business transformation. while also is their largest asset. To learn more about why this paradox exists and where businesses need the most help, we commissioned a study from Forrester Consulting to dig deeper.
The resulting survey, based on a survey of 4,036 senior executives responsible for their companies’ data strategy, titled: Uncover data challenges affecting businesses around the world,, is available to read now.
Honestly, the survey confirms our concerns: during this decade, data has become a burden and an advantage for many companies – depending on how ready the business is for data.
While Forrester identified several paradoxes with data that hinder business today, three major contradictions emerged for me.
1. The paradox of perception
Two-thirds of respondents would say that their business is run by data and say that “data is the lifeblood of their organization.” But only 21% say they treat data as capital and prioritize their use throughout business today.
It is clear that there is a break here. For greater clarity, Forrester has created an objective measure of the readiness of business data (see figure).
The results show that 88% of enterprises have yet to advance either in data technologies and processes and / or in their data culture and skills. In fact, only 12% of companies are identified 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 have too much data to deal with at the moment: 70% say they collect data faster than they can analyze and use it, but 67% say that they constantly need more data than their current capabilities offer.
Although this is a paradox, it is not so surprising when you look at the study as a whole, such as the share of companies that have yet to provide data advocacy at the boardroom level and return to an IT strategy that cannot be scaled (ie. bolts for multiple data ponds).
The consequences of this paradox are deep and far-reaching. Six out of 10 companies are struggling with data silos; 64% of respondents complain that they have so much data that they cannot meet the security and compliance requirements, and 61% say that their teams are already overwhelmed by the data they have.
3. The “seeing without doing” paradox
While economies suffered during the pandemic, the demand sector grew rapidly, igniting a new wave of initially data-based firms that pay for what they use and use only what they need — definitely from the data they generate and analyze.
Although these enterprises are emerging and doing very well, they are still relatively small. Only 20% of businesses have moved most of their applications and infrastructure to a model as a service — although more than 6 in 10 believe that a model as a service will allow companies to be more flexible, large-scale, and seamlessly deliver applications. .
Achieving a breakthrough together
The study is sobering, but there is hope on the horizon. Businesses are seeking to rethink their multi-cloud data strategies by moving to a data model as a service and automating data processes with machine learning.
Of course, they have a lot to do to charge the data distribution pumps. However, there is a way forward by first upgrading their IT infrastructure so that they can collect data where they live, on the edge. This includes bringing business infrastructure and applications closer to where data needs to be captured, analyzed and impacted, while avoiding data scattering while maintaining a consistent multi-cloud model.
Second, through optimization data pipelines, so that data can move freely and securely as it grows from AI / ML; and third, by developing software to provide the personalized, integrated experience that customers crave.
Stunning data volume, diversity and speed may seem superior, but with the right technology, processes and culture, businesses can tame the data beast, innovate with it and create new value.
To learn more about the study, visit www.delltechnologies.com/dataparadox.
This content is produced by Dell Technologies. Not written by the MIT Technology Review.