Ten years of data, data is both an advantage and a burden


In 2016, Dell Technologies commissioned us to conduct the first DT Index study to assess the digital maturity of global companies. Since then, we commissioned this study every two years to track the digital maturity of the company.

Sam Grocott is the senior vice president of marketing for the business unit of Dell Technologies.

Our third issue DT indexLaunched in 2020 (the year of the pandemic), it shows that “data overload/inability to extract insights from data” is the third-ranked transformation barrier, higher than the 11th place in 2016. This is a huge jump from the bottom to the top of the list of barriers to digital transformation.

These findings point to a strange paradox-data may become the number one obstacle to business transformation Meanwhile also Become their greatest asset. In order to learn more about the reasons for the existence of this paradox and where companies need help most, we commissioned Forrester Consulting to conduct a study for more in-depth digging.

The final study is based on a survey of 4,036 senior decision makers responsible for the company’s data strategy, titled: Uncover the data challenges affecting global companies, You can read it now.

Frankly speaking, this study confirms our concerns: in this data decade, data has become a burden and advantage for many companies-it depends on the company’s data readiness.

Although Forrester has identified several data paradoxes that hinder the development of enterprises today, I think the three major contradictions are prominent.

1. Perception paradox

Two-thirds of respondents would say that their business is data-driven and said that “data is the lifeblood of their organization.” But only 21% said they treat data as capital and prioritize the use of data throughout the enterprise today.

Obviously, there is a disconnect here. To provide some clarity, Forrester has created an objective measurement of enterprise data readiness (see figure).

The results show that 88% of companies have not yet improved their data technology and processes and/or data culture and skills. In fact, only 12% of companies are defined as data champions: companies that actively participate in these two areas (technology/process and culture/skills).

2. The “want more than they can afford” paradox

Research also shows that companies need more data, but they now have too much data to process: 70% of companies say they collect data faster than they can analyze and use it, but 67% of companies say they continue to need more data than they currently Ability to provide more data.

Although this is a paradox, it is not surprising when you consider the research as a whole, such as the proportion of companies that have not yet secured data advocacy at the board level and have returned to an IT strategy that cannot be scaled (i.e., lock in more data Lake).

The implications of this paradox are profound and far-reaching. Six out of ten companies are struggling with data silos; 64% of respondents complain that they have too much data to meet security and compliance requirements, and 61% of respondents said that their teams are already owned by them The data is overwhelmed.

3. The “see but not do” paradox

Although the economy suffered during the pandemic, the rapid expansion of the on-demand sector has triggered a new wave of data-first and data-ubiquitous companies that pay for what they use and only use what they need—— Determined and analyzed by the data they generate.

Although these businesses are emerging and performing very well, their numbers are still relatively small. Only 20% of enterprises have moved most of their applications and infrastructure to an as-a-service model-although more than six in ten companies believe that the as-a-service model will make the company more flexible, scale and configure applications without complexity.

Achieve a breakthrough together

The research is thought-provoking, but hope is on the horizon. Companies hope to modify their data strategies in a multi-cloud environment by moving to a data-as-a-service model and using machine learning to automate data processes.

It is true that they have a lot of work to do to start the pump of data explosion. Nevertheless, there is still a way forward. The first is to modernize their IT infrastructure so that they can process data where it exists and at the edge. This includes keeping an enterprise’s infrastructure and applications closer to where data needs to be captured, analyzed, and processed by maintaining a consistent multi-cloud operating model, while avoiding data sprawl.

Secondly, through optimization Data pipeline, So data can flow freely and safely while AI/ML is enhanced; third, through the development of software to provide the personalized and integrated experience that customers desire.

The amazing amount, variety, and speed of data may seem unstoppable, but with the right technology, process, and culture, companies can tame the data beast, use it to innovate, and create new value.

To learn more about the research, please visit www.delltechnologies.com/dataparadox.

This content was produced by Dell Technologies. It was not written by the editors of MIT Technology Review.


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