Roll Out Data-Driven Digital Transformation in Just 4 Steps

The amount of data your company is generating has grown by leaps and bounds over the past few years — according to Dave Reinsel, senior vice president, IDC’s Global DataSphere, 64.2 zetabytes of data was created or replicated last year, an 8.8% rise from 2019. And there’s no signs of that data generation slowing – or the demand for business uses of that data.

The problem the enterprise often faces: The tools and strategies to analyze that data and put it to use have not necessarily expanded to match the data-driven digital transformation measures that are critical to maintaining and growing a business.

Part of digital transformation entails putting data analysis tools and strategies in place. Effectively dealing with data unlocks new business potential, and not dealing with it hampers your transformation process. We’re at the precipice of the Data Age and a new age demands new tools.

“There is an argument to be made that there can be too much data, but only if the data has not been efficiently organized,” said Salinder Kohli, lead developer at consumer hardware maker Coffeeble. “It boils down to the tools and resources at your disposal.”

The explosive growth of data has led to the integration of a host of data-dependent technologies that are pushing data-driven digital transformation forward at enterprises of all types. But at the same time, the Data Age is also one of stalled-out transformation when that data isn’t properly stored or analyzed. 

Two-thirds of surveyed organizations expect the sheer quantity of data to grow nearly five times by 2025, according to research from Splunk. And instead of fueling innovation, data overload makes it harder to extract insights and hampers digital transformation efforts, according to the 2020 Dell Technologies Digital Transformation Index. 

“The abundance of data in today’s business landscape is overwhelming, making it incredibly difficult for decision makers to assess what matters,” said Krishna Tammana, CTO at Talend. We’re long past the point where the volume and diversity of data an enterprise deals with can be managed through human curation alone, Tammana said.

Enterprise leaders see the potential in that data explosion. Four out of five told Splunk it was valuable to their company’s overall success, for example. Digital transformation is simultaneously made possible by data and required to deal with that data. “The exponential growth of business data, coupled with advancements in cloud computing, AI, and IoT has unleashed an era of digital transformation initiatives,” said Kathy Brunner, CEO of Acumen Analytics. 

Having data-driven digital transformation plan is essential for success in the Data Age. However, even when companies see data as a key part of their growth, they don’t necessarily put that belief into action in their business operations. Although 64% of respondents said that their business was data driven, less than a quarter said that they treated data as capital or prioritized its use across their business, according to a May 2021 report from Forrester Consulting commissioned by Dell Technologies.

Fortunately, there are ways to move from knowing data is important to enterprise operations to reflecting that belief in those operations. 

With a scalable data architecture in place, your team can focus on other areas while making the best use of the data relevant to their operations, said Nir Livneh, CEO at big data-processing platform Equalum. 

“Figure out which departments and lines of businesses need what type of data processing, at what speed and with what frequency,” Livneh said, “and then build a cohesive data architecture that can support the growing data demands across the organization.”

It’s important for a company’s data-driven digital transformation strategy to assess and prioritize use cases, he said.