Digital Transformation
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Roll Out Data-Driven Digital Transformation in Just 4 Steps

A key component of digital transformation is improved data analysis and insight. The right process can improve the odds of effective enterprise data-driven transformations.

To see the other technologies and approaches highlighted in our Digital Transformation series, read our report: An Enterprise Guide to Digital Transformation in 2021.

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.

Understand the Difference Between Data Innovation and Overload

“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. 

Build and Manage the Architecture

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. At the same time, good data governance and literacy is important not only for allowing non-technical employees to make the best possible use of data, but also for ensuring regulatory compliance and that both the existing and planned data architecture is not overloaded by unnecessary users.

Have a Plan for Growth

“If you are looking to leverage data to give your business the boost it needs, take a long hard look at two things that will help you do that: the tools you are using to generate said data, and the resources you are using to analyze this data,” said Kohli.

In the Splunk report The Data Age is Here. Are You Ready?, plenty of enterprises indicated that they were, in fact, not. In particular, leaders reported feeling unprepared to deal with the expected data growth over the next few years. More than half of respondents said their data was growing more quickly than their organization could keep up, and 47% acknowledged that they would fall behind.

“Embarking on a digital transformation project is always complicated — robotics, Internet of Things, AI, etc.,” Brunner said. “Success starts with senior leadership backing the initiative and having a defined goal with clear governance.”

Improve Your Data’s Health

Healthy data is high quality, trustworthy, cleaned up and compliant with relevant industry, state and national regulations. And it’s also complete, accurate, and of high enough quality to be used in reliable analytics. Given the glut of data many organizations are dealing with, many of them are falling behind on ensuring that data is healthy.

“Data health is a persistent problem that’s been plaguing organizations for years, but it’s never been addressed,” Tammana said. “While execs focus on solving issues like data storage and integration, they’ve forgotten to check if that data is even trustworthy.”

Concerns about data quality are a barrier to data integration projects, the first step in digital transformation initiatives, Brunner said. “Many find data teams spend too much time on average cleaning and prepping data for analysis or find that staff will only trust insights from data if it confirms their gut feel.” 

Dealing with dark data — information that has been generated, processed, and stored but isn’t put to use — is another key part of ensuring overall data quality. In 2020, Splunk found that two-thirds of organizations believed that more than half of their data is dark — an increase of 10% from the previous year. Reducing that number through proper data classification both unlocks the potential of the valuable data and reduces data clutter.

Ensuring the health of the data a company deals with has to be a pervasive priority at a company, as data health is relevant to every employee who deals with that data. 

By understanding all the aspects of data quality, organizations set themselves up for the long-term practice of good data health,” Tammana said. “And the more you practice good data health within your organization, the less risk you have of data issues leading to bad decisions or security breaches.”

Staff As the Data Demands It – Or Upskill the Staff You Have

In an effective data-driven digital transformation strategy, dealing with data involves tools, processes and people. Unfortunately, in many organizations, staffing is inadequate to deal with – or make the most effective use of – data. 

Very few if any companies have the sufficient talent or skills in house and readily available,” Brunner said. “Finding the staff with the right skills and enough of them is a significant challenge to success in data transformation projects.”

Hiring is one path to solving this problem, of course, but training existing staff is another option. It’s important to help your existing staff gain the skills that support them in making sense of your company’s data, as appropriate for their wider roles, Kohli said. 

“I am by no means saying that everyone should become a data scientist,” he said. “All I am saying is that understanding data, and drawing out conclusions that can help you do your job better, are always something every professional should learn.”

Develop a data-driven culture at your organization through initiatives like teaching staff how to leverage data at various stages and providing data analysis training, Kohli suggested. “The data is only as useful as the individuals and tools using it.”

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