Kevin is the CEO at Syniti, a global leader in enterprise data management.
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Most organizations today (93%, according to one study) have adopted or plan to adopt a digital transformation strategy. This strategy must accomplish two things: It must make the process faster and also support better business outcomes.
To accomplish this, you need a data-first approach. This will not only help ensure the success of current digital transformation efforts, but it will also prepare your organization for future endeavors. So, what is this approach? And why is it so crucial?
A Data-First Approach Is The Necessary Foundation
One main part of this strategy involves beginning with data work before diving into the actual design phase of a project. Most transformations do the opposite. Sadly, organizations sometimes fail to address the quality, size and scope of their data before embarking on transformation activities.
Data cleansing should really start six to eight months ahead of your global design. Don’t just find it; fix it in existing systems, and you will see benefits both now and down the line. In the past, it was exceedingly difficult to start data work without the global design completed, but today, that’s no longer the case. There are modern tools available that enable you to start the data work beforehand and clean up your existing data.
Data must be fit for the purpose of delivering on the promise of digital transformation projects and driving real value. No organization has ever lamented, “We started data cleansing too early.”
When Data Isn’t At The Core
If you’re undergoing a digital transformation, you must also undergo a data transformation. Not addressing data early can result in delays, untrustworthy analytics, cost overruns and even failure. By next year, according to a recent McKinsey report, organizations will shell out about $100 billion on wasted data migrations.
Here’s a real-world example from a large manufacturing company I worked with. The company's head of supply chain knew the company had made significant strides in innovation. However, after multiple acquisitions and scaling efforts, they needed to undergo a digital transformation to support these efforts and continued growth. After speaking with consultants, moving to SAP S4/Hana seemed like the right approach. However, the supply chain head was hesitant because he felt that if he started the process now, it would fail, and he’d be told it was because of his data. The data needed to be addressed first—not afterward and, in his case, not in tandem.
All too frequently, data management’s complexities get classified by IT leaders as the responsibility of the business unit, and they don’t get the prioritization they deserve. Such complexities restrain agility, heighten risk and reduce an organization’s innovation capabilities. A survey by Enterprise Strategy Group and HPE found that for 74% of participants, keeping their data management processes up to speed with the ever-increasing pace of business was an ongoing struggle.
Another hurdle can be that data work is viewed primarily as a technical challenge. However, every business problem is a data problem, so addressing these projects will require business as well as technical expertise.
Focus On Quality And Leader Buy-In
The success of a company’s AI initiatives hinges on good data. According to one survey, 87% of analytics and IT leaders said advances in AI make data management a high priority; 92% said the need for trustworthy data is higher than ever.
So, the keys to success with AI are beginning the data work before the digital transformation project starts and making high-quality data the main goal. This is critical for organizations that want to use generative AI to innovate and drive value. Data transformation is digital transformation. Without high-quality data, organizations can end up with challenges like biased results, useless “guidance” and incorrect recommendations. Any of these have the potential to harm your brand reputation—or worse.
Following these best practices will help your organization stay ahead of the curve. According to the report by Enterprise Strategy Group and HPE noted earlier, data-first leaders are faster to market. Any leader who has gone through multiple digital transformations will say when asked about the longest pole in the tent, “It’s always about the data.”
However, 37% of participants in another study blamed CEOs and boards of directors for hindering a company’s digital transformation project. The senior executive team came in second at 32%. It’s vital, then, to make sure leaders are fully on board before starting a data-first transformation initiative.
Putting Data First
Digital transformation requires data transformation. With a data-first approach, leaders and their teams prioritize data cleansing before the digital transformation effort begins. Because leaders create most of the bottlenecks affecting these efforts, it’s pivotal to ensure they are all on board from the start.
Once buy-in has been established, make high-quality data your goal. If your data is of poor quality, any number of reputation- and business-damaging results may occur. And you’ll have wasted a lot of time, money and effort in the process.
A data-first strategy will become increasingly necessary as new and emerging technologies like generative AI take hold. Since AI must have high-quality data to function properly, bad data can jeopardize your generative AI projects, too. Save your organization a lot of toil and expense by taking the time to put data first.
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