Chief Data Officers are tasked with using data to positively impact the business, maximize value, and make key decisions across the supply chain.
Using Data As a Business Asset for Executives
A good decision with bad data is still a bad decision. So what makes an effective data management strategy and what do you need to achieve success?
Jan 20, 2022
In a recent interview with Enterprise Management 360 (EM360), Syniti CEO Kevin Campbell discussed findings revealed in Syniti’s recent Global Data Management Report. According to this report, only 5% of C-level executives trust their data, despite confirming that they consider it a pivotal business asset.
Why Don’t 95% of Executives Trust Their Data?
According to Campbell, executives typically lack trust in their data due to misleading promises like “dump all your data into a data lake, we’ll run analytics on it, and it’ll be perfect” - only to find out that their data lake quickly turns into a data ocean, which quickly turns into a data swamp. Managing that data soon becomes impossible because users can’t integrate the data, figure out the source, or even govern it.
But as Campbell puts it, “you can't trust [your data] from the outside in, you’ve got to do it from the inside out.” When it comes to putting confidence back in this key business asset, executives and leaders need to know “where the data came from, what’s the trusted source of the data, and how it’s governed,” he explains.
Every Problem is a Data Problem
At Syniti, there’s a saying: “A good decision with bad data is still a bad decision.”
Whether supply-chain, accounting, or inventory-related, leaders don’t truly know what they are basing critical decisions to drive the business on without trust in data.
For example, every business uses data for informing or improving customer relationships and credibility. They spend a lot of time and money acquiring a massive proliferation of customer records and data points within numerous systems and platforms. Still, there’s no clear indication of where that customer information came from or what or who it’s governed by. Without knowing the source, users can't be sure the data will produce the kind of insights trustworthy enough to put in front of a customer.
What Makes an Effective Data Management Strategy?
As revealed in Syniti’s Global Data Management report, 90% of C-level executives assert that data is critical to their company’s success. Despite this, only 23% of C-level executives have implemented a consistent and policy-aligned strategy at scale across their organization.
If access to high-quality, reliable data is critical, why do so many businesses lack an effective data management strategy? While many aren’t sure how to get started, the reality is building a data management strategy can actually be quite...well, manageable.
An effective data management strategy is essentially comprised of “a thoughtful way to lay out what your data is, classify and/or tier the data as to what’s the most important data, and determine what data is driving the company's decisions," explains Campbell.
Don’t focus on addressing all the data and the various systems within the organization at once. Instead, start first with the most essential elements, then figure out with that data, what the source of the data is, and how it is updated and maintained – in other words, how that data is governed throughout its lifecycle. “Now I can say, I have a foundation, I got a set of data, I know how it’s handled, and now I can go and can be expanded to the rest of the company, Campbell sums up.
Which Tools Should SMBs Consider Adopting and Investing In?
When it comes to managing enterprise data, too many people try to treat it all, do it all, or just hope it will magically get better by piling on new platforms and resources; however, “the most important thing is clean data,” says Campbell.
Whether it’s artificial intelligence, robotics processing, or machine learning (ML), there’s a lot of fascinating new tech that people are eager to utilize. “The problem is that,” Campbell warns, “if your data is bad, you’re still going to get bad answers with that technology.” Before amassing leading, advanced technology, you need quality data before using the tools.
“The first thing is: get the data clean,” says Campbell. Once that is achieved, you can look at solutions that take manual tasks and automate them to save people time. Advances in things like machine learning can basically “train” algorithms over time to make inferences and connections between data that can be pointed out faster than a human. All these techniques are essential for people to look at and can be critical technology to build upon.
How to Action Mergers & Acquisitions with Data
According to Syniti’s Global Data Report, urgent action is needed to deliver the quality of data required to achieve business viability and ensure Mergers & Acquisitions (M&A) success in 2022.
M&A is an important tool for all CEOs. Leaders of business today usually want to increase revenue, decrease cost, or increase compliance. One way to do that is by buying or selling something.
Due to the pandemic and shifts in the market, “we’re predicting huge surges in M&A activity over the next 18 months,” notes Campbell. People are rapidly getting in and out of markets and reshaping how they are delivered, and they’re going to need added capabilities to do that.
At the core of whether M&A projects are successful is data quality. Executives should treat data as another asset you’re buying. When it comes to M&A, ask yourself, “what’s the data that’s associated with that? What’s the quality of that data?” Is it good quality data that you can access, understand, and know who the customers are? Whether you’re looking to make a merger, acquisition, or divestiture work, you have to understand the data and the quality of that data to understand what needs to get done.
Listen to the podcast here: https://em360tech.com/tech-podcasts/bad-data.
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