Manufacturers Beware! Avoid These Six Common, Costly Mistakes in Data Management

If you’re in the process of evaluating your data management system or implementing a new master data management strategy, there are six common mistakes you’ll want to avoid.

Although it has been said that money makes the world go round, a quick Google search shows the expression is increasingly being rephrased: data makes the world go round. And it’s also been said that data can make or break a company, depending on how it’s used.

According to IBM, 2.5 quintillion bytes of data is created every day. With that much big data available, manufacturers might have trouble harnessing and distilling it into master data; but they can’t afford to ignore it. From 3D printers accelerating product development to augmented reality (AR) and wearables helping humans on the factory floor, manufacturing is changing with new technology and relying on data to stay competitive.

With increasing demands to simultaneously reduce time-to-market and keep up with suppliers, distributors, and end users, manufacturers sometimes discover that growing data works against you, rather than for you. Without consistent access to clean, current data, overcoming these challenges becomes increasingly difficult, if not impossible.

So, if you’re in the process of evaluating your data management system or implementing a new master data management (MDM) strategy, there are six common mistakes you’ll want to avoid.

Mistake No. 1: Not thinking strategically before selecting a data management solution

All too often, companies execute an MDM strategy without understanding how it fits into the overall business strategy. In fact, a report from PwC and Iron Mountain shows that 76 percent of companies gain little value from their data because of a lack of focus and understanding of its benefits. Without syncing their data strategy with the broader business strategy, manufacturers often have trouble navigating the sea of data.

Secondly, manufacturers may choose the least-expensive data management solution available, or one that only fixes a short-term issue. While setting data management objectives, it’s critical to consider the entire value chain to ensure that regardless of where you are in the process, centrally located data is accessible.

And thirdly, data strategies can flop when you fail to choose a customer-centric provider because you might not get support when it’s needed most—during implementation.

Mistake No. 2: Using low-quality, dirty data

Gartner estimates that poor data quality costs companies $15 million per year. Without identifying and course correcting errors before implementing data management technology, dirty data could remain hidden for years, leading to inefficiencies, dissatisfaction, and a shrinking bottom line. Cleaner data leads to better, more accurate decisions that improve customer satisfaction and contribute to your company’s profits. However, businesses must understand the true meaning of quality data. Quality can be measured in many ways. What may be perceived as “quality” for one individual or use case, may not be seen as “quality” for another. Quality is measured on how it meets or complies with requirements. Gone are the days where “one size fits all.” Today is about personalization, to support successful sales strategies and to satisfy individual consumer needs. Therefore, one needs to consider aspects such as completeness, accuracy, integrity, compliance, uniqueness, consistency and timeliness.

Mistake No. 3: Lacking ownership

There are times when manufacturers jump on the data bandwagon without determining which employee(s) will drive strategy implementation and ensure budgets and timelines are met. Someone must take accountability, so the project stays on track. MDM allows you to set up data governance teams to keep the data management technology momentum going.

Mistake No. 4: Overlooking stakeholder engagement and executive sponsorship

All too often, those responsible for launching a successful MDM strategy are on a different page than leadership, especially when senior leadership has a mindset that data is solely an IT function. Since a successful transformation involves company-wide change, it’s paramount to gain buy-in from stakeholders and executives throughout the organization. This can be accomplished by educating your team on the benefits data management technology provides the entire enterprise.

Mistake No. 5: Not considering the cultural transformation needed

Although leadership acceptance is crucial, so is buy-in from every employee in every manufacturing location. So how do you cast a wider net? If you champion the idea that data-driven change heralds opportunity instead of threat, you’re more apt to gain widespread adoption of your MDM strategy. To enable this cultural transformation, articulate the importance data brings in staying competitive and keeping up with customers’ increasing needs.

Mistake No. 6: Forgetting data is an ingredient of information

A database could effectively manage data, but it’s only useful when data is informative. When selecting a data management solution, understand how different stakeholders plan to use their data and present it within a useful, actionable context. A data management solution must have the ability to access, interpret, combine and present data so it can be used effectively to drive positive business outcomes.

Learning from data technology errors

Although mistakes will likely happen when implementing data management technology, learning from others’ mishaps can lessen their impact. With the right strategy and tools in place, data can indeed help make your manufacturing world go round.

Ian Piddock is director of solution strategy for product master data management at Stibo Systems.

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