Reported by IBM, 2.5 quintillion bytes of data is created every day. With that much big data available, manufacturers might have stress harnessing and distilling it into master data; but they can’t afford to disregard 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 growing demands to simultaneously reduce time-to-market and keep up with suppliers, distributors, and end users, manufacturers every now and then discover that rising data works against you, rather than for you. Without frequent access to clean, current data, overcoming these challenges becomes rapidly 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 popular mistakes you’ll want to avoid.

 

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

 

More often than not, companies execute an MDM strategy without understanding how it fits into the overall business strategy. In reality, a report from PwC and Iron Mountain shows that 76 percent of companies get minimal 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 could possibly choose the least-expensive data management solution available, or one that only fixes a short-term issue. While setting data management objectives, it is essential to think 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 simply because you might not get support when it is demanded most—during implementation.

 

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

 

Gartner estimates that poor data quality costs companies $15 million per year. Without figuring out and course correcting errors well before implementing data management technology, dirty data could remain hidden for years, ultimately causing inefficiencies, dissappointment, and a shrinking bottom line. Cleaner data leads to better, more precise decisions that improve customer happiness and contribute to your company’s profits. In spite of this, businesses must know 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 all about modification, to support successful sales strategies and to satisfy individual consumer needs. For this reason, one needs to consider aspects such as completeness, accuracy, integrity, compliance, uniqueness, consistency and timeliness.

 

Mistake No. 3: Lacking ownership

 

There are instances when manufacturers jump on the data bandwagon without determining which employee(s) will drive strategy implementation and assure budgets and timelines are met. Someone will need to take responsibility, 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

 

Commonly, those accountable for launching a successful MDM strategy are on a different page than leadership, specifically when senior leadership has a mindset that data is purely 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. Sometimes it is accomplished by educating your team on the benefits data management technology provides the total enterprise.

 

Mistake No. 5: Not considering the cultural transformation needed

 

Although leadership acceptance is critical, so is buy-in from every employee in every manufacturing location. Hence, how do you cast a wider net? If you champion the idea that data-driven change heralds opportunity in place 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 staying on par with customers’ increasing needs.

 

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

 

A database could appropriately manage data, but it is useful when data is informative. When selecting a data management solution, understand how many 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 goof ups will almost certainly happen when implementing data management technology, learning from others’ mishaps can decrease their impact. With the right strategy and tools in place, data can indeed help make your manufacturing world go round.

 

This article is originally posted on tronserve.com