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Author: Tronserve admin

Friday 30th July 2021 05:25 PM

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


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Even though it has been said that money makes the world go round, a quick Google search shows the expression is progressively 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.


Based upon IBM, 2.5 quintillion bytes of data is created every day. With that much big data available, manufacturers might have hassle harnessing and distilling it into master data; but they cannot 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 escalating demands to simultaneously reduce time-to-market and keep up with suppliers, distributors, and end users, manufacturers often times uncover that expanding data works against you, rather than for you. Without persistent access to clean, current data, overcoming these challenges becomes significantly 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


Too often, companies execute an MDM strategy without understanding how it fits into the overall business strategy. In point of fact, a report from PwC and Iron Mountain shows that 76 percent of companies gain tiny 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 crucial to think about the entire value chain to ensure that regardless of where you are in the process, centrally located data is available.


And thirdly, data strategies can flop when you fail to choose a customer-centric provider for the reason that 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 identifying and course correcting errors before implementing data management technology, dirty data could remain hidden for years, causing inefficiencies, dissappointment, and a shrinking bottom line. Cleaner data leads to better, more appropriate decisions that develop customer satisfaction and contribute to your company’s profits. Even so, businesses must perceive 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 mostly about personalization, to support successful sales strategies and to satisfy individual consumer needs. Thus, 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 guarantee budgets and timelines are met. Someone need to 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


Too often, those responsible for launching a successful MDM strategy are on a different page than leadership, mainly when senior leadership has a mindset that data is merely 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 is often accomplished by educating your team on the benefits data management technology provides the full 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. 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 top of customers’ increasing needs.


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


A database could proficiently manage data, but it's just useful when data is informative. When selecting a data management solution, understand how several 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 slips will likely happen when implementing data management technology, learning from others’ mishaps can limit 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 manufacturing.net


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Posted on : Friday 30th July 2021 05:25 PM

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


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Posted by  Tronserve admin
image cap

Even though it has been said that money makes the world go round, a quick Google search shows the expression is progressively 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.


Based upon IBM, 2.5 quintillion bytes of data is created every day. With that much big data available, manufacturers might have hassle harnessing and distilling it into master data; but they cannot 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 escalating demands to simultaneously reduce time-to-market and keep up with suppliers, distributors, and end users, manufacturers often times uncover that expanding data works against you, rather than for you. Without persistent access to clean, current data, overcoming these challenges becomes significantly 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


Too often, companies execute an MDM strategy without understanding how it fits into the overall business strategy. In point of fact, a report from PwC and Iron Mountain shows that 76 percent of companies gain tiny 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 crucial to think about the entire value chain to ensure that regardless of where you are in the process, centrally located data is available.


And thirdly, data strategies can flop when you fail to choose a customer-centric provider for the reason that 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 identifying and course correcting errors before implementing data management technology, dirty data could remain hidden for years, causing inefficiencies, dissappointment, and a shrinking bottom line. Cleaner data leads to better, more appropriate decisions that develop customer satisfaction and contribute to your company’s profits. Even so, businesses must perceive 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 mostly about personalization, to support successful sales strategies and to satisfy individual consumer needs. Thus, 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 guarantee budgets and timelines are met. Someone need to 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


Too often, those responsible for launching a successful MDM strategy are on a different page than leadership, mainly when senior leadership has a mindset that data is merely 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 is often accomplished by educating your team on the benefits data management technology provides the full 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. 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 top of customers’ increasing needs.


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


A database could proficiently manage data, but it's just useful when data is informative. When selecting a data management solution, understand how several 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 slips will likely happen when implementing data management technology, learning from others’ mishaps can limit 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 manufacturing.net

Tags:
datakit database data analysis data breaches data management