Survey: In Manufacturing Data We Trust—Mostly
The Manufacturing Leadership Council’s latest survey reveals steady progress toward data mastery, alongside persistent gaps in verification, integration and untapped potential
KEY TAKEAWAYS:
- Manufacturers are improving decision-making with data, but still underutilize most of it
- Formal data strategies boost business alignment, yet only half of manufacturers have one
- Legacy systems and unverified data remain the biggest barriers to true data mastery
“In God we trust, all others must bring data.”
Most commonly attributed to statistician and business theorist William Edwards Deming, those nine words make for a popular post on LinkedIn and elsewhere online. Irony being what it is, there is no data to verify that Deming was their original source. There is no record of him ever saying or writing them, and no direct reference exists in any of his published works.
Data has a way of poking holes in long-held, even cherished beliefs. For the purposes of the Manufacturing Leadership Council’s Data Mastery and Analytics survey, a different Deming quote (one that’s verified by the Deming Institute) may prove more illuminating:
“Data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action.”
Since the earliest days of manufacturing’s digital era, the operational holy grail has been achieving a single source of truth that could provide insights for action. Data provides a way to capture and verify what has long been driven by intuition, experience or opinion.
If Deming were around today it’s likely that he would be proud of the way many manufacturers are approaching their collection, analysis and use of data. Many are applying the rigor of continuous improvement to their data projects just as they would on any other operational project. And while the road to data mastery remains a long and challenging one, manufacturers remain dedicated to a course that promises valuable rewards.
SECTION 1: The Good, Bad and Ugly of Operational Data
The majority of respondents (80%) said that their organization has a measured value for their operational data—most commonly, its impact on operational performance (chart 1)—and 89% say that using data has improved their decision-making processes (chart 2). Many report positive impacts on quality, productivity, cost reduction and decision-making speed (chart 3).
There are still troublesome gaps, though, on the path to data mastery. Perhaps most notably, most manufacturers (63%) say they are actively analyzing or using less than 50% of their manufacturing data (chart 4).
1. Performance is Top Data Value Measure
Q. How do you measure the value of the operational data in your organization? (select up to three)
2. Most Say Data Has Improved Decisions
Q. Has the use of data improved your company’s manufacturing decision-making process? (select one)
3. Quality, Productivity Improved with Data
Q. What degree of impact has manufacturing data had in improving your manufacturing organization since the start of your digital journey?
4. Much of Manufacturing Data Goes Unanalyzed
Q. Approximately what percentage of your manufacturing data is actively analyzed or used in decision-making today? (select one)
SECTION 2: Corporate Strategy and Data Leadership
Manufacturers who aim to succeed in getting the most competitive advantage from their data are frequently dedicated to creating formal corporate-wide guidelines and strategy around that data. About half of respondents (48%) said that their company has such a plan for how operational data is collected, organized, accessed and utilized (chart 5). Of those who said that they had such a plan in place, it stands to reason that 87% said their data strategy had either high or moderate alignment with the company’s overall business strategy (chart 6).
Getting it right with data also can carry a personal incentive for some company leaders—for example, almost 70% of respondents said that operational data management is included in annual incentives or business imperatives for at least some of their company leadership (chart 7). And while many manufacturers describe their digital journey as a continuous tension point between IT and OT teams, it appears that data has become a point of convergence, with 40% of respondents saying that joint IT-OT leadership most frequently has primary ownership of manufacturing data strategy (chart 8).
5. Nearly Half Have a Corporate-Wide Data Strategy
Q. Does your company have a corporate-wide governance plan, strategy, or formal guidelines for how operational data is collected, organized, accessed, and utilized? (select one)
6. Data Strategy Moderately Aligns with Business Goals
Q. If so, how closely do you feel this data governance strategy is aligned to your company’s overall business strategy? (select one)
7. Leadership Often Incentivized to Make the Most of Data
Q. Is operational data collection/management/analysis included in annual incentives, KPIs, or business imperatives for operational company executives/leadership? (select all that apply)
8. Data Ownership is a Joint OT-IT Project
Q. Who has primary ownership of manufacturing data strategy in your organization? (select one)
SECTION 3: Decisions, Decisions, Decisions
As the vast majority of respondents to this survey have stated, data has helped their organizations to make better decisions. However, manufacturers most frequently continue to make those decisions after an event has already occurred, rather than before that event (or disruption) has taken place. While growing usage of AI and machine learning can allow for earlier detection of anomalies, most manufacturers continue to make decisions in a reactive manner (69%) vs. a proactive, predictive or preventative one (chart 9).
That said, the frequency with which manufacturers are making data-driven decisions is strong, with 54% saying that they make data-driven decisions half the time or better (chart 10). Additionally, manufacturing data is becoming actionable at an accelerated rate, with 38% of respondents saying they are able to get insights from their operational data within hours or even in real time (chart 11).
When it comes to the obstacles standing in the way of data-based decision mastery, most frequently it is the challenge of extracting data from legacy systems (43%) or integrating data from different sources (37%) (chart 12).
9. Data is Most Often Used for Reactive Decisions
Q. Which of the following best describes how your operational data is used in manufacturing decision-making today: (select one)
10. Data-Driven Decisions Made More Frequently
Q. How often would you say your organization makes data-driven decisions today? (select one)
11. Making Progress from Data to Insight
Q. How quickly can your organization turn manufacturing data into actionable insight? (select one)
12. Legacy Systems, Disparate Sources Hinder Data-Driven Decisions
Q. What are the most important challenges or obstacles hindering your organization from making more data-driven decisions? (select top three)
SECTION 4: Data Readiness and Expertise
The advent of data-driven manufacturing means that manufacturers are hiring for data-specific roles to enhance their data readiness. 63% said that their company has data/business intelligence analyst roles, and 47% said they are employing data engineers. While only 19% report having a chief data officer, perhaps more worrisome is the 21% who said they have no data-focused roles within their company.
That said, additional data expertise might be useful and even necessary to improve manufacturing’s data acumen. Most respondents ranked their company’s ability to collect the right data for business needs as moderate (65%, chart 14), and nearly half said that their operational data had low AI readiness, e.g. data that is well-labeled, contextualized governed and accessible (49%, chart 15).
13. Data Architects, Engineers and Analysts Find a Home in the Factory
Q. Does your company have any of the following roles (select all that apply)
14. Most are Moderately Skilled at Collecting Data for Business Needs
Q. How would you rank your company’s ability to collect the right data the business needs from your manufacturing operations? (select one)
15. Operational Data Frequently has Low AI Readiness
Q. What degree of AI-readiness does your operational data have today? (e.g., well-labeled, contextualized, governed and accessible) (select one)
SECTION 5: How Data is Collected and Shared
For the systems and tools that are used to analyze manufacturing data, Microsoft Excel remains the undisputed champion (76%), combined with other systems including statistical analysis program (63%), shop floor analytics (59%) and corporate analytics systems (50%, chart 16).
One of the survey’s most concerning findings is how few manufacturers verify their data’s accuracy and quality—just 42% said their company has such a process in place (chart 17). But while data might not be frequently verified, manufacturers are often sharing it internally, as 34% reported they are sharing data routinely across most functions or enterprise-wide (chart 18).
16. Excel, Business Intelligence and Shop Floor Analytics Systems Most Common
Q. What systems do you use to analyze the manufacturing data you collect? (select all that apply)
17. Fewer Than Half Verify Manufacturing Data Accuracy
Q. Does your company have a process to verify the accuracy and quality of manufacturing data? (select one)
18. Data Sharing Becomes More Systemic and Routine
Q. To what extent is operational data routinely shared cross functionally in your company? (select one)
Penelope Brown
Senior Content Director, Manufacturing Leadership Council
