Survey: Manufacturers See Data, Lack Strategy
Despite persistent challenges, data has made measurable impacts on performance and decision-making.
KEY TAKEAWAYS:
● Manufacturers recognize that data is the essential fuel for digital transformation, but without governance and analytics, they lack the “map” to turn data into strategy.
● While many companies report measurable gains in efficiency, cost reduction, and decision-making from data use, nearly half still lack a corporate-wide governance plan and processes to ensure data quality.
● Disparate systems, legacy equipment, and limited organizational data skills remain major obstacles, yet manufacturers increasingly view data as a quantifiable business asset tied to strategic outcomes.
Manufacturing data is the commodity that promises manufacturers a pathway to operational excellence. It can also act as an accelerant toward discovering new markets, new customers and innovation for both products and processes. Coupled with the urgency that many companies feel to bring AI into the business, there is no question that data is the necessary fuel for the digital transformation journey.
But if data provides the pathway, then governance and analytics are the map. Creating a map that is accurate relies on validated, contextualized, high-quality data. But met with the common challenges of disparate systems, legacy equipment, and a lack of organizational data skills, many manufacturers are instead left frustrated that the wayfinding they so desire is just out of reach.
Despite those challenges, manufacturers are seeing significant progress in utilizing data to make better decisions, while also increasing the frequency with which those data-driven decisions are made. They are seeing improvements in cost reduction, speed and quality. A growing number of companies are determining how to best put a quantified value on their data, adding a direct tie to the bottom line.
For manufacturers who diligently strive toward forging that data-driven pathway, improved business outcomes lie on the route ahead.
SECTION 1: Data is Valuable – Strategically and Financially
It could be said that data, like happiness, really can’t have a price tag, because it is by nature priceless. In the quest to assign its best value to align with business goals, companies value data differently. The greatest share of respondents reported that data’s value was measured by impact on operational performance (41%), while around a fifth (21%) said that data is assigned a dollar value.
Still, nearly a third of respondents (28%) said that they do not have any such measure for their manufacturing data (Chart 1). Without such quantifiable measures, leaders can find it challenging to gain support and investment for data-related investments or to initiate data pilot projects.
Likewise, the structure for making that aforementioned data-driven map is lacking at nearly half of all manufacturers—46 percent of respondents said that their companies lack a corporate-wide data governance plan (Chart 2). As data-sharing is fairly common for internal cross-functional teams (75%) (Chart 5), it seems likely that more companies will move toward a more strategic approach.
For companies that do have a data strategy, there is a payoff in alignment with overall corporate business strategy—70% say their company’s data strategy aligns with overall business strategy either entirely or very closely (Chart 3). And while only 17% of executives currently find their annual incentives or KPIs tied to data collection and management (Chart 4), it’s not hard to foresee a future where this becomes more ubiquitous.
1. Data is Valued by Performance and Cost Savings
Q: How do you measure the value of the data in your organization? (Select one)
2. Companies Divided on Corporate-Wide Governance
Q: Does your company have a corporate-wide governance plan, strategy, or formal guidelines for how data is collected, organized, accessed, and utilized across the enterprise, including manufacturing operations? (Select one)
3. Data Strategies Closely or Partially Align with Business Strategies
Q: How closely do you feel this data strategy is aligned to your company’s overall business strategy? (Select one)
4. Data Management has Tentative Ties to Executive Incentives
Q: Is data collection/management/analysis included in annual incentives, KPIs, or business imperatives for company executives/leadership? (Select one)
5. Cross-Functional Data Sharing Has Limited Reach
Q: Is data routinely shared cross functionally in your company? (Select one)
SECTION 2: The State of Data Sourcing and Quality
Most frequently, the well of manufacturing data is made up of enterprise-level data (ERPs), shop floor systems, and quality control systems (Chart 6). That said, 79% of manufacturers also include manually entered data—a potential source of data contamination downstream. Microsoft Excel still reigns as the undisputed champion for data analytics (Chart 7).
Respondents were somewhat pessimistic in their companies’ ability to collect the right data for business needs (Chart 8), but far more untrusting in the ability to collect AI-ready data (Chart 9). Alarmingly, only 39% said that their company has a process to verify data accuracy and quality (Chart 10).
6. ERP, Shop Floor and Quality Control Systems Lead the Data Supply
Q: What are the sources of your manufacturing data today? (select all that apply)
7. Microsoft Excel is the Most-Used Data Analytics Tool
Q: What systems do you use to analyze the manufacturing data you collect? (Select all that apply)
8. Companies are Moderately Skilled at Collecting the Right Business Data …
Q: How would you rank your company’s ability to collect the right data the business needs from your manufacturing operations? (Select one)
9. … But are Less Skilled at Collecting AI-Ready Data
Q: How would you rank your company’s ability to collect AI-ready data? (Select one)
10. Only 4 in 10 Have a Process to Verify Data Accuracy or Quality
Q: Does your company have a process to verify the accuracy and quality of manufacturing data? (Select one)
SECTION 3: Data Has Proven Value – But Challenges to Realizing It
What stands between manufacturers and this promising data-driven future? The most persistent challenges include data from disparate sources (46%), the ability to extract data from legacy systems (45%), and the lack of quality data (34%). (Chart 14)
Despite the halting steps along the way, manufacturers are seeing true value from operational data. The highest level of impact has been seen on cost reduction, efficiency and quality. (Chart 11) Overwhelmingly, respondents say that data has improved their company’s decision-making process (93%). (Chart 12)
11. Cost Reduction, Speed and Quality All See Improvements
Q: What degree of impact has manufacturing data had in improving your manufacturing organization since the start of your digital journey?
12. 93% Say Using Data Improves Decision-Making
Q: Has the use of data improved your company’s manufacturing decision-making process? (select one)
13. Data-Driven Decisions Gaining in Frequency
Q: How often would you say your organization makes data-driven decisions today? (Select one)
14. Integration, Extraction, Quality Create Barriers to Leveraging Data
Q: What are the most important challenges or obstacles hindering your organization from making more data-driven decisions? (select top three)
About the author:
Penelope Brown is the Senior Content Director, Manufacturing Leadership Council