In a recent whitepaper, “Data Visualization & Analytics: Six Keys to Unlocking Operational Excellence”, I outlined some key methods to unlocking operational excellence in the process industries. One of those methods was Rapid Problem Solving Skills. Quick action shortens the duration of a production problem and yet few companies measure and track the response times of their team.
A few suggestions:
- Invest in IO/OT technology that reduces the skill and effort for preparing data for analysis.
- Look for solutions with strong visual and programmatic features for data transformation and manipulation.
- Invest in manufacturing master data management to build diagnostic data hierarchies.
- Add capabilities in production to capture unstructured data from operators, engineers and maintenance staff for use in future prescriptive applications.
- Invest in software that creates a bridge between production and business systems.
Unfortunately, many organizations have to contend with poor data quality which results in poor decision-making. After all, decisions are no better than the information on which they're based. Reliable, relevant, and complete data supports organizational efficiency and is a cornerstone of sound decision-making. So what are some of the consequences of sub-par data quality?
Mistrust. Poor data quality often breeds mistrust among internal departments.
Two departments, call them A and B, each needed data about parts. Their needs overlapped considerably, but each needed a few fields that the other did not. Initially the data was maintained by department A, but the quality wasn’t high enough for department B, so it developed its own database. Soon the databases became horribly inconsistent. The issue became a "lightning rod" and it became impossible for the two departments to work together.
Poor or delayed decisions. If you suspect you're dealing with unreliable or incomplete data, you might delay making your decision If you don't feel confident in your data, why would you feel confident in making a decision that could come back to bite you?
Wasted money. According to a recent study in the UK, US and France, 16.6% to 18% of departmental budgets are lost due topoor data quality. The research also indicates that 90% of surveyed companies admit that inaccurate data – such as duplicate accounts, lost contacts and missed sales opportunities – contributes to budget waste.
There isn’t a decision being made in boardrooms today that hasn’t been shaped at some stage by the data. Yet there remains a deeply-rooted skepticism about the use of data to drive decisions. The explosion of data, new analytics techniques and derivative business models are confounding the issue: Are we working with the wrong data? Are we thinking the right way about using it to compete? Can we make fact-based decisions in the needed timeframe?