In this — our first installment of our PRAGMATIC MANAGEMENT series — we discuss the various process and systems data types needed by organizations, and how this data can be used to make better decisions.

So we all know that you need data in order to manage processes and systems. Compliance data — in various forms, e.g. Weekly On Time Deliveries — is a basic metric used to check how well we (or our processes / systems) are performing. But surely there must be other, more compelling reasons as to why we need data.

Why do you need Data?
You need data in order to make Informed Decisions.

Any process or system that is not providing you with data is a blind spot — and thus, when problems arise with that process or system, or any other processes or systems they interact with, your actions will be more reactive or remedial; rather than preventive or predictive.

Many things that we take for granted these days — like weather predictions, GOOGLE MAPS / WAZE routes and arrival estimates — are only possible due to large amounts of analyzed data.

With enough data, you will be able to create models of the processes and systems you are studying, with potentially mind-blowing results. Predictions and trends can be determined, and actions to address expected issues can be taken — all before any issues arise.

Here is one example — the National Oceanic and Atmospheric Administration (NOAA) Forecast for Hurricane Laura. The NOAA forecast not only indicated the current location of Hurricane Laura (as of August 26, 7AM), but it then went on to indicate the expected path of the hurricane for the next five days.

Photo credit: NJ.com

With enough data, you will also be able to create data models of your processes and systems, and make similar mind-blowing analyses and predictions (like accurately predicting product sales for the next five days, and highlighting potential shortfalls in supplies on a per location basis), at a simple click of a button.

Processes and Systems can generate different types of data — not only data models — and these provide different insights into how the Processes and Systems are functioning. Let's quickly look at the various types of data we can gather and how they can be used to help make decisions.

Process and System Data Types

Whenever we talk about using data for decision-making, it must be stressed that the data to be used must be of “ impeccable quality” — that is, we are confident that the data is correct, accurate, true and valid, and was gathered using the correct methodology.

On Data Integrity and Availability
Data Integrity ¹ and Data Availability ² are prerequisites for Timely and Informed Decisions.

¹ Data Integrity = Ensuring that data is correct / valid / gathered properly
² Data Availability = Ensuring that data can be accessed by authorized parties when they need it, in the format needed.

After ensuring that the data is fit for use, we must then ensure it is available for use by authorized parties (in the required form or format) when needed.

The Process and System Data Types (i.e. Noncompliance / Compliance / Capability / Efficiency) above also reflect the general stages (i.e. changes in focus and priorities) that data management programs go through, over time. Each data type is a prerequisite for the succeeding type, thus ensuring the natural progression through the stages.

Photo credit: Army Staff Sgt. Brendan Stephens (Public Domain via Wikimedia Commons) — Server Wire connections

During the initial phases of implementing a data management system, data gathered focuses more on non-compliance and compliance to requirements. As the data management system matures (and the data management efforts gain support from management and stakeholders), it moves on to gathering data on process and system capability — for use in appropriate improvement efforts.

At this stage, data management starts relying on statistical techniques in order to make sense out of the chaos and amount of data available. Various statistical techniques can be used to analyze data, and reveal insights that are not visible to the naked eye — like Process mean and standard deviation, correlations between variables and Process Capability indices. These insights are the basic building blocks for building process capability and efficiency.

Then, after building up enough expertise in providing the required capability data, focus is then moved to providing the efficiency data needed to support the waste reduction programs of the organization.

At some point in time, the data management system will then start looking outward — towards customer / supplier / stakeholder data — as these will become more important (especially when making Strategic Decisions) to the organization as it grows larger, and its processes and systems mature.

Conclusion

Since decision making drives the need for data, the data needs of an organization are (by nature) tightly interconnected with its current concerns and challenges.

Evaluating your organizations' systems and processes — and determining current challenges — will allow you to define and establish a data management system that addresses your needs directly. Focusing on data that does not address your current needs or match your maturity level will not be productive — as the data provided will either not be actionable (or appropriate) at that point in time.

As processes and systems mature, so must the data management systems, evaluation techniques, managers and employees needed to support them and implement them.

Building up and improving data systems require time, money, knowledge, manpower and most of all; commitment — but properly implemented and maintained data management systems will provide benefits that far outweigh any investments made — either in the form of reduced problems, increased customer + stakeholder satisfaction, increased market share and / or bottom-line profitability.