Process vs. Data: Which Comes First

Data is the lifeblood, and the process is the body function of enterprise. 

Data is like the life blood of business, processes are like body functions, made up of steps that convert inputs to outputs. process management is to manage known from the flow. Process vs. Data, which one should come first?

Logically, the process always comes first: Processes exist whether they are undocumented/ documented. Businesses have processes before flow-graphing was invented and before computers existed. The process is "what are you trying to do, what result are you trying to achieve." Then, you can ask, "what data do you need." If you don't know what you are trying to do, how can you possibly know what data you might need? At the same time, you can see the value of collecting lots of data, since you never know what might be needed in the future, but that is a different question.

In an ideal world, the process should be primary. Whereas businesses usually use inherited systems as data sources, the process has not to be constrained by them. You should have to construct data adapters for the process to work properly. But in the real world, you have always been constrained by existing systems and databases. The process never stands alone, and rarely is the sole source of data for any meaningful process. So while you consider process primary, you must consider existing data sources as constraints. Ignoring them while designing processes is likely to lead to a bad result and rework.

It’s just another chicken-egg debate: As long as you have the freedom to design what you want to do. Then you design the process, define the data you need along the way... All is good. In many cases, you are often forced to look at existing processes, existing data collection, and the challenges are to make things "lean." Do you need to collect the data, or is there a way to streamline the process and get the same data leaner. Do you collect Big Data and are you benefitting from it? When you have to mix Data Management initiatives and Business Process improvement, only the concerted approach will work and the overlap is inevitable. So it turns to be another chicken and egg debate.

Data drives processes, when multiple groups and / or department and / or people involved in the process. For example, customer onboarding process, dispute tracking process is highly dependent on data, data elements, and business rules for process optimization. In these scenarios, data has a significant role in defining the process and data comes first. A good process includes an understanding of value, function, Data, responsibility, and control flow. Function and Data are thus integral to a good process.

Always emphasize on the Data part: Though process may come first, always put emphasis on the data part. Each step generates data ranging from a simple "done" declaration by an individual performing the step, to the collection of, for example, various data elements needed by steps downstream from a step that has the focus. First keeping the practice you want to incorporate as processes. Data quality, the frequency of occurrence, impediments of decisions for Data variance and many analytical factors have a major influence on the fine parts of a Process Definition. Process definition though has been marked as the principal skeleton of an Enterprise Business Regulations, yet to define it to the finest position, data science has a huge contribution. When you talk about the Business Decision Management, you should have Data analysis part first to get all Social education embedded within a process. Recent development on Digital Transformation can also be invited to make your processes implanted in the womb of OMNI Channels when data analytics and science have been well thought off and recognized within the definition of your process.

Like so many things, it depends: The technical answer would be processed first because someone had to do it the first time in order to measure it. But the reality is that over 90% of the processes of today's world are based on some previous iteration. As new mediums and environments emerge, the initial balance starts heavy on processing data, and then shifts toward data over time. An outsider who comes into an organization and is unable to find anyone who can describe a particular process can reverse engineer that process by carrying out data mining. If the process is mostly linear it is easy to reverse engineer a process. If the process is complex with multiple branching points where different pathways are undertaken on the basis of data, experience, judgment, intuition, and, at times, arbitrary action, it becomes more difficult to derive a process from data.

Hence, it is just another chicken-egg debate, process and data go hand in hand. Processes are defined around some outcomes/KPIs and objectives to improve in a defined region where Data is important to be defined for the same. Similarly, Data has no meaning until it is owned and given some identity. Processes give the Data a shape to be quantifiable as well.