Traditional Process Digitalization vs. Machine Digitalization

When data volumes are huge like with machine data, there is no other way than to automate data collection, to digitalize it. Sensors can collect almost unlimited amount of data without overload or time limits. When question is about traditional process digitalization with humans involved, human resistance aspect appears: Why should I complete this information? What should I write in this field? What’s in it for me?

Data is often gathered manually, recorded with keyboard method. Much too often speech-recognition, smartphone based barcode readers, or augmented reality applications are considered too futuristic options. Data quality and hopefully user experience for data capturing could be tremendously improved by utilizing new digital capabilities.

Digitalization Has Taken Place Already

There are plenty of hype terms such as digital disruption, big data, augmented reality and Internet of Things around digitalization. Software vendors provide new and innovative technologies for each of these hypes, and magazines are full of articles about digital innovation.

Somehow it has been left in the background that digitalization has already happened in many areas: traditional business process development with CRM and ERP solutions, different internet services have replaced calls and papers, and we very seldom send letters – instead we e-mail. So digitalization has already happened during the last decades, now it is just stepping to a new area of machines in a broader sense.

Digitalized Process Change Requires Optimization

Business process change does not happen without pragmatic actions and constant reminder about the goals and benefits the company wants to achieve. In many companies business processes such as sales, delivery, and invoicing are already digitalized. Still, digitalization in these cases is not fully optimized: processes and IT systems are not integrated or there are multiple redundant and manual data entering. Yes, process is digitalized, but not optimized and simplified.

Change for optimized digital process demands strong change management and far too often companies are stumbling on practical level implementation. Since process is mainly done by humans, have we copied and pasted the same way of doing things too many times and just digitalized it without thinking thoroughly new ways of doing them? Results in IT systems are not very convincing, even though decades and decades of time has passed to improve digitalized way of working.

Utilizing the Digitalized Information Is a Challenge

The world of the Internet of Things does not have the burden of old ways of doing things: pen and paper. Machines don’t complain about changes in the way of working. Data is transferred and processed as intelligently as humans have dictated. The only limitation is where you can put the sensors, what information they can collect and are there enough resources for implementing new technology changes.

Analyzing digitalized data requires very similar capabilities both in traditional digitalized process data and machine data. It requires analytical solutions and humans who have competence to understand what data can speak and tell. Technologies vary, data structures vary, deviations can be highlighted, but actual analyzing is still depended on capable human competences.

So where then is the difference? In both aspects data must be collected, processed, analyzed, and actions must be done to react to findings. In machine world the question is more about limitations in technology, whereas in human world it is more about managing change and people.

"Change for optimized digital process demands strong change management"

In both aspects, the hardest part is to utilize information available and make changes in daily work based on findings: change process, change product offering, change own thinking, be innovative based on the findings. Competences, innovation and capability to change can be a matter of life and death for a company and thus for the job security of each of its employees.

At the end the question is about the change, regardless whether it is driven by machine data utilization, or by process or business development change.