How Process Analytics will revolutionize the way you do business, and how you can adapt to the changes
It seems almost impossible to avoid reading about Artificial Intelligence or Machine Learning these days. My daily social media stream seems inundated with posts and articles that often predict a world where machines will overtake our daily lives, a life where we can kick back and let our jobs be done by machines (and possibly losing my job in the process).
While nobody can accurately predict the future, there is no denying that every business (big or small) will be impacted by the rise of smarter and self-learning algorithms. There has certainly been a lot of activity in the field of Machine Learning as of late, where many new startups and tech giants alike are applying these machine learning processes to different domain areas, discovering new patterns in human behavior, and creating ever more efficient - and sometimes useless - products and services.
Why the sudden increase in Machine Learning usage?
Most of the algorithms used by Machine Learning are not new, so often it can be confusing as to why these processes have just recently started gaining traction. The main reason behind the advent of Machine Learning can be attributed to an availability of low-cost computing, made possible by Moore’s Law and Cloud service. Having an accessibility to theoretically unlimited computing power has made it easier than ever for anyone to experiment and apply learning models to different domains, such as interpretation of complex topics like image and speech recognition, and cybersecurity.
Cheap storage is also another factor giving impetus to Machine Learning, enabling collection and storage of enormous amounts of data for enterprises, even if they haven’t yet the slightest clue what to do with the data. Once this data has been collected, the next step is to apply Machine Learning to discover interesting patterns and answer questions they could never ask before. Therefore, the race for better machine learning solutions will be won by the companies that possess unique domain datasets.
Finally, we'll continue to see experience 'smarter' devices, services in our daily lives due to advances in real-time streaming technologies have also afforded enterprises the ability to deploy machine learning algorithms close to “edges” and automatically predict and respond to business events. One can the trends at the annual Consumer Electronic Show between 2016 and 2017 to see the explosion of A.I. based products being launched. Make no mistake, there will be no consumer or enterprise products/services that won't be affected by this trend.
The Rise of Process Analytics
For a long time, machine learning practitioners had access to two data science approaches: a model-based mining technique and/or a data-oriented analysis technique. While these are good frameworks, it always felt that there was a missing element. A new branch of machine learning is trying to close the gap between a model-based analytics and the traditional data-oriented analysis techniques. This new branch is known as “Process Mining”, techniques and algorithms specially designed to "learn and interpret" process data and aid the analysis of atomic business flows.
Despite being a fairly new concept, the rise of Process Mining is already well underway in the tech world and especially in places like Western Europe. Even today, analytics tools supporting the analysis of business processes are often difficult to implement and a hassle to use. This is largely because the analytics tools, algorithms, and data storage technologies were not meant to be process centric. That is, until as of late.
With a completely new generation of tools and software coming into the light, Process Mining tools have become surprisingly powerful to use, but more importantly, much easier to implement and use, and are proving to revolutionize the way companies process and analyze their data.
Process mining and analytics use machine learning algorithms to automatically detect and represent process "as-is" model flows (a Petri net), allowing business owners the ability to automatically observe processes as they are utilized, instead of doing so in a series of disconnected KPIs. These process analytics tools are also able to attributions/variables that impact process performances, helping to spot process bottlenecks and their root causes, target and execute process improvements and more.
Simply put, process analytics provide enterprises full transparency insights into their processes, making for more informed decision making processes and analytics. And with the tools now being developed, the implementation and process mining itself is becoming much easier for the everyday enterprise.
Bridging the Domain Expert - Technologist Gap
Understanding business processes and mapping them out with underlying data can give a business owner a lot more information than just applying machine learning to the data without any context. Thus, leaving us looking for an easier solution.
Without a doubt, there is a growing need for platforms that will bridge this gap between domain experts and data scientists. The benefit of a process centric analytic platform is that it's easy enough for domain experts who might not necessarily be technologists because it represents data into business flows that domain experts already understand and can capture contexts in a clear and concise way, all the while collecting data that the data scientist can take and start working a model almost immediately.
These process analytics platforms correlate the context with underlying data to provide perspective to the data, thus helping the data scientist with the preparation of data based on the perspective, in order to apply Machine Learning algorithms. In turn, the data scientist will have a richer path to generation key performance indicators (KPI’s) and predictions and can convey this to the business owner in an easy to understand and timely manner.
What can Process Mining do for my organization?
Imagine if an enterprise is collecting data from their marketing operation software, CRM software, and Enterprise Resource Planning software. A data scientist, completely unaware of the flow of a customer journey between these systems, often "mines" the data from these individual systems in isolation.
What this type of method generates is the data scientist coming up with interesting KPI’s from each of the individual systems, like what bottlenecks in renewal for existing customers, or where the variations and waste are in their production cycle, yet it cannot find the relationship between customer demand and production output since those analyses are done in silos.
Moreover, an organization’s value chain process may not be as straightforward as it always seems, and most demand/supply processes are not linear. This makes it vitally important that enterprises be able to understand ALL different deviations of their value chain processes in order to remove inefficiencies, increase customer satisfaction and, therefore, increase profitability.
Using modern process analytics software, an organization can support ever more common “lean management” practices, and thus start to connect the dots between data spread across disparate supply chain systems.
And this process is not one for just a data scientist, but rather, everyone in the organization, as the never-ending process of creating value for their own customers is the mission for all organizations. Process Analytics bridges the gap between modern machine learning solutions and process improvement initiatives. If not, perhaps a machine will do this for us in the near future?