The Roman philosopher and poet Seneca was known for saying that if you do not know what port you are sailing to, no wind can help you. And it seems that in the flood of data we see in business environments today, many companies are sailing aimlessly.
What creates today's flood of data? Notably the Internet of Things (IoT). Things or devices and systems that talk to each other constantly. Some also with users. But let's focus on companies, specifically manufacturing companies. They have a wealth of data at their disposal, but cannot use it efficiently. Implementation of the Industrial Internet of Things (IIoT) initiatives requires knowledge that is seldom lacked by today's businesses.
Gartner estimates that 20.4 billion devices will be connected to the Internet of Things this year, and McKinsey has calculated the potential of using the Internet of Things in modern factories – that could generate from 2 and 3.7 trillion US dollars between now and 2025. Such numbers are hard to ignore. So how do you capture that wind in the sails and overtake the competition?
At the same time, well-known consulting houses point to a large number of failed initiatives in IoT. According to Cisco, up to 60% of the IIoT projects are in the concept phase. The reasons for this can be found in the complexity of their implementation and the fact that many of the implementations are not based on business goals or defined business challenges that they are supposed to solve.
When launching the IIoT in business, companies should ask themselves the following questions:
It’s true, there are many specific questions. Some of them are answered by technology, while others require the answers of company executives and data professionals.
There is no doubt that without the competencies and talents, predominantly knowledge and technology, there is no data engineering that companies crave for. There is no wind in the sails and no sailors. And that's why many IIoT projects are doomed to fail. If you do not have the talent and knowledge to set the sails properly and operate the rudder, no wind, even so strong, will help you reach the finish line.
For a successful IoT project, a company needs good project management and knowledgeable staff. The path to success lies with data engineers and architects, as well as software engineers who can put together the right puzzle that connects the edge to the center, data, and business applications. They are the creators of a data environment that will deliver business information and help deliver better results. These are the people who have the required architectural and data skills, such as capturing data from industrial devices and sensors, linking locations and systems, and designing an architecture that will meet all current and some future requirements. A business needs to establish an environment where data becomes visible, easily accessible and shared or exchangeable.
The technological part of the data-sailing equation is supposed to be the simplest, at least in theory. You build a modern data platform and connect it to data sources. In practice, however, this is all but an easy step. Old anchors, this is an analogy for old systems with silo arrangements for data capture and storage, in many places hold on tight. Therefore, a great deal of effort is required in the business environment or a manufacturing company to achieve that data is captured, cleaned, normalized, processed, stored and shared in the manner required by modern business.
Data engineering brings businesses the knowledge of building robust and reliable data paths. Businesses typically pump key data into data warehouses, or return it (refined, of course) to systems (from where it was captured). Robust implementation of intermediate and transformational data paths is crucial, as production environments require operational reliability and fault tolerance. Production must continue as soon as possible even in the event of a failure, therefore a rapid recovery of the interrupted data path must be possible.
Modern business is increasingly looking towards the cloud, or to be more precise, into the cloud. Production environments were, and are, in many places, separate islands at each location. But the competitive advantages are mainly due to the rapid transfer of data and information. This is where data engineering comes to the fore again, regardless of whether the new data solution will still only work on a single location, in the cloud, or will maybe represent a combination of the two, the so-called hybrid. If a company chooses to have a hybrid layout, then a solution that can simplify data management should be chosen, otherwise the complexity can greatly increase. To do this, the company (again) needs engineers who are familiar with communications and systems protocols, machine language, applications…
Yes, even with oars you can get far, but with the wind in the sails you get there much faster. Every job in the business requires choosing the right tool for the job at hand. The same is still true when you are working with data. Businesses need tools that can refine data, calculate KPIs, correlate them with legacy data, and refine with data from systems such as ERP, MES, CRM, and other business systems or applications.
In the case of IoT projects, these are often custom applications, especially in the field of manufacturing. For this reason, in addition to application building knowledge, companies also need analytical skills, as they often aim for better visualization and insight into the business (possibly also using existing BI tools and data of better quality). A really strong wind in the sails is delivered by advanced machine learning models that make sure individual business processes are on steroids, especially predictive analytics and predictive maintenance.
It is no secret that in the business world there is a real battle for talent. Knowledgeable people are the key creators of success today. And because we live in a data-driven economy, the companies that know how to attract better professionals are winning. But at the same time, they need to understand where competences and motivation lie with data engineers or data scientists. Today, one creates a competitive edge in the field of the Internet of Things with data engineers, architectures and data itself.