Let's make one thing clear: we won't tell you that data is the new oil because you probably heard enough about it. Surely, data is a valuable source for businesses, but manufacturing industries realize that data alone will not magically solve every issue they might be having. Huge Mizal at Copperberg finds out more…
The confusion on how to use and monetize data was one of the leading topics at our 2018 roundtable discussion on Aftermarket where industry experts voiced their concerns on having lots of data but not knowing what to do with it. A well-thought-out data strategy, along with smart data management practices, can guide businesses in making sense and use of their data.
What is data management?
Sabrina Schiele, Head of Data Analytics of Vaillant Group, defines data management as the "task of storing the right data properly, then combining and transforming that information, and making it available in a meaningful form at the right time to create value for the recipient." This is a good definition for several reasons. First of all, it calls to attention that the businesses need to be selective about the data that is required to accomplish a particular task. Then, it emphasizes the transformation of data into information, just like processing ore to gold, for it to be useful at the right moment. Moreover, it manifests the main purpose; providing better services or products for the customers. This may seem like a daunting task, especially when the amount of data businesses have is considered, but with the right approach and tools, it's doable and, more importantly, worthwhile. So, let's dig a little deeper into this definition to make the most of your data.
Determine what you want to achieve with your data
A common misconception on data and data processing technologies such as AI and machine learning is that they are so smart that they can make all kinds of decisions on behalf of us. The truth, however, is far from that. Gerd Leonhard, the European futurist, speaker, and author who was also at our Aftermarket Business Platform 2018, says that while today's machines are better at analyzing data, they are nowhere near human intelligence. So, the responsibility to ask the right questions still falls on the human's shoulders. In that case, thinking about the end goal is a good way to start because it's easier to identify the steps that are needed along the way once you know your destination. Schiele shares a similar approach; 'You'd typically start with what you have, but actually, you need to turn it upside down. The question becomes, "where do I want to go?" and "what do I need to get there?". If you know what you want to do, you can ask yourself what process is needed for every action.'
Nick Frank, the co-founder of Si2 Partners, also points out the importance of defining the business problems at the beginning. Once the problem is apparent, businesses can start looking at data for identifying potential solutions and data needs.
Harmonize data whenever possible
Data harmonization is the act of taking data from different sources and combining them after clearing away inaccurate items. When done right, data harmonization enables businesses to get a glimpse of the big picture and supports the decision-making process. Of course, combining data that used to be stored in different systems and various formats is not an easy task. Schiele is familiar with the struggles that come with harmonizing data:
"The Vaillant Group is a traditional industrial company. If you consider the numerous countries we operate in, you may observe local differences. However, the company has very much become aware that fragmentation of data is not enabling us to our fullest. That is why harmonization is important to us."
So how can businesses proceed with data harmonization? As there is no one-size-fits-all solution, the answer will depend on organizational structure, volume, and variety of data and available solution providers. One tip that Sabrina Schiele gives is to be bold and mindful at the same time, because although data harmonization is feasible, it's a long-term process with many decisions to be made.
Collect data for your customers, not for the sake of data
With the hype around big data and technological advancements, it's easy to get excited and even overwhelmed by the possibilities. However, in the end, a good data strategy should take into account the customer needs and expectations because it's the core of any business. Data management is a powerful tool for enhancing customer satisfaction through better products and services.
It's also important not to forget that the most valuable data is usually provided by the customers. Thus, gaining their trust in how that data is handled and used is crucial. If the customers are not getting anything in return in terms of service, they won't cooperate. The lesson here is not to fall into the pitfall of collecting data for the sake of data. Instead, businesses should make it their priority to use their data to improve their services and build lasting relationships with their customers.
Interested in learning more on data management or the latest trends in the Aftermarket? Then have a look at Sabrina Schiele’s session on “Why Artificial Intelligence is not doing the thinking for you” and Stephan Boch’s speech; “Smart Connected Assembly” at the Copperberg's Aftermarket Europe Conference which will be held in Nyköping, Sweden, on 16th- 18th October 2019.