Find out how much your company data is worth

“Everything around us can be reproduced and understood through numbers. If numbers of any system are displayed graphically, they create patterns. Therefore: Patterns exist everywhere in nature”. This was the hypothesis of a math genius in 1998, expressed in the low-budget film “Pi” – an American experimental thriller by Darren Aronofsky. Data is generated by all of us, at all times. Machines generate data and this is often available in different forms: texts, numbers, images, position and much more. Newly combined and visualised, it leads us to the above-mentioned patterns, doesn’t it?D

Such patterns are ubiquitous and serve as the basis for decision-making in everyday life. Let’s take a weather app: Based on a huge amount of data, such as air pressure and temperature, the app presents this data in a user-friendly way so that the user can make decisions, for example regarding a planned two-day mountain trek. In addition, data on social media also helps, where people who are currently in the same area post reviews in real time. Data not only helps us make decisions, it can also relieve us from having to make decisions, thus increasing our comfort. One example is automatic speed control for cars. This adjusts the speed for slow-moving vehicles. The car is therefore already making decisions without the driver having to get involved.

Concern about data protection is increasingly intense. With new provisions such as the European General Data Protection Regulation (GDPR) that entered into force in May 2018, the EU intends to regulate the use and handling of personal data. Switzerland is following up with a corresponding regulation. Data and data protection are therefore on everyone’s mind.H

Apart from employee data and possible customer data, company-owned data can actually be used safely. It should even be used and processed in the company’s own interests. Today, however, data sets are more likely to be seen as a cost. They grow and they have to be stored laboriously. Storage costs notoriously need to be optimised in a company. And with digitalisation, the amount of data is increasing even faster; and with it, its hidden value is also increasing!

That’s why it’s time to go in search of these unknown unknowns and uncover the value! Companies need to ask themselves how they can use their data to serve their business. Or even generate new value and innovation. Given the extreme amount of data, of course, this is not an easy task. It can, though, be supported by tools and structured approaches, in the sense of Design Thinking.

Interdisciplinary teams with different perspectives on a topic, together with the company, can promote creative processes and innovation. You can organise things so that a company’s existing data can be converted into value. Bringing together and visualising different data sources in short and rapid iterations helps to identify patterns. These patterns lead to new insights and trigger new inquisitiveness. You get inquisitive about carrying out further analyses and to bring in new data sources. And suddenly new patterns are emerging! So we get back to the initial quote: “If the numbers (data) of any system are displayed graphically, they create patterns.” Patterns that can provide the company with valuable insights and, once again, new directions for action.

“The best course of action is a combination of technology and innovation processes”

Data-driven business models are becoming increasingly important. However, Swiss companies are still making insufficient use of this potential. Martin Gerber, Big Data and Analytics Consultant for the Data Mavericks at Acceleris, explains what matters to SMEs in the interview below.

In your technical contribution, you emphasise the value of data. How far away are Swiss companies from being involved in the exploitation of their data sources?

I am convinced that there is still huge potential in all sectors. Based on existing and daily generated data, completely new statements can be made with other data sources, for example about the quality of a product being produced. This data can be generated in conjunction with customer satisfaction, which enables the next steps for its improvement. Even better: You can use this ability to derive data-driven and automated forward-looking recommendations for action.

What kind of approach would you advise companies to implement in order to take advantage of existing data and thus be able to offer new services?

That is a very important point! It is not a question of becoming a “data-driven org” overnight through huge projects. The appropriate approach involves a combination of technology and creative innovation processes. Technology should help to process and visualise large amounts of data, regardless of its source and format, in a simple and fast manner. In order to uncover the greatest value of the data for the business, great effectiveness is reached through a creative process based on design thinking, by a team which should be as interdisciplinary as possible! Customer value-oriented, the team iteratively develops results which can provide the company with new insights in small steps, at the same time driving innovation and uncovering new, hidden clues.

What tools or skills do you need?

As regards the technical aspects, tools which can easily search for patterns and then display these results almost in real time are required. As regards skills, industry-specific knowledge is very important as it leads to the goal faster. The interdisciplinary team complements such specific knowledge with further perspectives, making use of collective intelligence. The key is also the correct setting: an agile process, led by great inquisitiveness and openness!

Can you give an example which illustrates which additional value companies can draw from their data?

Take a Swiss SME which produces consumer goods in Switzerland. The quality of the products is at the top of their priority list. These are checked through complex testing procedures prior to delivery to ensure marketability.

The stored data can be reused if necessary. The value is now in the test results’ sum. The analysis can provide a new picture of the variance of quality, such as which tests met the requirements and which were below the required guide values. An analysis of these results, for example in correlation with other data sources such as room temperature, state of the machines, specific product features or even with weather data for this location at the appropriate time, leads to new findings. This way, measures to improve quality can be implemented in a more targeted manner. In this case, only accessible company data and publicly available information were processed.

To the Data Mavericks blog.

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