Data: important but largely untouched

The importance of data is constantly increasing, especially with the emergence of new technologies and Industry 4.0. However, companies do not use all the data, which means that ‘dark data’ remains untouched. Nevertheless, the benefits of workable data are great. Among other things, it can make for a more efficient way of working. The predictive nature of data should also not be overlooked.

The fourth component in the PPT model

Every company knows the PPT model, which refers to the combination of people, processes and technology. The three components are connected to each other and cannot function without each other. Employees and processes without technology create frustration and inefficiency. Processes and technology without employees create alienation, while people and technology without processes lead to automated chaos and substandard customer service.

With the rise of Industry 4.0, data appears as the fourth component in this model. Your company will not function optimally without a clear data strategy. In the following sections, we will discuss the power of data and how it makes better business operations possible.

What do companies do with the data in their organisation?

Companies can learn a lot from data, but the reality is that not all data is used. There is a large amount of unused data or ‘dark data’ at present. A study by Splunk, consisting of more than 1300 business leaders from different parts of the world, shows that more than half (55%) of the data in a company is unusable. A remarkable fact is that the French respondents in the survey said that they do not use 42% of the data. This is in contrast to the Chinese respondents who claim to only have 15% dark data.

Dark data is a global phenomenon

The main reason that companies do not use data is the fact that they do not have a tool to record and analyse the data. Furthermore, the data would also be incomplete and the abundance of data would make it difficult for companies to use it. Not using data is disadvantageous, because storing this so-called ‘dark data’ costs money. As mentioned earlier, data is becoming increasingly important. Below you will find three main advantages regarding the use of data.

Three major advantages when data becomes workable

Advantage 1: Work more efficiently through insights

Efficiency is an important objective in every company. Business processes are adapted in various ways to work as optimally as possible. One of those ways is by looking at the company’s data. The industry is currently undergoing a real digital transformation towards Industry 4.0. Data is becoming increasingly important in this respect. It has the power to reveal various inefficient processes. If you respond to data, you can adapt business processes so that they run more efficiently. Just think of the time to market that can run faster in this way, or your turnover that increases as a result, etc.

Advantage 2: Data can be predictive

Furthermore, data and Industry 4.0 also ensure that you can be more predictive as a company. Imagine if one of your machines or pieces of equipment no longer works. You do not know what the problem is so you have a look at it. Once you know the cause, you contact a repairman and see where you can order the damaged part. By the time you have gone through all these steps and the repairman has visited, you will quickly lose a few days. This costs your company money – money that is desperately needed. By equipping your machines with sensors that store and transmit all the vital machine data at all times, you know in advance when your machine will break down. This allows you to call a repairman in advance and order the necessary parts. This also gives data a predictive character. It may therefore be important to invest more time and energy in generating and analysing data.

Advantage 3: Increased transparency in the company

The transparency of your company can also be increased by means of data. By gaining insight into all processes, you will be able to find out all relevant information. That way, you may be able to solve anomalies. When a machine equipped with sensors reports that there are 300 items rolling off the conveyor belt per hour, but there are not 300 items in stock, this provides food for thought. Maybe some items are rejected? Or are the stock figures wrong? You can solve these issues by analysing data.

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