The BI market is growing continuously, there are now thousands upon thousands of business intelligence and data analytics providers worldwide. Despite this oversupply, many companies still have a need for solutions that help them digitize their processes and analyze their data. What and which applications the terms “business intelligence” and “data analytics” include is still a mystery to many companies. For a long time now, it has not been acceptable to speak of just nice-looking pie charts.
Data analytics should not only be about simple data visualization, but also about “reading” company data correctly (more on this in my next blog). To do this, one must first determine whether the company is already collecting data that has not yet been converted into information. It is also about understanding the processes in the company, the industry or the customers. Only then is it possible to bring together the right data sources and prepare them in a targeted manner.
In my opinion, the following are important points for preparing a future in companies in which data serves as added value:
Start – the data preparation
The first question companies should ask themselves is: How can we use data and analytics to create new business opportunities? And how can the applications be integrated into my processes in a way that makes them easy and convenient to use?
The first point always concerns data preparation – an important pillar of Advanced Analytics. According to the management consultancy firm McKinsey, companies waste up to 70 percent of their data-cleansing efforts due to the wrong approach. Often, this waste is caused by the fact that in the first step all data is cleaned up across the board without having previously defined a strategy. Before the cleansing, it should be clarified which use cases a company has or could have and whether the necessary data is available. Linking the data to real use cases in the company is the foundation for the profitable use of analytics.
Creating convenient, efficient solutions – data analytics
This brings me to my second point: the application of data analytics systems in daily work. Efficient solutions do not require experts in the company, all important information should be accessible to all relevant employees if possible. After all, one of the functions of business intelligence and data analytics applications is to present data in an understandable way, especially to employees who do not regularly work with certain data. For this function, the above-mentioned pie charts then also gain in importance again for many users, but with the focus on the decisive information and a clear presentation. After all, it is often the case that the core issue is actually the use of data analytics or reports for decision-making. Reporting functions help here to distribute the relevant key figures to the right people.
The icing on the cake – data science
However, if all data is already optimally prepared, the data analytics solution is set up correctly and every employee works efficiently with the application, there is still untapped potential. This is where the need goes beyond classic data analytics. “The electric light did not come from the continuous improvement of candles,” said Oren Harari, professor of economics at the University of San Francisco. And in my view, this also applies to data analytics: You shouldn’t just rely on paths that have already been taken.
My colleagues and I experiment a lot with data sets – I think it makes sense to test new applications regularly. Especially in highly competitive industries, this can lead to completely new competitive advantages. Applications such as artificial intelligence offer undreamt-of potential and make working with data easier – and above all faster.
The digital transformation brings challenges to companies – but also many opportunities. Companies should seize these opportunities. The correct application of existing data plays a major role here. It is important to implement the core functions of data analytics correctly and thus enable a profitable application in the company as well as a correct interpretation of the data. Only in this way can data be transformed into added value for the company. To get the most out of this data, one should not be afraid to break new ground and test trend themes in data analytics.