How important is good data analysis
Data analysis: why it is becoming increasingly important for companies
“You know nothing, John Snow!” Is a catchphrase on the popular TV series Game of Thrones. Today more than ever, the discipline of data analysis proves that John Snow knew a lot.
The “real” John Snow is considered to be one of the pioneers of systematic data analysis. In the mid-19th century, the British doctor questioned the prevailing view that cholera was transmitted through the air. During the waves of illness, he systematically collected data, located it using a London road map, and thus recognized that all patients lived in the vicinity of a specific water pump. After this was shut down, the cholera outbreak was massively contained.
Today the buzzwords data analytics, data science and big data dominate the media - with good reason. With every transaction, booking and use of services, we generate data that can now be used better than ever. Data analytics makes it possible to combine, analyze and evaluate very large amounts of data from different sources.
This digitization of data analysis began with the spread of computers and the associated increase in computing power, and made another big leap with the introduction of SQL in the 1980s.
The hardware and software has continued to increase in performance since then. The Internet through the multitude of (freely) available sources and the expansion of digitization to more and more areas of life have also contributed to the fact that we now have an almost infinite amount of data.
Citizen data scientists have more power
When it comes to data analysis, the picture is still very much characterized by complicated evaluation tools and a few highly specialized data workers. These specialists are busy every day looking for the right data, merging and evaluating it, drawing the right conclusions from the volume of data and presenting them to the decision-makers in the company.
Many large companies optimize their day-to-day business with the help of data. The German sports brand Adidas, for example, examines sales data for products such as model, color and size, takes into account the surfing behavior of users and marketing activities and uses this to determine a data-based forecast. In this way, the company optimizes its cooperation with suppliers as well as its own production and marketing measures in order to react agilely to increasing or decreasing demand and thus save costs and time.
However, since more and more data is being generated in more and more areas that contain knowledge potential, if only interpreted correctly, the data specialists quickly reach their time limits. In addition, not every company has the capacity to employ highly sought-after analysts.
A solution for this is provided by self-service offers - intuitively applicable software with which data from various sources and formats can be merged and individually tailored workflows can be created easily and flexibly without the need for time-consuming programming.
These employees, who evaluate data but do not program it themselves, are called Citizen Data Scientists. Although you do not have a data science or IT background, you are nevertheless familiar with the basic functions and models for data evaluation, carry out analyzes yourself, make predictions and data-based decisions. This is becoming more and more important in a globalized, connected world - in order to open up markets, optimize processes and improve products and customer loyalty.
For many companies, Citizen Data Scientists offer an optimal solution for carrying out data analysis and not leaving the potential unused. For highly specialized tasks, professional data scientists and data analysts will also be needed in the future. However, citizen data scientists can provide active support in many application areas - after all, you don't have to be a professional car mechanic to drive a car.
Data analysis is becoming more and more important - let's get ready for change
In addition to the question of qualified personnel, companies now have to face another challenge: the data jungle. The multitude of data and data sources is both a blessing and a curse. Big data and increased storage capacity can secure more and more information, but the trick is to understand which data is suitable for which type of data analysis - and which is not. In addition, the databases must be properly maintained and protected and, above all, used in accordance with data protection guidelines.
So what does the future look like with the increasing importance of data analysis?
First of all, companies need to prepare for change. Old processes are being broken through and increasingly being replaced by automated ones in day-to-day business. Employees have to learn to trust them and make the right decisions thanks to the analysis reports.
Once these initial growing pains are over, companies will be able to work more efficiently and effectively. In any case, one advantage is that enormous amounts of data are already buried deep in the databases and structures of companies that are just waiting to be lifted, cleaned up and used. Now all that remains is to activate the employees, introduce them to the world of data analysis and exploit the analysis potential with both hands.
The fact is that in the future it is unlikely that any company will be able to avoid data analysis in order to be and remain competitive. In the 21st century, the motto is: The more employees are able to analyze data, the better.
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