If a company wants to be successful as part of digital transformation, there is no way around data and analytics. Even though there are many buzzwords buzzing around and corporates think that they have been using a BI tool for a long time, in Austria in particular, people are usually still a long way from a data-driven corporate culture.
However, it is precisely this fact that means that now is an ideal time to recognize data analytics as a decisive competitive factor.
According to a Gartner survey, less than 50% of corporates currently mention data as an essential strategy to promote and communicate corporate values. However, this will change rapidly by 2022: by then, according to the forecast, 90% of all companies will recognize analytics as a decisive success factor and treat it as such.
Leading companies are therefore now positioning themselves with analytics so that they can make faster and better decisions based on data and also provide a glimpse of the future through forecasts.
BI, also known as 'business analytics, 'organizes and optimizes the collection and structuring of company-relevant data. This process improves data quality and removes the typical data silos, often consisting of an unmanageable number of Excel files, where apples are often compared with oranges in board meetings and there is confusion and uncertainty.
In addition to the central and structured provision of data ('data governance”), BI offers a holistic view of current events, insights into whether business goals can (can) be achieved and whether there are business-critical outliers somewhere. It is the central tool for people who have to make decisions on an ongoing basis, i.e. typically management and corporate governance.
Business intelligence therefore tells you the current status, the trend and what you need to pay attention to.
Even though AI ('artificial intelligence') is still in an early development phase, there are gradually more areas of application where artificial intelligence can also be used to support the corporate sector. This now goes beyond conventional image recognition for quality assurance in the manufacturing industry.
For example, modern analytics platforms such as Tableau offer automatic recognition of 'outliers' in charts and the software supports users with AI algorithms that provide clues as to how outliers occur. Natural language processing (NLP) also supports the analysis of data and the creation of dashboards to simplify access to data and the resulting insights.
Artificial intelligence can therefore be seen as a valuable extension of a modern business intelligence strategy in companies. It will never be able to replace entrepreneurs and managers, but anyone who makes use of AI-enriched BI will be the decisive step ahead of the competition.
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