Using AI and Business Data Analytics to become a data driven enterprise.

Data competence and technical foundation are important.

Every company has data - and lots of it. Often, however, the necessary expertise is lacking to draw concrete added value for the business from the data or to achieve better deals through more and qualified data. For the evaluation and analysis of the data, employees need the corresponding know-how and the appropriate technical infrastructure. Further training and a modern data infrastructure are therefore two important foundations for the correct analysis of existing and new data. Only then can companies use data to improve their decision-making processes and draw well-founded conclusions.

Data competence and technical foundation are important.

Every company has data - and lots of it. Often, however, the necessary expertise is lacking to draw concrete added value for the business from the data or to achieve better deals through more and qualified data. For the evaluation and analysis of the data, employees need the corresponding know-how and the appropriate technical infrastructure. Further training and a modern data infrastructure are therefore two important foundations for the correct analysis of existing and new data. Only then can companies use data to improve their decision-making processes and draw well-founded conclusions.

• With descriptive analytics, companies depict the past. Reports with current trends, developments or other insights from the company are thus obtained from the raw data.
• Diagnostic Analytics goes one step further and investigates why something happened. It identifies the factors that led to certain trends or events. This enables companies to understand cause-effect relationships and improve decision-making.
• Predictive analytics uses data and modern technologies such as artificial intelligence and machine learning to predict future trends and events. Companies can use it to make forecasts and informed decisions. 
• Prescriptive analytics goes one step further and provides recommendations for action to achieve goals. This combines predictions with optimisation algorithms to determine the best possible course of action.

Examples from practice.

Depending on the issue, the analysis methods described above are used. An example: A real estate investment company initially wanted to process the existing data for operational decision-making and thus become a data-driven enterprise. Employees were trained and a single source of truth was created - all data came from one source, above all always up-to-date and cleansed. With access to the data and with the defined key performance indicators, asset managers can now analyse and optimise their portfolio. Descriptive and diagnostic analytics thus support management at C-level and in the specialist departments in making operational and strategic decisions.

The predictive and prescriptive analytics of business analytics, on the other hand, support companies in improving their business processes and exploiting new opportunities, as another example shows: A construction company wanted to better deploy its machines on the various construction sites and thus better utilise their capacity. It also wanted to improve its IT service management (ITSM) by predicting when to expect an increased volume of IT tickets in order to analyse the required team size. Both could be identified through business analytics and their predictions. So using such analytics not only significantly improves business processes, but also reduces costs at the same time, increasing opportunities and profits for companies.

Become a data-driven enterprise now.

If the right data sets are available and analysed with the right method, the data helps to better understand customer behaviour, analyse market trends, optimise processes, evaluate employee performance and much more. Companies should therefore consider data as a strategic resource and put it at the centre of their decisions and activities. By establishing a modern data infrastructure and developing the data competence of employees, companies can take advantage of the benefits of a "data-driven enterprise" and increase their success.

The Bechtle Data & Analytics business unit will be happy to provide you with comprehensive advice on this - from data storage and integration to data analysis, data visualisation and the development of a data strategy, and will take over these processes completely for you if you wish.