While large providers such as AWS offer various solutions for data processing and analysis, the question for many companies is how they can use these technologies effectively.
Current challenges in data analytics
Despite the increasing importance of data, there are various challenges:
- Complex systems: Many companies do not have the specialized knowledge to use modern data analytics tools efficiently.
- Data quality & integration: Data comes from different sources and must first be processed before it can be used sensibly.
- Gain in knowledge: Large amounts of data alone are of little value - only the right question leads to relevant analyses.
AI can play a decisive role here.
How can artificial intelligence solve these challenges?
AI-supported analyses make it possible to recognize patterns in data and automate processes. Possible areas of application are
- Automated KPI analysesthat identify relevant key figures and generate regular reports.
- Intelligent assistance systemsthat answer specific questions, e.g. on the performance of products or the efficiency of certain processes.
- Decision-making aidswhich provide data-based suggestions and support companies in developing their strategy.
The quality of the results depends heavily on how well the underlying data is prepared.
The role of real-time analysis
In addition to automation through artificial intelligence, real-time analysis is also becoming increasingly important. Companies want to evaluate data from various sources in the shortest possible time in order to be able to react more quickly to market changes.
As it is often impractical to process all raw data in real time, many modern systems rely on so-called data products - specifically prepared subsets of data that are used specifically for certain issues.
Structured data preparation as a basis
Automated and targeted processing is needed to make data usable efficiently. A structured approach can help here:
- Automated processes reduce the manual effort required for data preparation.
- Focused data usage ensures that companies work with the relevant data instead of sifting through large raw data sets.
- Flexibility in the analysis facilitates integration into existing processes.
Conclusion
Data analytics is constantly evolving. AI and real-time analysis offer great opportunities, but require a well-structured database. Companies are faced with the challenge of preparing their data in such a way that it can be used in a targeted and efficient manner.
At doubleSlash, we are working intensively on the question of how companies can meaningfully integrate AI-supported analyses into their data strategy. In the coming weeks, we will be presenting some tools that are already helping to efficiently implement automated KPI analyses and intelligent data preparation.
One example of such a solution is our Data Factorywhich supports companies in structuring their data in a targeted manner and making it usable for analyses. Find out more here:
