We live in challenging times, with conflicts affecting almost all areas of society and economy. The impact of these crises on the Business Intelligence world is also reflected in the BI and data trends for 2023. We take a look at some of the most important developments in the data sector and venture a forecast for the near future.
The world is currently being really buffeted by the geopolitical, social and economic situation. Already during the pandemic, many companies had to upgrade technologically in order to maintain their operations at all. But recently, numerous other conflicts have swept through society and the economy – with no end in sight. This global circumstance is finally having an impact on the Business Intelligence sector.
Data and analytics professionals face increased fragmentation resulting in, for example, distributed data centers, supply chain disruptions, constant innovation and increased skills shortages. It is becoming increasingly important to stay ahead of the curve, to be prepared for future crises and to be able to react appropriately at any time.
Arming against supply chain disruptions with real-time data and automation
The pandemic and Ukraine conflict in particular have caused huge supply chain disruptions and bottlenecks in the recent past. In the event of such disruptions, it is always important to take the necessary measures immediately to prevent worse. This should be done based on contingency plans and ideally preventively. Forecasts and consideration of possible future scenarios, for example, are particularly helpful in this respect.
In order to be prepared for further crises, companies should therefore place greater emphasis on the analysis of real-time data and modern functions such as alerting and application automation in the future. In this way, acute conflicts can be responded to as quickly as possible, and the appropriate measures can be initiated in good time.
No-code and low-code continue to gain ground
In recent years, more and more low-code tools have been made available for application development, enabling even non-technical users to create their own apps. In this way, a much larger group of people in the company can benefit from data and the resulting insights.
According to experts, round about 60% of new applications will be developed on no-code or low-code platforms in the near future, compared to only around 30% in the past. But the use of low-code should not exclude high-code. On the contrary, its use should be optimized in such a way that it is aimed at maximum productivity and optimum business results. Existing competencies should never be disregarded in the process.
Man vs. machine
Artificial intelligence has long found its way into many areas of life and business – including data analysis. Natural language models here are now trained using machine learning with huge amounts of data and are becoming increasingly sophisticated. In the field of data and analysis, the possibilities for using natural language will have an enormous impact on the retrieval, interpretation, and presentation of information in the future. Users will not only find the data they are looking for, but also new, extensive information that they would not have thought of. In the coming years, the artificial intelligence used in this process, especially using natural language, will become even more established in data analysis.
Data storytelling must lead to action
For data analytics to be as efficient as possible, it is important that the right information is always made available to the right person at the right time. One magic word here is data storytelling. This is considered to be the best way to prepare data for users in a meaningful way. After all, stories – better than data alone – can appeal to people emotionally and thus encourage them to act. Data storytelling must therefore be more than just diagrams and dashboards. Rather, it must go hand in hand with appropriate measures. In today’s fragmented data world, this is often not so easy to implement.
For data storytelling to be successful, it is therefore also advisable to use alerting, reporting and automation to ensure that the stories flow into the workflows at the right time. According to analysts, data stories will be the most common way to use analytics in the next 2-3 years – much of it will be generated automatically using augmented analytics techniques.
New opportunities through market consolidation
Despite the fragmentation mentioned above, a consolidation of previously separate systems (e.g. for data integration and management, analytics and AI, visualization, data science and automation) can also currently be observed, which simplifies collaboration between the producers and users of data.
According to experts, the market for stand-alone data preparation will disappear more and more and the corresponding functionalities will be increasingly embedded in modern tools for data management, analytics and data science.
In addition to the aforementioned trends and developments, the BI and data world is of course also making steady progress in other areas. Thus, topics such as data governance or data management in general also remain current and relevant.