AI in business intelligence: Between hype and reality

AI in business intelligence: Between hype and reality

8 min read Jan 22, 2024

The transformative evolution of data analysis by artificial intelligence (AI) is bringing business intelligence (BI) into focus. The ongoing hype regarding AI, particularly as a result of ChatGPT, is not only highlighting the opportunities but also the critical issues relating to the realistic integration of AI into BI. This article discusses the challenges, opportunities and ethical issues resulting from this symbiosis.

The hype surrounding AI often leads to overblown expectations. Viewing AI as the ultimate solution to every challenge is resulting in an overabundance of ideas. In practice, however, their implementation is highly complex. Companies may emphasize the integration of AI into their software, but what’s involved is often little more than a rebranding of simple functions. This results in overblown expectations, often with a dearth of tangible real-life applications. The gap between ideas and practical applications is a major challenge.

In terms of BI, it is also true that not every application of AI makes sense. Companies are faced with the challenge of integrating AI in a targeted way and in the right areas in order to generate real added value. The potential of AI in BI lies in particular in the explorative area and in the use of natural language query. Another aspect of interest, which has not yet received sufficient consideration, is the development of automated machine learning (AutoML).

Natural language query: communicating with your own business data

Natural language query (NLQ) is playing a key role in the transformative evolution of data analysis by artificial intelligence (AI) in the field of business intelligence (BI). NLQ enables users to make data-related queries while the algorithm processes these queries in the background and generates precise responses, summaries or diagrams. The intensity of the interaction with business data depends on the precision of the queries submitted, the data quality and the input data.

The use of NLQ offers clear added value, particularly for users with no experience of programming or machine learning. Used correctly, it can offer a fast and efficient way of working with your own data. The new challenge, however, is in creating precise queries and validating the results. NLQ represents a significant step toward democratizing data access because it makes it possible for people outside the technical disciplines to interact with BI data actively and efficiently.

AutoML: untapped potential for data-driven decision-making

The integration of NLQ in BI is being driven by the parallel development of technologies such as AutoML. This technology makes it possible to use machine learning without any extensive programming expertise. Although the opportunities for using it in analytics and reporting are wide-ranging and its potential is enormous, only a handful of experts have addressed this field in any depth to date. Qlik is one company offering this AutoML function.

Thanks to AutoML, users of the analytics platform can conduct ML experiments, train models and make predictions for the future based on fully verifiable data with ease. The integration of AutoML into Qlik SaaS facilitates interactive analysis and scenario planning. The ability to generate, refine and explain models transparently and with ease illustrates the potential of AI in specific BI applications.

Using AutoML models makes data analysis and decision-making more efficient by drawing on historic data to gage probabilities for the future. This approach not only offers fascinating insights but can also make a significant contribution toward obtaining in-depth information from models and improving decision-making in various areas of application such as analytics and visual reporting.

A more intensive analysis and integration of this approach could help to enrich existing platforms with valuable information and further promote data usage within the company. Having said this, it is important to remain skeptical, particularly regarding the verification and falsification of responses. Companies should be aware that the use of AI is not only associated with opportunities, but also with risks that must be addressed proactively.

Furthermore, BI tools are an excellent way of visualizing and processing external results within the context of machine learning. This facilitates data exploration, thus creating the ideal basis for the analysis, interpretation and refining of results.

Machine learning products from BE-terna

BE-terna is one of Europe’s leading providers of specialist business software solutions for a range of industries. In the area of supply chain optimization, the company uses Qlik and Power BI Reports to help visualize forecasts for demand, stock levels and order proposals. Users have the option of adjusting this data as required and sharing it with external tools, such as ERP systems, for processing.

BE-terna also offers solutions that use business intelligence applications as user interfaces. These solutions cover various areas, such as the optimization of production planning, customer segmentation and the analysis of camera monitoring systems on the conveyors, including the automated identification of defects.

From model development to explainable AI

The seamless integration of AI into BI requires a comprehensive quality assurance approach. One major hurdle here is data quality. Poor quality or incomplete data may lead to models drawing erroneous conclusions or providing distorted insights. Furthermore, it is essential to consider the metadata. Metadata serves as the key to interpreting the underlying data by providing context, sources and structure. Without clear metadata, AI may find it difficult to properly understand the data and draw appropriate conclusions.

Users and stakeholders must be in a position to be able to understand and reproduce the way AI models work and how they lead to specific results. This need for transparency is not only important for ethical reasons but also for the acceptance of the generated results and user trust. The field dealing with these issues is known as “explainable AI.” Overall, the careful consideration of data quality, metadata and explainability underlines the complexity and wide range of challenges in the successful integration of AI into business intelligence. 

Manipulation of models and the importance of data literacy

One challenge that must not be underestimated in the context of AI in business intelligence is the potential for models to be manipulated. Particularly in an era of radical technological change where cybercrime is becoming more prevalent, companies not only need to consider the verification of their models but also proactively develop strategies for preventing manipulation.

The topic of data literacy is increasingly significant in this context. Data literacy describes the ability to not only interpret data, but also question and understand it. Only if we have an in-depth understanding of the data used by AI models can we detect and remedy any potential manipulation. Data literacy not only enables data controllers to understand the model’s results, but also to verify the integrity of the underlying data.

With regard to the breakneck pace of development in the field of AI, it is critical that companies train their employees in data literacy – not only as a proactive security measure to prevent manipulation but also in order to tap into the full potential of AI in the area of business intelligence. A data-literate workforce is not only better able to deal with the results of AI models, but also to maintain the integrity and authenticity of the data – a key aspect for the responsible use of AI in a corporate environment.

A look to the future: regulation, ethics and corporate responsibility

The promising advances made by artificial intelligence in business intelligence give cause for optimism with regard to the future. User-friendly BI tools that integrate AI are particularly impressive. Despite positive developments, companies are facing real challenges and ethical dilemmas. It is therefore essential to take a realistic view of past developments and future challenges.

The increased integration of artificial intelligence in the field of business intelligence sharpens the focus on issues relating to regulation and corporate responsibility. Clear ethical guidelines and transparent mechanisms are vital in order to strengthen public trust in the use of AI in BI. The development of guidelines for the ethical use of AI technologies is essential, and an open discussion with regard to ethical standards and corporate responsibility is imperative.

Overall, the reality of AI in business intelligence reveals a mixture of promising developments, practical challenges and ethical dilemmas. Despite the ongoing hype surrounding AI, it is important to make an objective analysis of the actual advances and challenges posed by this revolutionary technology. The future of AI in BI will largely be shaped by the extent to which companies can integrate the technology into their processes in a logical and responsible way.


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