How will BI tools evolve to meet business needs?

The major shift will happen when 'self-service BI' tools start providing the promised 'self-service' to daily decision-makers. So far, most of them are targeted at 'analytics producers'—developers and analysts—but not common business users. User experience for this particular category of users needs to be improved dramatically until the disappointment hasn't turned into rejection.

The BI tools of today are mostly data aggregation and visualization tools. The intelligence part is still missing for the most. They need to evolve first to generative actionable insight, not leaving it to the end user to derive it. Then their is a need for these tools to provide real time intelligence in solving problems, resulting in reduce MTTR. Next the big shift is required in making them proactive by predicting the problem before they may occur and suggesting how they can be prevented. The final stage should be an autonomous system which takes care of end to end tasks, without any human intervention. The day these tools became intelligent and self reliant for handling business like an SME, they would full fill their purpose.

BI tools are expected to evolve in several ways to better meet evolving business needs. Firstly, they will become more user-friendly and intuitive, with advanced self-service capabilities allowing non-technical users to access and analyze data independently. Secondly, there will be increased integration with artificial intelligence and machine learning algorithms, enabling BI tools to provide predictive analytics, automated insights, and advanced data visualization techniques. Thirdly, there will be greater emphasis on real-time analytics, allowing businesses to make faster decisions based on the most current data.

Overall, self-service BI tools are evolving to meet the growing demand for data-driven decision-making within organizations. By combining user-friendly interfaces with advanced analytics capabilities, these tools empower users at all levels to harness the power of data to drive business success. I am especially excited for advanced AI tools. AI-driven features such as predictive analytics, automated data cleansing, and anomaly detection enhance the capabilities of self-service BI tools. By automating repetitive tasks and offering intelligent recommendations, these tools will streamline the data analysis process and enable users to focus on deriving insights rather than data preparation.

One vision for the future of data analysis is the ability to 'interview' your data using natural language processing. This involves building a specialized model that can access your business intelligence (BI) data, understand complex queries, and provide answers based on solid evidence. This can be further enhanced by integrating BI tools or using simple programs like Excel or PowerPoint to generate powerful yet easy-to-understand visuals

Augmented Business Intelligence (BI) is set to evolve to: - Automate the data analysis processes to make BI tools more intuitive and capable of generating insights faster. - Design BI tools for easy integration into existing business applications and workflows to reduce the learning curve and foster wider adoption. - Utilize metadata and real data to provide rich, contextual insights, coupled with predictive analytics for more actionable information. - Enhance data cleansing and preparation features to improve data quality and make complex analyses accessible to non-technical users.

Probably the next evolution of BI Tools is toward 3 main pillars : - Lower technology barrier for BI Users - Enhance the value realization - Improve forward looking 1) Lower the technology barrier by the use of AI and LLM capabilities to allow users to navigate data using natural language, sharing prompt, ask question on data 2) Improve the value realization by imbedding AI/ML so the users can already focus on trend, anomalies, outliers and uncover insights more rapidly 3) Promote forward looking , traditionally BI is about past Data, business event that have occurred, the inclusion of prescriptive and predictive model will have users future looking of business outcome, possible recommending action to take for the desire output

I am excited to see more data integration with other platforms (such as social media). By connecting BI capabilities with communication channels that employees already use, these tools make it easier for teams to share insights, discuss findings, and collaborate on data-driven initiatives without switching between multiple platforms. When employees across departments can easily access and discuss data, it promotes transparency, accountability, and alignment towards common business goals.

Embedded BI tools represent a pivotal shift in data accessibility, seamlessly integrating analytics into familiar applications like CRM, ERP, or e-commerce platforms. This convergence enhances user experiences by providing contextual, real-time insights. For instance, in CRM systems, sales teams can access customer data alongside sales figures, streamlining decision-making. Moreover, these tools empower proactive actions, like triggering alerts for low inventory in an ERP system, optimizing supply chain operations. By embedding BI into everyday workflows, organizations unlock the full potential of their data, fostering informed decisions and operational efficiency.

Die wichtigste Frage zu Embedded BI ist: Wer besitzt die Daten? Sind es Benutzer, die sich berechtigen wollen, wie es z.B. bei Power BI Pro der Fall ist? Dann ist Embedded BI nicht die richtige Antwort. Lautet die Antwort: Die Anwendung, in der mein Dashboard eingebunden ist, dann is Embedded BI genau das Richtige. Die Anwendung, z.B. Sales Force, steuert, wer Zugriff auf die Analysen und Berichte hat. Die Anwendung filtert kontextbezogen Dashboards vor. Ich bekomme alle Sales-Daten zu genau einem Kunden, oder die Umsatzzahlen gefiltert auf mich als Vertriebsmitarbeiter. Tools wie zum Beispiel Microsoft Power BI Embedded werden zu einem Bestandteil der L?sung. Der Endanwender kann zwischen App und Embedded BI gar nicht unterscheiden.

Mobile BI tools empower users to access data and analytics anytime, anywhere, and on any device, enabling them to make informed decisions on the go and stay productive even when away from their desks. By providing seamless access to critical information and enabling interactive analysis on mobile devices, these tools enhance the agility and responsiveness of organizations in today's fast-paced business environment. I am especially excited to see the advance of Location-Based Services to provide contextually relevant information based on the user's location. Combine this with push notifications to keep users informed and engaged and prompt timely actions based on real-time updates and alerts.