Are AI and ML the end of business intelligence ?
Introduction
In today’s data-driven world, analytics plays a vital role in decision-making. The ability to collect and process vast amounts of data has led to the development of various analytics techniques that allow businesses to gain insights and make better decisions. Four main types of analytics emerged: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics describes what has happened in the past, providing a summary of historical data. This type of analytics is often used to create reports and visualizations that show the performance of a business or organization over time. For example, a retail company may use descriptive analytics to see how sales have changed over time or to identify patterns in customer behavior.
Diagnostic analytics investigates specific issues or problems in order to understand their causes. This type of analytics is often used to troubleshoot problems or to identify areas that need improvement. For example, a manufacturing company may use diagnostic analytics to understand why a particular machine is failing or to identify the root cause of a quality control issue.
Predictive analytics uses historical data and statistical techniques to make predictions about future events. This type of analytics is often used to identify trends, patterns, and relationships in data that can be used to make predictions about future performance. For example, a financial institution may use predictive analytics to identify potential fraud or to predict how likely it is that a loan will be repaid.
Prescriptive analytics uses data, statistical techniques, and optimization algorithms to recommend actions that will achieve specific goals. This type of analytics is often used to identify the best course of action for a business or organization. For example, a logistics company may use prescriptive analytics to determine the most efficient route for a delivery truck or to identify the best inventory levels to maintain to minimize costs and maximize profits.
Traditional Business Intelligence
Business Intelligence has been around since the nineties, focusing on descriptive and diagnostic analytics, providing insights on the past performance of a business.
Data Warehouse
The core component of a traditional Business Intelligence solution is Data Warehouse.
It receives data from various sources (e.g. ERP, CRM and other transactional processing applications), applies ETL (Extract, Transform, Load) process and then stores the data in a multidimensional format, such as a star or snowflake schema, which allows for easy querying and analysis.
OLAP
On top of the Data Warehouse sits OLAP (Online Analytical Processing) Server. It provides multidimensional analysis capabilities, allowing users to drill down, roll up, and slice and dice the data.
The analysis results are presented to users using reporting tools such as Crystal Reports or even an Excel spreadsheet.
The full cycle of processing requires lots of processing power and time, therefore it usually runs in batch mode. Such batch generation is not a problem for descriptive and diagnostic analytics as they are integrated into a longer cycle of quarterly and yearly strategic decision making which does not need a quick reaction to incoming data.
The main goal of traditional BI is to provide insights into what has happened in the past, which can be used to make informed decisions about the present. However, in today’s fast-paced business environment, insights based on past data alone may not be sufficient to make strategic decisions. This is where the integration of Artificial Intelligence and Machine Learning comes in.
Machine Learning and Artificial Intelligence
Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the way businesses use analytics. ML is a subset of AI that enables machines to learn from data without being explicitly programmed. AI, on the other hand, goes beyond ML by incorporating the ability to reason, plan, and make decisions.
A shift in the way businesses use analytics
The integration of AI and ML with Business Intelligence has led to a shift in the way businesses use analytics. With the help of ML and AI, businesses can now use predictive and prescriptive analytics to focus on the future and make predictions about future performance.
One of the main benefits of using predictive and prescriptive analytics is the ability to anticipate future trends and make proactive decisions. This can help businesses to identify potential risks and opportunities, and take appropriate actions to mitigate risks and capitalize on opportunities.
For example, a retail company may use predictive analytics to forecast sales trends, and use this information to adjust inventory levels, pricing, and marketing strategies.
The ability to process large amounts of data in real-time
Another benefit of using AI and ML in BI is the ability to process and analyze large amounts of data in real time. This is particularly important in industries such as finance, healthcare, and manufacturing, where large amounts of data are generated every day. With the help of AI and ML, businesses can process and analyze this data in real time, providing insights that were previously not possible.
A profound impact on the architecture of Business Intelligence solutions
On-boarding of AI and ML has a profound impact on the architecture of Business Intelligence solutions:
- Instead of a data warehouse, modern BI solutions have a data lake at its core which is more suitable for storing huge volumes of data collected by business and used as an input for business decisions
- If traditional BI used to work with tabular data, AI/ML opens a way to process and analyze unstructured data, like text, images, and audio, which require new MLOps pipelines, powerful computational resources and specialized algorithms to train and operate on the data. This often requires the use of specialized hardware such as GPU clusters, and the integration of new software frameworks such as TensorFlow and PyTorch.
- Furthermore, the integration of AI and ML also requires a more robust security and governance process, as the data used to train AI and ML models can be sensitive and confidential. This requires the implementation of strict data access controls, data encryption and protection, and monitoring and audit capabilities.
A shift in the way businesses use BI
The development of new tools and applications such as real-time dashboards and control towers has also led to a shift in the way businesses use BI. Real-time dashboards provide a comprehensive view of the data, allowing businesses to monitor key performance indicators (KPIs) in real time.
Control towers, on the other hand, provide an overview of the entire supply chain, allowing businesses to monitor and optimize all aspects of their operations. Unlike traditional analytical reports, such applications deliver real-time actionable insights.
For example, if ML-based analytics predicts an imminent failure of industrial equipment, it can raise an alert to the maintenance personnel or even trigger automatic actions, like shutting down the equipment and creating a service request.
The real-time aspect becomes crucial in control and optimization scenarios that require a continuous cycle of making predictions about the future, prescribing corrective actions (to maintain optimal performance), and then acting according to these prescriptions. It completely changes the static nature of traditional Business Intelligence.
Does it mean the end of Business Intelligence?
Business Intelligence is still an important aspect of data-driven decision-making, but it has evolved to integrate with new technologies such as ML and AI. The new generation of BI tools like Power BI, Tableau, and Qlik have emerged, which allow for the integration of real-time data and advanced analytics into dashboards. These tools provide a more comprehensive and actionable view of the data, helping businesses make informed decisions.
This new generation of BI tools also offers advanced visualization and data exploration capabilities, making it easier for users to uncover hidden insights and patterns in the data. Additionally, they also offer collaboration and sharing features, allowing teams to work together and share insights, regardless of their location.
Another major advantage of these new BI tools is their ease of use. These tools are designed for non-technical users, allowing them to create and customize their own dashboards and reports without the need for IT support. This has significantly reduced the time and resources required for the deployment of BI solutions.
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Conclusion
In conclusion, the integration of AI and ML with Business Intelligence has led to a shift in the way businesses use analytics. From focusing on the past to focusing on the future, businesses can now use predictive and prescriptive analytics to make data-driven decisions in real-time. The future of BI looks promising, with the integration of advanced analytics and the emergence of new BI tools.
But that’s not over! The next generation of Business Intelligence is expected to move from dashboards to story-telling, using bots and natural language processing technologies like Chat GPT to present insights in a more engaging and easily understandable format. This will allow businesses to easily communicate their data-driven insights to their stakeholders, regardless of their technical expertise.
Furthermore, the integration of IoT and big data analytics is also expected to play a major role in the future of Business Intelligence, allowing businesses to collect and process data from a wide range of sources in real-time, and providing insights that were previously not possible.
Business Intelligence, as a decision-support tool, is not going to go away. It will rather evolve incorporating new technologies and better integrating into the decision making process. With hyperautomation on the horizon, more and more decisions will be shifted from humans to intelligent agents. However, whatever form the agent will have, it will always need Business Intelligence to rely on!