Integrating AI for Improved Business Intelligence

Understanding the Integration

The evolution of artificial intelligence has emerged as a game-changer in the realm of business intelligence. How? What exactly did AI improve in BI practices and solutions? How to use AI for business intelligence? First, let’s have a look at AI and BI separately. What is AI and BI? The terms Artificial Intelligence (AI) and Business Intelligence (BI) are not interchangeable.

According to E. Foley and M. G. Guillemette BI refers to the technologies, processes, and tools that organizations use to collect, analyze, and present business data to support decision-making processes. There are many benefits of the business intelligence, but the primary goal is to transform raw data into meaningful insights, helping businesses make informed and strategic decisions.

Key Components of BI

Here are the key components of BI in detail:

1. Data Collection: Gathering data from various sources such as internal databases, external databases, spreadsheets, and more. Extracting, transforming, and loading (ETL) processes to ensure data consistency and integrity.
2. Data Warehousing: Storing and organizing data in a centralized repository (data warehouse) for easier access and analysis and creating data models that facilitate efficient querying and reporting.
3. Data Analysis: Employing analytical tools and techniques to explore and analyze data. After generating reports, dashboards, and visualizations to communicate insights effectively.
4. Reporting: Creating and distributing reports highlighting key performance indicators (KPIs) and trends. Enabling stakeholders to monitor business performance and make data-driven decisions.
5. Querying and Ad Hoc Analysis: Allowing users to interact with data through queries and ad hoc analysis to gain specific insights. This component also allows supporting self-service BI, where non-technical users can access and analyze data independently.
6. Performance Monitoring: Tracking and monitoring key metrics and performance indicators in real-time and setting up alerts for anomalies or deviations from established benchmarks.
7. Business Process Improvement: Identifying areas for improvement based on data insights, as well as supporting strategic planning and forecasting through data-driven decision-making.
8. Data Visualization: Representing complex data sets through charts, graphs, and other visualizations to aid understanding. Additionally, enhancing the communication of insights to various stakeholders.
9. Predictive Analytics: Using statistical algorithms and machine learning to forecast future trends and outcomes and enhance decision-making by providing insights into potential future scenarios.
10. Data Governance and Security: Establishing policies and procedures for ensuring data quality, accuracy, and security and complying with regulations and standards to protect sensitive information.

Overall, BI tools and processes empower users at all levels of an organization to access and interpret data, fostering better decision-making and overall business success.

Key Components of AI

Regarding AI, during the 6th International Conference on Computer and Technology Applications AI was defined as a simulation of human intelligence in machines that are programmed to think and learn like humans. The goal of AI is to enable machines to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

Let’s have a look at AI’s main branches in more detail:

1. Machine Learning (ML): ML is a subset of AI that involves the development of algorithms and statistical models that allow computers to perform tasks without explicit programming. It relies on data to train models and improve their performance over time.
2. Deep Learning: Deep Learning is a specific subset of machine learning that uses artificial neural networks to simulate human-like decision-making. It excels in tasks such as image and speech recognition.
3. Natural Language Processing (NLP): NLP enables machines to understand, interpret, and generate human language. It is used in applications like chatbots, language translation, and sentiment analysis.
4. Computer Vision: Computer Vision involves teaching machines to interpret and make decisions based on visual data. Applications include image and video analysis, facial recognition, and object detection.
5. Robotics: AI is applied to robotics to create intelligent machines capable of performing tasks autonomously. Robotic process automation (RPA) involves automating repetitive tasks using AI-driven robots.
6. Expert Systems: Expert systems mimic the decision-making ability of a human expert in a particular domain. They use rules and knowledge bases to make decisions and solve problems.
7. Speech Recognition: AI is used to convert spoken language into written text, enabling voice-activated systems and voice assistants.
8. Autonomous Vehicles: AI is crucial for developing self-driving cars and other autonomous vehicles, allowing them to perceive their surroundings and make decisions in real time.
9. Cognitive Computing: Cognitive Computing involves creating systems that can understand, reason, and learn like human cognition. It aims to simulate human thought processes in solving complex problems.

AI and BI are two powerful technologies that have the potential to transform how businesses operate and make decisions. While BI focuses on analyzing historical data to gain insights and support decision-making, AI enables machines to learn from data, make predictions, and automate tasks.

Using AI in Business Intelligence

• • Data-Driven Decision Making:
    BI facilitates the collection, organization, and analysis of diverse data sources. When combined with AI, it empowers businesses with deeper insights, fostering data-driven decision-making across all levels. By leveraging BI and AI, companies can effectively mine valuable information from their data, identify patterns, and predict trends. This synergy enables them to make informed choices, optimize processes, and gain a competitive edge in the market. The integration of BI and AI is a powerful tool for modern businesses, driving efficiency and strategic decision-making through the transformative capabilities of data-driven insights.
  • Predictive Analytics:
    With the help of AI-powered algorithms, businesses can delve into historical data to forecast future trends and outcomes effectively. These predictive analytics play a crucial role in anticipating demand fluctuations, identifying potential risks, and optimizing operational processes. By harnessing AI’s capabilities, companies gain the ability to make proactive decisions, mitigate risks before they escalate, and capitalize on emerging opportunities.

    Predictive analytics empowers businesses to stay ahead of the competition by aligning their strategies with anticipated market shifts. Updated Technical Training Programs also play a vital role in empowering employees. This data-driven approach not only enhances operational efficiency but also fosters a customer-centric approach, enabling businesses to cater to evolving consumer preferences and needs.

    Gartner analytics ascendancy model explicitly shows how data analytics has evolved.

• Personalization:
  AI analyzes customer data to understand preferences and behaviors, enabling businesses to tailor products and services. BI measures the effectiveness of these efforts, ensuring precise targeting and enhancing customer satisfaction. With personalized offerings, businesses can strengthen customer relationships, drive loyalty, and boost revenue.

  By combining AI’s insights with BI’s evaluation, companies refine their strategies to meet individual needs, positioning themselves for success in competitive markets and framing good Technical Program Management Training for employees.

• Automated Reporting:
  Integrating AI with BI streamlines report and dashboard generation, freeing up employees to focus on insightful analysis rather than manual reporting tasks. Time saved by automation enhances productivity and decision-making efficiency. With AI’s ability to process vast datasets rapidly, businesses receive real-time updates, enabling timely responses to emerging trends.

  Automated reporting not only expedites data-driven decision-making but also ensures accurate and consistent reporting across the organization, fostering a cohesive understanding of performance metrics and facilitating strategic planning for the future.

• Fraud Detection and Security:
  AI algorithms excel at spotting anomalies and potential fraud within extensive datasets. Integrating AI with BI empowers businesses to fortify their security measures by swiftly identifying suspicious activities and potential threats. By leveraging AI’s proactive detection capabilities and BI’s comprehensive data analysis, companies can strengthen their defense against cyber threats, safeguard sensitive information, and protect their reputation.

  This powerful union ensures a robust security framework, instilling confidence in customers and stakeholders alike, and mitigating financial and reputational risks associated with fraud.

Reasons for Collaboration

It’s not exactly easy to take advantage of the collected data. Here lies the main reason for using AI in business analytics. Integration of AI and business intelligence can be the game changer here.

According to BARC Survey ‘The Future of BI & Analytics’ companies and organizations can expect to gain certain benefits from utilizing generative AI in business intelligence.

AI is capable of performing tasks close to human intelligence abilities like visual and auditory perception, NLP, pattern recognition, and decision-making, but comprehensively faster and at a larger scale. Therefore, AI-enabled business intelligence tools can analyze large amounts of data in real-time, generate insights, structure data, anticipate trends, or make recommendations.

AI and BI collaboration come in different forms and shapes. Let’s have a closer look at this union.

1. Augmented Analytics for Simplified Complexity
   Augmented analytics is known for democratizing data and reducing the time needed to build ML models. It enhances the use of data, automating routine manual tasks across the data analytics lifecycle, and shortening the time between the moment you collect the data and act on it.
2. Cognitive Search for Efficient Data Discovery
   Cognitive search, or AI search is a type of search technology that uses AI to understand and anticipate the user’s needs and intentions. It can deliver relevant and contextual information to the user by analyzing the patterns and relationships that exist within the data. The traditional search has been shown to have certain challenges such as unstructured data, poor search queries, and limited human bandwidth.

   AI search is a progressive improvement and a radical shift in how we find information. The cognitive search comes with these benefits:

   • enhances query relevance, thereby boosting productivity
   • provides precise results by understanding natural language and context
   • personalizes outcomes through machine learning, fine-tuning its accuracy and speed over time

   It is predicted that generative AI search, exemplified by Retrieval Augmented Generation (RAG), is the next step of cognitive search. It is a concept that merges the intelligence of AI search tools with the creativeness of generative models (like ChatGPT). This powerful combination promises a seamless and intuitive search experience that answers queries and provides precise references.

3. Automated Report Generation
   Automated reporting is about bringing users relevant useful information in a timely way, without the users having to seek out the information for themselves. It tells you what has happened and how different areas of a business are performing. Automated reports can be generated at fixed intervals. They may also be triggered by certain events, like a shipping backlog that has now increased to a critical level that must be resolved. But how can BI and automated reporting work together?

   A business intelligence application with built-in automated reporting can bring you the advantages of both approaches. By using the BI application as a single data repository, everybody can analyze the same versions of the numbers and work from a single source of truth to get information and insights. Simple connection of multiple data sources and easy creation of reports and dashboards let your users get what they need and get on with their jobs, with little or no help from IT.

4. Adaptive Data Governance
   AI tools can help organizations adapt their data governance policies dynamically. Which means they are based on changing business requirements, regulatory standards, and risk factors. By analyzing trends, patterns, and emerging threats in data usage, AI-driven analytics can inform adaptive policy management strategies, ensuring that data governance remains agile and responsive to evolving needs.

   More importantly, AI-powered techniques such as machine learning and natural language processing can enhance data privacy and security by identifying and mitigating risks associated with sensitive data exposure or breaches.

5. Automated Anomaly Resolution
   Since AI algorithms can analyze vast amounts of data in real-time it allows detecting anomalies more accurately than traditional methods. With AI-powered anomaly detection techniques, BI systems can identify deviations from expected patterns or behaviors with greater precision, reducing false positives and improving the accuracy of anomaly detection.
6. AI Boosts BI functionality
   The real potential of Business Intelligence can be gauged by breaking down a large volume of data into granular insights. It enables enterprises to comprehend smaller aspects of the big picture. AI boosts the capacity and functionality of BI applications too. However, the real impact lies in real-time insight. Actually, BI’s mainstream process is to process and visualize data. However, BI can not generate this data result and predict trends in real time. AI with the combination of the latest technologies like ML infuses the capacity of generating real-time insights and trends. Boosting BI functionality improves the value to the organization.

How AI Can Transform Business Intelligence

AI in business intelligence is making it more accessible to a wider range of users. AI-powered BI tools can be used by business users who do not have any coding or technical expertise. This is because AI-powered BI tools can understand and respond to natural language queries. Let’s look at examples and specific ways in which AI is transforming BI.

Another way of AI changing your business is by using it as a digital personal assistant. To be specific, AI can transform internal business operations through AI chatbots or ChatGPT integrations that act as personal assistants, helping to manage emails, maintain calendars, and provide recommendations for streamlining processes. Additionally, chatbots can help you grow your business by handling customer inquiries online.

By offloading various tasks to chatbots, you improve customer service while gaining extra time to focus on strategies to grow your business.

AI+BI: Real-World Use Cases

Looking at real-life examples we are faced with the question of how to integrate AI into your business process. Let’s a have better look at examples of AI in business intelligence applications or platforms. A number of businesses have successfully leveraged the integration of AI and business intelligence.

Waverley Software: Success Stories

Here at Waverley Software, we have an experienced team providing BI consulting services. They work closely with AI engineers to reach maximum results by integrating AI and business intelligence.

Challenges and Solutions

With all the opportunities offered by AI in business analytics, a number of challenges may arise.

1. Poor Data Quality. Having outdated or poor-quality data may lead to a process of optimization being inefficient.
   Solutions could be found in these processes: data profiling, data cleansing, data standardization, automated data quality checks, and data validation.
2. Lack of Technical Skills. Using AI in business intelligence requires a high level of technical expertise, that can prevent many companies from implementing BI and AI within their organizations.
   There is no solution other than training and skill development, collaboration with external partners, or creating cross-functional teams.
3. High Implementation Costs. As new emerging technologies the cost of integrating AI into BI (technology, training, expertise) could be significant.
   A part of the solution is to focus on your Return on Investment, because if implemented wisely, those modern solutions can bring a lot of value to your organization in the long run and make you more competitive. Also, when integrating BI and AI, we usually recommend our clients to start small.. Instead of trying to implement AI and BI solutions across the entire organization at once, start with a small-scale pilot project or proof of concept. To reduce costs, try resorting to outsourcing companies or external consultants.

Tips for Implementation

As we stated above, integrating AI into BI can enhance the capabilities of BI systems by adding predictive and prescriptive analytics, automated insights generation, and more advanced data processing techniques.

To reach maximum results from enhancing your BI services and ease the implementation process it is recommended to follow these steps.

1. Define Clear Objectives
   Clearly define the objectives and goals you want to achieve by integrating AI into your BI system. Determine specific business problems you want to solve or the insights you want to uncover using AI-powered analytics.
2. Evaluate the Quality of Data
   Evaluate the quality, completeness, and relevance of your data. AI algorithms rely on large volumes of high-quality data, so you need to ensure that your data is clean, well-structured, and properly labeled for training AI models.
3. Choose the Right AI Techniques
   Select AI techniques and algorithms that are well-suited to your business objectives and data characteristics. Common AI techniques used in BI include machine learning for predictive analytics, natural language processing for text analysis, and computer vision for image analysis.
4. Identify Use Cases
   Identify specific use cases where AI can add value to your BI system. This could include predicting customer behavior, forecasting sales trends, optimizing pricing strategies, or automating data processing tasks.
5. Integrate AI Models or Create Your Own
   Integrate AI models seamlessly into your existing BI infrastructure. This may involve leveraging APIs or SDKs provided by AI platforms or customizing your BI tools to support AI capabilities.
6. Provide User-Friendly Interfaces
   Design user-friendly interfaces that make it easy for business users to interact with AI-powered features within the BI system. This could include dashboards, reports, and visualization tools that present AI-driven insights in a clear and understandable manner.
7. Ensure Transparency and Explainability
   Ensure that AI-driven insights are transparent and explainable to users. Provide explanations or visualizations that help users understand how AI models arrived at their conclusions and recommendations.
8. Stay Agile and Adaptive
   AI technology is rapidly evolving, so stay agile and adaptive in your approach to integrating AI into BI. Keep abreast of the latest advancements in AI and BI technology and be prepared to adapt your strategies accordingly.

Explore Emerging Trends in AI and BI Collaboration

Over the past decade, business intelligence has changed considerably. 2023 was a particularly significant year for the business intelligence industry. In the comping years BI tools and strategies will become increasingly customized. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics, but what is the best BI solution for their specific needs?

But first, let’s have a look at BI statistics in numbers.

1. 1. As per Markets and Markets, the business intelligence market will be worth $33.3 billion by 2025, growing at an annual CAGR Of 7.6%
   2. The global mobile business intelligence market was valued at $8.8 billion in 2020 and is expected to reach $29.6 billion by 2027 at a CAGR of 18.9%.
   3. The worldwide business intelligence software market is projected to reach $17.6 billion by 2024 when run together with analytics tools, as per Statista.

Experts outlined a few emerging business intelligence and analytics trends in 2024.

Conclusion

AI-powered data collaboration platforms represent a significant investment for businesses. Especially if the businesses want to stay competitive in the near future. By facilitating efficient data collaboration, they enable businesses to harness the potential of their data, develop insights that lead to better decision-making, and achieve operational efficiencies. As businesses would need to process and present data into visual findings, software that can make exact trend predictions in real time would be of major significance. AI can show its real potential there and enable businesses to seek a breakthrough in the market.

If you want to be sure how to use AI and power BI and how to implement it within your business our specialists at Waverley Software will guide you through.