How Machine Learning Can Transform Your Business Intelligence

For decades now, Business Intelligence (BI) has been contributing to the success of organizations. However, the ever increasing data, dynamic business events, process-intensive workflows, and need for specialized skills has challenged traditional BI to evolve. With the advent of AI-based technologies such as Machine Learning (ML), BI is now able to expedite complex analysis and make available hidden insights to regular business users for their daily decision making.

Through this blog, let’s understand how Machine Learning is revolutionizing Business Intelligence.

What is Business Intelligence and Machine Learning?

Machine Learning, a key component of AI-based technologies, utilizes sophisticated algorithms and advanced models to unlock valuable insights from vast troves of data, empowering AI analytics to generate actionable intelligence. Machine learning in business intelligence does the heavy lifting by automating the data analysis process, thus saving valuable time and efforts of analysts. It frees the analysis from human error, bias, or delays to ensure accurate insights are available at the right time.

Business Intelligence is the outcome or insights received from data after analyzing it from various perspectives. Traditional BI was designed for data scientists and analysts who would clean and prepare data, and run SQL queries on a limited set of data for answering specific business questions. The insights would become outdated by the time they would reach the actual decision makers. Machine learning in augmented analytics with its powerful algorithms and ability to process vast amounts of data quickly. Any user with little or no machine learning experience can run advanced calculations and perform predictive analysis, without any delay or dependency.

Machine learning is also useful in testing hypotheses at a faster pace, recognizing patterns, identifying hidden relationships in data, and uncovering the underlying factors that influence business outcomes. For example, with only BI, you may see the sales of your product increase in a quarter as compared to the same quarter last year. But you may not know what is influencing the increase. With ML, you can come to know if a particular discount coupon, a new customer segment, or a newly opened store is driving the sales. Then you can focus your efforts and allocate your resources in a more efficient manner to ensure high profits.

How Machine Learning Impacts Business Intelligence

Machine Learning boosts the capabilities of BI in the following ways:

Expediting analysis with automation

Machine learning brings in the much needed automation in various time-consuming and repetitive data processing and insight generation processes. It also helps to streamline DataOps activities, process real-time data, and ensure that the latest data is always available for analysis.

Uncovering hidden Insights

With its powerful algorithms, machine learning is able to uncover insights that are not easily apparent or explicit to find in data by looking at static dashboards or lengthy reports. Advanced insights such as anomalies, analogies, outliers, trends, clusters, and predictions are effectively extracted from data and presented in time for users to understand business events.

Testing Hypothesis

Machine learning in business analytics helps in deeper exploration of data from all perspectives for testing hypotheses in a very short time. It provides various capabilities in terms of finding root causes, performing predictive analysis, identifying clusters, understanding relationships and influencing drivers, and providing meaningful contexts to insights.

Empowering business decision makers

Machine learning helps transform business users into citizen data scientists by empowering them to perform their own analysis easily. BI platforms with machine learning capabilities can learn from its users and provide personalized insights to address their specific use case and business needs. Empowering decision makers with self-service analytics capabilities, machine learning can promote a data-driven culture in organizations.

Boost productivity of analysts

By making business users self-reliant in managing their daily insight needs, machine learning frees up analysts and IT teams from repetitive and mundane tasks. Analysts can use this saved time and efforts to work on specialized use cases, resolve complex problems, and bring innovation to processes.

Applications of Machine Learning in Business Intelligence

Research shows that the global Machine Learning market is expected to grow from USD 21.17 Billion in 2022 to USD 209.91 Billion by 2030, at a compound annual growth rate (CAGR) of 38.8%. Machine learning in BI has found the following applications across industries.

Retail

Everyday, retailers collect vast amounts of customer data. This data can be analyzed by machine learning models to surface insights on customer preferences, products with high demands, lowest selling products, best performing outlets, emerging customer segments, and so on. It can also help retailers forecast demand, track inventory, decide pricing, and identify upsell and cross selling opportunities.

E-commerce

Machine learning models can be employed to understand buyers’ online shopping behavior, interests, and past purchase history. This can help e-commerce companies to create personalized offers, provide next-product-to-buy recommendations, maintain stock levels, and adjust prices dynamically based on demand.

Logistics and Supply Chain

Operational inefficiencies can take away a significant proportion of any organization’s revenue. For supply chain and logistics companies, there are several touchpoints where inefficiencies can arise. Machine learning can perform real-time processing and analysis of data on delivery times and schedules, transport routes, vehicle health, and so on. The insights can help such companies optimize operations, save costs, prevent delays, and strengthen networks to ensure seamless movement of goods.

Health Care

Machine learning can correlate millions of different data points in patient data to uncover hidden insights on patient progress, seasonal diseases, chronic illnesses, and effectiveness of treatments and medicines. BI platforms with machine learning capabilities can also help hospital management track inventory of medical supplies, manage bed allocations, schedule availability of medical staff, and ensure operational efficiency.

Marketing

Marketers have to justify every penny spent from the limited marketing budget. Machine learning can help them measure channels receiving the highest and lowest traffic and compare leads generated by different social media platforms in real time, so that they can adjust budget allocations swiftly and avoid losses due to underperforming channels. They can also use insights derived from machine learning to evaluate campaigns performances and ensure that the expenses do not exceed the marketing budget.

Challenge of using machine learning in business intelligence

There are certain challenges that affect the analysis performed by machine learning. However, an advanced BI platform can overcome these challenges and ensure that machine learning models and algorithms work effectively.

Lack of clean data

The effectiveness of a machine learning algorithm depends on the quality of data that it has been trained on. If the data is bad, that is incomplete, inconsistent, or inaccurate, then it affects the insights generated by machine learning. BI platforms offering machine learning must also have provisions to measure and report on data quality issues, so that those can be fixed before the data is used for training and analysis.

Rigid models lacking configurability

Organization may have unique or specialized use cases for which they require custom ML models. Lack of configurability and support for out-of-the-box models can restrict users from reaching insights and deriving actual value from their enterprise data.

No interpretation of insights

Organizations may be using the most advanced ML models and algorithms for extracting insights. However, if users find it difficult to interpret elaborate visualizations, spot minute but important differences in a butterfly chart, or know where to focus on in a scatter chart, the insights are lost on them.

Data noise

Too many insights can be very overwhelming for users to digest and apply in their work. ML models spewing batches and batches of irrelevant insights can actually discourage users from adopting a data-driven approach in their decision making. Such increased data noise may even hide significant insights that may be really valuable for users.

Lack of transparency

ML models may sometimes interpret data in a context that’s different from user expectations. So it becomes important to build trust and ensure transparency in the analysis process. Lack of transparency in how the insights were derived can affect the confidence levels users have in the generated insights.

How MachEye transforms Business Intelligence with Machine Learning

MachEye’s AI-powered Business Intelligence platform uses robust Machine Learning models to help all business users perform deep exploration, advanced calculations, root-cause analysis or why analysis, and predictive analysis easily and quickly.

Powerful and configurable ML

MachEye offers powerful and sophisticated ML models and algorithms for scalable analytics. Additionally, it also supports configurable or out-of-the box models to address specific business requirements and custom use cases of customers.

Automated data catalog and data quality index

With its automated data catalog and data quality index, MachEye measures data on various parameters such as completeness, clarity, interpretability, uniqueness, and consistency. Going beyond the quality check, MachEye also generates a comprehensive quality report with recommendations to fix the issues and improve the data quality. This ensures that MachEye’s ML models and algorithms always train on good quality data and generate accurate insights.

Text summaries and interactive audio-visual data stories

MachEye’s copilot for data analytics automatically generates interpretations for insights extracted by its powerful ML. Interpretations include text summaries, interactive visualizations, and audio-visual data stories which makes understanding insights easy and fast.

Personalized and focused insights

Instead of bombarding users with every correlation, analogy, or trend, MachEye offers provision to personalize insights and cater to specific user needs. MachEye’s ML models learn from users’ past searches, topics of interest, work area, and access privileges to send customized and focused insights that are of high relevance to users. With automated business headlines, users get to know insights on their preferred topics the moment they emerge from data, without having to explicitly search for them.

Transparency in analysis process

For every search query, MachEye publishes details of how the natural language query was interpreted and transformed into SQL statements to fetch answers and insights. By explaining users how the ML models work, MachEye brings transparency, improves understanding, and builds the user’s trust in the analytics process.

MachEye integrates the powers of Generative AI, Natural Language Processing (NLP), and Machine Learning (ML) technologies to understand simple language search queries, identify user’s intent behind a query, and provide instant contextual answers with actionable insights. With its intelligent search, interactive audio-visuals, and actionable insights, MachEye has revolutionized business intelligence and made it usable for business users across organizations. Exploring the trends of machine learning in business analytics, MachEye brings a new dimension to the way businesses leverage data for informed decision-making.