Why use Machine Learning to Scale Business Intelligence & Predict Data Outcomes

Since time immemorial, people have invented instruments and devices to simplify the work they do, and save their energy required to accomplish their tasks. The humans have inhabited almost all corners of the planet Earth, landed on the Moon, explored Mars by spacecraft and even sent the robotic arm to collect dust and pebbles from the asteroid. Everyone reading this article on a PC or on a smartphone is using the technology that has changed our lives unrecognizably just in a few decades. We have invented automatic machines that are capable of conducting tasks without human involvement, take coffee machines or windmills, as an instance. And those machines are generally much quicker, more precise and a lot better at doing their tasks than humans are.

By being clever, resourceful species, we are not only fully dependable on making use of tools and devices to survive, we are also exceptional in the sophisticated and advanced technology we have invented. We have been trying to embed intelligence into tools and devices we use since the dawn of technology. Spam filtering in the email clients, natural language processing of huge volumes of digital data, predictive analytics for business intelligence – those are just a few examples that have transformed human-machine interaction and raised our expectations even more. In this article we will explore how to utilize the full potential of data, making use of machine learning to augment existing BI capabilities. We will also take a look at several machine learning applications, such as natural language processing, customer segmentation, financial risk assessment and others.

How to realize the potential of data for fact-based decisions?

How does business intelligence affect the way we run our business? It’s all about cold numbers at the core. Again. In the beginning, the data revolution was about having more diverse, accessible and comprehensive data. As soon as the first wave of excitement was over, predictive analytics and the data-driven culture emerged. Having accurate and real-time data could provide a more granular and refined view of business’ performance. And the ability to react promptly on the changing environment is enabling firms to create more value.

It’s not uncommon to hear from C-level executives that they are disrupting their industries with data science, business intelligence and machine learning. Though the hype around big data started in 2004, its promises are yet to be fulfilled however. Not all companies have managed to realize the potential of machine learning to automate and create value.

There are a few explanations behind these unfulfilled expectations. One of them is the ability to form the right questions from the outset, while focusing on business challenges. It’s important to be clear about what business objectives we want to impact and what actionable insights can help us find unseen business opportunities. This can be done by breaking down the questions by asking: why do we want to achieve this, what KPI are we trying to influence, are there any correlations or causal effects from these business decisions, and is there anything to be improved

At the same time, it is not enough to have the data pool, invest into the technology and hire the talent. The inherent principle of a data-driven approach to business is that the data itself won’t explain to us what decisions to make or what scenario to opt for. Data only makes sense when worked on in combo with other instruments.

Using machine learning to get insights from data enables companies to understand the trends better and make weighted decisions. In the next paragraphs we’ll explore how business intelligence augmented by machine learning helps companies achieve competitive advantage.

Machine learning to augment your existing BI capabilities

According to the data from Statista, 33% of surveyed IT leaders are planning to use machine learning for business analytics.

There is a growing understanding that business intelligence (BI) and machine learning (ML) are facilitators of each other and perform best in conjunction, for large volumes of data being utilized for business needs. While business intelligence is the logical first step, machine learning, as a subset of data science follows to get deeper insight. Business intelligence uses basic calculations to provide answers, while machine learning with predictive, prescriptive and cognitive analytics use mathematical models at this point of work, to determine attributes and offer prediction.

→ Read about Avenga data science perspective on Covid-19: a real life example

In the last 10 years, we are witnessing the renaissance of machine learning. Businesses start to rely on algorithms like autoencoders that are able to identify the data points that couldn’t be quantified before. Machine learning helps to turn experience into expertise and extract meaningful patterns from huge amounts of data. It augments the abilities and results obtained from business intelligence, complementing it and performing tasks that are way beyond human capabilities. The ability to process and analyze complex datasets allows machine learning programs to identify the patterns that are out of the scope of human analysis. To give an example, if a doctor brings in a hypothesis there is a correlation between poor eating habits and diabetes, its a role of statistics to check patients records and confirm the validity of a hypothesis. In contrast, machine learning’s goal is to utilize the information gathered from medical data to produce the description of diabetes.

Machine learning utilizes aggregated data, with specific unit characteristics of every instance with multiple variables to be used, to detect patterns thus enabling predictive models. In other words, we can now evaluate, interpret and define specific future behaviors based on the interaction/synergy of existing systems like production databases, data cleansing, and data acquisition. That’s how predictive analytics bolster the business intelligence’s mission, progressing from retrospective answers to a focused predicting performance that advise specific actions based on it.

Machine learning is a tool that detects anomalies in the business intelligence workflow by receiving notifications about sore points or incidents in the critical KPIs. This helps in understanding where scaling of operations are required, in order to satisfy customer needs based on market demand or historical data, so that profitable opportunities are never missed.

Business Intelligence (BI) has already moved away from long-outdated static reports to interactive dashboards and real-time analytics and allows businesses to have a descriptive vision based on the accumulated visual data. See how to build custom data visualizations in Power BI.

In particular, machine learning algorithms, applied to datasets, can help to verify whether a hypothesis regarding the data is true or not. Basic machine learning algorithms are capable of determining patterns in the data that would be beyond the bounds of identification using manual methods.

Why do we need to integrate machine learning into BI?

What are the advantages of programming computers to do the task instead of completing it by hands? The are three main benefits of usage of machine learning:

• Machine learning can easily and quickly perform tasks that are too tedious and boring for humans to do. Image and speech recognition are the most vivid examples of such tasks. Machine learning has achieved quite good results in performing those tasks once exposed to a sufficient number of training phases;
• Machine learning algorithms are successful with performing analysis of large and complex datasets. To name a few, examples may include medical archives and clinical freetext, astronomical data, weather prediction, genomic data, etc. As the number of digitally recorded data is growing every day and achieve the volume 149 zettabytes (that’s 149.000.000.000.000 gigabytes) by 2024 as stated in Statista, it becomes clearly visible there are treasures of meaningful data buried in archives that are too huge for humans to make sense of. The ability to identify important and meaningful patterns in the large and complex datasets is a promising opportunity. That’s where the combination of machine learning algorithms that learn on its own, have unlimited memory and have a powerful processing speed opens new horizons.
• Machine learning algorithms are flexible, adaptable to input data. A popular example is a machine learning algorithm for optical character recognition, especially for handwritten text, that is able to adapt to handwriting styles of different users and smoothly decode it. Another example is smart spam filters, progressing in response to the changes in the nature of spam emails.

→ See hands-on stories of how PharmaTech, HealthTech, BFSI, security tech consultancies and others leverage software solutions to handle data diversity, capture, store, process, analyze, and visualize big data.

Six applications of machine learning

The applications of machine learning in business intelligence are numerous. Here are a few of them.

1. Natural language processing — one of the most popular applications of machine learning.

Natural language processing (NLP) or computational linguistics, is the combination of machine learning, AI, and linguistics that allows for machine-human communication. NLP and search-driven analytics could prove to have great potential in connecting businesses with data.

For instance:

NLP and bots are applied to deliver data insights outside of the usual dashboard environment. You can request specific stats or detailed analysis from the bot while at a meeting via Skype or Whatsapp, and they’ll send these breakdowns from your business’ entire pool of data to your conversation seamlessly.

– NLP for making BI more insightful and personalized. We are talking about one of the greatest potentials of machine learning. Predictive algorithms learn from the data and their models, and when integrated into applications, provide them with predictive capabilities. The models are re-trained periodically, so that they automatically learn new data for more updated results. An AI component is the next logical step after turning natural language into machine language. Chatbots get better at “understanding” the query and start to deliver answers rather than search for results. The computers are on their way to learning the semantic relations and inferences of the question in order to analyze metrics and return with actionable insights rather than simply showing dashboards and graphs. The next big thing is integrating BI analytics into every business layer, thus offering data-driven exhaustive analysis at every point.

– NLP has the potential to make data user-friendly. With NLP, data has the potential to become more easily managed, so that you’re able to get answers alongside liable metrics anytime and anywhere. Chatbots can turn business intelligence into a simple conversation, as easily as you asking it by text or voice command about revenue change over the last quarter or about today’s customer sentiment. Instead of the complicated processes of data mining and software experience, the processing is taken care of in the cloud.

→ Avenga research on Sentiment analysis. Google Natural Language Processing vs Custom Algorithm

– NLP tackles unstructured data. In order to produce in-depth answers, NLP aims for unstructured data to be understandable to a machine. Here is where sentiment analysis plays a leading part. Determining sentiment by machine means interpreting data, without human bias and spitting out quantitative answers with the highest possible accuracy. Sentiment analysis can provide tangible help for organizations seeking to reduce their workload and improve efficiency. As speech/face recognition techniques are getting more and more advanced, audio and video are getting more accessible as sources for machine analysis.

2. Predictive analytics to augment your existing BI capabilities

Business intelligence encompasses a set of analytics techniques to transform the raw data into meaningful and useful information.Business intelligence tools enable to extract relevant data, make it reliable, accurate and attractive to users. These data guides top managers and decision markets to the right decisions. Examples of business intelligence outputs include dashboards, data visualizations, reports, statistics, etc.

In its turn, predictive analytics is used to foresee the trends and patterns. It drives the transformation of business processes, changing how analysis is being done. The big promise of predictive analytics is to use vast amounts of information to expedite our ability to make sense out of the chaos of data.

Predictive analytics offers feasible solutions for the various business domains. It involves a statistical approach that consists of machine learning algorithms and predictive modeling techniques helping to determine future outcomes. Supervised machine learning algorithms, such as logistic regression, is one of the most commonly used methods to perform these forecasts.

Technological advancements in processing power and storage enabled economically feasible machine learning predictions that weren’t thought about before. Such predictions are able to enrich human intelligence by determining possible scenarios and choosing the one that will produce the optimal business results.

For example, the chart that can be seen below that estimates the risk of hospitalization and risk of death built using regression-based approaches, such as logistic regression and gradient-boosted trees. Gradient-boosted trees is a machine learning technique to make predictions by constructing a series of decision trees, and checking each one after another. In the case below, the algorithm checks whether the patient has a chronic liver disease first, and if positive, and the patient is male and over 65 years old, than he may experience a fatal outcome.

Source of the image: The Economist

Real-life benefits of the successful application of predictive analytics for businesses can improve efficiency in some complex systems within healthcare, IT, energy, pharma, logistics, etc.:

• Increase productivity and operational quality. Analysis-ready data allows for more proactivity while simultaneously setting achievable goals in future predictions, based on past data and not on traditional presumptions. So the decision-making process is faster and the overall team function on outcomes is much more efficient.
• Reduce costs and risk management. Sufficient data-based prediction leads to timely methods, and responds to challenges beforehand in real time, saving on the cost of delayed processing and the outcome of delayed reactions in critical market situations.
• Improve sales processes and optimize marketing. Machine learning/predictive modeling algorithms help build buyer behavior models to understand the customer’s journey which leads to more effective and cheaper campaigns, simultaneously retaining profitable users from the market.
• Optimize customer success with faster results. Predictions based on new trends and developments help to build customer acquisition models and reduce churn.
• Detect fraud. Methodologies relating to multiple layer analytics help detect cyber threats and fraud and prevent them by targeting vulnerabilities before any actual loss happens.

3. Customer segmentation as an application of machine learning

As the business competition is increasing, companies from all industries try to anticipate and foresee what their competitors are doing in order to provide better products and services and thus stay ahead of the competition. The urge to understand market trends, products and services comes as a determining part of making business.

Responding and fulfilling the needs of every customer is quite a complex task. Customers have different needs, demography, tastes, preferences and behaviours. These challenges spurred the adoption of customer and market segmentation, where they are divided into smaller segments, each of these segments having similar market characteristics or behaviors.

In addition, the ability to understand customers better results in leveraging targeted products and services and tailoring marketing campaigns in order to “hook” consumers. This understanding is possible via advanced customer segmentation, that empowers businesses to distribute tailored marketing messages to those potentially interested in their services/product.

Customer segmentation used to be a tedious and time consuming process, requiring hours of manual work. The adoption of machine learning fueled the approach to customer segmentation, transforming maula process into automated one, making the traditional customer segmentation techniques ineffective. Machine learning, in particular, K-Means algorithm is proven to be an effective technique to clusterize customer segments that share similar market characteristics.

The insights from customer segmentation include:

• Tailored marketing programs that fit each customer segments;
• Decision support for providing credit options for customers;
• Defining the products/services matching the demand and supply;
• Highlighting hidden relationships/dependencies between customers and products;
• A capacity to foresee customers’ behaviors and churn.

Big marketplaces, like Amazon and Ebay analyze a customer’s previous purchases, the viewed products and recommendations that he/she reacted to. Afterwards, the marketplaces identify customers with similar interests, and their purchases form the basis for further product recommendations.

For instance. Popularity-prediction algorithms build models combining user mobility information from social networks with geolocation data (like traffic intensity), alongside analysis of land prices, in order to give an almost all-inclusive understanding when deciding on the best retail store location or the next healthcare facility location.

4. Risk assessment and financial modeling – another application of machine learning

Applications of business intelligence in finance are pervasive and numerous. Predictive analytics models are utilized to forecast financial performance, evaluate the risk of investment projects and build financial instruments like derivatives, in order to gain a competitive edge and eliminate risks.

Financial modeling built on machine learning algorithms involves developing a mathematical model that translates the behavior of markets/agents into numerical predictions.

For example, prescriptive analytics models aid with creating optimal portfolios or financial assets and putting together the optimal capital budgeting options. GE Asset management utilities optimization models to choose how to invest the money of its clients. The estimated benefit from the model is over $75 million in a 5-year time frame.

Risk assessment allows for an exhaustive analysis of possible issues associated with a business. Data mining becomes a valuable mechanism in order to manage decision support systems that can accurately predict what business operations are profitable for the company.

Risk analysis helps decision makers to estimate the impact of certain decisions and assess the value of a payoff that may actually occur. This is especially relevant when the problem being faced is an uncertain and risk-filled pattern of future events that often occurs in the financial realm. The ultimate aim of decision analysis is to determine the best decision alternative or the preferable decision strategy, utilizing data about uncertain events, possible consequences and probable payoffs.

There are three possible approaches to making decisions without guesswork: the optimistic approach (estimates decision in terms of the best payoff the can possibly occur), the consevative approach (estimates the worst payoff that can occur) and the minimum regret approach (estimates how much the potential payoff a person would withhold by choosing a certain decision alternative).

However, if the probability assessments are obtained, an expected value approach can be used to identify the best decision alternative. Basically, it’s a sum of weighted payoffs for decision alternatives. The calculations to determine the decision alternative with the best expected value can be identified using the decision tree.

5. Market analysis, sales forecast and churn prevention

Marketing is leading in the applying business intelligence models augmented by machine learning. A clearer understanding of a customer behavior obtained from the scanners and social media posts has kicked off a growing interest in marketing analytics. Abundant marketing analytics, in its turn, led to more effective advertising budget usage, more efficient pricing models, more precise estimates of the product demand, better product-line management and improved customer loyalty.

Analysis of sales history and market survey events result in realistic sales predictions and planning, and this can be exploited by companies to address customer experiences, therefore increasing profit and reducing the attrition rate. Sales forecasting is equally applicable to short- or long-term improved forecast accuracy, which offers better insights on what the best course of action is for business planning. By analyzing a big existing customer’s data set, enterprises can build predictive models that lead to proactive customer relationships by the company; as losing an existing customer proves to be much more expensive than retaining an existing one.

Data models constructed from past data can be used to predict the future events or assess the impact of one variable on another. E.g., past data of product sales can be utilized to develop a mathematical model to predict future sales.

The model can include the seasonality based on the past patterns. The point-of-scale scanner data from retail shops can assist in predicting the increase in sales due to discounts. The data from surveys and past purchase behaviors can help to assess the market share of a new product.

By studying historical point-of-scale data, large retailers can create a targeted marketing campaign, in which data mining is utilized to predict which customers are more likely to make use or discounted offers by purchasing higher-margin products, thus increasing overall company revenue.

6. Healthcare analytics as an application of machine learning

The use of machine learning and natural language processing in healthcare is on the rise because of the constant pressure to optimize expenditures and deliver better treatments. The adoption of electronic health records across the medical facilities skyrocketed in the last 10 years, and resulted in a huge volume of medical data.

However, the mould of medical freetext, such as doctors notes, is often overlooked during the analysis. Machine learning models can take the full advantage of these unstructured pieces of information, classifying and clustering them to get insights.

Large-scale recurrent neural networks are showing impressive predictive results by utilizing unstructured and structured sources of data for semi-supervised learning. Neural networks are quite successful in:

• Gathering disease names, treatment and procedure names from electronic health records and unstructured clinical freetext;
• Joining together medical events and their corresponding times from medical freetext;
• Spot the relationships like medical product X improves/worsens the condition Y;

One of the goals that can be achieved by analyzing electronic health records using natural language processing and deep learning techniques is predicting patient outcomes. As for now, machine learning aids with predicting two aspects of patient outcomes:

• Static health outcome prediction, such as predicting heart failure rate, determining atherosclerosis risk factors, classifying diagnosis.

• Temporal health outcome prediction that aids with forecasting patient outcome within a set time frame or making a forecast on the basis of time series data, such predicting unplanned hospital readmission.

→ Read more about advanced technologies used in the pharmaceutical industry in our overview article.

Combining BI and machine learning – an insights-driven approach to your business agility

Ultimately, business intelligence is about making better decisions. The machine learning techniques we have introduced in this article help decision makers to optimize the analysis of existing data, predict future outcomes and assess recommended options. Through risk analysis, decision makers can estimate the probability of favourable and unfavorable outcomes if certain action is taken and choose the option that brings the best results.

Machine learning and business intelligence differ in functionality and analytics delivery, however, it’s when business intelligence makes use of machine learning that it has great potential for businesses. Starting from improved functionality of the existing analytics within logistics, then detecting hidden insights in the unstructured data within the behavioral analysis, and ending up with image recognition and sentiment analysis in customer support, the potential is huge for almost every vertical industry. Machine learning algorithms are about accumulating more and more data which makes them a perfect match for business intelligence. The more inputs that are fed to them, the more accurate the output becomes at effectively uncovering hidden patterns and insights in the data.

We believe that the combination of business intelligence and machine learning is invaluable in modern business in gaining a competitive edge. It offers the benefits of intelligence combined with agility. We, at Avenga, help businesses uncover insights in the BI workflow for better performance, increased automation and prediction abilities.