Gartner predicts that 45% of the digital giants will use more Machine Learning-backed products and applications, including some business intelligence (BI) platforms and they do actual human workers by the end of 2018.

In a customer-centric world, the fundamental challenge of how to get customer insights from data and how it actually drives the results of getting to the next level of growth, is gaining momentum.

There is a growing understanding that BI and Machine learning are facilitators of each other and perform best in conjunction, for large volumes of data being utilized for business needs. While BI is the logical first step, machine learning, as a subset of data science follows to get deeper insight. BI 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.

How do you apply machine learning concepts to your business intelligence strategy?

Predictive analytics to augment your existing BI capabilities

Business Intelligence (BI) has already moved away from long-outdated static reports to interactive dashboards and real-time analytics and allows businesses to have a descriptive vision based on the accumulated visual data.

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 (BI’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 BI 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.

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
• Reduced costs and risk management: Sufficient data-based prediction opts for timely methods, and respond 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.
• Faster results to optimize customer success: With the precision of desired outcomes, predictions based on new trends and developments, help build customer acquisition models and reduce churn.
• Fraud detection: methodologies relating to multiple layer analytics help detect cyber threats and fraud and prevent them by targeting vulnerabilities before any actual loss happens.

The applications of predictive analytics in Business Intelligence (BI) are uncountable. Here are just a few.

NLP is 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, 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/or anywhere. Chatbots have the capability to turn BI 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.

– NLP tackles unstructured data. In order to produce more exhaustive 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.

Customer segmentation as an application of machine learning

Customer segmentation empowers businesses to distribute tailored marketing messages to those potentially interested in their services/product. It has been proved that predictive analytics can identify target customers much better than traditional techniques, with all the due advantages for the business, like better communication with the customer, marketing campaign savings, and increase of profitability.

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.

Risk assessment and Financial modelling – another application of machine learning

Risk Assessment allows for 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. Financial modeling is a mathematical model which translates the behavior of markets/agents into numerical predictions. These predictive models are used for decision-making processes about investments, in order to gain a competitive edge and eliminate risks.

Market analysis, Sales forecast and Churn prevention

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 requirements, 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 enable proactive customer relationships by the company; as losing an existing customer proves to be much more expensive than retaining an existing one.

Combining BI and Machine Learning – An insights-driven approach for your business agility

Machine Learning and BI differ in functionality and analytics delivery, however it’s when BI 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 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 BI. 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 BI and machine learning is invaluable in modern business in gaining the competitive edge because it offers the benefits of intelligence combined with agility. Helping businesses uncover insights in the BI workflow, for better performance, using machine learning will increase automation and prediction abilities.

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