Did you know that 53% of companies use data analytics technology? Most of these companies have found that is is very useful. It can be even more valuable when used in conjunction with machine learning.
Machine Learning Helps Companies Get More Value Out of Analytics
There are a lot of benefits of using analytics to help run a business. You will get even more value out of analytics if you leverage machine learning at the same time.
Analytics has been influencing the income for companies for quite some time now. These days more and more organizations are embracing the use of analytics. They are digging deeper into their data to improve efficiency, gain a competitive advantage, and further increase their profit. This is why businesses are looking to leverage machine learning (ML). They need a more comprehensive analytics strategy to achieve these business goals.
For years, spreadsheet programs like Microsoft Excel, Google sheet, and more sophisticated programs like Microsoft Power BI have been the primary tools for data analysis. However, the rapidly changing business environment requires more sophisticated analytical tools in order to quickly make high-quality decisions and build forecasts for the future. These tools help companies boost productivity, reduce costs and achieve other objectives.
You definitely need to embrace more advanced approaches if you have to:
- process large amounts of data from different sources
- find complex hidden relationships between them
- make forecasts
- detect unusual patterns, etc.
In this article, we will share some best practices for improving your analytics with ML.
Top ML approaches to improve your analytics
Times are changing — for the better! Today, there are many advanced ML approaches that you can use to enhance your analytics and gain valuable insights on how to optimize business processes, improve decision-making, build the right customer relationships, and leverage your market proposition. Let’s dig deeper.
Сlustering is an approach where several data points are clustered according to the similarity between them, so they are easier to interpret and manage. Сlustering with ML methods creates computer clusters of data by using its numerous parameters or properties which is more difficult for human-led clustering. Once clustering is complete, domain experts can interpret these clusters to better understand the business or apply it to different classifications. There are a number of ready-made BI solutions that allow you to group data. However, if you need custom data processing due to the huge amount of data and specific tasks, or are willing to save money, you can benefit from creating a specific ML module that will process the data the way you need it.
There are many ways to use data clustering such as image processing,, customer segmentation, medical imaging, etc. For example, marketing managers can run a cluster analysis to segment customers by their buying pattern or preferences. Moreover, cluster analysis is typically used when you are dealing with large unstructured datasets. Clustering can help you process large datasets and quickly organize them into something more usable with no need to define a full algorithm. For example, insurance companies use cluster analysis to detect false claims, while banks use it to assess creditworthiness.
Predictive analytics uses historical data to predict future trends and models, determine relationships, identify patterns, find associations, and more. Basically, predictive analytics answers the question, “What is most likely to happen based on my existing data, and what can I do to change that result?” Although most BI tools have out-of-the-box solutions for predictive analytics, there are prerequisites and limitations. They usually have expensive licenses, are simplified for the average customer, and block solutions within a single vendor. While the existent tools cover typical use cases, the next step is to set up a custom forecasting module to perfectly meet your needs and configuration.
The applications of predictive analytics based on ML are countless and include sales forecasting, risk evaluation, financial modeling, predictive maintenance, inventory forecasting, etc. Predictive analytics has been successfully used in different industries such as ecommerce, telecommunications, marketing, banking, insurance, or energy, to name a few. For example, using predictive analytics, product managers can predict and reduce customer churn with much greater accuracy in comparison to basic analytics tools.
Anomaly Detection (AD) systems are either created manually by experts configuring thresholds for the data or created automatically by examining the available data using ML. For an environment where data changes eventually, such as fraud, manual deployment may not be a good solution as it builds a system that cannot adapt. ML is in line with the engineer’s goal of creating an adaptive AD system with better performance. Anomaly detection powered by ML enables large amounts of data to be processed automatically and detects even single occurrences of anomaly while it would be left undetected in aggregated data analysis.
With anomaly detection, you can easily identify suspicious groups of users, defective products, or abnormalities in the client’s data. Anomaly detection has been successfully used to optimize business operations in a variety of industries such as banking, financial services, retail, manufacturing, IT and Telecom, defense and government, healthcare, and more. For example, manufacturing companies rely on anomaly detection to quickly pinpoint equipment failures.
In areas where technology affects people’s lives, it is vital to understand what is behind the AI algorithms and on the basis of what decisions are made. A typical concern for potential AI adopters is that it is usually unclear how the technology comes to certain conclusions. This is where Explainable AI comes into play. Explainable AI is the next generation of AI that opens a black box so people can understand the logic that goes inside AI algorithms. According to NMSC, the global explainable AI market will reach $ 21.78 billion by 2030.
Explainable AI finds its application in areas where AI models’ credibility and intelligibility are essential, e.g. in healthcare, automated transportation, manufacturing, banking & insurance, as well as mission-critical applications such as predictive maintenance, natural resource exploration, and climate change modeling. For example, explained AI could help doctors diagnose diseases more accurately, such as detecting cancer with an MRI image that identifies suspicious areas that could be tumors.
The potential benefits of machine learning have tremendous appeal, which is why many companies are looking to invest in advanced analytics solutions. The logical step for organizations seeking to maximize the potential of data is to leverage ML to analyze data. Those who adopt these advanced approaches the fastest will optimize their business processes and also get the highest return on investment.
You can find expertise related to ML by partnering with outsourcing data scientists (which may be familiar to you from the dedicated development team model) or find the right specialists for your in-house team.