Predictive analytics is the practice of applying mathematical models to large amounts of data to identify patterns of previous behavior and to predict future outcomes.
The combination of data mining, machine learning and statistical algorithms provides the “predictive” element, allowing predictive analytics tools to go beyond simple correlation. In business, predictive analytics has a wide variety of uses, including:
More and more software tools are incorporating predictive analytics, making it much more accessible to users in organizations of any size. Predictive analytics provides a real opportunity to “lift all boats,” offering practical value to users who are not trained in data science or advanced analytics. This trend is often referred to as “data democratization,” the concept of making data available across an organization so that anyone can use it to make better decisions.
Below, we’ll look at why predictive analytics is valuable, how it relates to other technologies like machine learning and data mining, the role of models, and some tips on getting started.
What Is Predictive Analytics: Contents
Why is predictive analytics important?
Predictive analytics is important because it lets businesses and organizations make critical decisions based on actual data — predicting probable outcomes — at a scale that was previously impossible. All enterprises survive or fail based on their ability to forecast, plan and operate efficiently while meeting the needs of their customers. Key decisions based on intuition, guesswork and historical information have led companies to lose billions — or fail — by launching new products they thought the public would love.
What are the three types of data analytics?
The three types of data analytics are descriptive, predictive and prescriptive.
What are the outcomes of predictive analytics?
Nearly every human endeavor in the 21st century generates data, so nearly every business, organization or industry can get value from predictive analytics. Here are a few of the hundreds of potential predictive analytics use cases.
Predictive analytics is valuable across the spectrum of banking and financial service activities, from assessing risk to maximizing customer relationships. Predictive analytics are used to:
Predictive analytics in retail:
Retailers, whether online or brick-and-mortar, need to manage inventory and logistics. Predictive analytics tools let retailers correlate huge amounts of information — historical sales data, buying habits, geographical preferences, even weather data — to optimize performance.
Predictive analytics in healthcare:
Drawing from global disease statistics, drug interactions, individual patient histories and more, predictive analytics can help medical professionals provide better care and run more efficient and effective practices and hospitals.
Predictive analytics in manufacturing:
In a modern, highly automated factory, predictive analytics tools can be used to monitor and optimize each step in the manufacturing process, including design, purchasing, production, quality control, inventory management, delivery and more.
Predictive analytics in marketing:
Consumers are bombarded with advertising and marketing everywhere they look, making it harder than ever to attract and retain their attention.
Predictive analytics and big data
You’ve no doubt heard plenty of statistics about the growth of data. According to a 2018 study by market intelligence firm IDC, worldwide data creation will grow to 163 zettabytes (ZB) by 2025 — that’s 10 times the amount of data produced in 2017. The Internet of Things (IoT) is a key driver. In 2006, there were around 2 billion connected devices in the world, according to a report from Intel. By 2020, they project there will be 200 billion. Each one of those devices creates data that can be used to provide better customer service, optimize networks, target marketing messages more effectively, increase data security and about a dozen additional uses.
The value of predictive analytics continues to increase alongside the growth of data. The sheer volume of information generated every day by billions of people, devices and networks creates both challenges and opportunities that cannot possibly be addressed by the human brain alone. Predictive analytics is a huge step toward realizing the promise of big data, offering an unprecedented ability to analyze data and make predictions about future outcomes.
Predictive analytics and other emerging technologies
Predictive analytics is often conflated with other developing data and analytics technologies. Three technologies often confused with predictive analytics are machine learning, predictive modeling, and data mining.
What’s the difference between an algorithm and a predictive model?
Algorithms are the mathematical basis of predictive analytics. They are the series of steps, like a recipe, executed to achieve a result or solution. Models define the way the algorithms are applied to solve a particular problem. The model is the framework that defines the questions, and the variables considered in answering them. The algorithms are the steps used to weigh variables and arrive at answers.
A quick web search will reveal that many people use the terms “algorithm” and “predictive model” interchangeably. The word “classifier” is also used in the same context. Again, while the terminology is fluid, “classifier” is generally used to indicate an algorithm specifically designed for classification.
What types of models are used in predictive analytics
The most common models used in predictive analytics are classification algorithms and regression algorithms.
What are the most common models used in predictive analytics?
The most common models used in predictive analytics include linear regression, logistic regression, linear discriminant analysis, decision trees, naive bayes, K-nearest neighbors, support vector machines, random forest and boosting. A more complete description of each is included below.
Data scientists use a variety of predictive models based on the type of outcome they are hoping to achieve. The math behind each algorithm is complex and beyond the scope of this article, but here are a few of the most popular predictive analytics algorithms and a brief description of how they can be used.
Predictive analytics in banking and financial services: Predictive analytics is valuable across the spectrum of banking and financial service activities, from assessing risk to maximizing customer relationships. Predictive analytics are used to access the following:
What are neural networks?
Neural networks are mathematical models designed to approximate the function of the human brain. Neural networks are effective in complex pattern recognition problems and finding nonlinear relationships in data, where one or more variables are unknown. Self-driving cars rely on neural networks, because of the enormous amount of data that must be analyzed instantaneously to make driving decisions.
What is the difference between data analytics and data analysis?
Data analysis describes the process of analyzing data and drawing conclusions from it. It could also be described as the job performed by a data analyst. Data analytics is an umbrella term for the various techniques used to identify, categorize and organize data to make it ready for analysis.
How do you find the best predictive analytics software?
The best predictive analytics software is the one that most successfully meets your specific needs and budget. There are as many different types of predictive analytics tools, including:
As the discipline has become better-known and more widespread, more software vendors are incorporating predictive analytics, or versions of it, into their tools. The challenge for the buyer is to determine which tools provide actual predictive analytics, which ones use only basic algorithmic functions, and which have just appropriated the term.
Moreover, many software platforms (including Splunk) incorporate predictive analytics into various elements of their solution. The portfolio of offerings may include some solutions that include predictive analytics and others that perform functions where predictive analytics isn’t required. In other words, just because a vendor says they have predictive analytics, they might not, or might incorporate it only into certain products.
How do you get started with predictive analytics?
The best way to get started with predictive analytics is to create a plan to understand what problems you can and can’t solve, define the most critical problems to tackle, identify the gaps in your skills and technology, then run a pilot project.
Predictive analytics is the future — and the present
Predictive analytics is no longer a new science; it’s a practical tool that businesses and organizations of all sizes are using to solve their biggest business problems. No matter where you are in your predictive analytics journey, from exploring your options to fine-tuning an existing implementation, it’s vital for you to keep on top of the changes in this fast-moving discipline.
Organizations need an approach that transforms previously complex and chaotic data into an opportunity instead of a risk or an impediment — and that’s where process mining comes in. Above all else, it represents a better way to analyze and correlate disparate and seemingly unrelated information, identify weaknesses and quickly take action. Rather than wasting hours, days or weeks of your time tackling process dysfunction on spreadsheets, adopting the right process mining tool will enable you to use the data you have more effectively and drive more business value. And while tackling the data chaos in your organization might seem like a daunting task, putting the wheels into motion now will reap a multitude of rewards down the road.
To find out more about predictive analytics and the ways that it can be applied to your IT infrastructure, download The Power of Predictive IT, from Harvard Business Review and Splunk.