ByKatrina Wakefield, Marketing, SAS UK
For many organisations, big data –incredible volumes of raw structured, semi-structured and unstructured data – is an untapped resource of intelligence that can support business decisions andenhance operations. As data continues to diversify and change, more and moreorganisations are embracing predictive analytics, to tap into that resource andbenefit from data at scale.
What is predictive analytics?
A common misconception is that predictive analytics and machinelearning are the same thing. This is not the case. (Where the two do overlap, however, is predictive modelling – but more onthat later.)
At its core, predictive analyticsencompasses a variety of statistical techniques (including machine learning, predictive modelling and data mining) and uses statistics (both historical andcurrent) to estimate, or ‘predict’, future outcomes. These outcomes might bebehaviours a customer is likely to exhibit or possible changes in the market,for example. Predictive analytics help us to understand possible futureoccurrences by analysing the past.
Machine learning, on the other hand, isa subfield of computer science that, as per Arthur Samuel’s definition from 1959, gives ‘computers the ability to learn without being explicitly programmed’.Machine learning evolved from the study of pattern recognition and explores thenotion that algorithms can learn from and make predictions on data. And, asthey begin to become more ‘intelligent’, these algorithms can overcome programinstructions to make highly accurate, data-driven decisions.
Howdoes predictive analytics work?
Predictive analytics is driven by predictivemodelling. It’s more of an approach than a process. Predictive analytics and machine learninggo hand-in-hand, as predictive models typically include a machine learningalgorithm. These models can be trained over time to respond to new data orvalues, delivering the results the business needs. Predictive modelling largelyoverlaps with the field of machine learning.
There are two types of predictivemodels. They are Classification models, that predict class membership, and Regression models that predict a number. These models are then made up of algorithms. The algorithms perform the data mining and statistical analysis, determining trends and patterns in data. Predictive analytics software solutions will have built in algorithms that can be used to make predictive models. The algorithms are defined as ‘classifiers’, identifying which set of categories data belongs to.
Themost widely used predictive models are:
- Decision trees:
Decision trees are a simple, butpowerful form of multiple variable analysis. They are produced by algorithms that identify various ways of splitting data into branch-like segments.Decision trees partition data into subsets based on categories of input variables,helping you to understand someone’s path of decisions. - Regression (linear and logistic)
Regression is one of the most popular methods in statistics. Regression analysis estimates relationships among variables, finding key patterns in large and diverse data sets and how they relate to each other. - Neural networks
Patterned after the operation of neuronsin the human brain, neural networks (also called artificial neural networks) are a variety of deep learning technologies. They’re typically used to solve complex pattern recognition problems – and are incredibly useful for analysing large data sets. They are great at handling nonlinear relationships in data – and work well when certain variables are unknown
Other classifiers:
- Time Series Algorithms: Time series algorithms sequentially plot data and are useful for forecasting continuous values over time.
- Clustering Algorithms: Clustering algorithms organise data into groups whose members are similar.
- Outlier Detection Algorithms: Outlier detection algorithms focus on anomaly detection, identifying items, events or observations that do not conform to an expected pattern or standard within a data set.
- Ensemble Models: Ensemble models use multiple machine learning algorithms to obtain better predictive performance than what could be obtained from one algorithm alone.
- Factor Analysis: Factor analysis is a method used to describe variability and aims to find independent latent variables.
- Naïve Bayes: The Naïve Bayes classifier allows us to predict a class/category based on a given set of features, using probability.
- Support vector machines: Support vector machines are supervised machine learning techniques that use associated learning algorithms to analyse data and recognise patterns.
Each classifier approaches data in adifferent way, therefore for organisations to get the results they need, theyneed to choose the right classifiers and models.
Find out more about Machine Learning algorithms
Applicationsof predictive analyticsand machine learning
For organisations overflowing with databut struggling to turn it into useful insights, predictive analytics and machine learning canprovide the solution. No matter how much data an organisation has, if it can’tuse that data to enhance internal and external processes and meet objectives,the data becomes a useless resource.
Predictive analytics is most commonlyused for security, marketing, operations, risk and fraud detection. Here arejust a few examples of how predictive analytics and machine learning areutilised in different industries:
- Bankingand Financial Services
In the banking and financial services industry, predictive analytics and machine learning are used in conjunction to detect and reduce fraud, measure market risk, identify opportunities and much, much more. - Security
With cybersecurity at the top of every business’ agenda in 2017, it should come as no surprise that predictive analytics and machine learning play a key part in security. Security institutions typically use predictive analytics to improve services and performance, but also to detect anomalies, fraud, understand consumer behaviour and enhance data security. - Retail
Retailers are using predictive analytics and machine learning to better understand consumer behaviour; who buys what and where? These questions can be readily answered with the right predictive models and data sets, helping retailers to plan ahead and stock items based on seasonality and consumer trends – improving ROI significantly.
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Developingthe right environment
While machine learning and predictive analytics can be aboon for any organisation, implementing these solutions haphazardly, without consideringhow they will fit into everyday operations, will drastically hinder their ability to deliver the insights the organisation needs.
To get the most out of predictive analytics and machinelearning, organisations need to ensure they have the architecture inplace to support these solutions, as well as high-quality data to feed them andhelp them to learn. Data preparation and quality are key enablers of predictiveanalytics. Input data, which may span multiple platforms and contain multiplebig data sources, must be centralised, unified and in a coherent format.
In order to achieve this, organisationsmust develop a sound data governance program to police the overall managementof data and ensure only high-quality data is captured and recorded. Secondly,existing processes will need to be altered to include predictive analytics and machine learningas this will enable organisations to drive efficiency at every point in thebusiness. Lastly, organisations need to know what problems they are looking tosolve, as this will help them to determine the best and most applicable modelto use.
Understandingpredictive models
Typically, an organisation’s datascientists and IT experts are tasked with the development of choosing the right predictive models – or building their own to meet the organisation’s needs. Today,however, predictive analytics and machine learning is no longer just the domain ofmathematicians, statisticians and data scientists, but also that of businessanalysts and consultants. More and more of a business’ employees are using it todevelop insights and improve business operations – but problems arise whenemployees do not know what model to use, how to deploy it, or need informationright away.
At SAS, we develop sophisticatedsoftware to support organisations with their data governance and analytics. Our data governancesolutions help organisations to maintain high-quality data, as well as alignoperations across the business and pinpoint data problems within the sameenvironment., Our predictive analytics solutions help organisations to turn their datainto timely insights for better, faster decision making. These predictiveanalytics solutions are designed to meet the needs of all types of users andenables them to deploy predictive models rapidly.
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