How to choose the best predictive analytics software in 2019

If only Kodak or Blockbuster could have seen into the future, what would they have done? If they’d had access to the latest predictive analytics solutions, things may have worked out very differently for their companies. Predictive analytics allows you to gain actionable insight from historical data to accurately predict future outcomes. This is leading to rapid growth with the value of the market for predictive analytics solutions set to pass the $12 billion mark by 2020. So, what should you look for when selecting the best predictive analytics software?

What is predictive analytics?

Let’s start with a simple definition. This kind of software identifies the patterns and trends in historical data – both structured and unstructured data – and uses predictive modelling and advanced algorithms to generate realistic predictions of future events. The more data that the predictive models have, the more accurate the predictions they will deliver.

Predictive analytics have been around for some time, but the growth in Big Data has meant this technology has now come of age. Enterprises today have so much information that making sense of it would be impossible without some element of automated analysis. In addition, companies want to combine information from a wide range of internal and external sources. For example, the best predictive analytics tools enable transactional data to be mixed with behavior data to provide much more comprehensive view of your customer.

It’s important to keep in mind that predictive analytics solutions don’t deliver conclusions, they provide recommendations. This relies on two elements. First, unlike some early forms of analytics, predictive analytics can’t be left to the data scientists. The best predictive analytics software isn’t just describing what has happened and delivering statistics on past performance. It’s delivering actionable insight. This means that in-depth knowledge in the business domain is as important as an understanding of the various analytics techniques or the ability to code analytics solutions.

The reason for this lies in the second element: You need to ‘train’ your software. Predictive analytics modelling is the heart of your solution but it is only as good as the analytics algorithms applied and the data on which it is fed. Business domain knowledge is vital to make sure that that predictive modelling takes place using the correct assumptions and information. For example, predictive analytics can spot buying trends and patterns, but it takes someone with an understanding of the market to help the software interpret them and assess their relevance.

Key features of predictive analytics software

The best predictive analytics software delivers a wide range of business and operational benefits. For instance, manufacturing and logistics operations are quickly applying predictive maintenance – based on predictive analytics models – to ensure maximum performance and uptime for their assets. Financial services and retail organizations are using Predictive Analytics tools to help with many key business functions including personalized marketing and fraud detection.

So, what should you look for in the best predictive analytics software?

Predictive modelling

A predictive model uses statistical techniques to identify patterns and trends within historical data. Predictive analytics algorithms are then applied to predict certain outcomes or behaviors. Although this is based on historical data, the predictive model must enable fresh and near-real-time data to be fed into it so that the predictive model is continually learning and the accuracy of its predictions continually improving. The best predictive analytics software will allow you to create a wide range of data models such as regression, classification, clustering, decision tree and time series models.

Predictive algorithms

Predictive algorithms turn the data within your predictive model into actionable insight. However, there is not one single algorithm that will answer all your questions. The algorithm is likely to need customization for a specific business problem or question, such as the most likely points of disruption in your supply chain. Often, experimentation is required when creating a Predictive Analytics model and you must be prepared to try different algorithms on a problem until you find the most effective solution. The best Predictive Analytics solutions will support an extremely wide range of predictive algorithms to support the creation and testing of many predictive models.

Data mining

Like any analytics solution, your predictive analytics tool is only as good as the data it’s working. Garbage in, garbage out. Data mining is the process of extracting information that can be fed into your predictive analytics models. This information will come from a wide range of structured and unstructured data sources – both within and external to your organization. For example, social media gives an increasingly valuable source of data for predictive analytics software where customer sentiment analysis is important. This data has to be brought together, cleansed, and converted into a format that your predictive analytics solution can use.

Data visualization

Data visualization is one of the most important elements of any analytics solutions. This is especially true for predictive analytics software where the results provide actionable insight to improve decision-making. Enterprises can’t afford to leave the interpretation of the results to data scientists; they have to be easily digestible by the people who will actually work with them – end users and business managers. Most Predictive analytics solutions provide a range of data visualization capabilities including charts, graphs, reports and dashboards. The best predictive analytics software will give you easy-to-use, self-service features where users can define their own visualization capabilities to display the results in the way they want.

Text analytics

Another important feature of your predictive analytics software is text analytics. This feature allows you to extract meaning from written communications. It allows your to identify and topics of interest within the text upon which you can decide the best action to take. It is this capability that enable predictive analytics solutions to tap into the unstructured data held in documents, spreadsheets, emails and social media posts.

Open source

Most organizations will find advantage in selecting solutions that are open source. Some predictive analytics software available today delivers proprietary models and algorithms that can’t be changed. This is known as ‘black boxed’ features and has inherent drawbacks in terms of collaboration and innovation. Being open source allows communities of common interest to quickly and collaboratively develop predictive analytics solutions designed for their specific industry.

Why choose OpenText for Predictive Analytics?

OpenText™ is a leader in the development of analytics solutions Predictive analytics is key element in OpenText™ Magellan™ – a comprehensive AI-powered analytics platform.

The solutions is flexible artificial intelligence (AI) and analytics platform that combines machine learning, advanced analytics, and enterprise-grade business intelligence (BI) with the ability to acquire, merge, manage, and analyze structured and unstructured big data. It enables you to perform predictive analytics tasks and have the results presented in easy-to-understand, interactive reports and dashboards.

Editor’s note: This is an installment in our “AI Glossary” series of blog posts, offering guidance on key areas of artificial intelligence and analytics. Look for future posts in this series over the months to come.

Zachary Jarvinen

Zachary is the Product Marketing Lead for Analytics and Artificial Intelligence at OpenText. He previously worked at Global Fortune 500 Epson and the U.S. State Department, and was part of the 2008 Obama Campaign Digital Team. Zachary speaks fluent Spanish and Portuguese, and holds an MBA/MSc from UCLA and the London School of Economics.

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