All businesses, big or small, want the same thing: To be profitable now and in the long run.
No tricks and tactics on that – except there are.
With intense competition and uncertainty in virtually every industry, the way businesses use historical data to predict future outcomes has emerged as one of the most effective tools for survival. Data is now more valuable than ever, but only if collected, analyzed, and used correctly.
That's why predictive analytics is such an important part of business intelligence.
But what exactly is predictive analytics? Is it a worthwhile investment, especially for companies with limited data management resources? More importantly, what are the prerequisites for adopting and implementing predictive analytics?
Read on to find out.
What is predictive analytics?
Predictive analytics is a branch of high-level analytics that examines available data to forecast future outcomes.
It uses historical data, machine learning, and artificial intelligence to predict future trends. It also makes use of statistical tools such as data modeling, multivariate statistics, regression analysis, forecasting, and other techniques to serve its purpose.
Why predictive analytics? Why now?
Predictive analytics isn't new. It's a technology that's been around for several decades, but underutilized and often neglected. However, more businesses are now turning to predictive analytics as a means to gain competitive advantage and grow their bottom line.
The big question is, why now?
Here are some explanations:
Exponential growth in computing power
Cheaper computing and storage
More user-friendly software
Increased data volume and types, coupled with growing interest by management to use data to make informed decisions and provide valuable insights
Tougher market conditions, which give rise to the need for competitive differentiation
Who can use predictive analytics?
With the emergence of user-friendly, more interactive software, predictive analytics is no longer a statisticians-and-mathematicians-only domain. Today, line-of-business experts, business analysts, and managers are using these technologies too, and to good effect.
Better still, this business intelligence tool doesn't discriminate. Businesses of any size and industry can use predictive analytics to optimize their operations, reduce risk, and increase revenue generation.
How can predictive analytics help your business?
As a business intelligence tool, predictive analytics can help you discover new opportunities and address difficult challenges in the following ways:
1. Fraud detection
By combining multiple methods of analytics, predictive analytics enhances pattern detection, which is critical in identifying and preventing criminal behavior.
With cybercrime being a major concern today, you can use behavioral analytics to examine on-network activities in real-time. This enables you to spot inconsistencies which may indicate zero-day vulnerabilities, advanced persistent threats, and fraud.
2. Optimization of marketing campaigns
You can use predictive analytics to determine consumer behavior, product preferences, as well as responses to pricing and certain types of marketing tactics. With the resultant findings, you can customize your marketing campaigns to suit future market trends, thus optimizing them.
3. Streamlining operations
Predictive models can help you forecast inventory requirements so you can schedule deliveries on time and manage resources more efficiently. They can also help you predict future labor requirements so that you prepare accordingly.
4. Risk reduction
By predicting future outcomes, predictive analytics can help you avoid risky investments.
What do you need to get started?
The first thing you need to implement predictive analytics is to define your business problem and its scope.
What future outcome do you want to know based on what happened in the past?
Which specific aspect of that outcome do you seek to understand and forecast?
How will you use the predictions?
These are all questions you need to address before you begin implementing a predictive analytics solution.
Second, you need data – lots of it. At the very least, you need several thousands of records, all with a sufficient volume of negative and positive outcomes. But simply having the data isn't enough. You need a data management expert to clean the data and prepare it for analysis.
The records you provide act as training data, which is used to create a predictive model that applies to your current data. Be sure to provide accurate data, as predictive models are only as accurate as the information they're built upon.
With your data ready, the next requirement is a software suite that's powerful enough to rapidly parse through your information sets and provide predictions. You can purchase the software and do it in-house or outsource to an experienced service provider. For the former, you'll need a team of dedicated data scientists.
Bottom line on predictive analytics
There's no denying that predictive analytics can be the difference between failing and thriving businesses for the next couple of decades.
But, managers need to keep their expectations in check when using this business intelligence tool.
You need to keep in mind that your predictive model will only be as accurate as the data used to create it. And even with the right data, the predictions you get only provide the likelihood of an outcome. As such, you need to leave some room for error and/or uncertainty.
That said, using powerful software, the right data, and making good use of data management experts can give you almost certain predictions and significantly enhance your managerial decisions.
How Can DashboardFox Help With Predictive Analytics
While DashboardFox has no machine learning or artificial intelligence algorithms built-in, DashboardFox can be a component of a predictive model solution.
DashboardFox can provide the data access and presentation layer components of a predictive analytics tech stack.
DashboardFox makes it easy for business users to create datasets from one or more data sources and as discussed above, data (and lots of it) is critical for predictive analytics.
DashboardFox allows you to quickly organize and query live data and via its API, you can even programmatically access that data. For those in a more manual process, it’s simple to export any data set directly to Microsoft Excel or CSV files for use by a predictive analysis engine.
DashboardFox also provides the presentation layer. Converting the results of any analysis that may be stored in a database (or spreadsheet), DashboardFox can convert that data into interactive dashboards and reports. Users can segment data via dashboard filters, drill down into more details, and schedule, share via direct link, or embed results both to secure internal users or anonymous public facing websites.
To learn more about DashboardFox, schedule a live demo and speak with one of our technical experts about your environment.