How to hire predictive analytics experts
Predictive analytics isn’t a single skill–it’s a combination of disciplines, including statistical analysis, predictive modeling, data mining, machine learning, visualization, and more, in addition to the engineering skills that may be required to gather and process the data.
How to shortlist predictive analytics specialists
Data scientists have a number of options when it comes to programming languages and libraries. Java, R, Python, and Scala are all popular choices with a number of high-quality open-source libraries for common predictive analytics tasks, from machine learning to data mining and visualization. Which one is best for your project will depend on what your team is used to working with and what your specific goals are. Java and Python are generally thought to be best-suited to building production apps, while R is favored for heavy-duty analytics work. Scala is increasingly popular, especially with teams who need a highly scalable language and who work with Apache Spark.
You might also look for proficiency with business intelligence (BI) tools that support some degree of predictive analytics. Three of the most popular options include:
- Tableau, a BI and analytics platform that specializes in rich visualizations. It can connect to literally hundreds of different data sources, including Excel, SAP, Salesforce, SQL databases, AWS, JSON files, Google Analytics, and more.
- Microsoft Power BI, a cost-effective and scalable BI tool. It lacks some of the analytics and visualization capabilities that Tableau has, but it boasts more complete R integrations, as well as real-time streaming capabilities. As a Microsoft product, it also plays well with Excel and Microsoft Azure, which could be a big advantage for certain enterprise teams.
- QlikView is an analytics-focused tool that also comes with robust API support. Unlike other analytics platforms, Qlikview processes data using system RAM. By processing data “in-memory,” QlikView is able to achieve much greater speeds than systems built on traditional relational databases.
Predictive analytics FAQs
What is predictive analytics?
At its heart, predictive analytics is about using historical data to make predictions. Your credit score is a good example of predictive analytics in everyday life. That score is based on your past credit history and is used to predict how likely you are to repay your debts. While predictive analytics has been used for decades in the financial services industry, it’s only just recently become an important tool in other industries. The advancement of data collection and processing technologies has made it possible to apply predictive analytics to nearly every aspect of business, from logistics to sales to HR.
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Why hire predictive analytics experts?
When organizations want to make data-driven predictions about future events, they rely on predictive analytics. Driven by the explosion of big data, predictive analytics is fast becoming an important part of many industries and functions.
While predictive analytics has been used for years in financial services, it’s now an integral part of many industries and businesses. The massive increase in data collection abilities coupled with the widespread availability of commodity hardware are the two trends that have made the spread of predictive modeling a reality.
- Customer Relationship Management (CRM). Using a combination of regression analysis and clustering techniques, CRM tools can separate your customers into cohorts based on their demographics and where they are in the customer lifecycle, allowing you to target your marketing efforts in ways that are most likely to be effective.
- Detecting outliers and fraud. Where most predictive analytics applications look for underlying patterns, anomaly detection looks for items that stick out. Financial services have been using it to detect fraud for years, but the same statistical techniques are useful for other applications as well, including medical and pharmaceutical research.
- Anticipating demand. Now retailers are able to anonymized search data to predict sales of a given product down to the regional level. Amazon has even patented a process it calls “anticipatory shipping,” which aims to ship products it expects customers to buy before they’ve even placed an order.
- Improving processes. For manufacturers, energy producers, and other businesses that rely on complex and sensitive machinery, predictive analytics can improve efficiency by anticipating what machines and parts are likely to require maintenance. Using historical performance data and real-time sensor data, these predictive models can improve performance and reduce downtime while helping to avert the kinds of major work stoppages that can occur when major systems unexpectedly fail.
- Building recommendation engines. Personalized recommendations are relied on by streaming services, online retailers, dating services, and others to increase user loyalty and engagement.
- Improving time-to-hire and retention. Even HR departments are beginning to use predictive analytics to improve their hiring and management policies. Companies can use data from the HR systems to optimize their hiring process and identify successful candidates who might be overlooked by human screeners.