How to get the most out of data analytical programs

Data analytics can provide valuable insights about your business and its customers, but to get the full benefit, businesses must know how best to analyse it

CREDIT: This is an edited version of an article that originally appeared on Business News Daily

Business analytics has three primary components: descriptive, predictive and prescriptive. Descriptive analytics is a basic statistical analysis that summarises raw data. It includes social engagement counts, sales numbers, customer statistics and other metrics that show what’s happening in your business in an easy-to-understand way.

Predictive and prescriptive analytics isn’t as straightforward; they take descriptive data and transform it into actionable information. We’ll dive deeper into predictive and prescriptive analytics, explain how they compare to each other, and show you how to put analytics to work to make better decisions.

Predictive vs. prescriptive analytics

Predictive and prescriptive analytics inform your business strategies based on collected data. Predictive analytics forecasts potential future outcomes, while prescriptive analytics helps you draw specific recommendations.

Predictive and prescriptive analytics are tools for turning descriptive metrics into insights and decisions. But you shouldn’t rely on one or the other; when used together, both analytics types can help you shift your business strategy to create the best possible outcomes.

“Predictive by itself is not enough to keep up with the increasingly competitive landscape,” said Mick Hollison, president of enterprise data management company Cloudera.

“Prescriptive analytics provides intelligent recommendations for the optimal next steps for almost any application or business process to drive desired outcomes or accelerate results.”

What is predictive analytics?

Predictive analytics is an advanced analytics category that helps companies make sense of potential outcomes or a decision’s repercussions. By leveraging mined data, historical figures and statistics, predictive analytics uses raw, up-to-date data to peer into a future scenario.

Until a few years ago, predictive analytics was the province of enterprise-level businesses – the only ones able to afford to parse and interpret reams of data from multiple sources.

However, the growth in software as a service (SaaS) providers and CRM analytics means even small companies can access valuable data analytics. A key aspect of predictive analytics involves segregating superfluous or misleading data that could distort the insights.

What is prescriptive analytics?

Prescriptive analytics also looks at future scenarios, but it employs a more technological approach. It uses complicated mathematical algorithms, artificial intelligence and machine learning to take a deeper look into the ‘what’ and ‘why’ of a potential future outcome.

Prescriptive analytics can also help a company see multiple options and potential outcomes. As more data comes in, prescriptive analytics can alter its predictions and suggestions accordingly.

“Prescriptive analytics can help companies alter the future,” said data-driven digital strategist, Immanuel Lee. Predictive and prescriptive analytics is “both necessary to improve decision-making and business outcomes,” he added.

Putting analytics to work

Here are a few tips to help you get the most out of your analytics programs:

Start small with data analytics

Data analytics is a complex subject that can be overwhelming, and you don’t want your best insights to get lost. Lee advised thinking big with your overarching analytics strategy but starting small tactically.

“With the complexity of big data and the systems that manage and process data, we can easily overlook the fact that sometimes there’s a solution in the simplest thing,” he said. “Small wins will help earn support for long-term analytics projects.”

Create rich data sets

There are many what-if scenarios when you run and market a business, and predictive analytics doesn’t always account for alternate possibilities. Mathew said looking at your predictive analytics more closely to create richer information sets (by accounting for demographics like gender and age) will yield better results from your prescriptive recommendations.

“Social media marketers care about maximising engagement and reach on their social posts,” he said. “Prescriptive analytics can make data-driven recommendations, such as the use of a specific hashtag or emoji, to maximise social traction with a specific audience segment.”

Understand the reasons behind prescriptive recommendations

Sengupta emphasized the importance of fully understanding the logic, nuances and circumstances behind the results of prescriptive analysis before taking action. Be prepared to prove that your results are statistically sound.

“Pretty graphs can be very compelling, but this is only software, and its analytical power is only as accurate as the human who designed it and the data we feed it,” Sengupta said. “It’s critical that business users understand the ‘story’ behind the results and the prescriptive action suggested.”

Keep your systems up to date

As your business grows and evolves, so should your algorithms. Hollison noted that both predictive and prescriptive analytics should be updated continuously with the latest data to improve predicted and prescribed actions based on real-time successes and failures.

“Predictive and prescriptive analytics depend on a solid data foundation,” Mathew added. “The analytics are only as good as the data that feed them.”