The Future of BI: Actionable Intelligence

Business intelligence isn’t a new concept, but it is one that has made leaps and bounds in the past few years thanks to the proliferation of big data, the development of sophisticated analytics tools, the Internet of Things (IoT) and the advent of artificial intelligence. These technologies show no signs of slowing down, but how will they impact the future of BI?

Traditional Analytics Focus on Historical Data

Traditional analytics, once the next big thing in BI, provide historical data, allowing companies to examine the past trends and spikes, and even hypothesize the factors that influenced those trends with reasonable accuracy. It’s certainly possible to make inferences from historical data, such as “if x, then y” types of educated guesses based on similar trends and patterns in the past. In fact, historical data is crucial for accurate forecasting.

However, hypothesizing a likely outcome based on historical patterns doesn’t take into account the many other variables that may be influencing the outcome. Traditional analytics are the foundation, and historical data is important, but it falls short of providing the predictive power of more sophisticated analysis.

An Example: The Shortcomings of Historical Data

For example, a customer may be following the same path to purchase as many customers in the past, hitting all the right touchpoints and navigating through the buyer’s journey in a largely predictable manner. You might think this is a guaranteed sale, but you’d be wrong.

  • What if the prospect in question has a pain point that differs substantially from that of your typical buyer persona?
  • What if the prospect is about to be offered a deal they can’t refuse from a competitor?
  • What if the prospect’s needs are evolving? What if their situation is changing?

By focusing on one aspect of the journey – the path – without considering the influence of other variables, you’re selling yourself and your data short.

Additionally, this scenario implies that the company isn’t effectively segmenting prospects and developing buyer personas, which provide data that can account for the differences between your typical buyers. You don’t know this for sure, of course, until the future actually happens. Customers want to be treated as individuals and they want personalized experiences – but these are not achievable goals if you focus too narrowly on a single component of the buyer’s journey.

What happens when that same prospect nears the end of the funnel and is disappointed that your marketing and lead nurturing materials haven’t yet touched on their primary pain point? They may drop out of the funnel to seek answers elsewhere. Effectively managing the customer experience requires looking at each individual as a unique person; it’s the only way to provide the truly personalized experiences today’s consumers demand. While historical data can help to fill in this unique, individual profile of a prospect or customer, it doesn’y:

  • Recognize that they may be about to drop out of your sales funnel
  • Identify the likely reason they’re about to drop out of your sales funnel
  • Tell you what to do to keep the prospect in your funnel
  • Evaluate the likely impact of external influences

Business Intelligence is More Than Just Data Analytics

Analytics is a key component of business intelligence, but BI really refers to the whole system, including the technologies and processes required to:

  • Collect data
  • Aggregate data
  • Analyze data
  • Present data in meaningful ways
  • Support business decision-making

This can be carried out to some extent with traditional (historical) analytics alone, but this information is not actionable – it merely tells you what has happened in the past given a particular set of circumstances and variables. It doesn’t:

  • Suggest actions most likely to result in desired outcomes
  • Reveal what will happen if you decide on a particular course of action
  • Tell you what will happen if you do nothing – unless all extraneous variables are identical to a historical scenario

Predictive and Prescriptive Analytics Provide Actionable Insights

Today’s businesses need to drive decision-making with data, and that requires predictive and prescriptive analytics. With predictive analytics, you can analyze data to determine the outcome most likely to occur, and with prescriptive analytics, you can run hypothetical scenarios to determine the most likely outcome to occur if you take a certain action.

If taking Action A is likely to produce an undesirable outcome, you can halt those plans immediately. On the other hand, if taking Action B is likely to generate the results you’re looking for, you can pivot on the spot and influence future outcomes. It’s the closest thing you’ll probably get to a crystal ball – at least for now. With more brands engaging in experiential marketing and similar strategies, the need to take a customer-centric position is more apparent than ever. And to do so, you’ll need to leverage sophisticated analytics to get to know your customers better than ever before.

The Future of BI is Actionable

Business intelligence is an evolving field, but to take full advantage of it, you’ll want a sophisticated solution that makes use of the most advanced analytics techniques in order to glean actionable intelligence. While colorful pie charts and sleek-looking graphs can impress in the boardroom and C-suite executives value the stories that BI can tell about what your data reveals, even more important are your next steps:

  • What actions will you take to overcome those bleak sales figures from last quarter?
  • How do you plan to address the uptick in customer churn?
  • Are you able to explain which customers are leaving, why they’re leaving and whether there are specific actions you can implement to boost retention?

You need a plan of action. In today’s fast-paced, highly competitive environment, this is the information that matters most, not so much what happened in the past.

On the BI Horizon

That’s not to say that historical data isn’t valuable, it’s critical for any level of analysis, providing the foundation on which more sophisticated analytics are built. But business intelligence is quickly evolving into a hybrid of its traditional form (pure BI, such as reports on last quarter’s sales data) to include business analytics (predictions about next quarter’s sales) plus the predictive power for analyzing what-if scenarios.

Machine learning is already a reality. On the horizon looms a transformation: BI driven by artificial intelligence (AI), which is already set to transform healthcare data through better patient risk management and enable automation and scale that, at least in the healthcare field, has been somewhat lacking to date in comparison to other industries.

Business intelligence, it seems, is destined to follow a similar trajectory as the analytics field itself, acquiring greater capabilities as more sophisticated methods of analysis are possible. So, today’s businesses can choose to keep pace with advancing technology or remain fixed in place, but this is one what-if scenario for which no crystal ball is required to know the outcome.

Author Bio: Amy Cross works at NGDATA as a Senior Content Marketing Manager. She has over 10 years of marketing experience, with a focus on content, digital, strategy and design.

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