A snapshot of Google Trends reveals that the term “embedded analytics” has been garnering increasing interest with a steady rise in searches over the last five years and even more so, recently.
Similarly, “predictive analytics” too has exploded in popularity with fast rising search volumes over the past few years.
Combining these two technologies offers the heady potential of infusing significant value into your overall business operations- embedding predictive analytics into your DNA, to make it an enduring and integral part of how you want to do agile business.
In several ways, the rise of embedded predictive analytics marks a natural evolution in the business intelligence market.
From IT-led, system-of-record reporting, we are now embarking on a new wave where users expect to benefit from data analysis in every application that is used on a daily basis and make predictions in real time, rather than waiting until the end of the day or end of the week to run an analysis.
Embedding predictive analytics across business applications can help users perform what-if analysis to maximize or minimize a value, or can be applied against whole new data sets to project a future outcome such as “projected revenue for the next few quarters” or “a customer’s likelihood of purchasing a product.”
This is especially valuable in today’s big data scenarios, where it can mean millions of dollars in savings/profits and, in some industries, might as well be the difference between life and death.
Why Smart Businesses Must Embed Predictive Analytics
A business without embedded analytics is much like a car minus GPS navigation, while legacy enterprise solutions are akin to those paper maps that need you to plan your route in advance before undertaking the road trip. Just as the GPS navigation guides your route in real time, you can drive business actions automatically by embedding predictive analytics into operational applications.
While traditional BI applications still have their place, they force users to deal with the hassle of switching between multiple dashboards and interfaces in order to derive the information that they need.
A recent embedded analytics report from Nucleus Research reveals that switching between applications leads to waste of one to two hours per employee each week.
And, that doesn’t factor in the extra time employees need to invest in filling their standalone analytics applications with data.
Embedded analytics, in contrast, tightly integrates analytical data with primary applications, thereby enabling users to access all the required data in a much more seamless and efficient manner.
Said another way, it puts intelligence inside existing systems (ERP, CRM, marketing automation, and/or financial systems) to bring additional context that satisfies users’ analytics needs at the exact moment they might question something within the applications they use every day.
Most of an organization’s legacy applications do not currently leverage predictive analytics to drive business actions. The pervasiveness of embedded analytics helps to remarkably improve the BI value within applications users already know while allowing organizations of all sizes access to valuable insights faster and at a lower cost.
What types of decisions can be improved?
Examples include embedding customer churn predictions into CRM solutions or injecting risk scores into legacy underwriting applications to improve the accuracy of decisions already being made.
Similarly a credit score can be delivered to a CRM solution and then used by a customer routing script to route customers who have low credit scores to agents specializing in helping those with poor credit. Or a Web recommendation engine may create the rule, “If a customer who just bought this item exhibits a product affinity score of 0.8 or greater, then display pictures of the following items with the text, ‘You may also be interested in purchasing these other items: ’”
Steps to Embed Predictive Analytics into your Business
So, it’s not hard to see why effective predictive analytics offers organizations potentially remarkable strategic advantages, adding improved decision making to their applications and processes powering more effective, more precise, more profitable behavior.
However, to achieve the end goal of having the best predictive analytics models in place, one has to begin by embedding the practice of predictive analytics into the broader business.
Embedment is a process that consists of multiple stages – the five most important components of which are detailed here.
Get started by focusing on decisions
The value to your customers will come not from a predictive analytic model, but from the decisions that can be made more precisely, more reliably or more profitably thanks to it.
Since not all decisions would be equally impacted by predictive analytics, it is crucial to identify and focus on such decisions that can show momentum and establish the value of each predictive analytic model in business terms. Said another way, it’s wise to reach for the “low hanging fruits”, the quick wins to start with.
Suitable decisions are usually data rich, measurable, action-oriented and valuable for the user. For each decision, it should be easy to assess —“THAT, which if only we knew would help us make a more accurate decision.”
Develop an Analytics Maturity Model
Not only does such a model provide the big picture of a predictive analytics program, but it also helps to identify where your initiatives need to go, where you should concentrate your attention to create more value for your data, and chart a roadmap for improvement. Besides, an effective model is also instrumental in helping businesses with actionable recommendations to rapidly advance their analytics initiatives and achieve enterprise goals.
Embed in Multiple Stages
Care must be taken not to bite off too much at once. It makes sense to start with a localized effort and expand systematically once you get going. An iterative approach works best since it provides measurable wins at every stage, which can be communicated across the entire organization.
Sync up analytical modeling & reporting infrastructure
The analytics and business teams cannot get out of sync at any point- they have to be true partners to build the sort of agile, test-and-learn approach, which is needed throughout the embedment process. To achieve this, it is necessary to bring business and IT people into a multidisciplinary team early on in the process. This will allow end-users to gain a much more granular understanding of the wizardry that data analysts may seem to be performing, while enabling IT to significantly benefit from the expertise of business users.
Continuously Monitor Your Model’s Validity
In order to ensure that your results deliver the required degree of precision over the longer term, you must be prepared to invest a certain amount of rigorousness into predictive analytics.
It is imperative to continuously monitor the predictive power of the models in use, identify which ones are working well and which ones need to be refreshed. This approach ensures that the models in use stay current and effective.
Once a model’s success rate drops below a predefined level, it’s necessary to create a new model that is capable of predicting more reliable results. The challenger model must further be compared against multiple predictive models– by testing actual outcomes against prediction.
While all of this may appear relentlessly time-consuming and arduous, it would be foolish to overlook these aspects if you want to see the value of an investment in embedded analytics.
According to Nucleus, within seven years 90 % of business users will interact with analytics at least once a day – however, merely 15 % will actually realize it. Organizations face two choices- either figure out how to embed predictive analytics to gain the competitive edge or get left behind by competitors that do.
Latest posts by Krittika Banerjee (see all)
- Aspire is Back Again at Temenos Community Forum 2018! - April 23, 2018
- Discover the Hyper-testing Marvel at STAREAST 2018 - April 11, 2018
- Enhancing Customer Loyalty using Market Basket Analysis - April 2, 2018