The LOB Follows The Right Analytics Strategy

There’s no shortage of strategic advice for the LOB manager when it comes to formulating a strategy for bringing IoT analytics into the division. Any “tools” vendor worth their PowerPoints has a quick answer, often before the question is asked…and the answer typically suits that vendor best of all.

Make no mistake, the outpouring of self-service analytic tools, is compelling. But a collection of tools does not equal a strategy. LOB managers are best advised to start at the beginning, with a basic understanding of the analytics pipeline, and work outward from there.

Of course, the real world of applications is anything but a simple, sequential series of steps. Like businesses themselves, IoT analytics applications differ markedly and often change as they grow more adept. And many applications will use a subset of the five pipeline components. But for planning purposes it’s best to view the pipeline as a linear, logically connected entity, featuring these main stages:

Descriptive Analytics
Data coming in from IoT devices – often at the rate of a quarter-million or more events per second – hits the first stage, descriptive analytics. Here, data is correlated and categorized via processes such as pattern matching and anomaly detection. Behavioral analytics would typically be in play for a customer-journey application; geospatial analytics for an autonomous-driving application. These analytics use contextual awareness of historical data, combined with situational intelligence that comes from overlaying current conditions. Because these processes involve data that is already known, the analytics are typically rule-based, so performance characteristics mainly have to do with the speed and quality of the data and the rules engines.

Diagnostic Analytics
Diagnostic process now ask the question Why – why did events occur as they did? Since the answers are unknown, these typically employ probability- rather than rules-based functions. Here is where business domain expertise is especially valuable, and where teams of business-expert and analytics developers can have significant impact on results. Here, root-cause analysis might play a part in a manufacturing scenario, where analysts are investigating the causes of machine slowdowns. In a customer-journey application, analysts might ask why a large number of customers have decided to switch to a lower-cost product, giving up certain high-end features that the designers thought would be attractive.

Predictive Analytics
Also largely probability-based, predictive analytics uses the results generated by the descriptive and diagnostic functions to suggest future outcomes. Again, business-domain knowledge is essential in configuring the various analyses. Here, analysts typically employ sophisticated, compute-hungry modeling – to explore decision trees, for instance, or regression and classification tests. In the customer journey application, this is where the business unit manager might want to find out if a new set of high-end features would turn customers back to the higher-priced product. Or a railway might ask if a specific cost incentive would be enough to promote greater usage of the railroad during weekdays.

Prescriptive Analytics
Prescriptive analytics essentially suggest what actions are most likely to succeed, based on results from the previous tests. Machine-learning and other rules-based processes use classification and optimization algorithms to score prescriptive models. The decision processes may seem simple, since many are based on “if-then” logic. But the computations can be immense because of the many factors to be taken into account. Prescriptive analytics must also abide by regular organizational policies regarding appropriate courses of action in specific circumstances. After all, they typically represent the last step before action is taken.

Process Automation
Here is where the rubber meets the road, so to speak, or the product reaches the shoppers online cart. To be effective, prescriptive analytics typically should create instant action, triggering a process that shuts down a faulty machine before it can cause any additional damage or putting a product offer before the eyes of a prospect before that person leaves the web page. Close integration with business processes is vital here, as is super-fast computer processing. A healthy, end-to-end analytics pipeline might have undertaken several thousand descriptive-diagnostic-predictive-prescriptive and automated processes in the time it took to read this last paragraph.

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