As organizations invest in analytics, predictive analytics is becoming a focus area. These advanced, mathematically based approaches are a powerful tool for leveraging data.

Meanwhile, organizations are tackling the challenges of big data, handling more data in more formats that arrives and changes more quickly than ever before. As this tidal wave of data comes crashing down, traditional approaches to storing and managing data are being challenged. So, too, is the mindset that data must be queried and reported or visualized to be used. This combination of predictive analytics and big data should create new opportunities for more specific, future-oriented analysis of large amounts of data which, in turn, leads to actionable insight.

Yet most organizations are not combining predictive analytics and big data in this way. The usability and functionality of both predictive analytic and big data technologies have improved dramatically in the last few years and technology incompatibilities are not the problem. Many predictive analytic tools support access to a wide range of data sources, including those typically branded “big data,” such as unstructured text , or semi-structured Web logs and sensor data. The problem is that organizations are trying to apply these technologies to the wrong problem.

When large investments must be made in new technologies, new approaches and even new roles (such as data scientist or data miner), organizations worry about showing a return on this investment. When they look for problems to solve, they focus first on the big hairy problems  figuring these will offer the best return if they can be solved. When it comes to data and analytics, organizations are no different. Realizing that better data and its subsequent insight can improve decision-making, organizations seek to influence big, strategic decisions. They try to use data and analytics to answer questions, like should we acquire this company, continue the development of this blockbuster drug or sponsor the Olympics.

Instead, they would do better to focus on operational day-to-day decisions. These are the decisions about which companies accumulate big data volumes and where new data sources are proliferating. These are the decisions that are best suited to improvement using more advanced analytics, like predictive analytics. Plus, the cumulative value of these decisions is often far greater than the value of those one-off decisions. Putting big data and predictive analytics to work on these decisions requires decision management.

Operational Decisions Drive Big Data

Where does big data come from? Primarily, it comes from the increasing digitization of businesses and of life, in general. Every interaction now creates data about what we did and where we did it. From a business perspective, it is our day-to-day operations that create this data, as we record more information about our customers, our products, the transactions we process, our machines and vehicles, and the goods that flow through our business. This data can be structured data, stored because we use enterprise applications to process our transactions, or semi-structured data Web log data from our websites or sensor data, as products move through the supply chain. It can also be unstructured data, found in emails or call center notes; increasingly, even phone calls and security video are being recorded. All this data is operational data.

In addition to the data we capture in our systems, we have access to a rapidly expanding array of new data sources as cloud-based data services become prevalent. Demographic and behavioral data about consumers dominate these data sources. In the past we could get a summary of the kinds of people in our stores; now we can get purchase history, demographic and other data about individual consumers or tiny clusters of them. More such sources are coming online all the time, contributing to the variety and volume challenges of big data and creating a richer picture of our customers and their world.

As far as data is concerned, whether it comes from our own systems or from third-party data sources, our day-to-day operations are where big data happens. Huge volumes and a wide variety of data are available. The increasingly real-time nature of our systems is also driving this data to arrive and change more rapidly, especially in an operational context, completing the volume, variety and velocity picture. Big data is operational data.

Predictive Analytics Add Value to Operational Decisions

If big data is focused on our operations, what about predictive analytics? Predictive analytic techniques generally take large amounts of historical data and detect patterns in said data that can be used to predict the future, often by assigning a probability to how likely something is to occur. A wide variety of techniques can be used to build predictive analytic models:

  • Neural networks can assess how likely it is that a credit card transaction is being performed by the cardholder by evaluating how close this transaction is to the patterns predicted by that person’s past behavior.
  • Regression models can determine which characteristics of a customer make it more likely that they will churn, or attrite, enabling a calculation of the risk of future churn.
  • Response models can predict how likely a particular person is to respond to a particular marketing offer, based on the success or failure of offers made in the past.
  • Predictive scorecards can determine the likelihood that someone will fail to make payments on his or her loan in the coming year.

These predictions of risk, fraud and customer opportunity are all created from large amounts of historical data. To build these models, you need not just data, but data over time. Data changing over time reveals behavior and patterns. As noted above, we have the most data for our operations. Therefore, our operational environment is where we have the data we need to build predictive analytic models.
One other note: it is often true that, in order to build solid predictive analytic models you must experiment. For instance, it is hard to build a response model to see how people might respond to a particular offer if you have never tried that offer on anyone. You need to conduct an experiment to see how it is received by your test group before you can build a model for your whole customer base. Your operational environment is where you can best experiment because large numbers of interactions allow you to try different approaches and gather experimental data.

Operational Decisions add up for Large-Scale Impact

Despite the fact that our operational environment is where we collect big data and that operational data is exactly what we need for predictive analytic models, the decisions involved in operations can seem unimportant. Surely, the big strategic decisions we make as an organization are more important than decisions about individual customers or individual transactions, right? Yet there are four compelling reasons to focus on these operational decisions.

First and foremost, operational decisions matter because they make your strategy real. For instance, you can have a strategy to grow revenue by effectively retaining customers. However, unless you change the operational decisions you make about which customers to target with retention offers or which retention offers are the best to use you will not succeed. Great strategic decisions must be made real by a series of sound operational decisions.

Secondly, operational decisions are where customer centricity happens. You cannot take a customer-centric approach, putting the customer at the center of your business strategy, unless you make decisions about each customer specifically. If you are going to treat each customer as an individual and consider everything you know about him or her when making decisions about appropriate treatment,, you must make unique, operational decisions about each customer.

Thirdly, through operational decisions, companies can be susceptible to increased risk and fraud. Credit risk is accumulated one bad loan and one bad credit limit at a time. Fraud is acquired one transaction at a time, as you fail to detect a fraudulent transaction. Supply chain risk is acquired one order at a time, when you pick an unreliable supplier. Operational decisions about a single transaction are critical to avoid, or at least manage, risk and fraud.

Finally, operational decisions scale up for potentially massive impact because of their sheer volume. Compare a strategic decision to renew the contract of a major channel partner and an operational decision to retain a specific customer account. The channel renewal decision will be made once every year or two, and because it impacts sales, margins and market share, most companies will invest a lot of time and money trying to ensure the best result. A retention decision for a specific customer will also be made once every year or two. Rather than being a unique decision, it’s one of thousands, maybe hundreds of thousands, of similar decisions made within that time frame. Even though the value of retaining a single customer is much less than the value of a channel partner, the cumulative bottom line impact could be much higher, as the value is multiplied by the thousands or perhaps millions of retention decisions that must be made across the whole customer portfolio.

Decision Management puts Predictive Analytics to Work in Operational Decisions

To effectively put big data and predictive analytics to work improving these operational decisions, organizations can follow the simple three-step process at the heart of decision management.

Discover

Start by determining which operational decisions matter most to your organization. Which are most relevant to the business strategies at the top of its agenda? Which could have the strongest impact on the business drivers and measures that chart progress toward these goals? Define these operational decisions separately from your business processes and IT systems. Describing and modeling operational decisions helps business stakeholders understand and take ownership of how decisions should be made and enables decision-making to be explicitly linked to KPIs and other metrics. 

Build

Predictive analytic models can now be built to influence these decisions, predicting risk, fraud or opportunity. Because operational decisions are voluminous and often need to be made in real time, these predictive analytic models must be built into automated decisioning solutions. Creating reusable decision services to replace decision points currently embedded in business processes and operational systems allows your operational environment to rely on these decisions. The building blocks of decision services are business rules and predictive analytics. The business rules comprise expertise, policies and regulations that determine how your organization does business. The predictive analytic models predict customer behavior patterns; classifications and population segmentation drive analytically precise decisions.

Improve

Once built, these decision services need to evolve and improve to both increase the quality of decisions made and to ensure that changing circumstances are reflected in decision-making. Decisions are highly variable components, impacted by changes in markets, consumer behavior, regulation and data. Collecting data about what works and continually refining and optimizing your predictive analytic models ensures the best possible outcomes.

Big data and predictive analytics come together in the operational environment. By focusing on operational decisions, we can put big data to work, using it to drive predictions that improve our ability to make good decisions at the operational level. Because these operational decisions manage risk and fraud, focus us on customers and scale up, the ROI of this is significant. By applying the techniques and tenets of decision management, we can discover, build and continuously improve our decision-making components.

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