Let’s look at the case lifecycle in more detail – to understand exactly how analytics impinge upon the operational case. Recall the diagram:
1. Know When to Act
What triggers a case? Systems watch data and send signals when significant events occur. For example, a Holter monitor indicates that the patient is experiencing arrhythmic heartbeats. A stock tracking system sends out an alert that the Dow Jones average has dropped below 10,000. An inventory management system signals that the number of iPads in stock has become dangerously low.
Knowing how to respond to such events is precisely the domain of case management. Think of tax audits, mergers & acquisitions, anti-terrorism, healthcare, and customer churn. In some cases you are looking for something subtle, like patterns of events, such as a recurring dip in the price of gold on Tuesday mornings. Such advanced forms of monitoring are the domain of Complex Event Processing, which performs event monitoring, reporting, recording and filtering.
2. Inline Analytics
A case includes some form of decision and often many decisions. Should I approve this loan application? What raise should I give employee John Kowalski? Some decisions are based on subjective judgment but increasingly important decisions need to be based on hard data. This is where big data and analytics play a critical role.
What is the meaning of “inline analytics?” It is simply a collection of reports, graphs and other analytical capabilities that help the case worker to understand the data and make the best possible decisions on the case in progress. There are different levels of analytics as shown below.
Here are a few examples to think about.
- Calculate insurance premium
- Compute total cost of products customer has ordered this year
- Graph, chart, gauge
- Scatter plot
- Heat Map
- Geographic maps.
Here’s a colorful visualization from Time magazine:
- Case type is simple, average, or complex
- Customer is at risk for churn
- Customer is entitled to extended warranty
- The income of the customer is greater than $200K
- Mean time to failure for this product is greater than 1 year
- It will take 4 days to ship the product the customer ordered
- The likelihood is 75% that this claim is fraudulent
- Target promotions based on purchase history
- Recommend that the customer perform preventive maintenance
- Reject mortgage application
- Recommend diet and exercise program to patient
- This customer, J. Bananas, is likely to default on his loan application
- Amount of rain for September will exceed 5 inches
- Order volume will be down 10% in Q4
- The S&P 500 will reach 13,000 in November
The first two types are deterministic whereas the others result in probabilities. As you progress the level of analytical sophistication increases.
3. Business Rules
With analytics you can create “fuzzy business rules” for example, the likelihood is 80% that customer will not repay the loan, so it may be the right decision to reject the loan application. Rules can be exercised through human judgment calls by a case worker or they can fire automatically.
4. Learning from Experience
Inline analytics depend on background analytics. A background analytical agent discovers unknown patterns. For example, to make a recommendation requires that a data mining algorithm detects relationships between variables. (The classic example is a market basket of beer and diapers).
So the exhaust from case processing creates valuable data that needs to be stored and analyzed. Much can be learned from experience. Here are three examples.
- Operational Metrics indicate how well your business is performing, whether you are meeting your goals. It gives you hard data on Service Level Agreements and Key Performance Indicators, without which you can’t manage the business.
- Predictive Analysis enables you to discover which inputs contribute to successful outcomes and which result in failure. For example, you can predict which customers will default on their mortgages or determine which students are on the path to failure.
- Process Improvement means that you should change the way you handle cases, either by restructuring the processes, re-training employees, or adding automation.
Learning from experience means that you can continually change and improve the algorithms that you use to support decision making based on real metrics.
The explosion of big data is the outcome of recent technology innovations. Increasingly, businesses will need to incorporate mobile data, social networks, digital video, and sensor data into their case management processes. They will need to extract semantic content from documents and use it to make decisions. The big data lifecycle makes it easy to recognize the touch points between big data and case management. Among these are signals to act, better decision making, and learning from previous cases. By applying big data and analytics, businesses can improve the timeliness, efficiency, and quality of their case management.