There was an article in The New Yorker last week entitled, “Why Smart People are Stupid.”
Its premise stated, “When people face an uncertain situation, they don’t carefully evaluate the information or look up relevant statistics. Instead, their decisions depend on a long list of mental shortcuts, which often lead them to make foolish decisions. These shortcuts aren’t a faster way of doing the math; they’re a way of skipping the math altogether.”
Given all the work organizations do to collect and align data, there really is no reason why foolish decisions should be made any longer, especially when there’s a huge price tag associated with bad decisions.
And, when you think about how many decisions an organization makes on a daily basis (thousands, millions?), being foolish is no longer an option – especially calculating the cost between one foolish decision and a million foolish decisions.
And, most of these transactional or tactical decisions need to be made in an instant, such as a customer service agent deciding to give a customer a discount to combat churn; an insurance claims system determining whether a potentially fraudulent activity should be escalated for investigation; or, a logistics manager deciding if a truck is safe to put on the road for the next delivery.
To end this foolishness, IBM has introduced Analytical Decision Management to help organizations automate and optimize decision making in real time to ensure the best outcomes occur every time.
Essentially, Analytical Decision Management takes the complexity out of big data by quickly analyzing and embedding analytics directly into business systems (in a call center, on a website, on the manufacturing floor) to empower employees and systems on the front lines with the ideal action.
It also allows business users to run multiple “what if” simulations, compare the outcomes of different approaches and test the best business outcomes before the analytics are deployed into the operational system. Even analytics follow the old adage, “Measure twice, cut once.”
IBM Analytical Decision Management
According to IDC, the Decision Management software market is expected to exceed $10 billion by 2014. To meet this growing demand, IBM Analytical Decision Management is the first in a series of IBM Smarter Analytics innovations that will change how organizations weave analytics into the fabric of their business, fueling all systems, decisions and actions to consistently deliver optimized outcomes, while adapting to changing conditions.
The newly released Analytical Decision Management combines and integrates predictive analytics, business rules, scoring, and now, optimization techniques, into an organization’s systems to:
· Maximize every customer interaction to grow revenues and increase loyalty
· Detect and prevent threats and fraud in real time to reduce risk
· Proactively manage resources by predicting equipment failure, staffing downtime and service disruptions to contain cost
For example, Santam Insurance is using Analytical Decision Management to transform its claims processing by enhancing fraud detection capabilities and enabling faster payouts for legitimate claims. In fact, in the first four months of use, Santam saved $2.4 million on fraudulent claims. (Read the full case study.)
Santam can now automatically assess if there is any fraud risk associated with incoming claims and allow frontline claims representatives to distribute claims to the appropriate processing channel for immediate settlement or further investigation, which in turn, optimizes operational efficiency.
As all customers and claims are not created equally, Analytical Decision Management adapts its recommended actions in real time to accommodate changing conditions as new data is collected and outcomes are recorded.
Analytical Decision Management is also equipped to automatically prepare, cleanse and transform data for the best possible analytics through the new Entity Analytics capabilities.
There can be challenges when diverse enterprise-wide data is integrated – especially when this data contains natural variability (e.g., Bob versus Robert), unintentional errors (e.g., a transposed month and day in a date of birth), and at times professionally fabricated lies (e.g., a fake identity).
The Entity Analytics feature allows data scientists to overcome some of the toughest data preparation challenges and create the most complete view of an individual record. Users can generate higher quality analytic models and, as a result, organizations will enjoy better business outcomes whether the goal is detecting and preempting risk or better responding to a customer’s needs.
For more information:
· Attend the IBM Innovations in Business Analytics Virtual Launch on demand and learn all about the new Analytical Decision Management solution
· Watch the demo for the new IBM Analytical Decision Management
· Read the whitepaper on how to use decision management for a competitive advantage