Too often we see decision makers take the wrong approach to decision making. If we want timely, insight-driven decisions to be the norm, we need to educate executives at all levels on how to ask questions, not ask for data or technology. Combining better processes and smart tools with a shift in skills and resources, and greater use of personalization, will increase the speed to insight, enable more effective decisions, and reduce the cost of business operations.
In this series we will explore how enterprises can transform the impact data and analytics can have on decision making.
When business executives need to decide on a course of action they typically ask IT for an extract or report from a central system, maybe the ERP or the data warehouse, and then use the data to prove an underlying belief about the cause or effect of a situation. These beliefs are based on experience but may not reflect the underlying truth that only a deeper interpretation of the data can reveal. As Malcom Gladwell outlines in Blink, we all rely on our unconscious mind to make decisions, but when it comes to complex, infrequent, or critical decisions, intuition will not deliver the best outcome.
Let me give you an example: Do you think the senior executives at 20th Century Fox used intuition or insight to sign over product merchandising rights for all Star Wars films to George Lucas in return for a cut to his studio pay check in 1977? That decision is believed to have cost the studio billions of dollars, as the franchise grew and revenues from merchandise outstripped those from cinema viewings and content licensing.
An alternative approach
As analytics, and in particular predictive analytics, becomes more prevalent, business executives are hearing of new ways to gain more insightful information for better business decision making. Big data, cloud, the internet of things, data visualization, agile—they all have a role to play, but the key element is the process, especially the start and end points.
By shifting the point at which the business engages with technology and combining traditionally separate functional teams we believe that we can rewire the analytics process and reimagine the way businesses industrialize decision making.
Alternative stages in decision-making approches
Identify issue Find evidence to support belief Manipulate data to match belief Respond or take action based on belief
Sense issue and opportunity Investigate cause and effect Predict impact Assess potential actions Agree strategy and response
Engage a network of sensors and processes to detact and identify changes and anomalies Use analytics to ask open-ended questions of the data across and outside the enterprise Rapidly identify implications through advanced analytics, functional experts, and agile tools, team and methods Consider and model options and scenarios to determine the optimal choice of actions Use insights to explain the optimal response, action taken, and impact measured against predicted outcomes
Alternative stages in decision-making approaches
- Sensing issues and opportunities – Give people critical lead time to move from reactive to proactive decision making by using sensors and implementing processes that listen to the heartbeat of the business through available data. The internet of things and machine learning are key to this capability.
- Investigating cause and effect – Fully investigating the drivers of specific issues and the extent of their effect on the business reduces the risk of snap decisions having huge, unexpected, and unsustainable knock-on effects. Shifting your business intelligence or analytics centers of excellence to a more real-time operation is key to establishing this capability.
- Predicting impact – Organizations can quickly work through the positive and negative implications of both issues and opportunities coming from within the business or from external forces, such as markets and customers, by combining advanced analytics with digital, operational, and functional expertise.
- Assessing potential options – Optimization technologies have transformed the ability of large, multidivisional companies to support complex decision making. Rapid access to data and high-power computing allows you to quickly provide statistics on the implications of multiple options at levels of accuracy and confidence that allow for faster decision making based on science rather than gut feel.
- Agreeing strategy and response – Having a rich and complete view of a situation enables you to get buy-in to change, execute against the plan, and implement the change. Establishing tracking and monitoring tools and processes provides the means to measure the commercial impact of intelligence-driven decisions.
Many industries are at an inflexion point in the adoption of analytics. By following these principles for giving users access to intelligence, we can reduce turnaround time, improve decision quality, drive greater impact, and industrialize the ability to answer complex business questions quickly and iteratively.
The leaders will be those that shift from a traditional heavy-lift approach to one based on agile and collaborative decision making.