What Makes Decision Intelligence a Better Framework for Decision-making Models
Decision Intelligence is A Hot Topic, But What Does It Really Mean?
First, we need to sort out exactly what this rather abstract concept really means. For example, Senior Analysts at Gartner define Decision Intelligence as follows:
A framework that brings multiple traditional and advanced techniques together to design, model, align, execute, monitor, and tune decision models and processes.
Conversely, the Head of Decision Intelligence at Google defines it somewhat differently:
A new discipline that brings together the best of applied data science, social science, and managerial science to use data to improve the lives of business and lead AI projects.
A common thread in both perspectives is that the latest technological advances can augment an organization’s ability to make a beneficial business decision in real-time.
We already know that data and analytics leaders and AI enthusiasts across the globe use increasingly complex algorithms and big data in a concerted effort to improve organizational decision-making capabilities. Despite these initiatives, evidence from major research companies suggests that businesses are failing to take advantage of technology-focused breakthroughs that can vastly improve the efficiency and effectiveness of decisions. Why is that?
The Missing Link
While there is widespread acceptance of the fact that the implementation of certain decision-making technologies can result in superior business outcomes, there is a gap between this belief and the reality of software capabilities. This ambiguity is accelerated by certain challenges in the organisations:
- While many technologies tout their ability to improve the decision-making process, these fail to define and model decisions in a concerted and thorough manner that ultimately leads to the desired outcome.
- The last mile of analytics conundrum- this involves using different types of analytics while failing to adequately integrate the algorithmic results within mainstream systems and processes, and thereby missing the opportunities necessary to influence change.
- While a singular approach to decision-making may be adopted based on team preferences, the fact is that most business landscapes are far too complex to rely on a solitary technique.
- Data and analyst leaders as well as AI practitioners are often guilty of focusing more on the accuracy of their algorithms than in taking the time to understand the impact and effectiveness of the resulting business decisions on day-to-day operations.
- Cultural change within an organization is a key component for the widespread adoption and successful implementation of any decision automation technology, yet many companies experience a consistent disconnect between IT and human resources in this regard.
The real promise of this new discipline of “Decision Intelligence” is that it can address all of these challenges by providing a unified delivery framework.
How Should the Decision Intelligence Framework Be Applied?
Businesses typically face several challenges when it comes to embracing a new principal, especially one as involved as Decision Intelligence.
Obviously, it is critical that companies hire the right sort of talent to solve their particular business challenges, whether these involve everything from the building of AI algorithms to the usage of advanced technologies to streamline operations. In the case of the latter, there is little point in a skilled team of data scientists working on an algorithm for 99.99% accuracy if this does not meet expectations for operational efficiency.
While the Decision Intelligence framework provides high-level guidance to those who wish to avail of its capabilities, it does not offer any insight as to how to align a complex business problem with organizational objectives. This absence of ‘best practices’ in this regard makes it difficult to quantify the ROI and real business value of the framework.
As such, a decision-centric approach is a crucial complement to any Decision Intelligence framework, as it will render a detailed design for successful implementation by identifying all of the relevant technologies and the attributes needed to meet a particular business need.
The last requirement is a technology that pieces together of all the relevant parts and components of the ultimate solution. Any such solution must allow for iteration and flexibility through orchestration of multiple smaller parts that have been built using different techniques and technologies. For example, the usage of an integrated solution with predictive and prescriptive analytics as well as decision robotics to convey decision outcomes to management.
Discover more about Decision Intelligence here: https://www.flexrule.com/archives/decision-intelligence/
