Data, while it is arguably invaluable, will not provide the perfect innovation or solution.
Ask any small or big organization and they know exactly why data is significant to their business. Yet no matter how advanced their data platforms are, or how rich the data, it is not a guarantee that these insights will direct them to their next best decision in their industry.
But if the best software and relevant data won’t work, what will?
In this article, learn more about increasing business agility and producing better outcomes with one of today's innovative business models: decision intelligence.
What makes decision intelligence different?
Gartner identified decision intelligence as one of the top strategic technology trends in 2022.
The firm defined decision intelligence as follows:
“Decision intelligence is a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor and tune decision models and processes.”
This framework includes decision-driven disciplines such as decision management and decision support, and incorporates human and machine decision-making processes to come up with precise business decisions.
Therefore, decision intelligence is not to be confused with business intelligence—which is data-centric in nature and mainly focuses on AI and data models. These models, however, are helpful in making the decision intelligence process a success.
Why decision intelligence matters?
Emerging technologies and data sources continue to increase. Organizations need to make faster and better systems that do not simply rely on human-driven processes, especially now that 80-90% of data is unstructured or not organized in pre-determined models or categories. So, industry leaders incorporated machine learning models to their decision-making processes.
Yet even with advance technologies, Gartner predicted that 85% of big data projects will still fail. What is more is only one out of every 10 data science projects can make it to production.
There are many reasons why data fails. Most of these failures points to poor data quality, lack of data literacy among the organization, and the absence of data governance systems.
And these are the very things that decision intelligence can actually solve.
How can I get started with decision intelligence?
The decision intelligence framework involves preparing, executing, and monitoring decision-making processes that involves the organization’s teams and their data technologies or systems. Here are three action plans to successfully incorporate decision intelligence in your corporation today:
#1 Incorporate different types of decision models in workflows.
Since decision intelligence is built around decision-driven processes for both humans and machines, organizations must optimize and familiarize their various decision models.
Here are the three primary types of decision models that corporations need to incorporate for decision-driven workflows:
- Human-based decision models
In these models, humans make the decisions based on the data provided. Some examples of human-based decision models are rational models (where corporations use structured processes for their operations) and creative models (where the decision maker is given tools and insights to create solutions for a problem).
- Machine-based decision models
For these models, corporations based their decisions on the algorithms from their AI systems. These decision models may vary from the technologies and systems used by each organization.
- Hybrid decision models
These models are the collaboration of AI systems and humans to arrive on a certain business decision. Though the decision-making process that is allotted for the workforce and the organization’s technology systems may vary, the models incorporate both human and machine-based decisions for creating innovations.
#2 Invest on decision intelligence technologies.
Today’s corporations will definitely have their own decision-making and data management tools. However, a decision intelligence framework may need more or less tools in its ensemble.
Here are the primary inclusions for building a decision intelligence framework:
- Decision modelling software for data gathering and modelling
- Business rule management software for developing decision rules
- Machine learning stacks or models for developing algorithms
- Data platforms for real-time data management
- Data visualization tool for creating human-based decisions
Note: While these tools center on automating decision-making processes, it is strongly discouraged to automate everything. As Gartner puts it, hyperautomation has its place and that is only for routine or repetitive tasks.
#3 Get everyone onboard in the decision-making framework.
According to Forbes, one of the ways to avoid data project failures is to ensure that everyone in the organization understands and knows how to use data. With data literacy, corporations can avoid data silos and challenges in data security. Here are some webinars and courses for decision intelligence and data literacy for non-data scientists:
The course provides an in-depth discussion on machine learning methods and techniques to handle and interpret big data for better business decisions. This course is available on Coursera and is offered by the University of Pennsylvania.
This course helps learners discover the fundamentals of data fluency: from preparing data, to exploring data visualizations, and interpreting the gathered data. The course is available in LinkedIn Learning.
This complimentary webinar discusses the key to effective decision making, and how to integrate decision intelligence to business processes and applications. The webinar can be accessed in Gartner’s Webinars page.
This on-demand webinar explores the lessons and techniques from companies that have successfully incorporated disruptive technologies and better decision cycles through decision intelligence. The webinar can be accessed in Aera Technology’s website.
Conclusion
Unlocking data fast and effectively in a data-driven world is undoubtedly challenging. To produce better innovations and solutions, decision makers must always leverage AI and their best workforce to use data—rather than be used by data themselves.
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Reference Links
https://www.datascience-pm.com/project-failures/
https://www.gartner.com/smarterwithgartner/how-to-make-better-business-decisions
https://sisudata.com/resources/guides-and-whitepapers/ultimate-decision-intelligence-guide
https://www.pcmag.com/how-to/how-decision-intelligence-can-seriously-boost-your-bi-game
Additional References
Baker, P. (2022). Decision Intelligence for Dummies. John Wiley & Sons, Inc.
Young, M. (2018). Ogilvy on Advertising in the Digital Age. Bloomsbury.