Traditional decision-making processes are great at helping businesses analyze data from the past and make predictions about the future. Such processes usually involve questions like, “What time of year do we sell the most products, and how can we optimize marketing and the supply chain to support and grow those sales?”
But today’s organizations need better decision-making processes. Nowadays, decisions have more wide-ranging effects on the business. And decisions can be fueled by data in a way they’ve never been fueled before.
By adopting decision intelligence (DI), organizations can gain the ability to make better decisions. And many are. Thanks in large part to its data leaders—people who can facilitate a data-centric mindset and enable employees with data in their everyday work—such organizations aren’t asking what the data is telling them. They are posing forward-thinking questions like, “What will my customers want to buy next year?”
By asking such questions, these organizations can use their DI models to figure out what data they need to gather, deploy tools for analyzing that data, make informed decisions quickly, and more easily monitor the impact of those decisions across the organization.
As Pam Baker recently wrote for PCMag.com, DI does not have “a single umbrella process.” How you choose to build your data and decision architecture will depend entirely on your business, the questions you want to answer, the data you’re gathering, and the tools you are utilizing (or will utilize).
Regardless of how you implement DI, you’ll consistently work with your organization to ask questions related to the future and discover what data is needed to support those decisions.
Best practices in decision intelligence for data leaders
Evolving your decision processes with enhanced intelligence starts with adopting the following practices in multiple areas of your organization:
- Making relevance, transparency, and resiliency the key pillars. You need to implement decision intelligence in a way that will support its impact and longevity. Gartner recommends adopting models that are focused on being more understandable rather than completely accurate every time. Transparency is key. Focus on the sustainability of cross-organizational decisions by building models using principles aimed at enhancing their traceability, replicability, pertinence, and trustworthiness.
- Having a global vision. The central focus of decision intelligence at your company should be on the global impacts both macro- and micro-decisions will have. For example, launching a new product is a macro-decision that will obviously affect multiple departments. But a micro-decision like changing key messaging on a website page will also need to be evaluated for its global impact. You can monitor and incorporate unpredictable emerging analytical behaviors as soon as they arrive. You’ll need to track dependencies and assumptions to see how they perform over time.
- Employing a modern intelligence technology stack. Reduce complexity and have tools that work together. It’s especially important your tools have two-way communication so you can consistently and automatically provide feedback on the data and refine automated models.
How business leaders can share the vision of DI
Business vision leaders—those driving the overall direction of the business—are instrumental in getting cross-organization support for decision architecture. Here’s how to ensure that the intelligence is applied across decisions and the effectiveness of decisions for the organization is measured:
- Monitor decision frameworks. If decision intelligence is “the discipline of turning information into better actions at any scale,” then track how your organization is affected by your new decision frameworks. Champion successes and work with others across the organization to make improvements to processes as needed.
- Lead with a global vision. Gartner recommended in a 2020 report titled Improve Decision Making Using Decision Intelligence Models that business leaders “ensure that peers and direct reports look at the ‘big picture,’ the ‘Global Outcome,’ while creating a decision model. The idea of serving a global outcome is that even highly localized decision models should always contribute to the bigger picture, and be integrated into an existing decision-making architecture.”
- Recruit stakeholders early on. Involve the stakeholders that will be directly affected by new decision models, have business leaders share feedback and train them on the transparency of the models from the start, and make sure your models favor explainability over aggressive accuracy. Because when business leaders can understand the models, they can be more effective at securing buy-in from others in the company.
Using Domo to drive decision intelligence
Here are some examples of how Domo can be used to help drive decision intelligence in your business:
- Demand forecasting. Multivariate time series modeling at the SKU level. Leverage hyper-granular forecasting to properly plan across the supply chain and beyond. Couple with Domo’s best-in-class visualizations for prescriptive planning.
- Predictive systems maintenance. Use best-in-class outlier and time series modeling approaches to proactively identify outages. Leverage model insights in order for targeted mitigation strategies for machine servicing and bespoke action.
- Resource planning. Leverage SKU-level demand forecasting in order for accurate resource planning across the enterprise. Affirm likely shortfalls and overages ahead of time in order for proper adjustments and action.
- Employee retention. Proactively identify employees who are at-risk to leave the organization and the associated drivers for leaving. Model insights allow for mitigation strategies to be developed ahead of time.
- Loan default risk. Optimize underwriting via applicants who pose the greatest risk for default. Leverage model insights in order for prescriptive early warning systems to target and remediate default risk ahead of time.
- Cash management. Ensure proper amounts of cash are allocated across various functions and areas of the business. Leverage likely cash consumption and demand modeling in order for requisite proactive planning.
Contact a Domo sales rep to learn more about decision intelligence and how Domo can help your organization achieve true business intelligence with DI.