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Businesses live and die by the decisions they make, but too many of these crucial selections are made with old-fashioned guesswork or mere gut feeling, opening organizations to a lack of transparency, few audit trails, and poor outcomes. Utility companies in Texas and California recently demonstrated that ignoring data can lead to cascading failures, and they reminded us that data silos limit an organization’s ability to capture externalities and avoid crises. When enterprises make decisions on assumption rather than data, they run the same risks.
General Motors (GM) recognized the importance of data and making informed decisions when designing new automotive features and preferences. The automaker teamed up with researchers at Penn State and Northwestern University to augment their in-house analysis and accurately simulate market activity, layering in survey data, sales volume, revenue, and market share. By incorporating data from the market and inputting historical consumer preference, GM optimized the vehicle development process and introduced features that were verified by the data. The model quantified uncertainty and reduced the risk of GM’s next rollout.
The evidence firmly establishes the immense need for powerful augmented analytics, and it should also inspire organizations that are still building the foundations for their own digital transformations. Like GM, organizations that seek to establish and enhance their intelligence need to outline processes that will enable scalable and informed decisions that can quantify uncertainty and reduce risk. Here’s how they can get started:
1. Understand how managers make decisions, even before data comes into play.
Organizations are overwhelmingly influenced by strategic decisions, which classically set the principles, objectives, and priorities for the business, and then filter down to operational and tactical decision makers. In a data-driven world, however, decisions are more continuous, more complex, and more connected. Tactical and operational choices often affect the C-suite as much as they do middle-managers or customers.
One consequence of this new age of decision-making is that the real-time insights operational teams typically use to drive decisions can also serve strategic needs. Consider, for instance, supply chain disruptions caused by a grounded ship. Operations must consider how to respond to this disruption in order to maintain business and meet customer needs. In parallel, the CEO and the board can access the same data in order to weigh the strategic fallout of the incident and determine next steps.
2. Ensure tactical decisions are on target.
Managers who are tasked with making tactical decisions typically deal with a high volume of data and thus require increased governance, collaboration, and augmentation. E-commerce wine company Enolytics is an outstanding example of how analytics solutions can cut through great quantities of complex market data to give producers real-time visibility into the internal and external events impacting their operating conditions. With Enolytics, wine producers can use analytics to capture the tactical decisions that wine producers are making every day so that they have the comprehensive, always-on context they need to tackle everything from customer behavior to market fluctuations and avoid unnecessary tactics.
3. Empower employee efficiency by automating operational decisions.
Operational decisions typically have a short shelf life. They are the day-to-day decisions that are made over and over again. While not every decision is suited for automation, the predictability and routine nature of operational decisions make them good candidates for AI and machine learning (ML). However, a fluid exchange of information across the organization is doubly important for an automated decision. For instance, if a business automates data entry from a cloud-based CRM or ERP, or deploys automation to handle other linear tasks, then the team will be free to focus on other, more valuable initiatives. They will have one less thing to worry about and can instead concentrate on nonlinear objectives, critical problem-solving, and creative thinking. This will empower the team to make smarter, more informed decisions. And when those decisions are driven by data, they can be confident that they are making choices that will positively impact the firm, its directive and its employees.
It’s a New Day for Intelligent, Highly Informed Decisions
Using data to mitigate risk and improve productivity is no longer a question of how much data you have or where it sits. Modern enterprises also need to know how they make the decisions to which data is applied. Businesses must find a way to bring together the wealth of real-time, hyper-contextual data while building on the most powerful human capabilities of experience, collaboration, and contextual awareness. In doing so, they will be able to make intelligent and highly informed decisions that fulfill goals by better serving their customers.