4 Ways to Integrate Decision Intelligence for More Effective Business Strategies

Succeeding in today’s ultra-competitive marketplace is highly dependent on a consumer goods company’s deftness in reaping value from data. The more seamlessly this capability is woven into the organization’s workflows, the more powerful the potential benefits.Decision intelligence is all about leveraging the latest in analytics and AI to enable this highly integrated, tech-enabled approach to decision-making. At the leading edge of decision intelligence, decision-making is well-orchestrated and enterprise-wide. How can today’s CGs better understand how decisions impact every level of their operations to implement more agile, integrated, and inclusive decision intelligence processes? Consider these four strategies for careful planning and commitment, taking learnings from companies like Estée Lauder, Proximo Spirits, Colgate-Palmolive, and Mars. Alex Hanmer, global consumer revenue growth management leader at EY, describes decision processes to CGT as promoting clarity, data utilization, inclusivity, transparency, and agility. They also feature a conscious and continuous feedback loop to evaluate and refine the process, he says.A number of consumer goods organizations fall short of that vision. Thirty-percent of decisions made in an organization remain undocumented, only 20% of decision makers feel completely comfortable with the number of decisions they need to make daily, and 25% of decisions that should be made, are not made, according to cross-industry research by IDC. Study participants report that only 55% of executives mostly or fully know how lower-level decisions are made. To close these gaps, Hanmer says: “Consumer goods companies may need to reassess and modernize their decision-making frameworks, invest in new technologies, foster a culture of data-driven decision-making, and streamline their organizational structures to be more responsive and agile.” 1. Perform a decision-making auditDue to the limitations of earlier decision support technology, and the processes that grew around it, many consumer goods companies largely based key data analysis and forecasts on historical insights. This is no longer good enough, according to Kalindi Mehta, VP, consumer foresight and predictive analytics at Estée Lauder. “Insight is almost an irrelevant term in today's world, which is so fast-changing,” Mehta recently told Forbes. “Analytics and insights should not just explain the past but tell you what the business needs to do tomorrow, creating consumer foresight that leads to action.” So one way decision-making must change is to be more forward-looking.EY’s Hanmer recommends adopting a comprehensive approach to assess the quality and effectiveness of decision-making across the organization. CGs should use both quantitative and qualitative measures as well as internal and external assessments in this analysis, he says. It’s also important to assess both the inputs and outputs of decision-making processes, Hanmer adds. For inputs, for example, “they should be looking to elements such as how much time is spent on the process and how much of the process is adhered to,” leveraging tools such as process mapping. “In terms of outputs, they should seek to assess the impact of the decision; for instance, has it met the KPIs?”IDC analysts contend that many past investments in technology to improve decision-making have not delivered sufficient ROI because they fail to include an understanding of how workers make decisions. So a thorough assessment of current approaches to decision-making is a critical part of understanding how to use decision intelligence to enable those teams.2. Prepare tech stacks for modern integrationsGood decisions start with good data. Unfortunately, master data management is historically underfunded in many organizations. According to CGT’s Retail and Consumer Goods Analytics Study, while 38% of CGs feel their data quality is at par with industry leaders, 55% feel they are lagging or significantly lagging. Lack of clean data is proving costly as more companies embrace decision intelligence and other applications of AI, so analysts note budgets for master data management are finally starting to rise as the importance of AI has permeated boardrooms. Clean data is also essential to meet regulations such as serialized item-level reporting for the Drug Supply Chain Security Act (DSCSA) and food traceability regulations. Beverage company Proximo Spirits is still fairly early into its decision intelligence journey but has already seen the critical nature of clean, standardized data. “Never underestimate the dramatic importance the master data has,” Luis Gonzalez, Becle global supply chain director at Proximo, previously told CGT. “If you don’t have the right quality of your data or the data is not the right structure, you will have to work it out in order for the tool to provide the right outputs,” he stresses. CGs also need to take a close look at their tech stacks to ensure they have everything they need to enable better decision-making. They should be considering whether their decision intelligence tools can integrate with others across the data ecosystem, says Hanmer.“Tools should be borderless, enabling cross-system exchanges and collaboration, such as via APIs,” he says. Beyond that CGs should consider decentralization and reusability by breaking down applications into smaller independent services in both data management (e.g., data mesh) and system design (e.g., microservices) to enable improvement with limited disruption while fostering innovation. Ensuring solutions are user-friendly is also key to maximizing their adoption, he adds. 3. Formulate a tracking plan and stand by your metricsSetting long-term priorities and objectives for how the organization will make decisions is important to guide the overall transformation. Still, interim steps are essential — as well as metrics to measure progress along the way. Those KPIs may be results-oriented, such as improving accuracy or impact; tracking things like adoption rates and productivity rates; or having input metrics such as decision cycle times. Hanmer has even seen a company measure the number of incidents raised, treating it as a proxy for amount of usage and engagement.It can be helpful to start with high-importance, frequently made decisions that are well supplied with data as pilots to test out this new approach to decision-making. Colgate-Palmolive’s decision intelligence pilot focused on optimizing product deployment decisions throughout its Hill's Science Diet and Hill's Prescription Diet fulfillment network, and its success led to expanded efforts. Six months into its pilot, Proximo’s proof of concept in its U.S. tequila market started bearing fruit by learning demand patterns while reducing forecast error to 25%. While business objectives are essential guideposts to set goals and metrics, users must be at the center of any effort to revamp decision-making, says Guillaume Doriry, digital transformation senior director, global supply chain and ERP, at Mars. “Start bottom up and not top down,” Doriry advised in a recent Tech Transformation podcast episode. “Top management needs to be very clear about what kind of change you're looking for, but it has to start with the end user in mind,” since they’re the ones using a decision intelligence application. ”Accept the fact that you need to go for an iterative learning exercise with them.”4. Get employee buy-in with data literacy trainingChanging how decisions are made is a fundamental transformation in how workers do their jobs. It’s important to convey to employees why faster, better decisions are critical, and gain buy-in for using the tools to support how they work, versus displacing jobs. One key to adoption is that users feel in control of the decision-making process, says Doriry, a lesson his organization learned when the adoption of a previous AI tool lagged because users felt the decisions were hard to explain and they didn’t have that control. Tools that offer users control and transparency build trust. Then if they feel confident that the algorithm has learned enough about the decision, they can let the tool manage the process, understanding it's under their control and can be rolled back, he says. Colgate-Palmolive created an organizational coach position to help its employees with analytics enablement. The job includes educating, socializing, and facilitating analytics within Colgate-Palmolive, developing and delivering its data literacy program, sharing best practices, and managing the intranet-based practitioner community.Going All in on Decision IntelligenceCompanies taking a well-considered approach to adopting decision intelligence are reaping the rewards, according to IDC, showing notable improvements in key business metrics such as customer retention, employee retention, and product or service innovation.As the challenges of operating in the consumer goods marketplace continue to escalate, the ability to make better decisions will only increase. Supply chains are a case in point, says Doriry. “There is no place in this operation where we don't want visibility and control. But by doing that, we generate a lot of data.” Employees can’t be expected to manage it all without tools, he says. “So it's very important that you are helping them to feel better equipped, [so] that they are capable to do the work that they need to do.”

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