If you work in analytics or depend on someone who does, you probably recognize the value of focusing on business outcomes first, before considering the “how” to achieve your objectives.
There is often a difference between an analytics use case (such as forecasting), and the contribution analytics automation makes to realizing the business outcome (improved forecasting). Stated differently, analytics automation can account for, speed, and improve results for any stage of an analytics development process. So, there are really two ways to view the potential value of analytics automation in supply chain and beyond:
- First, how analytics automation is used in isolation to functionally support a step within a use case – such as accounting for the suboptimal data preparation or predictive modeling steps of forecasting. Many analysts adopt and recognize value through this lens, saving a great deal of time and effort easily justifying the investment.
- Second, how well analytics automation supports the overall project to realize the business outcome (such as more timely and insightful forecasting leading to improved sales). Here, analytics automation plays a valued role alongside other necessary elements (like coding and enterprise applications). The value here is of a far greater magnitude.
The first scenario above “nests” within the second, and in some instances, serves the totality of the customer’s requirements. For example, any analyst can benefit using low and no code analytics automation to reduce manual effort, relative to spreadsheets, coding, and BI tools. Somewhat like the value immediately apparent in this webinar with Kraft Heinz.
This is the fastest and easiest path to some value but does not represent the greatest value possible.
Analytics automation may also support complicated data preparation as part of a data scientist building and testing a forecasting model, who then deploys it into a business application, after which results are evaluated in a Business Intelligence dashboard. This use case is framed around the entire analytics development process, not just the functional capabilities available in analytics automation that ease an analyst’s workload.
This approach surfaces much greater and measurable business improvement beyond benefits to an analyst’s productivity. An example is what you see in this webinar with Amway.
Achieving ultimate value
The payoff for unifying these scenarios into an intentional plan is that analytics automation becomes a force multiplier to rapidly improve many of the most important decisions facing supply chains today.
Customers have a growing and confusing choice of analytics tools, technologies, and applications. There are tradeoffs among them all. Former Gartner analyst Howard Dresner recently cited the following research illustrating this trade off:
“46 percent of organizations that use ERP software prefer to source their BI and analytics capabilities from third-party vendors. A key reason for this is flexibility: In general, third-party solutions support many more and broader business use cases, and can more easily integrate and work with multiple data sources, compared to the BI and analytics capabilities included with ERP systems.”
It’s very likely these organizations lean toward large, global enterprises with mature analytics teams supporting many functions. Even so, it’s that very flexibility that others crave but struggle to obtain given the presumed resources required.
Analytics automation serves to speed a use case such that those without coding or domain expertise can contribute. These are the two greatest barriers to analytics success today.
Hesitation is why few organizations partake in the benefits of analytics done well at scale. Minimizing risk by employing solutions which strip out uncertainty while speeding time to value and requiring fewer skilled resources, demonstrates the value of analytics automation and upskills the entire workforce.
Automation permits manual recurring analytic tasks (called workflows) to be offloaded to a system of governance. That’s why many ideal analytics automation use cases support daily decisions on which organizations depend. Such as demand forecasting, inventory management, assortment and merchandise optimization, and machine maintenance.
Recalling the Kraft Heinz example, the ultimate benefit of the use case discussed in the webinar was inarguably business value critical, even though the title suggests otherwise (How Kraft Heinz Saved 5,500 Hours with Analytics Automation).
The value of low/no code
Almost every organization must rationalize investments and expectations for analytics across a variety of functions, skillsets, and technologies since no single one addresses all requirements.
This is how market leaders win with analytics and achieve the most value from investments across people, processes, and technology. The CDO, or Chief Data Officer, is increasingly expected to bring together all the right elements.
This diagram shows how analytics automation offers a path to improving many use cases through a combination of strategy, re-usability, upskilling and collaboration among business stakeholders and analytics teams. In this fashion, more use cases at a greater velocity come to market, at a faster pace, and contribute greater business value with each use case.
Analytics automation offers what amounts to a platform for change, improvement, and business value that acts as a force multiplier to any existing analytic use case. To realize this requires aligning the benefits available through analytics automation with use cases targeted for improvement, or wholly new use case opportunities.
Often the case, saving time and manual effort yields significant improvements in time to value and the quality of insight. Chokepoints across supply chains are among those that stand to benefit the most.