Best practices

Reinventing the Analytics Supply Chain: Finding Clarity in an Obscure World

“It’s a lack of clarity that creates chaos and frustration. Those emotions are poison to any living goal.”

― Steve Maraboli, Life, the Truth, and Being Free

When an executive asks why something happened, or tries to forecast for the next year, the goal is to provide clarity and direction for their team and the greater organization. As the opening quote states, a lack of clarity leads to negative results. This is the world of legacy analysis. The analytics supply chain (data-to-analytics process) is broken.

This problem is front and center when dealing with analytics on corporate systems. Take, for example, Oracle E-Business Suite (EBS). One of EBS’s greatest strengths is its extensible and flexible architecture, which allows organizations to develop highly-customized EBS deployments that meet their unique business needs. Unfortunately, this architecture also requires customers to build cumbersome, custom reporting and analytics solutions, rather than leverage pre-packaged tools. As a result, many EBS customers often begrudgingly resort to using multiple tools and high-cost experts to generate even the most basic reports—reports that leave much to be desired when it comes to performance and functionality.

Take, for example, supply chain analysis. The first priority in supply chain analysis is visibility. You must have visibility into shipments operations to understand landed cost and ongoing availability. Without this foundational information, you are making directional guesses as inventory and staffing forecasts. Any plans made based on those guesses live on a shifting foundation as the users are blocked from the details in the data (blocked from the truth).

Figure 1 – With a legacy approach, moving from user questions to answer is time-consuming and complex. The access to the transactions (the truth) is often blocked. The analytics supply chain is lengthy and expensive.

Historically speaking, the nature of data analysis mean users are expecting a pretty stark reality:

  • The analysis is a series of fixed reports. Any modification to these analyses needs to be done by a power user or IT. If new data is required, then IT must be involved.
  • The time to adapt the analytics supply chain is 8-16 weeks. As such, users are used to building their own models and trying to predict the future based on partial information.
  • Predictions and analysis are directional at best and often incorrect as the entire analytics supply chain is focused on old questions, not fluid needs.

The big secret in the world of analytics, is that this reality does not have to be the case. Modern approaches to the analysis process lets organizations move away from old, expense, ineffective processes and move toward an agile, user-driven approach.

To continue with the example of the EBS supply chain analysis. In the legacy approach you have to

  • ETL the data from EBS (an other systems);
  • Create a data warehouse ETL the data to a series of purpose build data mart structures (to answer specific types of questions);
  • Map that data into an enterprise BI environment;
  • And then finally allow users access. If there are new questions, new data, any changes that process starts again.

Compare this to a modern approach. In the modern analytics supply chain you:

  • Load data from the existing data warehouse to a modern platform;
  • And provision users for packaged and self-service analytics.

Figure 2 – Incorta eliminates the need for secondary modeling, enable user self-service, and greatly shortens the analytics supply chain.

Note: In the previous example, the user of a data warehouse in the modern approach is optional. If your organization does not have a warehouse in place, analysis can be done without one. If one is already in place, you can skip all of the secondary data mart structures and modeling to expedite the overall analytics process.

An example of this modern approach is Shutterfly. Using a modern data-to-analytics platform, they were able to enable user-driven forecasting and analysis to support their Just-In-Time (JIT) inventory processes. Over a period of six weeks, Shutterfly deployed five customized analytics applications, that helps their team of inventory analysts avoid out-of-stock situations in their busiest time of the year. Moreover, their team is able to work with data that is near real time.

In the new reality, the analytics supply chain is about empowering the user community to make informed decisions with processes and platforms that adapt to the fluid nature of the modern business world. It is about finding clarity in a world that often obscures it.

To learn more about how Incorta can modernize your supply chain analytics:

Find out why Shutterfly chose Incorta over other solutions and how Incorta helps Shutterfly:

  • Optimize inventory, with fewer stockouts and E&O expenses
  • Streamline exception management workflows
  • Improve supply chain planning processes
  • Spend less time running manual reports
  • Give its executive team visibility into supply chain metrics

Want to learn more about Incorta? Contact me at