In 2020, consumers spent $861.12 billion online with U.S. merchants, up an incredible 44.0% year over year, according to Digital Commerce 360 estimates. That’s the highest annual U.S. ecommerce growth in at least two decades and is also nearly triple the 15.1% jump in 2019.

This unprecedented growth has stretched legacy fulfillment channels to their capacity limits and businesses have been forced to consider new and creative ways to keep up with demand resulting in coordinated efforts across both digital and brick and mortar fulfillment. Failure to seamlessly orchestrate operations can result in negative customer experience, driving lost sales, reduced market share, and lowered profit margins.

The stakes have never been higher and adapting quickly to the dynamic landscape is critical to any retailer’s success and ultimate survival.

Consequently, teams are forced to move quickly to ensure functional continuity by developing and integrating various management software systems. Analyzing and making sense of data generated by these systems, however, is often an afterthought requiring armies of analysts, deep pockets, countless meetings, and endless patience to even scratch the surface of what’s possible.

This is where having the right modern analytics platform comes into play and becomes a competitive advantage. When the right data can be presented in the right way to the right people empowered to make the right decisions, operations teams greatly increase their speed to value in driving growth.

Let’s review the questions to ask across several key factors when comes to delivering speed to value in a modern analytics platform along with our Elevate perspective in italics:


  • Does your team know which data to look at and how to make sense of it?
  • Is your data structured to drive essential actions?
  • Are you able to compare actual data to targets, plans, or goals to make sense of results?
  • Can your platform send alerts and notifications based on conditional criteria?
  • A modern analytics platform should be able to clearly identify and prioritize which data deserves attention at which points in time to drive business critical outcomes.

Effort & Cost

  • How difficult is the initial implementation process? How much on-going effort is required for maintenance and support?
  • What’s required to bring data together from different data sources?
  • What does it take to build a new report? To modify an existing report?
  • What are monthly platform costs? How much do changes cost?
  • A modern analytics platform should offer near-turnkey implementation, can seamlessly integrate multiple data sources, and have relatively low total cost of ownership.

Recency & Timing

  • How fresh is the data from which decisions are being made?
  • Is your platform able to ingest updates in near real time?
  • Are the decisions you’re making based on stale data?
  • Is data from different sources and timezones normalized to ensure synchronization?
  • A modern analytics platform should have the capability to ingest and normalize updated data at least hourly from multiple sources.

Data Proximity

  • How quickly can contextual secondary and tertiary be referenced to better understand what’s going on and what to do about it?
  • Are you able to actively drill into your data from the summary down to the line level?
  • Can data sourced from different systems be brought together in a meaningful way?
  • A modern analytics platform should be able to structure data from across sources for quick comparison and drills to provide relative context and insights.


  • Can you quickly provide different views to the data?
  • Are you able to add data elements and make new calculations in the platform on the fly?
  • What approvals or processes are in place to make changes?How long does it take for a change to be implemented?
  • Do you have sufficient analytics and technology resources? Are these resources constrained?
  • A modern analytics platform should provide dynamic capabilities and have the plasticity to be appended, changed, reformatted, and adjusted quickly to meet business needs, enabled by distributed development resources.


  • Are key decision-makers able to access the information they need?
  • Are stakeholders aligned on terminology, definitions, and calculations for KPIs?
  • How quickly can information be shared? Through which means?
  • Do team members know what they need to do based on analytics to deliver outcomes?
  • A modern analytics platform should serve as a point of alignment and be easy to distribute across all levels of various stakeholders in a retail organization.

With advanced cloud computing and visualization capabilities coming of age, it’s time for analytics platforms to evolve in order to step up to the challenges presented by the monumental shifts in online retail. A platform that increases an organization’s speed to value can be a significant strategic advantage in capitalizing on these growth opportunities.

If you’re interested in learning more about factors to consider in delivering speed to value and how a best in class analytics platform achieves these, reach out to us at