The Complexities of Diverse Informational Needs

Data and analytics commodities are just that… commodities

You have hundreds of reports, dashboards, and Excel files at your disposal but are still missing signals or nuggets of insights that could help you dramatically change the trajectory of your business. Sound familiar?

For years, organizations have struggled to meet the diverse analytics needs of different users. From finance teams demanding structured tabular reports (rows and columns), to executives relying on dashboards for quick insights, to data or business analysts and data scientist performing advanced analytics, and power users building intricate Excel models—each role requires a unique and fragmented approach to working with data.

Commodity tools like SAP Business Objects, IBM Cognos Analytics, Power BI, Tableau, and others are scattered across corporations, each providing a slice of data to business users. Behind all these tools is an army of data engineers, ETL, tools, data bases, and data warehouses. These traditional approaches require data lakes or data warehouses to enable reports and dashboards, invariably adding costs, long lead times, and complexity. This diversity has led to a disjointed approach to business intelligence (BI), analytics, and data warehouse platforms.

Adding to this complexity are the intricate natures of ERP systems with convoluted data schemas, metadata dictionaries, and cryptic table and column names. ERPs like Oracle JD Edwards, Epicor, Oracle eBS, SAP, and Workday are challenging for most data engineering teams. Almost as if they were specifically designed to keep IT teams focused on non-strategic tasks. Organizations need a way beyond this traditional ETL to data warehouse process.

Companies often rely on multiple tools and platforms, each catering to different user needs. This means vast resources are dedicated to building, maintaining, and evolving reports, dashboards, data pipelines, and integrations, often not integrated across different teams or departments. The complexity only grows as new requirements emerge, diverse data sources get added, new applications purchased or created, regulatory standards change, and business priorities shift.

Often this complexity drives organizations to buy more tools and over engineer their data and analytics landscape that is costly, difficult to scale, and challenging to maintain. The result, more complexity. IT teams are burdened with endless report requests, analysts spend too much time reconciling data across systems, and business users struggle to get timely answers. Many business users are still stitching data together in Excel from diverse system CSV exports. This traditional approach—multiple tools, hundreds of dashboards, and layers of manual intervention—is no longer sustainable. When what they really need is a new approach to data and analytics.

And because many organizations spend so much time combining data and preparing reports, they have little time for deeper analysis. Insights, trends, and patterns are hiding just beneath the surface of reports and dashboards but are often overlooked. Organizations are missing important opportunities to optimize their business or think differently about the decisions they could make to change the trajectory of their business. A recent report consumer quote: “I’ve been looking at this 10-page report daily for years, and I still don’t know how I should optimize my inventory to reduce wastage.”

The days of managing hundreds of reports and dashboards across disparate platforms are coming to an end. Organizations must rethink their analytics strategy and move towards a more unified, streamlined, and intelligent approach to business intelligence. Insights are hiding in their data and users want guided and interactive analytics to help them see beyond the rows and columns.

From Hindsight to Foresight

It’s time for a new approach to data and analytics that moves beyond historical reports and dashboards, one may call deterministic reports, to a more probabilistic workflow with your data. Organizations need to empower business users with data democracy, that allows them to combine any data quickly, with accuracy to go from hindsight to? foresight.