A Step-by-Step Guide to Conducting an Analytics Assessment
A retail bank launches a lending campaign expecting strong results. Early dashboards look promising, but soon, conversion rates drop.
Marketing, risk, and operations teams each see different numbers, and no one can pinpoint the issue. By the time teams align, the opportunity to act has passed.
This isn’t a data problem. It’s an analytics problem.
This is where an analytics assessment comes in. It helps you see what’s holding you back and move forward with confidence using analytics solutions for sharper, faster decisions.
Why Conduct an Analytics Assessment?
An analytics assessment helps you understand how well your data supports decision-making. It checks if your data is accurate, easy to access, and useful.
For example, a finance team may have multiple dashboards tracking risk exposure, but if each pulls from different data sources, the insights can be inconsistent and difficult to act on.
This highlights how data and analytics gaps can directly impact business performance.
This happens frequently. Gartner estimates that poor data quality costs businesses an average of $12.9 million per year. This demonstrates how analytics and data gaps can directly affect company performance.
Conducting an analytics assessment helps address these challenges by:
Identifying inconsistencies and gaps, you ensure leaders are working with reliable, aligned data.
Making it easier to manage risk and support effective risk analytics services.
Enabling better use of existing financial analytics solutions.
Want to see how modern analytics and data enhance customer outcomes? Read our blog, “Leverage Data and Analytics to Deliver Superior Customer Experiences.”
How to Conduct an Analytics Assessment: Step-by-Step
An effective analytics assessment goes beyond systems. It starts with business needs and looks at your data, tools, and teams to understand what’s working and what isn’t.
With 34% of companies already using AI to deeply transform their business, building a strong analytics foundation is now essential.
Here’s a simple step-by-step process to assess your analytics and identify areas for improvement:
Step 1: Pinpoint High-Impact Decisions and Pain Points
List 5–7 decisions that regularly slow down or get debated, such as monthly financial forecasts or risk exposure reviews.
For each, note what data is used, where delays happen, and what questions remain unanswered. This will help you:
Concentrate your evaluation on choices that have a significant impact on business.
Determine which data gaps are impeding the results.
Prioritize areas where analytics can provide instant benefits.
Step 2: Trace Data Back to Its Source
Start by tracking each metric back to its source using one or two crucial reports.
Additionally, as they are frequently the source of discrepancies and delays, be sure to record data sources, ownership, including any unstructured data, and any manual actions required.
Additionally, to ensure computations are consistent across reports, make a point of verifying the rationale for each metric.
Over time, this will lessen inconsistencies, standardize definitions, and increase confidence in your data.
Step 3: Test Data Consistency
With your data sources mapped, actively test the reliability of your data in real-world scenarios.
Choose two to three crucial KPIs, then compare their values across various dashboards, teams, and reports. If the numbers don't add up, note the precise difference and identify the source, transformation logic, or data extraction procedure.
Next, validate metrics against historical data or benchmarks to spot anomalies. Also, run spot checks for missing values or duplicates, and recreate reports using raw data to verify accuracy.
Step 4: Identify Reporting Gaps
Now that you’ve validated your data, shift focus to how it is being used across reports and dashboards.
List all of the current reports for a certain function, such as risk or finance, to start. Examine them collectively to find reports that are infrequently utilized, overlapping insights, or duplicate metrics.
You can also consider engaging end users to understand which reports they actually use and which they ignore. This helps separate what is truly valuable from what simply exists.
Read Also: Empowering Data Analytics with Generative AI: Best Practices to Follow, to explore how you can enhance your analytics capabilities with AI-driven approaches.
Step 5: Audit Tool Usage and Integration Points
At this stage, shift your focus to the tools powering your analytics. Instead of just listing them, evaluate how well they support your key use cases, especially for critical functions like risk analytics services.
Start here:
Map each tool to a specific use case to identify overlaps and gaps
Track how data moves between tools and flag any manual handoffs
Review actual usage to spot underutilized features or redundant tools
Step 6: Document Findings and Create an Assessment Summary
Consolidate all observations into a structured assessment report. Clearly outline key gaps, root causes, affected processes, and supporting examples.
Make sure you group findings into themes such as data quality, accessibility, tools, and governance.
Gartner predicts that by 2030, 50% of AI agent deployments will fail due to weak governance and poor system interoperability. This makes it even more important to clearly document gaps and address them early during your analytics assessment.
Here’s how this will help:
Give stakeholders a clear, structured view of where analytics is falling short and why
Align business and data teams on key issues, reducing confusion and misinterpretation
Make it easier to prioritize actions based on impact, urgency, and effort
Provide a documented baseline to track improvements and measure progress over time
How Straive Helps You Build a Future-Ready Analytics Foundation
Turning an analytics assessment into real impact requires the right expertise to act on it.
Straive helps bring data, analytics, and AI into everyday workflows, improving efficiency and decision-making. From managing unstructured data to streamlining data extraction and reporting, Straive makes your analytics more reliable and useful.
Overall, Straive improves data quality, manages unstructured data, and streamlines data extraction and reporting to make your analytics reliable and scalable.
Want to turn your analytics assessment into a measurable impact? Partner with Straive for better, faster decisions.