Why More Dashboards Aren’t Giving You Better Answers and How Self-Service Analytics Can
15 Min Read
Your business may have more dashboards than ever and still struggle to answer a basic question when it matters.
A manufacturer sees a margin shift and wants to know what changed. A distributor notices inventory moving differently across locations. An operations leader sees an unusual trend and wants to know whether it is isolated or part of something larger.
The dashboard shows the result, but then the questions start.
Which products caused the change? Did it happen everywhere? When did it begin? Is there a customer, supplier, location, or operational factor behind it? Is the underlying data current enough to trust?
If answering those follow-up questions means opening another dashboard, exporting data into Excel, asking an analyst to build a report, or waiting for someone to reconcile information from several systems, the organization does not necessarily have a dashboard problem.
It has an analytics access problem.
That is where self-service analytics can save the day.
More Dashboards Do Not Automatically Create Better Decisions
Dashboards are useful because they make complex information easier to monitor. They can show sales performance, inventory levels, production metrics, service trends, financial measures, customer activity, and hundreds of other indicators.
The problem begins when every new business question produces another report.
Over time, organizations can end up with dozens or hundreds of dashboards built for slightly different purposes. Similar metrics may be calculated differently. Some reports refresh at different intervals. Different departments may rely on different source systems.
Instead of creating clarity, the reporting environment becomes another layer people have to navigate.
The issue is not that dashboards are outdated or unnecessary. Tools such as Power BI remain extremely valuable for recurring reporting and visual analysis.
The issue is expecting a predefined dashboard to anticipate every question a business user will eventually want to ask. It cannot because a dashboard is designed around questions someone already knew were important.
The Real Problem Often Exists Beneath the Dashboard
Adding another visualization cannot fix inconsistent data underneath it. If one system identifies a customer differently than another, a new dashboard will not resolve the discrepancy.
If departments calculate the same KPI differently, visualization does not create a shared definition.
If operational data sits in disconnected applications, a report may still require complex preparation before the information can be analyzed together.
And if employees do not have appropriate access to the data they need, giving them another reporting tool does not make the organization more self-sufficient.
Before self-service analytics can work well, organizations need to address several foundational questions.
Is the Data Connected?
Important information may live across ERP systems, CRM platforms, manufacturing applications, spreadsheets, databases, cloud platforms, and third-party systems.
Users cannot easily explore relationships between those datasets if every question begins with manually bringing the information together.
GDC’s System and Data Integration services focus specifically on connecting information from disparate products and legacy environments so data can be used more consistently across the organization.
Are Departments Interpreting Jargon/Abbreviations in the Same Way?
Words such as revenue, active customer, margin, inventory availability, on-time delivery, or conversion can sound straightforward until different departments calculate them differently.
Self-service does not mean every user should create a personal definition.
The organization still needs agreed-upon business logic, data models, and definitions so users are exploring the same underlying truth.
Can Users Trust the Data?
Data quality becomes more important when access expands. Missing data, duplicate records, inconsistent formats, inaccurate mappings, and outdated information can all produce technically valid analysis with the wrong conclusion.
This is where processes such as data cleaning, validation, transformation, and data governance become part of the analytics strategy rather than back-end technical work no one else needs to think about.
Should Everyone See Everything?
Usually not. Useful self-service analytics requires governance around access.
Finance data, employee information, customer records, operational metrics, and other sensitive information may require different permissions. Depending on the environment, organizations may use role-based access and row-level security to control what individual users are allowed to see.
Self-service should remove unnecessary barriers to insight without removing appropriate controls.
What Self-Service Analytics Changes
The goal of self-service analytics is not to turn every manager into a data scientist.
It is to reduce the distance between a typical business question and a trustworthy answer.
Instead of submitting a request every time someone wants a slightly different view of the data, authorized users can explore trusted information themselves.
That may mean filtering or drilling into existing reports. It may mean creating their own analysis from governed datasets. Increasingly, it can also mean asking direct questions and allowing an analytics or AI layer to interpret the request against approved enterprise data.
A good self service analytics platform gives users more freedom without abandoning the controls that make the answers reliable.
The organization still needs data engineers, analysts, business analysts, data scientists, and other technical professionals for more complex work. Self-service does not eliminate those roles.
It allows those teams to spend less time answering repetitive report requests and more time working on the questions that require deeper analysis.
Self-Service Analytics Still Depends on Data Engineering
The simplest analytics experience often has complicated work behind it.
For example, a user might ask: “Why did sales decline in the Northeast last quarter?”
But answering that may require data from sales systems, customer records, inventory, financial applications, product tables, location hierarchies, and historical transactions.
Before the question can be answered, those sources may need to be:
- Ingested into a common environment
- Cleaned and standardized
- Mapped to consistent business definitions
- Modeled so relationships are understood
- Validated for accuracy
- Secured according to user roles
- Refreshed at the frequency the business requires
Those are the data pipelines, transformation, modeling, governance, and validation processes that make the final experience possible. The conversational interface is the visible part.
The quality of the data foundation determines whether anyone should trust the answer.
Choosing a Self-Service Analytics Platform
There is no universal platform that makes sense for every organization.
The right architecture depends on what you already use, the types and volume of data involved, the technical capabilities of your team, governance requirements, analytics and AI goals, and how people need to consume information.
Three platforms are likely to enter many enterprise conversations.
Microsoft Fabric
Microsoft Fabric can be a good consideration for organizations already working extensively with Power BI and the Microsoft data ecosystem.
Fabric brings together capabilities for data ingestion, engineering, data science, warehousing, real-time analytics, databases, and Power BI within one SaaS analytics environment. Microsoft’s current architecture centers those workloads around OneLake and shared governance capabilities. Explore Microsoft Fabric’s current architecture.
For organizations already standardized around Microsoft technology, that integration may be an important factor.
Databricks
Databricks may deserve stronger consideration when data engineering, data science, machine learning, and production AI workloads are central to the organization’s requirements.
Its current platform supports the machine-learning lifecycle from data preparation and exploratory analysis through model training, deployment, monitoring, and MLOps. Review Databricks’ current data science and machine learning capabilities.
That makes team skills and anticipated workloads particularly important when evaluating fit.
Snowflake
Snowflake may be an even better option for organizations looking to centralize governed enterprise data for analytics and AI across different teams and tools.
The platform can support data engineering, analytics, sharing, AI, and other workloads while allowing organizations to control access around the underlying enterprise data.
That can be especially relevant when data needs to move beyond individual dashboards and become a shared resource across the organization.
Choosing among these approaches should not begin with which platform has the longest feature list.
It should begin with what the business needs its data environment to accomplish.
Where Power BI Fits
For many organizations, moving toward self-service analytics does not mean abandoning Power BI.
Power BI can remain an important visualization and business intelligence layer even as the underlying data architecture becomes more sophisticated.
For example, Snowflake can serve as a centralized data platform while Power BI provides reports and interactive analysis to Microsoft-oriented business users.
Microsoft currently provides a supported Snowflake connector for Power BI that can use Import or DirectQuery configurations, and organizations can configure Microsoft Entra ID and single sign-on for supported Snowflake environments. Learn More Here: Microsoft’s Power BI and Snowflake connection documentation.
That means the question is not necessarily if you should choose Power BI or Snowflake.
They can perform different jobs within the same architecture.
GDC has Power BI experience as well to bring to your team. Our Application Services team created a data pipeline and Power BI reporting solution for York County Probation to replace a labor-intensive process involving the manual extraction and review of data. Read GDC’s Power BI case study.
What Manufacturing and Distribution Organizations Are Already Doing
The value of this shift is easier to understand through real-world examples.
| Toyota Motor Europe describes using Snowflake to reduce the complexity of accessing data from multiple applications and databases while creating reusable data products that can serve as a consistent source of truth across teams. Read the Toyota Motor Europe customer story. | Kraft Heinz has discussed using Snowflake to bring together information such as purchase orders, inventory, manufacturing, sales, and distribution data while collaborating with retail partners. The objective was not simply another dashboard. It was reducing delays and creating a more consistent view of supply-chain information across organizations. Read the Kraft Heinz example. | Scania has publicly discussed its use of data mesh principles and Snowflake as it distributes data ownership across business domains and supports greater reuse and collaboration around operational data. Read about Scania’s data mesh approach. |
These examples differ in architecture and use case, but the broader lesson is similar.
Better analytics begins with making trustworthy data easier to access and reuse.
The dashboard comes later.
From Self-Service Analytics to Conversational Analytics
The next evolution is making analytics easier to access without requiring every business user to understand query languages, database structures, or data-model design.
Snowflake CoWork, formerly Snowflake Intelligence, is one example of that shift.
Snowflake currently describes CoWork as a governed agent experience that can work with structured and unstructured enterprise data, answer natural-language questions, generate visualizations, and support analysis and actions based on the access available to the user. Explore Snowflake CoWork.
Snowflake’s documentation explains that CoWork uses agents connected to semantic views, semantic models, Cortex Search services, and other tools. It is powered by capabilities including Cortex AI Functions, Cortex Analyst, and Cortex Search.
Conversational analytics should not be interpreted or simplified to: Connect the data, turn on AI, and every answer will automatically be correct.
The same fundamentals still apply.
The data needs to be available. Definitions need to be clear. Access needs to be governed. Relevant business context needs to exist. Data quality needs to be monitored, which is now easier through natural-language analytics.
It does not eliminate the work required to create a trustworthy data environment.
Self-Service Does Not Mean Ungoverned
There is a distinction between giving employees access to information and giving them uncontrolled access to data.
The best self service analytics tools allow users to explore information within clearly defined boundaries.
A plant manager may need detailed production information but not employee compensation data. A regional executive may need results for several facilities but not every division. A sales leader may need customer and revenue information without access to sensitive HR or financial records.
Governance makes those distinctions possible. It also gives business users more confidence that the information they are working with is approved and consistent.
That is why governance is not the opposite of self-service.
Done correctly, governance is what makes self-service safe enough to scale.
Start With the Question Your Business Cannot Answer Today
If your organization already has dashboards, the next analytics investment should not automatically be another dashboard.
Start with the questions people still struggle to answer.
- Which decisions are slowed because information has to be gathered manually?
- Where do teams disagree about the numbers?
- Which questions repeatedly get sent back to analysts?
- What data is trapped inside systems that cannot easily be analyzed together?
- Which employees should be able to explore information directly but cannot today?
Those answers help define whether the next requirement is better reporting, system integration, a new data platform, improved governance, conversational analytics, or some combination of them.
For organizations using Microsoft Dynamics 365 Business Central, creating more connected operational data can also provide a stronger starting point for downstream analytics. Learn more about Microsoft Dynamics 365 Business Central and how finance, sales, inventory, and operations can work from a more connected foundation.
And when the challenge extends beyond one application, GDC’s System and Data Integration services can help organizations connect disparate sources, modernize data flows, and build an architecture that supports reporting, analytics, and future AI initiatives.
More dashboards may tell you more about what has already happened.
The better goal is giving the right people a trustworthy way to ask what happened, understand why, and determine what to do next.
Contact GDC to discuss how your current data environment could better support self-service analytics and decision-making.
About CJ Kozarski
Chester “CJ” Kozarski is our Senior Solutions Director of Application Services. He is a Senior Technologist and Enterprise Solution Architect who drives measurable business value through leadership, innovation, and delivery of IT services across software, data, business intelligence, analytics and architecture domains. He holds a Bachelor in Computer Science from Shippensburg University and has over 15 years of enterprise IT experience of broad-based, hands-on management in systems design and development, implementation and support.
