Most small and mid-sized businesses will spend money on AI tools this year and get almost nothing back. The technology isn’t broken. The data feeding it is.

According to a 2024 RAND Corporation study, more than 80% of AI projects fail — twice the failure rate of traditional IT projects. Gartner goes further: through 2026, organizations will abandon 60% of AI projects that aren’t supported by AI-ready data. And in S&P Global’s 2025 survey, 42% of companies abandoned most of their AI initiatives in a single year, up from just 17% the year before.

If you’re an SMB CEO wondering why your Copilot rollout, your AI chatbot, or your “AI-powered” analytics tool hasn’t moved the needle — this is why SMBs fail at AI.

Key takeaway: AI doesn’t fail SMBs. SMBs fail AI by trying to layer it on top of broken data. Fix the data, and AI starts working almost on its own.

Why Do Most SMB AI Projects Fail?

After 100+ implementations across SMBs in oil & gas, manufacturing, healthcare, retail energy, and financial services, we see the same five patterns every time. None of them are about the AI.

1. Your data lives in silos

Sales tracks customers in HubSpot. Finance tracks revenue in QuickBooks. Operations runs on spreadsheets. Marketing pulls from a fourth platform. When you ask Copilot for your top five customers by margin, it doesn’t know which version of “customer” to believe.

Real scenario: A 75-person manufacturing company we worked with asked AI for its top customers by profitability. Sales said one list. Finance said another. Operations had a third. Three answers, zero trust, project shelved within 60 days.

Do this week: Make a one-page inventory of every system where “customer” exists. You’ll be shocked at the count.

2. No one owns the data

When nobody is responsible for data, quality collapses. Fields go unfilled. Definitions drift. “Active customer” means one thing to Sales (anyone we’ve quoted in 12 months) and something else to Finance (anyone who paid an invoice this quarter). The AI inherits all of it — and amplifies it.

Gartner research found that 63% of organizations either don’t have or aren’t sure they have the right data management practices for AI.

Do this week: Assign one named human as the owner of each core data entity (customer, product, revenue, employee). Not a committee. One person.

3. You have tools, but no strategy

The pattern is familiar: a dashboard from one vendor, a chatbot from another, an automation tool from a third, and now an AI copilot on top. There’s no underlying plan for how data gets collected, cleaned, standardized, or governed. Every new tool is built on quicksand.

This is why MIT’s 2025 Project NANDA study found that 95% of generative AI pilots delivered zero measurable financial return. The pilots worked in demos. They collapsed in production because the data layer underneath them was never designed.

Do this week: Before approving the next tool purchase, write one sentence: “This tool will succeed if [specific data] is clean and [specific person] owns the result.” If you can’t fill in the blanks, don’t buy it yet.

4. Leaders don’t trust the numbers

If your CEO doesn’t believe the monthly revenue report, they will not trust an AI recommendation based on the same source. Without trust in the foundation, AI becomes theater — pretty dashboards no one acts on.

We see this every week. A leadership team will spend $50,000 on an AI initiative and then make the actual decision based on the CFO’s gut, because nobody trusts the underlying numbers enough to bet on them.

Do this week: Pick one number your leadership team argues about. Trace it to its source. Fix that one definition. Trust is built one metric at a time.

5. You’re starting with the wrong question

Most SMB AI efforts begin with “Where can we use AI?” That is the wrong question. It puts technology in front of the problem. The right question is: “What is our biggest operational problem, and could AI actually solve it?”

Technology-first thinking almost always fails. Problem-first thinking almost always works — because it forces you to identify exactly which data needs to be clean, and exactly which person needs to own it.

What Percentage of AI Projects Actually Fail?

The numbers are remarkably consistent across independent research:

The pattern is impossible to miss: the failure is in the foundation, not the model.

What Is AI-Ready Data?

AI-ready data is data that is consolidated, consistently defined, actively governed, traceable to a single owner, and continuously quality-checked at the source. Traditional data management runs on quarterly audits and monthly reconciliations. AI runs in seconds. The data feeding it has to keep up.

In practice, AI-ready data has five attributes:

1. Consolidated — one source of truth per entity (one customer list, not seven).
2. Defined — every key term has a written, agreed-on definition.
3. Owned — one named person is accountable per data domain.
4. Governed — there are rules for how new data enters the system.
5. Trusted — leadership uses it to make actual decisions.

If any of those five is missing, your AI will struggle. If three or more are missing, your AI will fail.

Data-Ready vs. Data-Immature SMBs

If you read down the left column and see your company, you are not behind. You are normal. You are also exactly the SMB that AI vendors are happy to sell to and walk away from.

The 90-Day Data Foundation Roadmap

You don’t need a $500k transformation program. You need 90 focused days.

This is the work that separates the 40% who succeed from the 60% Gartner predicts will abandon their AI projects.

What Should an SMB Do Before Buying AI Tools?

In order:

1. Identify the business problem first. Not the technology.
2. Verify the data exists. And that it’s not split across five systems.
3. Assign ownership. Of the data, the metric, and the outcome.
4. Build the dashboard before the AI. If a human can’t trust the report, AI on top of it will be worse, not better.
5. Then — and only then — layer in AI.

The boring work is the work. Skip it, and you’ll join the majority who quietly write off their AI investment by year-end.

How CDO Advisors Helps SMBs Get AI-Ready

We’ve run this exact playbook with 100+ SMBs. It’s the foundation behind every successful AI rollout we’ve seen.

• AI Launchpad — our structured program to take SMBs from “AI-curious” to “AI-producing” in 90 days, anchored on data readiness.
• Power BI Quick Start — working dashboards from a clean, consolidated source in as little as 4 weeks, from $10,000.
• Power BI Rescue — for teams whose existing BI is the broken layer underneath the AI ambition.
• BI as a Service — ongoing data stewardship that fixes the “no one owns the data” problem permanently.
• Free Data Assessment — 30 minutes, no commitment, honest answer on where you actually are.

Frequently Asked Questions

The Bottom Line

The companies winning with AI aren’t the ones with the biggest budgets or the most advanced models. They’re the ones who got their data house in order first.

Stop chasing shiny AI tools. Fix the data. Everything else gets dramatically easier.

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Every Monday, leadership teams across the country sit down for their weekly check-in.
The agenda is straightforward: review last week’s numbers, align on priorities, move
forward.

But that’s not what happens.
Instead, the first 30 minutes disappear into a familiar spiral:
“Where did you pull that number?”
“That doesn’t match what I’m seeing in my report.”
“Which version of the dashboard are we looking at?”
By the time the SMB data debate settles—if it settles—half the meeting is gone. Energy is
drained. Decisions get pushed. And everyone leaves with a lingering sense that maybe,
just maybe, nobody actually knows what’s going on.
Sound familiar?

The Problem Isn’t the SMB Data. It’s the Ownership.

After auditing dozens of SMB data setups, we’ve seen this pattern repeat with striking
consistency. And while the symptoms vary—conflicting dashboards, outdated
spreadsheets, metrics that don’t reconcile—the root cause is almost always the same.

No single person owns the data.

When we ask leadership teams “who’s responsible for data quality?”, we hear some
version of:

• “IT manages the systems.”
• “Finance owns the numbers.”
• “Each department tracks their own metrics.”
• Silence.
  Here’s the truth: when everyone owns the data, no one owns it. And when no one owns
  it, no one trusts it.
  That lack of trust doesn’t stay contained to dashboards and spreadsheets. It seeps into
  every decision, every meeting, every strategic conversation

The Four Patterns of SMB Data Dysfunction

Through our work with growth-stage businesses, we’ve identified four recurring patterns
that erode data trust. Most organizations we audit exhibit at least two. Many have all
four.

Pattern 1: Multiple Versions of Truth

This is the most common—and most damaging—issue we encounter.
The same KPI means different things to different departments. Revenue gets calculated
three different ways. Customer counts don’t match between sales and success.
Everyone has their own spreadsheet, their own formula, their own definition.
The result? Three or more “versions of truth” floating around the organization, each one
defended by the team that created it.
Leadership doesn’t know which number to believe. So they either pick one arbitrarily,
average them together, or—most often—delay the decision entirely.

Pattern 2: The Excel Email Chain

We consistently find businesses running critical reporting infrastructure through Excel
files emailed back and forth.
Someone updates a spreadsheet. Emails it to three people. Those three people make
their own edits. Email their versions to five more people. Within a week, a dozen copies
exist—none of them matching, none of them clearly authoritative.
There’s no version control. No audit trail. No way to know which file reflects reality.
And yet, decisions get made based on whichever version happens to be open when
leadership asks for an update.

Pattern 3: The Ownership Vacuum

SMB Data governance requires someone to be accountable. Not for building dashboards—
for ensuring the numbers are accurate, consistent, and trusted.
In most SMBs, that person doesn’t exist.
IT maintains systems but doesn’t validate business logic. Finance reconciles their own
reports but can’t speak to operational metrics. Department heads track what matters to
them without coordinating definitions across teams.
The result is a patchwork of disconnected data fiefdoms, each operating with its own
standards, its own sources, its own version of reality.

Pattern 4: Decision Paralysis

All of this compounds into a single, costly outcome: leadership teams that can’t move
decisively.
When you don’t trust the numbers, you hesitate. You second-guess. You ask for “one
more report” before committing. You spend meetings debating data instead of
discussing strategy.
Over time, this paralysis becomes cultural. The organization learns that SMB data is
unreliable, so people stop expecting it to inform decisions. Gut instinct fills the vacuum.
And competitors who have figured out data governance? They’re moving faster, with
more confidence, while you’re still debating which spreadsheet is correct.

The Hidden Costs You’re Not Calculating

Data dysfunction doesn’t show up as a line item on your P&L. But the costs are real,
and they compound.
Wasted time: How many hours per week does your leadership team spend reconciling
conflicting reports? How many meetings get derailed by data debates? Multiply that
across a year.
Delayed decisions: Every week a strategic decision gets pushed because “we need
better data” is a week your competitors might be acting.
Eroded confidence: When leaders stop trusting the numbers, they stop using them.
Decisions become political instead of analytical. The loudest voice wins, not the best
insight.
Missed opportunities: You can’t optimize what you can’t measure reliably. Growth
levers go unpulled because no one can agree on what’s actually working.
Talent frustration. Your best people—the ones who want to make data-informed
decisions—get demoralized when the data can’t be trusted. Some of them leave.

What Data-Mature Organizations Do Differently

The businesses that get this right aren’t necessarily using better tools. They’ve made
different organizational choices.

They assign clear ownership.

Someone—whether it’s a fractional Chief Data Officer, a data-focused ops leader, or a
designated owner within finance—is accountable for data quality across the
organization. Not just system maintenance. Actual data integrity.

They define terms once.

What does “revenue” mean? When does a lead become an opportunity? How do you
calculate customer churn? Data-mature organizations answer these questions once,
document them clearly, and enforce consistency across all reporting.

They create a single source of truth.

Not multiple dashboards with different numbers. One authoritative source that everyone
references. When discrepancies arise, there’s a clear process for investigating and
resolving them.

They build trust incrementally.

Data governance isn’t a one-time project. It’s an ongoing discipline. The best
organizations start small—one critical metric, one reliable dashboard—and expand from
there, proving the value of trustworthy data through quick wins.

The Question Worth Asking

Here’s a simple test for your organization:
If your CEO asked three different department heads for last quarter’s revenue number,
would they all give the same answer?
If not, you have a data ownership problem. And it’s costing you more than you think.

Where to Start

Fixing SMB data dysfunction doesn’t require a massive technology overhaul. It requires
clarity, accountability, and incremental discipline.
Start here:

1. Identify your highest-stakes metric. What’s the one number your leadership
   team debates most often?
2. Trace its origins. Where does that number come from? Who calculates it? What
   assumptions are baked into the formula?
3. Assign an owner. One person responsible for that metric’s accuracy. Not the
   system—the number itself.
4. Document the definition. Write down exactly how it’s calculated, where the data
   comes from, and how often it’s updated.
5. Communicate the standard. Make sure everyone who references that metric is
   using the same definition from the same source.
   Then repeat for the next metric. And the next. It’s not glamorous. But it works

The Competitive Advantage of Trusted SMB Data

The businesses that figure out data governance don’t just run more efficient meetings.
They make better decisions, faster. They stop relitigating the same debates every week. They spot problems earlier and opportunities sooner. They build organizational confidence that compounds over time. In a market where speed and clarity win, the gap between data-mature SMBs and
everyone else is widening.

Which side of that SMB data gap is your business on?

CDO Advisors helps growth-stage businesses build data foundations that leadership
teams actually trust. If the patterns in this article sound familiar, [let’s talk]

Read More :

State of SMB Data

SMB Data and AI

You did everything right.

You made the case to leadership. You got the budget approved. You bought the Power BI licenses. Maybe you even brought in a consulting firm to stand it all up.

And yet — six months later — your team is still emailing Excel files.

You’re not alone. A global survey found that BI adoption sits at just 26% on average. That means nearly 3 out of 4 people at companies that invested in business intelligence tools are still ignoring them. The dashboards sit untouched. The reports break when data changes. And leadership is starting to ask the uncomfortable question:

“Was this investment worth it?”

Here’s the hard truth: the problem usually isn’t Power BI. The platform is genuinely powerful. The problem is how it gets sold, implemented, and handed off. In this article, we’ll break down exactly why Power BI adoption fails — and what you can do about it without spending another $200,000.

The Uncomfortable Reality of Power BI Adoption

Let’s paint two pictures. Both are incredibly common.

If you’re new to Power BI:

• You don’t know where to start — and nobody on your team does either.
• IT quoted 6+ months and $200,000+ just to get something in front of users.
• Other consulting firms want $250–$400/hour with a team of 4–5 people.
• You’re worried about making an expensive mistake that’s impossible to undo.
• Meanwhile, your competitors are pulling ahead with faster, cleaner data decisions.

If you already have Power BI:

• Adoption is stuck at 26% or less — sound familiar?
• Your team still emails Excel files instead of logging into the dashboard.
• Reports break constantly when data sources change.
• Nobody really understands how to use what was built.
• You spent a fortune and have very little to show for it.

The common thread in both scenarios? Traditional BI consulting is broken.

Why Traditional Power BI Consulting Fails

The average Power BI implementation through a traditional consulting firm takes 4–6 months and costs $150,000–$300,000. For that investment, you might expect bulletproof adoption and a team that can’t imagine going back to spreadsheets.

What you usually get instead:

• Overly complex solutions designed to justify the engagement size — not your actual needs.
• Dashboards built without enough input from the people who have to use them.
• A handoff with documentation nobody reads and training nobody attends.
• A bill that includes bench time, account managers, and project managers who never touched the data.
• A dependency on the same firm for every update, change, or fix.

The result is a technically functional Power BI environment that nobody trusts, nobody uses, and nobody maintains. And you’re left holding the invoice.

The 5 Real Reasons Power BI Adoption Fails

After 100+ Power BI implementations across healthcare, oil & gas, manufacturing, retail energy, and financial services, we’ve seen the same failure patterns repeat. Here’s what they are — and how to fix each one.

1. The Dashboards Were Built For IT, Not Users

When dashboards are designed by data engineers without regular input from the people who will actually use them, you end up with technically correct but practically useless reports. Users don’t adopt what they don’t understand — and they don’t understand what wasn’t designed for them.

The fix:

Involve end users from day one. Interview stakeholders. Build a proof-of-concept with real data before investing in a full build. Design for the person who will open the report on their phone on the way to a meeting — not the person who built the data model.

2. Nobody Was Trained to Use It

A 60-page PDF and a 90-minute Zoom call is not a training program. Most Power BI rollouts include some version of this and then wonder why adoption is low. People go back to what they know. Excel is familiar. The new dashboard is not.

The fix:

Build training into the project — not as an afterthought, but as a core deliverable. Create champions in each department. Make the dashboards so intuitive that they barely need a training manual.

3. The Reports Break — and Nobody Fixes Them

Data sources change. Columns get renamed. A server gets updated. And suddenly three of your five dashboards show errors. When there’s no owner and no support plan, broken reports kill adoption faster than anything else.

The fix:

Design reports with maintainability in mind. Build automated refresh schedules. Establish governance practices. And have access to an affordable consultant who can fix issues fast — without a six-week engagement to change two lines.

4. The ROI Case Was Never Made to Employees

Leadership sees the value of data-driven decisions. But the analyst who spent 10 years mastering Excel pivot tables sees a threat, not an opportunity. Without a compelling case for what’s in it for them, behavioral change doesn’t happen.

The fix:

Show users what they gain — faster answers, less manual work, better decisions. Celebrate early wins publicly. Make Power BI the easiest path to the insights people already want.

5. The Implementation Was Too Big, Too Slow, Too Complex

Six-month projects build momentum in the wrong direction. By the time a traditional implementation concludes, business requirements have changed, the project team is exhausted, and enthusiasm has faded. Users never connected with the outcome because they were never close to the process.

The fix:

Start small. Deliver something working in 4 weeks. Show value early, build confidence, then expand. Incremental wins drive incremental adoption.

A Better Way: The CDO Advisors Model

At CDO Advisors, we built a Power BI consulting model around everything traditional consulting gets wrong. Here’s how the two approaches compare:

What a Power BI Engagement With CDO Advisors Looks Like

Whether you’re starting from scratch or rescuing a failed implementation, our process is designed to get you to working dashboards fast — and keep your team using them.

Week 1: Discovery & Data Transformation Plan

We start by understanding your data sources, your business goals, and the specific decisions your team needs to make faster. We interview stakeholders, review existing reports, and audit your current data landscape. The output is a clear roadmap — not a proposal designed to maximize engagement size.

Weeks 2–4: Build & Deliver Working Dashboards

We build your first production dashboard with real data, automated refresh schedules, and mobile optimization. Not a prototype. Not a placeholder. A working solution your team can use on day one.

Week 5+: Train, Expand & Drive Adoption

We train your team, publish to Power BI Service, document governance practices, and stay involved as long as you need us. Need to pause? We stop billing immediately. When you’re ready to build the next dashboard, we pick up where we left off.

Real Results From Real Clients

With 50+ combined years of Power BI experience and over 100 successful implementations, we’ve worked across some of the most data-intensive industries:

• Healthcare — Patient outcome dashboards, financial performance tracking, and clinical analytics
• Oil & Gas — Production monitoring, well performance analytics, and regulatory reporting
• Manufacturing — Production line monitoring, quality control, and supply chain analytics
• Retail Energy — Customer analytics, churn prediction, and operational dashboards
• Financial Services — Executive KPI dashboards, portfolio tracking, and risk management

“CDO Advisors provided us with the technical expertise we needed to implement data and analytics. They assessed and partnered with our team to deliver value quickly.”

“CDO Advisors led our successful new BI solution with a Data Vault and Power BI.”

Ready to Get More From Your Power BI Investment?

If your team is still living in Excel despite a Power BI license sitting unused, you don’t need another six-month engagement. You need results in four weeks — at a price that makes sense.

Here are your four paths forward:

• Free Power BI Proof-of-Concept: See your actual data visualized in Power BI in as little as 5 business days. Zero obligation.
• 2026 Quick Start Package — $10,000: A working dashboard in 4 weeks. Fixed fee. No surprises.
• Hourly Consulting — $150/hr: Flexible support billed only when we work. Start, pause, or stop anytime.
• BI-as-a-Service: Ongoing dashboard development and support for a low monthly per-user fee.

No sales pressure. Just expert guidance.

Visit cdoadvisors.com/affordable-power-bi-consulting to schedule your free consultation or proof-of-concept today.

Building a Modern Data Platform with Snowflake and Microsoft Azure

In today’s data-driven world, the ability to harness your data effectively can significantly differentiate your business from competitors. CDO Advisors, with their expertise in data strategy and technology, offers a tailored solution to build a modern data platform using cutting-edge technologies like Snowflake and Microsoft Azure, enhanced further by AI solutions. Here’s how this approach can transform your business operations and boost your revenue.

1. Scalability and Flexibility with Snowflake

Data Warehousing Reimagined: Snowflake’s architecture is uniquely designed to separate storage and compute, allowing for independent scaling based on demand. This means your business can handle data growth without the constraints of traditional data warehouses. CDO Advisors leverages Snowflake to ensure your modern data platform can scale effortlessly, supporting everything from analytics to data science without performance degradation.

Multi-Cloud Support: Snowflake’s cloud-agnostic approach means you can deploy across AWS, Azure, or Google Cloud, providing flexibility in your infrastructure choices. This is particularly beneficial for businesses looking to avoid vendor lock-in while maximizing resource efficiency. With CDO Advisors, you can utilize Snowflake on Microsoft Azure for seamless integration with other Azure services, enhancing your overall data strategy.

2. Robust Infrastructure with Microsoft Azure

Comprehensive Service Integration: Microsoft Azure offers an extensive suite of services that complement Snowflake’s capabilities. From Azure Synapse Analytics for big data solutions to Azure AI for intelligent applications, Azure provides the infrastructure to not only store but also process and analyze data at scale. CDO Advisors uses Azure to create a cohesive environment where data can flow securely and efficiently across different applications and services.

Security and Compliance: With global compliance standards and robust security features, Azure ensures that your data is not only scalable but also secure. CDO Advisors ensures that your modern data platform is built with compliance in mind, reducing risks and safeguarding your business against data breaches or regulatory fines.

3. AI-Driven Insights for Revenue Growth

Enhanced Business Intelligence: By integrating AI and machine learning solutions, you can move from data storage to data intelligence. Azure AI services, combined with Snowflake’s data handling capabilities, enable predictive analytics, anomaly detection, and customer segmentation, among other uses. CDO Advisors helps businesses to implement these AI solutions to uncover hidden patterns in data, leading to better decision-making and targeted marketing strategies that can significantly increase revenue.

Real-Time Decision Making: AI can analyze data in real time, offering insights that traditional analytics might miss. Whether it’s optimizing supply chain operations, improving customer service through chatbots, or personalizing user experiences on your digital platforms, real-time AI insights can lead to quicker, more informed decisions, directly impacting your bottom line.

4. Streamlined Data Management and Governance

Unified Data Experience: CDO Advisors helps in creating a unified modern data platform where all data is accessible, yet governed. This means less silos, more collaboration, and a single source of truth for your data, which is crucial for maintaining consistency across all business units.

Automated Compliance and Data Quality: With Azure and Snowflake, automated tools can manage data quality and compliance, ensuring that your data is clean, consistent, and compliant with various regulations. This reduces the administrative burden and allows your team to focus on strategic tasks rather than data management.

5. Future-Proofing Your Data Strategy

Innovation at Your Fingertips: By partnering with CDO Advisors to leverage Snowflake and Azure, your business isn’t just adapting to current needs but is also equipped for future innovations. This setup allows for easy adoption of new AI models, integration with emerging technologies, and scalability to meet future data demands.

Cost Efficiency: The pay-as-you-go model of both Snowflake and Azure means you invest in what you use, significantly reducing the cost of ownership and making it easier to experiment with new solutions without large upfront investments.

Conclusion

Building a modern data platform with CDO Advisors using Snowflake and Microsoft Azure, alongside AI solutions, isn’t just about managing data; it’s about transforming data into a strategic asset that drives business growth. From scalability and security to AI-driven insights and cost efficiency, this approach offers a comprehensive strategy to not only handle today’s data challenges but also to prepare for tomorrow’s opportunities. Engage with CDO Advisors to start this transformative journey and see your business revenue soar.

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Use Power BI to Transform Your Data – Transform your business with Data

I often see reports that were built in Power BI only designed for 1 audience in mind.  A typical reporting project involves putting in a request to the BI team to get a new report.  Then you meet and provide requirements for the report and views for your report.  While designing the report other business views will most likely be needed.  Yet they are often an after thought and require additional time and work.  A best practice that I have developed involves thinking about the 3 business views in Power BI that business users will need. 

Rarely does someone just ask for a report that only they will use.  Managers will want an aggregated or trend report.  Everyone should be looking for data quality issues to make the data better and more accurate.

Improve your reports and report requirements session by considering these three business views in Power BI reports for each report request.  First the business view that need to use to run their business.  Second what view or views will their management team want.  Third what types of exceptions should be reported and how.

1 – Operations Views to Run Your Area

What do you need in this report to run your business and make data driven decisions?  What source data is being displayed, business logic that is applied, etc.  95% of reports focus on this aspect when being requested and developed.  Take advantage of the BI resources when you have them working on your projects to get not only the views that you need.  But take the extra time to build out the other views.

2 – Management View to Share Outside Your Area

The next area to think about with every report request is who else in the organization may want a view of the data.  Do you need to create views or tables that highlight summary data that management teams or other departments would need to help them make better decisions?  If so, create some views designed and labeled for customers outside your department. 

How will these users get the reports?  Will you email them the pages via a subscription?  Do they want the data in email and only want a screenshot?  Do you need to provide an easy way to export the data and send them an file?

Your users are unique and how they want the data varies between them.  Design external business views in Power BI that make it easy for you to share your data with external teams.

3 – Exception Views for Data Quality

Data quality is a constant battle in every organization.  One way to make data quality a primary goal is to build exception reports into each report that help users find data that is suspect.  Once users can easily identify data quality issues users are more likely to update the source system to correct the data.

Add at least 1 page focused on exceptions.  Ideas include minimum and maximum dates.  Do you have dates that are years in the future that should not be?  Business rules to check for conditions in your data.  One example could be closed sales status with a stage that is awaiting client feedback.  

The specific rules that you build should be related to the report or the dataset used in the report for users that have the ability to change the data in the source systems.

Looking for help to create business views in Power BI?  Fill out the contact form for a free consultation.

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As you get started with Power BI for reporting and dashboards.  Setting up data governance is often overlooked in order to get data to insights to users quicker.  Data governance can take many forms and be overwhelming to get started.  Governance focuses how people interact with the reports and ultimately the data in the reports.

Power BI governance can be applied regardless of the level of you Power BI maturity.  Below are 3 best practices for you to review and consider in your Power BI journey.

1 – Power BI Security

2 – Deployment Strategy

3 – Workspace or Report Ownership

1 – Power BI Governance Security

Time to check your  implementation and how Power BI security is setup.  I often find workspaces with people added that are no longer with the company.  Workspace owners that moved on to new roles and the new owners have not reviewed the users in the workspace. 

Do you have a process for authorizing and granting users access to workspaces?  Who needs to approve the request? What type of access does a user need in each workspace?  Do you have sensitive data that you need to protect?  

Do an audit of your environment and see what you find and what improvements may need to be implemented to more securely access and protect  your data.

Read More – Roles in Power BI Workspaces

Read More – Power BI Security

2 – Deployment Strategy

How do you deploy your Power BI reports as part of your Power BI governance?  Do you have your files in source control?  Do you deploy to a UAT workspace or do you just overwrite the old version of the report?  How do you capture the approval to deploy the report to production?

One of the most common patterns that I see is having a Sandbox or UAT workspace for testing.  The reports in this workspace are pointing to production data and are ready for users to test.  Once validated the reports are moved to the final workspace and then removed from the UAT.

The data quality and profiling options are built into the Power Query Editor on the View tab as shown below.

3 – Workspace or Report Ownership

Do you have ownership defined for your Power BI workspaces, reports and data?  If not, you should start small and get a champion that is easy to work with in your business.  

Power BI governance includes defining workspace ownership to approve who and what level of access a user can have in the workspace folder.  Then for each report you need to define the report owner and what they are responsible for.  Common responsibilities include approving changes or logic updates.  Each report should have this information documented so everyone understands who the report belongs to.

For more information on data governance – check out The Data Governance Institute.

Looking for help?  Fill out the contact form for a free consultation.

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Get Power BI Consulting for 50% less than Big Firms

I work with clients looking for high-quality Power BI consulting services without the overhigh of larger firms.  Business leaders that need quick implementations or flexible timelines, hire me to implement their Power BI solutions.  I’ve done work with companies that included 40 hours over 2 months to full-time work over months to complete their projects.  

No need to pay for a project manager, architect, visualization expert, ETL developer, or data engineer.  I’ve been building and delivering BI solutions for over 23 years.  Unlike other firms, I handle all of the work and roles.  

Need a Power BI project?  Give me a call, my upfront rate is $200 per hour and is billed only when actually working on your project.  

No guessing what you are paying for or what resources on a team are doing.

Other Firms typically require a mix of these roles based on what you are trying to do.

• Project Manager – $150/hr
• Data Architect – $175/hr
• Data Visualization – $150/hr
• Data Engineer – $150/hr
• Solution Manager – $200/hr

Do you have a backlog of projects that need to get knocked out?  In need of consulting help but don’t have a large budget?  Have you delayed projects because other projects prevented you from dedicating resources full time?  I can help with fast and flexible Power BI consulting services that get you what you need without breaking your budget. 

Typical Power BI Consulting Services

Below is an example project breakout that shows the cost savings for an 8-week Power BI consulting project.   It shows my rate versus typical engagements and save over 50% with an all in one resource.

Request a Free Consultation

I work with these quick-win solutions often and have plenty of references for you to talk with on the solution, costs, and delivery.  Often the Power BI consulting services projects that I complete are done faster and under budget than is shown here. 

Do you have a project or backlog that you need help with?  Contact me to discuss and let’s get your business better data to insights with Power BI consulting services.

How it works:

1 – Pick a project

2 – Set your budget

3 – Get Your Solution

Power BI Consulting Services I offer:

• New Implementation
• Report Development
• Report Enhancements
• Support
• Power BI Governance
• Power BI Adoption
• Converting Excel to Power BI

Request a Free Consultation


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Power BI Consulting as a Service – Low Monthly Fees, Pay Per Month Per User

Microsoft Power BI Demo – Compare your current reporting to Power BI

Power BI Quick Start Solutions – Ready to start or enhance your Power BI?

Microsoft Power BI Consulting – Learn more about our Consultants

Use Power BI to Transform Your Data – Transform your business with Data

Data quality is a problem for every company.  Legacy systems, new systems, and data acquired from mergers or acquisitions have one common theme. Insufficient data and poor data quality are everywhere.  Leverage options in Power BI to create Power BI data quality dashboard examples to start your journey to better data without investing in another software platform.

Your First Power BI Data Quality Project

Getting started with a data quality project is often where I see companies get stuck.  They are unsure how to start or which systems to start with.  With data quality projects, don’t let great be the enemy of good. Start small, pick some critical data that you know has data quality issues and start there.  

1. 1. Select the data to profile, start small 2o columns or less
   2. Assign the data owner
   3. Assign the data steward
   4. Profile the data with Power BI
   5. Review outputs with Data stewards and data owner
   6.  Create processes to clean up source system inputs

PowerSchool Power BI Data Quality Examples

To show how Power BI can be used to build a Power BI data quality dashboard.  I will use some columns from the student table in PowerSchool.  Having accurate data about students is critical for various aspects of administration and even funding.  

The data quality and profiling options are built into the Power Query Editor on the View tab as shown below.

To see how each option works, turn them on one by one and review the output.  To see column quality select the checkbox and Power BI will add a window above your columns with additional data.

After enabling “Column Quality” you can see the percentage of records that are valid, empty or have errors when imported into Power BI.  This is a great quick way to get a sense of what is in your data.  For instance, the DOB field shows 100% valid.  This means that all rows have data that matches the data type.  It does not mean that all dates of birth are accurate or in a certain format.  The column “Street” shows 92% valid and 8% empty.  This lets you see how populated your data is in a few simple clicks in Power Query.

The next option is Column Distribution in Power Query.  Uncheck Column Quality and select Column Distribution.  You will now see bar charts and data summaries that show you what is in the data.

As you can see from these examples, you can quickly get insights from your data.  Start you data quality journey with Power BI data quality dashboard examples to show your users the impact of bad data on decisions they are making.

Contact me with questions.

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CDO Advisors Microsoft Power BI Solutions:

Get Power BI Consulting – CDO Advisors Increase Power BI Adoption

Increase Power BI Adoption – See how we help you get more from your investment

Power BI Consulting as a Service – Low Monthly Fees, Pay Per Month Per User

Microsoft Power BI Demo – Compare your current reporting to Power BI

Power BI Quick Start Solutions – Ready to start or enhance your Power BI?

Microsoft Power BI Consulting – Learn more about our Consultants

Use Power BI to Transform Your Data – Transform your business with Data