Your data problem is about to get worse—unless you take these steps to fix it

A business manager reviews her quarterly product performance report. The data shows that Product A is generating higher margins than Product B. 

Based on this information, she redirects marketing resources, adjusts purchasing plans, and reallocates staff. Six months later, she realizes someone had been incorrectly categorizing certain costs between the product lines. Product B had actually been the better performer, and now the company faces inventory imbalances and customer delivery delays.

​Poor data quality poses significant risks to organizations, leading to misguided decisions and operational inefficiencies. According to recent studies, these problems are widespread: 40% of CFOs don’t completely trust their organization’s financial data, and 50% of senior finance and accounting professionals lack full confidence in the data they’re working with daily. Gartner estimates that poor data quality costs organizations nearly $13 million per year on average, yet nearly 60% of organizations don’t measure these financial costs.

As AI tools become integrated into business operations, data accuracy has taken on greater importance. The traditional boundaries between sales teams, operations, and accounting are disappearing as companies move toward a model where information flows freely between departments, with everyone sharing responsibility for data quality. 

Let’s take a closer look at why this data dependence matters and discuss steps to address these challenges within your organization.

AI’s data dependency

“We’ll implement this AI solution now and clean up our data later.”

This approach is common among businesses eager to adopt AI, but it misaligns with what artificial intelligence needs to work. Unlike conventional software that simply follows programmed instructions, AI systems learn patterns from your existing data.

A recent report from Precisely highlights this disconnect: while 60% of organizations now consider AI a primary use case for their data, only 12% believe their data meets the quality and accessibility standards needed for AI to work effectively. 

If your sales team categorizes customers differently than your support team, or if your accounting department uses different revenue classifications than your sales department, AI tools will amplify these inconsistencies.

Think of it like hiring a new employee who works at lightning speed but follows instructions exactly as given. If you provide this employee with inconsistent or incorrect information during training, they’ll replicate those same errors across all their work—but faster and more consistently than a human would.

Breaking down departmental walls

Remember when accounting departments worked separately from other business functions (or maybe in your organization, this is still the case)? They were often physically separated—the people in the back office that other departments only interacted with when reconciling expenses or requesting budget approvals.

As business software systems became more integrated, accounting teams formed closer partnerships with IT departments. Technical staff needed to understand basic accounting principles, while accounting professionals had to grasp the capabilities and limitations of the systems.

More recently, as real-time financial data became integral for business operations, accounting teams began working directly with sales and operational departments. Accounting staff started collaborating on forecasting and analyzing business performance metrics in ways they never had before.

Today, we’re seeing the final breakdown of these silos. With AI tools requiring consistent data across departments, organizations are moving beyond simple collaboration to shared ownership of data. Companies can no longer maintain separate “sales data,” “operations data,” and “accounting data”—all information must work together for effective decision-making.

This shift is essential, as 76% of CFOs agree that without “one version of the truth” across business units, their organization will struggle to meet its objectives.

Your data strategy champions

As business operations become more integrated, the roles of Salesforce administrators and IT specialists are changing fast. While they used to focus primarily on keeping systems running, upgrading software, and handling technical issues, today they manage customer records and need a broader understanding of business workflows, operational processes, and financial accounting.

A skilled Salesforce admin today serves as a connector between departments, investigating the “why” behind business processes before implementing technical solutions. 

With AI implementation, these admins become invaluable assets. They create tools that help various departments make better decisions based on integrated data without requiring everyone to understand complex accounting principles or technical details.

For example, a capable Salesforce admin might develop an AI agent that helps sales representatives set appropriate discount levels based on current cash flow, profit margins, and customer value—all without requiring the sales team to learn financial analysis or accounting rules.

One platform, one source of truth 

Many businesses operate with disconnected systems—perhaps HubSpot for marketing, Salesforce for sales, and Oracle for accounting and finance. Each system maintains its own definition of customers, products, and revenue classifications.

This fragmented approach creates significant challenges for AI implementation. Before you can use AI effectively, you must first harmonize all this disparate data—a project that can consume months of effort, can be very costly, and still leave gaps and inconsistencies.

Consider this scenario: A company wants to use AI to recommend optimal pricing for each customer based on their history, market conditions, and current profitability. With disconnected systems, the AI tool would need to pull customer information from the CRM, financial data from the accounting system, and market trends from the marketing platform into a data lake—each with different data structures and potentially conflicting information—before it can even begin to analyze the information.

A unified platform eliminates this hurdle. When all departments work from the same core data model, AI tools can immediately begin providing valuable insights without the extensive manual work, spreadsheet exports, or creation of a data lake.

This approach also reduces technology maintenance costs. Instead of needing specialists for each separate system and complex integration tools to connect them, companies can operate more efficiently with a unified skill set focused on one platform.

Keeping your data safe

As AI adoption accelerates, security concerns multiply. Employees often experiment with public AI tools, potentially sharing sensitive company information without considering the implications.

This casual AI usage, sometimes called “shadow AI,” creates significant risks for data security and compliance. Without proper governance, employees might inadvertently violate NDAs or expose customer information by inputting sensitive data into public AI systems.

Organizations need clear AI governance policies and secure tools with appropriate safeguards to protect data while enabling AI functionality. This becomes particularly crucial for financial data, which often contains sensitive information subject to regulatory requirements and contractual obligations.

Steps to unify your data

Moving toward a unified data approach might seem overwhelming, but here are some practical steps that can help you make progress:

1. Start with a critical business process. Instead of trying to unify all your data at once, identify one key business process that would benefit most from improved data quality and AI insights. Build a successful pattern there, then expand to other areas.
2. Act despite timing concerns. There’s never a “perfect” time to undertake a major data initiative. The competitive landscape has shifted with AI, making it important to act rather than wait for ideal conditions.
3. Set achievable goals. Define clear, measurable objectives for your data unification efforts. Focus on specific business outcomes rather than abstract concepts like “better data quality.”
4. Stay flexible. Even organizations committed to bringing their data together will still use specialized tools for specific functions. The key is ensuring these tools integrate properly with your core systems. For example, a company might use a specialized expense management tool that feeds data directly into their main accounting system.
5. Create cross-departmental ownership. Form a data governance team with representatives from sales, operations, accounting, marketing, and IT. Give them joint responsibility for data quality and strategy across the organization.

See clearly, act faster

An unexpected benefit of breaking down data silos is increased visibility across the organization. Financial information once shared only in management meetings can now appear in company-wide dashboards accessible to team members at all levels based on appropriate role permissions.

This transparency builds confidence both internally and with customers. Teams better understand how their performance affects financial results, and customers gain trust in organizations that can provide consistent, accurate information.

It also speeds decision-making. Instead of waiting for monthly financial reviews, managers can make informed decisions based on current data that’s accessible across the organization.

Better data for a healthier business

AI is changing how organizations think about team structure and data management. For finance professionals, this means becoming strategic partners in business decision-making rather than isolated recordkeepers. For Salesforce admins and IT specialists, it means developing a deeper understanding of business processes and creating tools that connect departments that were previously separate.

The companies that succeed with AI will be those with the cleanest, most unified data. Building that foundation starts with breaking down the barriers between different departments to create a unified data strategy that supports the entire business.