Data Quality Helper | Boost Competitive Advantage by Improving Data

It may not be your competition or market conditions holding back company growth. Elevating the quality of your data might offer numerous opportunities across the enterprise. 

Poor data quality can be traced back to several sources. Most organizations experience several of them simultaneously, and they often interact and compound exponentially over time. Common sources include:

• Data silos create duplicates
• Manual entry introduces errors
• No data owner fixes them
• Definitions and formats lack standardization, differing across teams

The result is low data trust across the organization.

Then these root causes translate into measurable problems:

• Slower decision-making
• Higher operational costs
• Failed analytics initiatives
• Missed revenue opportunities
• Poor customer experiences

 Improving Data Quality Should Be a Company-wide Priority & Initiative 

Poor data is not just an IT issue—it is a material financial risk that directly affects revenue, cost, and profitability. Across industries, poor data quality typically results in:

• 10–25% revenue loss
• Millions in annual operational waste
• Failed AI/analytics initiatives
• Lower customer satisfaction and retention
• Lower enterprise morale and productivity

Bad data causes inefficient operations across supply chains, finance, and internal processes. Poor data quality causes employees to spend time fixing data instead of creating value. Instead of producing insights or innovation, teams are often doing manual repair work.

• 25–30% of business processes are affected by poor data quality
• Labor productivity can be reduced by up to 20%
• Operations costs can be 30% higher for companies with low data quality maturity
• Data quality issues account for 10–15% of data-related operating costs
• Data quality failures cause 70% of process failures in operational workflows

Poor data also damages strategy, innovation, and growth opportunities. When leaders don’t trust the data, companies delay decisions, underinvest in analytics, and miss market opportunities.

• 40% of the expected value of business initiatives is never realized due to poor data quality
• 85% of organizations fail to fully leverage their data assets
• Only ~35% of executives fully trust their company’s data
• 36% of executives avoid using data for key decisions because they don’t trust it

Poor data quality also affects how companies perform in the marketplace. Bad data leads to

marketing targeting the wrong customers, incorrect personalization, poor customer experiences, and reduced retention.

• Customer retention can drop by up to 15% due to data quality failures
• 25% of marketing efforts are wasted because of inaccurate customer data
• Customer satisfaction scores drop ~10% when data quality issues exist
• 75% of organizations lack confidence in their customer data accuracy

Poor data quality blocks the value of analytics and AI. AI and analytics amplify bad data, meaning technology investments fail without clean, governed data.

• 40% of AI and analytics projects fail to deliver ROI due to poor data quality
• 60% of organizations say data quality is the biggest barrier to digital transformation
• 40% of analytics failures are linked to poor data quality

 Of course, the key is making good decisions, the right decisions, to fulfill the promise of business AI. And this should allow the human element to remain front and center throughout.

High-Quality Data Is Required for Successful Business Outcomes

To be sure, a poorly constructed AI tech stack will create highly negative outcomes. The good news is that there are an overwhelming number of options available in the marketplace to help your business try to get it right. You are not alone.

But your data is YOUR data. It is not like anyone else’s data. The best or most expensive AI tech stack in the world will fail spectacularly if you feed it poor quality data. Ultimately, the primary responsibility of having high-quality business data is yours.

Businesses that decide to use the Salesforce^® and Agentforce^® platform have built-in advantages when it comes to the AI tech stack and a repository for data. But more work must be done on the data side.

Using Salesforce App Data Quality Helper Is a Wise Decision

Optimizing the use of Salesforce and AI is the responsibility of a number of the business’s IT stakeholders, first and foremost the Salesforce Admin. In Salesforce, default data quality tools are limited, so it is likely the Admin will make the decision to find additional solutions to elevate data quality.

The easy-to-use functionality of the free app Data Quality Helper offers various ways improve the quality of existing data and future data creation:

• Duplicate record detection and resolution
• Flexible enhanced validation rules
• Storage and deletion rules

Using this app will result in a better data set that will optimize the ROI of using Salesforce generally and produce successful outcomes for AI specifically. And there’s even more good news: The way the app works doesn’t leave the full responsibility for data cleanup resting solely with the Admin. Salesforce users are empowered to fix data issues—after all, they are the ones closest to the data, so they are the ones best suited to make the necessary decisions about quality resolution.

With better quality data, your business can:

• Create effective business strategy at the executive level
• Efficiently manage internal operations and data
• Productively work with external customers and stakeholders

What Can Be Done to Elevate Data Quality? 

The problems often originate with the lack of data governance. Organizations often focus on collecting data rather than managing it. Instituting the proper governance tools is a proactive approach to preventing the problems in the first place. Without governance, there is no:

• Quality monitoring
• Duplicate record detection and merging
• Validation rules
• Lineage tracking
• Standardized processes

According to research from Gartner, poor governance is one of the primary reasons data initiatives fail.

To limit costly and lengthy manual repair work required to get to a better future state, the right tools can streamline efforts across the entire company and accelerate data quality success. If your company uses the Salesforce^® platform as its single source of truth for data, the free Data Quality Helper app can empower admins and users together in every company function.

This 100%-native app offers quality monitoring for duplicate records, soft or hard validation rules, and data storage management and deletion. Admins create the rules, records and objects are flagged for review, and the users who best know the data track and fix the data issues. In real-time, 24/7, all existing data and newly created data are monitored and flagged with warnings that follow the data until resolution.

It couldn’t be easier. And improvement efforts don’t fall on a small group of people—quickening the pace of change. Plus, with data rules in place, new bad data is less likely to occur going forward.

Bad data quality silently sabotages business productivity, revenue, and growth everyday. Unaddressed, it gets worse over time. How much longer can you afford to delay action?

Learn more about Data Quality Helper, and install the free version of Data Quality Helper at the Salesforce AppExchange today.

(Statistical Sources: Gitnux, ZipDo, Data Ladder, and WifiTalents)