Bad data isn’t just a nuisance; it’s a significant liability for businesses.
According to Gartner, poor data quality costs organizations an average of $12.9 million annually. For consumer packaged goods (CPG) companies, the implications can be even more severe and widespread. Inaccurate sales forecasts, misaligned inventory, failed marketing campaigns, and operational setbacks can all arise from bad data, wreaking havoc across departments, damaging retailer relationships, and eroding consumer trust.
This post outlines seven key signs that indicate your retail data may be of poor quality. It will also discuss how these issues can impact sales, marketing, supply chain, IT, and finance. Finally, we will explore solutions to address these problems and minimize the costs associated with poor-quality data. Recognizing these warning signs is the first step in transforming your data into a strategic asset, accelerating your company’s digital transformation.
In the highly competitive CPG-retail industry, data drives critical growth-related decisions and day-to-day operations. Whether it’s determining how much stock to replenish, setting pricing strategies, or targeting the right market segment, accurate point-of-sale (POS) and inventory data is the foundation and lifeblood for success. Poor supply and demand data, on the other hand, leads to missed growth opportunities, lower ROI, wasted resources, lower productivity, operational inefficiencies, and more.
Consider this: if your inventory data is flawed, you risk stockouts or overstocking—both of which can be costly for brands and retailers. Similarly, inaccurate data could result in marketing campaigns targeting the wrong audience, wasting precious ad spend and time.
Data quality is directly linked to the quality of actionable insights and decision-making processes across the enterprise. High-quality and trustworthy retail data are also critical in powering various systems and solutions, from simple reporting and analytics and demand planning to machine learning models and generative artificial intelligence (AI) solutions.
By understanding the signs of bad data, you can mitigate these risks and turn your data into a competitive advantage and unlock your organization’s full potential.
No CPG company or retailer is immune from poor data quality. And without a sound, consistent data management strategy, poor data is practically inevitable. Making matters worse, bad data doesn’t always announce itself; often, it lurks unnoticed until a major issue arises.
However, if you know what to look for and where to look, you can more effectively address the factors that contribute to low data quality before they become larger and more costly issues. Here are seven common signs your retail data quality may be compromised:
Duplicate or repetitive entries for products, sales, or customers can cause overrepresentation of crucial information, create confusion and conflicts, and distort reporting and analytics. For example, having two entries for the same SKU can lead to incorrect inventory counts, causing out-of-stocks or overproduction.
Data that is stored in multiple locations takes up valuable space without providing any additional value. Storing redundant data can create unnecessary overhead for companies (e.g., increased data storage costs), increase their total cloud-related costs, and decrease the efficiency of their data systems.
Data that comes from various systems, datasets, and retailers often lacks alignment and synchronization. This inconsistency is evident in different data formats, such as varying date structures, measurement units, or naming conventions. As a result, it becomes challenging to integrate and analyze the data. While this issue can arise when examining a single retailer, it becomes even more pronounced during cross-retailer analyses.
Example: One region reports product weights in kilograms while another uses pounds, leading to errors in centralized reporting and analysis. This lack of consistency prevents a unified view and hampers collaboration across departments and regions.
Relying on outdated data and data that is no longer relevant results in misguided business decisions that don’t reflect current realities. For instance, utilizing POS data that is weeks or months old to evaluate the success of a new product launch doesn’t provide the timeliest insights to determine if you should promote, continue promoting, or stop promoting the item.
Critical gaps in data, such as missing product descriptions or customer lead times, can hinder operations and decision-making. Additionally, partial data, like truncated information, may exist due to errors or interruptions during the data collection process.
For example, missing lead times in your system can result in delayed product deliveries, which can create ripple effects throughout your supply chain. This may lead to insufficient retail replenishment, out-of-stock products, and ultimately, consumer frustration.
When different teams, departments, or systems maintain their own separate databases, datasets, and spreadsheets, discrepancies can occur that require reconciliation. This situation makes cross-functional collaboration challenging.
Data silos also generate conflicting insights and perspectives, which undermine trust in reporting and analytics, ultimately jeopardizing strategic decision-making.
Without a universal structure for capturing and categorizing data, where information does not conform to predefined standards, it becomes nearly impossible to align metrics across the organization for proper interpretation and analysis.
For example, if product categories are labeled differently across markets, it can be challenging to compile enterprise-wide sales data. This inconsistency can result in inaccurate reporting, poor sales forecasting, ineffective marketing initiatives, and distorted demand forecasting and planning.
Errors in manual data entry, verification, and reporting, along with system misalignments, can lead to inaccurate insights that may affect other systems and applications, such as trade promotion planning and demand forecasting. For example, a pricing mistake that results in overcharging customers can damage relationships and necessitate refunds.
As brands collect extensive and diverse retail data to support predictive analytics, leverage the potential of AI, and guide decision-making for improving sales and operational efficiency, enhancing customer engagement, and maximizing ROI, one critical aspect is often overlooked: the cost of poor data.
Neglecting data quality issues and making strategic business decisions based on unreliable data can lead to significant consequences for CPG companies, retailers, and consumers alike:
Errors in pricing, POS reporting, sales and demand forecasting, or inventory data can lead to flawed analyses, resulting in revenue loss and decreased profitability. If you cannot make informed and timely decisions due to unreliable data, you may miss out on profitable opportunities. This could mean missing the chance to enter a lucrative market, failing to launch an appealing new product, running an ineffective marketing campaign, or inadequately replenishing inventory for top-performing retailers. Each of these scenarios represents a potential loss of sales and growth opportunities.
Data quality issues can also affect communication and collaboration within your company and with retailer partners, leading to misinformed pricing strategies, ineffective retail execution strategies, and mismanaged inventory.
Employees frequently spend a significant portion of their workday—sometimes up to 30%—dealing with and reconciling inaccurate data, rather than focusing on strategic initiatives. The costs associated with correcting this data extend beyond the immediate expenses of the cleanup process, which can quickly deplete budgets. There is also the opportunity cost of diverting resources away from more strategic and profitable initiatives.
Low-quality and unreliable data can lead to inefficiencies in supply chain operations and increased expenses. For example, inaccurate inventory data may result in overstocking or understocking items, both of which incur significant costs. These costs include holding expenses for excess inventory, emergency orders for stock shortages, and incorrect orders. Furthermore, inaccurate customer data can lead to failed deliveries, miscommunication, and difficulties in effectively managing supply chain disruptions.
Inaccurate retail data can lead to poorly targeted marketing campaigns and ineffective communications, ultimately harming brand perception. This can quickly erode consumer trust and loyalty, causing shoppers to switch to competitors, including both brands and retailers. For instance, negative or inconsistent shopping experiences due to inaccurate data can result in revenue losses, as businesses may face decreased sales and challenges in acquiring and retaining customers.
Additionally, in today’s social media landscape, news about errors or poor shopping experiences—whether in-store or online—can spread rapidly. This might result in wider public relations issues, further jeopardizing partnerships with retailers, brands, and influencers.
Data and business analysts rely on timely, detailed, and accurate information to generate actionable insights, predict trends, and facilitate effective decision-making. Poor data ingestion processes and low data quality can adversely affect the speed of data consumption, interpretation, and decision-making. When retail data is inaccurate or incomplete, company leaders often make misguided strategic choices based on distorted insights, negatively impacting sales, supply chain operations, product availability, and profitability.
This can result in missed investment opportunities, lost strategic partnerships, failed product launches, and an inability to adapt to market changes. Brands cannot achieve the expected returns when using unreliable POS and inventory data, which obstructs intelligent decision-making.
Improving retail data quality, despite the source and format of data, requires proactive measures:
Preventing bad data from entering your technology ecosystem in the first place is far more efficient and cost-effective than addressing data quality issues later. By utilizing a modern retail data platform like VELOCITY®, brands can automatically collect, cleanse, and harmonize data from various retail sources. This approach allows companies to make strategic decisions based on one accurate version of the truth—a complete and unified view of their data across all retailers that is easily accessible to all business units.
To ensure that data is valid and usable for all interested parties, it is essential to establish structured guidelines and rules. Brands, whether large or emerging, should define, document, and communicate their data policies, procedures, standards, metrics, and clear ownership of the data. Failing to do so can result in different departments handling data inconsistently, leading to fragmentation, confusion, conflicts, misaligned strategies, and ultimately, negative impacts on business outcomes.
Creating a company-wide culture that prioritizes and values data quality can lead to departments taking responsibility for maintaining standards and holding each other accountable. When everyone acknowledges that ensuring data integrity and using it effectively is a shared responsibility, there is a collective effort to uphold these standards.
Additionally, breaking down data silos by facilitating seamless access and sharing of information enhances collaboration and alignment among departments such as sales, marketing, supply chain, and finance. This approach also fosters better collaboration and joint business planning with retailer partners.
Without regular assessments and data audits, even the smallest inaccuracy can escalate into a major problem across various areas of your business. Regular audits to ensure data accuracy and relevance over time are essential for maintaining business continuity.
Good data is essential for modern CPG companies and emerging brands to operate intelligently, efficiently, and profitably. Bad retail data, a common issue for many brands, can weaken a company's competitive position and impair critical business objectives, decisions, and outcomes.
Recognizing the seven signs of bad data and understanding their associated costs is the first step in addressing this challenge and unlocking a company's full potential. By proactively improving data quality, your organization can reduce costs, enhance efficiency, and achieve better results across all retail partners. Furthermore, if a brand intends to harness the value and potential of AI models and systems and truly become a data-driven organization, it is crucial to prioritize data quality and invest in advanced data ingestion, cleansing, and harmonization technology like VELOCITY.
How do you begin? How can you build a trustworthy data foundation for operations, analytics, and critical decision-making? Reach out to us today to explore how we can support you in this endeavor.
Wondering how good your data is? Download our Retail POS Data Self-Assessment.