Date Quality Can Make or Break Your B2B Ecommerce Business

Data Quality Can Make or Break Your B2B eCommerce Business

Data is key to B2B businesses. It drives all business operations. And if you’re selling online, data quality is critical to your eCommerce success. It allows you to reach your customers and provide a satisfactory buying experience. It makes your workflows more efficient, your strategies more effective, and your business more profitable. It allows you to scale.

What is eCommerce data?

What do we mean by data? At the very center of your eCommerce business is the numerical and descriptive information that is attached to your products, customers and suppliers. Besides the data that drives your eCommerce, analytical data (metrics) from eCommerce transactions, marketing campaigns and marketing research must support your eCommerce decisions and strategies.

What is data quality?

Data quality is the measure of how accurate your data is across all systems. For data to be effective and efficient, it first needs to be complete, consistent, current and correct across all your workflows, channels and systems. Good data quality eliminates workflow redundancies, production errors, customer complaints and returns. Good data quality optimizes your marketing strategy, expedites deliveries, bolsters your sales team and elevates your brand reputation.

Complete data contains all the necessary data elements. For example, all specified attributes of a product and complete contact information for vendors and customers. Consistent data doesn’t  have conflicts between the same data values in different data sets or systems. It conforms to company-wide business standards. Current data is up to date across all systems. Correct data is accurate, error-free data from a reliable source of information.

How does poor data quality affect eCommerce?

Unfortunately, a lot of B2B businesses have moved into eCommerce with bad quality data. They’re using data that is input by multiple sources who are using disparate terminology and methods. They’re manually collecting, amending and merging data while it’s being used across several systems—an unmanageable undertaking.

Other businesses have pivoted into eCommerce unprepared, with outdated systems and flawed data. They’ve strung together technologies into patched-up systems that cannot properly sync data. In several cases, their databases are spreadsheets. The resulting data contains duplications. It’s inconsistent, incomplete and incorrect.

The problem with flawed data is that it affects every part of your business: decisions, leads and sales, operational costs, customer retention and so much more. Businesses lose up to 20% of revenue due to poor data quality. Not surprising, considering that bad data costs U.S. businesses $3.1 trillion each year.

According to the Harvard Business Review, the cost of poor data quality can be approximated by the rule of ten: the operational cost to complete one unit of work is ten times greater when that task is completed with flawed data. The rule of ten does not account for the losses incurred when flawed data results in lost sales, customer defection, badly informed decisions and plummeting brand reputation.

Data is Siloed Across Systems

Businesses most affected by these losses are those that mistakenly believe the data management tools that support their eCommerce also support good quality data. In fact, the software that houses your data are siloed data systems. Consider:

  • Customer Relationship Management (CRM) is the sales and marketing tool you use to manage business relationships interactions with customers and leads. Examples: Salesforce, Infusionsoft.
  • Product Information Management (PIM) is a repository of product and service information. Example: Pimcore.
  • Enterprise resource planning (ERP) stores financial and business information. It handles inventory and transactional activities. ERPs are not master data repositories for customer, product or supply-chain data. Example: Netsuite.
  • Supply Chain Management software manages your vendor contacts and every aspect of your supply chain from procurement to delivery. Example: Supplier 360.

Businesses often utilize two or more of the above tools, providing separate sources of data which may be duplicated, incomplete or inconsistent across the systems. Unfortunately, integrating these siloed systems still doesn’t allow for harmonizing and synchronizing data to create a single version of truth for your business data. For that you need a Master Data Management (MDM) system.

Manage all your data with a Master Data Management solution

An MDM system is enterprise-wide software that integrates and synchronizes all your data sources. MDM solutions allow eCommerce businesses to create, consolidate, verify, update and manage shared data records across multiple workflows. An MDM provides the company’s single, trusted view of complete and consistent customer data, product data, financial data, and supply-chain data.Data quality is critical to MDM

MDM is a tool that enables eCommerce businesses to consolidate and correct data. While it makes good quality data possible across multiple workflows, it does not perform the task for you. And in fact, your single view of your master data will not be trusted if it starts with bad data. There are, however, data quality (DQ) tools that identify, understand and correct flawed data. A complete data quality platform, like Melissa Data Quality Suite will provide all of these tools:

  • Data profiling tools review and audit the condition of data stored across your business systems
  • Data stewardship tools manage the data lifecycle by defining data standards and implementing data governance
  • Data cleansing tools identify and correct inaccurate data
  • Data enrichment tools identify and supplement incomplete data
  • Data consolidation tools match and merge duplicate data

Bottom Line

The data that drives your eCommerce must not be duplicated, incomplete or incorrect. For your B2B eCommerce business to succeed in today’s market, you must first start with high quality data. This means investing in a data quality platform to clean your data and then improving the view of your data across all workflows with a master data management solution.

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