Why quality beats quantity even in the data sphere
Big data, those incredibly complex and voluminous data sets championed by powerhouses like IBM, is becoming an increasingly popular tool for detecting trends, patterns, and behaviors-even in the retail industry. However, big data has become so hyped that companies are now expecting it to deliver more value than it actually can. Companies continue to invest like crazy in data scientists, data warehouses, and data analytics software, without having much to show for their efforts (and it’s possible they never will).
The return on big data is considerably less than optimal for most organizations because they fail to ask the million dollar question: “is the data clean?”
Clean Data Powers Your Organization
Your database may be one of the most important assets in your company. It contains attributes and important product information that help retailers leverage your products so that consumers can buy them. Yet organizations don’t know how to manage the database. They don’t know if their data is clean or dirty.
Dirty data happens when a database record contains errors. Dirty data can be caused by a number of factors including duplicate records, incomplete or outdated data, and the improper parsing of record fields from disparate systems.
Garbage In, Garbage Out
When your organization is making decisions based on low-quality data, you can expect the results to be equally subpar. Dirty data can have a negative-and costly-impact on a brand. According to The Data Warehousing Institute (TDWI), data quality problems cost U.S. businesses more than $600 billion a year. Gartner’s research finds that poor-quality customer data leads to significant costs, such as higher customer turnover, excessive expenses from customer contact processes like mail-outs, and missed sales opportunities. Companies are now discovering that data quality has a significant impact on their most strategic business initiatives. It’s not just sales and marketing: back-office functions like budgeting, manufacturing, and distribution are also affected.
There is a bevy of bad data consequences. Read and weep:
● Lost revenue
● Wasted resources
● Decreased productivity
● Damage to credibility
● Risk of failure for marketing automation initiatives
● Fines due to compliance issues
● Inability to reach a prospect by email, phone, or mail.
Make Data-Driven Decisions With ItemMaster
Implementing rich, clean data is so beneficial that it can supercharge your business by generating quality leads and targeting your messaging to the right audience. By introducing data quality initiatives, some companies have added millions of dollars to their bottom line as they gain benefits such as increased sales, lower distribution costs, and improved compliance.
At ItemMaster, we perform Data Quality Assessments to ensure your most important asset is a clean one. Let us help you power your product content with clean, accurate data! Schedule some time to get started HERE.