Data has become a core part of every business operation. Enterprises rely on storing and analyzing data to understand customers better and improve business operations. Hence, ensuring data quality is vital for businesses.
Poor-quality data costs companies around $13 million every year, per Gartner. Further, 60% of businesses are unaware of the costs of poor quality data as they do not measure the impact. Quality data combines accurate values, ensures removal of duplicates, and enables consistent and complete data points. A slight compromise in one of these factors can be damaging for the business that uses the data.
Business Risks of Poor Data Quality
Hindrance to data quality can lead to significant revenue loss. The impact of poor data quality in sales and marketing can range from inaccurate marketing campaigns and sluggish sales pipelines that struggle to convert opportunities. It can also curb business automation initiatives. Here are some specific examples of the impact of poor quality data.
1. Missed Opportunities
Out of 3.64 million leads generated each year, 45% are filtered as bad leads due to duplicate data, invalid formatting, failed email validation, and missing fields. A business is prone to miss opportunities on multiple fronts if they have poor data quality across disparate datasets. Moreover, companies cannot tailor effective business strategies to stay relevant to agile market conditions without accurate data.
2. Customer Dissatisfaction
The ultimate objective of any business is to enable customers to fulfill their needs, thus improving customer retention. Customer dissatisfaction can take a severe toll on brand loyalty. Analyzing historical data equips businesses to curate and personalize services; however, most companies are yet to leverage data analytics. According to a study, out of 37% of respondents working on customer experience for external-facing processes, only 30% proactively monitor data quality impacts. Customer-generated data is essential for businesses to understand customer behaviors and preferences. If this data lacks quality, companies will make incorrect inferences about their customers resulting in misinformed business decisions.
3. Reputational damage from product return
In any eCommerce business, product returns can be an absolute nightmare. Poor product information is one of the biggest reasons customers return products. Consumer reviews also play a huge role in the company’s and product’s reputation. In 2021, 77% of consumers read online reviews before ordering, a considerable increase from 60% in 2020. And 62% of consumers believe they’ve seen a fake review for a local business. This can also adversely affect the reputation of a business.
4. Inaccurate sales campaigns
Many sales executives may feel they do not have enough information before making a call, and working with inaccurate data further compounds their ability to do their job well. However, with data hygiene, sales executives can barrel through call lists, spending more time connecting with quality prospects.
Reaching out to incorrect phone numbers, emails, and outdated accounts is time-consuming and affects productivity. A LeadJen study showed that SDRs wasted an average of 27% of potential selling time following bad data, resulting in a whopping loss of more than $20,000 in productive sales time per year for each SDR.
5. Increased financial costs
All these business risks mentioned above build up to one thing: it costs you more money than it would have if you had an end-to-end data quality management system in place. As discussed at the beginning of this blog post, companies lose approximately 13 million dollars annually due to poor-quality data. Hence, it is best to invest in implementing a single, complete data quality management system that cleans and prepares all different types of data handled at your organization to control the increasing financial costs.
Improving Data Quality
Now that we have seen what kind of irreparable damage poor-quality data can inflict upon a business, let us look at what you can do to improve data quality. The straightforward approach would be to handle the quality issue in its source (i.e., to streamline the data collection and accumulation process). The following are other options to ensure clean data:
1. Webforms
Webforms help capture first-party data — the most valuable business data. This includes data gathered via content downloads, blog subscriptions, webinar registrations, or anyone who shares their information on the website.
2. Quality check of second or third-party sources
This data is often purchased and should be thoroughly vetted before entering the downstream systems.
3. Fixing during ETL phase
In case the incoming data is of poor quality and a fix cannot be done at the source, the data may be fixed during the ETL (Extract-Transform-Load) stage. This may be especially useful for big or large-scale data (where checks at the source stage are not possible) by using cloud data engineering tools such as Azure Pipelines and Databricks.
4. Apply precision identity/entity resolution
This is likely the most challenging method of fixing data quality issues but the most effective. One of the most significant issues with many customer databases is that they have multiple records for the same customer and no way to tell that these pieces of information are interrelated. Applying precision identity/entity resolution can identify a customer/household in all its variations, which allows for more targeted and efficient marketing.
Good data = Smart business: The way ahead…
The cost of having bad data running through your system far outweighs the cost of introducing good data to your business. When it comes to critical business data, don’t simply look at the price tag; consider the return on investment in high-quality data and the time and money you could lose by making the wrong choice. Basing your business functions on high-quality data might be the difference between driving optimal sales and marketing that connects with your audience — or missing out on revenue and alienating potential customers.
In conclusion, implementing consistent, automated, and repeatable data quality measures can help your organization attain and maintain data quality across all datasets. We at LatentView Analytics help organizations assess the data quality, streamline them with proper checks, and maximize the customer’s profitability.
