The old saying “garbage in, garbage out” has been around since the early days of modern computing. Code for operator error or bad data, the adage implies that the output of a program is only as good as the input supplied by the user. With more data being collected, stored, and used than ever before, data quality at the point of entry should be a top priority for all organizations.

Now that data is informing more aspects of our businesses, it’s not difficult to imagine a future where data accuracy is vital. Think of delivery drones, which have been tested all over the US and UK in recent years. If a contact’s bad address information goes unchecked, it could feed a drone the wrong coordinates resulting in misdeliveries and lost products.

Data quality affects every aspect of your business, from sales and development to marketing and customer care. Yet a 2017 survey by Harvard Business Review found that “47% of newly-created data records have at least one critical (e.g., work-impacting) error.” So, what constitutes garbage input? It depends on the data and how it enters your system.

What Is Bad Data?

Of course, there are many kinds of data an organization may choose to collect, but here we will focus on one of the most critical – contact data. This includes a contact’s name, address, phone number and email, all of which are crucial to marketing, sales, fulfillment, and service.

Some common issues that make a contact record bad:

• Inaccuracies like bad abbreviations or missing zip code
• Typos caused by speed or carelessness
• Fraudulent information
• Moving data from one platform to another without appropriate mapping
• Data decay as contacts move, get new phone numbers, and change positions

So, how does a bad contact record make it into your database?

Contacts entering their data online, whether downloading a whitepaper or ordering a product, are usually the first to commit a data quality error. Filling out an inquiry form on a mobile device, rushing through a purchase, or providing inexact information (such as missing “West” before a street name) are all examples of bad data leading to inaccurate contact records.

Your sales and customer service team can compound poor data quality by manually updating information that looks incorrect and making mistakes in the process. Good business practices can help mitigate operator error, but if a record was poor to begin with that likely won’t matter – you already know what happens to garbage when it ages.

What Is the Cost of Bad Data?

Much like garbage, bad data only gets worse over time.

Poor data quality can cause an organization’s sales team to waste time and effort chasing bad leads. According to a 2018 study by SiriusDecisions, B2B databases contain between 10% to 25% of critical contact data errors. That means up to 1 in 4 leads could have bad phone or email information attached, so follow-up communications may never reach the intended contact.

The customer service department loses time and money first in dealing with unhappy customers – even if they provided poor contact information, they’ll still blame your business for a package that never arrives. Additional time is spent troubleshooting problems and clarifying bad information, leading to major inefficiencies and frustration.

Overall costs to businesses include reputation related losses that occur when upset customers take to the internet to air their grievances. Time is lost due to the hidden data factories that arise within an organization when individuals working with bad data take it upon themselves to make “corrections” without understanding why or how the data is incorrect. Lastly, poor data robs a business of the ability to take full advantage of business tools like marketing, sales automation systems, and CRM.

How Can My Business Fix Bad Data?

Tightening up business policies around collecting and managing data is a start, but implementing data validation services will help ensure your data is as genuine, accurate, and up-to-date as possible and keep contacts current through frequent updates. Contact data validation can be integrated in a number of ways to best meet the needs of individual organizations, including:

• Real-time RESTful API – cleanse, validate and enhance contact data by integrating our data quality API’s into your custom application.
• Cloud Connectors – connect with major marketing, sales, and ecommerce platforms like Marketo, Salesforce, and Magento to help you gather and maintain accurate records.
• List Processing – securely cleanse lists for use in marketing and sales to help mitigate data decay.
• Quick Lookups – spot-check a verbal order or cleanse a small batch.

Service Objects’ validation services correct contact data including name, address, phone number, and email and cross-checks it against hundreds of databases to avoid garbage input. The result: cleaner transactions and more efficient processes across all aspects of your business.

Contact our team to determine which of our services can help you collect and maintain the highest quality data and kick the garbage to the curb.

We talk a lot on these pages about how data quality affects your business. But once in a while, we also feel it is important to look at how data quality affects society as a whole. And one of the best examples of this in recent memory is the upcoming 2020 United States Census.

Every 10 years, the United States goes through a demographic headcount of its inhabitants. The results of this survey are pretty far-reaching, involving everything from how the Federal government allocates more than $600 billion in funding to who represents you in Congress. But this year, for the first time ever, technology and data quality loom among the biggest issues facing the next Census.

2020 Census Data Quality Doubts

These concerns are serious enough that the American Academy of Family Physicians, a healthcare advocacy organization, recently introduced a resolution entitled “Maintaining Validity and Comprehensiveness of U.S. Census Data” that has now been accepted by the American Medical Association together with other healthcare groups. It breaks down a number of data quality concerns currently facing the Census, including the following:

• This will be the first year that a majority of responses are planned to be collected online, introducing possible sources of data error.
• Sampling and data quality errors may disproportionately affect vulnerable populations subject to health care disparities, such as minorities and women.
• In addition to human and data errors, there are concerns that mistrust of technology and privacy may prevent some people from completing the Census survey.
• Above all, there are concerns over the impact of scaled-back funding for the 2020 Census, together with the departure of its director, in terms of how this will affect preparations for new technologies and survey methods.

Where Data and Politics Converge

It isn’t just stakeholders like healthcare providers who are raising a red flag about the next Census: the government itself shares many of the same concerns. In its 2020 Census Operational Plan, the U.S. Department of Commerce points to data quality as one of its key program-level risks, stating that “If the innovations implemented to meet the 2020 Census cost goals result in unanticipated negative impacts to data quality, then additional unplanned efforts may be necessary in order to increase the quality of the census data.”

This is a case where political issues also intersect with data concerns: in addition to the ongoing battle over funding levels for the 2020 Census, others have raised concerns over a proposed new citizenship question that is potentially a hot button for areas with large Hispanic and immigrant populations. According to the Brookings Institute, both of these issues may have far-reaching impacts on the quality of this next decennial Census, and recently the Attorney Generals of several states drafted a joint letter raising these as potential quality issues.

The Impact of Contact Data Quality

Finally, in an area near and dear to our hearts, the 2020 Census serves as an example of where contact data quality will have a huge impact on both costs and quality – because many addresses change over the course of a decade, and the current practice of canvassing non-responders on foot (up to six times) can be costly, time-consuming and error-prone. In 2015 the government responded to this issue by conducting address validation tests across a limited population sample, and to be fair, they must also contend with many non-standard locations (such as people living in basements, illegally subdivided units, or homelessness). But clearly, accurate address validation and geolocation will loom larger than ever for the census of the future.

These concerns are examples of some of the potential social impact of data quality issues, as society bases more of its decisions and funding choices on collected data. At a deeper level, they point to a world where data scientists may even ultimately have as much impact on these social issues as politicians and voters do. Either way, technology is playing more of a role than ever in social change.

The takeaway for all of us – in business, and increasingly in life itself – is that our world is increasingly becoming data-driven, and paying strategic attention to the use of this data is going to become progressively more important over time. And in the near future, this will include making sure that every American is accurately and properly counted in the next Census.