Cleaning up has always been a virtue. Thanks to Netflix and bestselling Japanese organizing expert Marie Kondo, who preaches a mindful approach to better living through tidying up, it has now also become a major viral trend. Today, I would like to help you explore another path to inner joy and peace in your business: cleaning up your contact data.

You see, your contact data assets are a bit like most people’s closets: they start off being functional, but without the right kind of effort, they decompose into clutter over time. (Over 70% of this data changes every year as people move, change jobs, their companies merge, and more.) Unfortunately, this clutter can cost you – in time, wasted marketing efforts, or even severe compliance penalties for unwanted marketing contacts. So here is a three-step process that will help ensure that your contact data is always genuine, accurate, and up-to-date.

First: Getting it Right at the Time of Data Acquisition

What is one of the more common sources of contact data error? Acquiring it in the first place. Most organizations have multiple touch points where contact data enters their system: web pages, inbound customer inquiries, lead processing, and more. Customers fat-finger their addresses or contact information, data entry team members are human and make mistakes, and sometimes fraud or fakery is even involved.

One productive solution to this is to use API-based tools the plug in to your sales, marketing or CRM platforms, to seamlessly help ensure contact data quality on the front end. For example, address validation services check inbound address data against USPS, international and other databases to ensure address accuracy or correct them if needed. For other forms of contact data, phone validation can identify a numbers owner and validity, as well as carrier, line type and geolocation data, while email validation verifies email validity and corrects things like common typos.

Other API tools bundle advanced services such as validating the quality of incoming leads, appending contact phone numbers, or linking your contacts to demographics data for business analysis or compliance purposes. Whatever level you need, implementing API-based services like these in your business automation platforms helps ensure getting the right contact data every time, at the point of entry.

Second: Cleaning Your Database

Congratulations – you’ve now entered accurate, validated contact data. Which leads to the next issue: practically the minute you get up to grab some coffee, your contact data assets are starting to decay. So periodically, it makes sense to bring this data back in line with reality, to maintain its usefulness for functions like market analysis, business intelligence, campaign planning and more.

In situations like these, batch or list processing services often represent a convenient way to clean an entire contact database at once. Our own batch services can process an entire list or database with little or no programming required. Tools like these are often a smart and simple way to make good data hygiene part of your regular routine.

Third: Lather, Rinse, Repeat

How often should you clean up your data? Repeat after me: every time you use it. Here’s why: accurate contact data may be important for things like market planning and business analytics, but it is absolutely critical when you actually get in touch with people on your lists. Direct mail campaigns have human and material costs tied in with bad address data, outbound telemarketing to changed numbers potentially risk severe penalties from the Telephone Consumer Protection Act (TCPA), and unwanted email contact can get you blacklisted.

The same tools you use to validate and clean your data are at your service here, each and every time you run a campaign or contact your customers. But here, the solution is as much organizational as technical: make sure someone is “on first” for ensuring your ongoing data quality and data governance.

Questions? We Can Help!

When it comes to cleaning your data, we actually do one thing much better than Marie Kondo: her bestselling book sprang from an infamous months-long waiting list for her organizing services, but our knowledgeable team of data experts will return your call in just 90 minutes or less! So whether it’s questions on international data, API interfaces, or simply discussing what strategy works best for you, contact us anytime and let us help you discover the life-changing magic of tidying up your data.

Unless you’ve been living under a rock for the past couple of years, you know that data privacy and security laws have become a big thing worldwide. Between Europe’s GDPR regulation, Canada’s PIPEDA laws and others, consumer’s rights over their own personal data became one of biggest issues of 2018 for CIOs and CDOs who do business internationally. But what about here in the United States?

Now we have some numbers behind public opinions on this issue, thanks to a recent survey from software giant SAS. The results show that many of the same concerns that led to regulations such as GDPR are top-of-mind among Americans, and should inform the way data professionals look at their contact data assets in 2019 and beyond.

What the survey says

In July 2018, SAS surveyed over 500 adult US consumers from a variety of socioeconomic levels about their opinions on data privacy. Here are some of the key conclusions from this survey:

People are concerned. Nearly three-quarters of respondents are more concerned about data privacy than they were a few years ago, with more than two-thirds also feeling their data is less secure. The biggest areas of concern? Identity theft, fraud, and personal data being used or sold without consent.

They want more regulation. 67% of respondents felt that government should do more to protect data privacy, while fully 83% would like the right to tell an organization not to share or sell their personal information. A large majority would also like the right to know how their data is being used, and to whom it is being sold.

Consumers are more savvy about privacy. Roughly two-thirds of respondents (66 percent) acknowledge that primary responsibility for their data security rests with them, and a majority are able do things like changing privacy settings. Notably, close to a third of people have reduced their social media usage and online shopping over these concerns.

Trust must be earned. Trust in organizations for keeping personal data secure vary widely, from highs of 46-47% for healthcare and banking organizations to roughly 15% for travel companies and social media.

Age matters. Older consumers value privacy more than young ones and are least willing to provide personal information in return for something (36% for Baby Boomers versus 45% for Millennials). However, this does not mean that young consumers live in a post-privacy world, with 66% of Millennials expressing concern over the security of their personal data.

What this means for data privacy – and for you

One important take-away from this study is that, whether or not we have a US version of GDPR some day – a direction favored by these survey results – the trend is clearly toward increasing consumer concerns over data privacy and security over time. This means that data professionals need to prepare for the very real possibility of increased regulation and compliance issues on the horizon.

These survey results also mean that even in the absence of regulation, your organization’s data policies can have a very real and tangible impact on brand image and consumer trust, which in turn affect your bottom line. The fact that some people are reducing their social media use and online shopping, for example, should be a warning for everyone to start paying more attention to data privacy and security concerns.

Finally, these results are another sign that more than ever, businesses need to get serious about contact data quality in 2019. Tools from Service Objects such as address, email and phone validation can help ensure that your contact data assets are accurate, and prevent unsolicited marketing contacts to mistaken or bogus entities – and in the process, give you higher quality leads and contacts.

Want to learn more? Contact us to speak with one of our knowledgeable product experts about improving your data quality in the new year.

Change is a constant in life. And when it comes to your data, even more so. Given enough time, migrating your data to new systems and platforms will be a fact of life for most businesses. Whether it involves a corporate merger, a new application vendor, or other reasons, data migration is one of those predictable “stress points” that can put your contact data assets at risk without the right strategy.

According to a recent post by Dylan Jones on Data Quality Pro, data quality issues are one of the key reasons for the high failure rate of data migration projects. He cites a recent survey showing that 84% of these projects run over time and/or budget – and in his view, an important part of avoiding this involves advance planning and modeling.

Data quality best practices for contact data migration

From our perspective, there are at least four best practices you should consider for preserving contact data quality during a data migration process:

Measure twice, cut once. Jones describes the use of what he calls landscape analysis, or a “migration simulation” in plain English, to anticipate problems before the migration begins in earnest. This involves testing a subset of your database against planned conversion rules and protocols, to help ensure that the results are likely to go as planned.

Validate addresses before conversion. The term “garbage in, garbage out” applies here, with clean data being an important factor on the front end of the migration process.

Validate addresses after conversion. What is the one thing that is worse than bad data? Reformatting data to make it even worse. Bad things can happen when old data goes into new fields, and if John Smith at 123 Mayberry Street becomes “John” at “Smith 123 Mayberry” in the new database, his value as a contact can go completely out the window. Never blindly trust converted data without a validation and review step to flag bad contact data in real time.

It ain’t over until it’s over. Yogi Berra’s famous baseball saying applies equally to data migration, because you aren’t finished with data quality when the initial conversion is done. Contact information is a perishable resource that goes stale over time, as people come, go, and change jobs and addresses. This means that your contact data migration isn’t really finished until you have implemented and tested an infrastructure for ongoing contact data validation and database cleaning.

Data migration: Not just a technical problem

According to a recent Infosys white paper, treating data migration as an “IT problem” is often a fatal mistake in terms of data quality – in their words, “Business has to not just care about data migration, but command it.” Put another way, no one can sweat the details on data quality like the stakeholders who will be using this data in the long run.

This raises one more important issue: a major data migration might also be the time to start thinking about a more formal data governance strategy, if one isn’t in place already. We’ve discussed this issue on our blog before, and it is particularly relevant here: major changes such as a data migration can often serve as a catalyst to build professional data expertise at a business-wide level. Either way, putting data quality front and center is one of the most important factors in creating a smooth transition to a new environment.

Have any additional questions on the important role data quality plays in the data migration process? Contact us and we will be happy to answer your questions.

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 APIs 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.