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The Data Quality Domino Effect


The Data Quality Dilemma

Download PDFMarketing and business development professionals spend a great deal of time carefully lining up plans to effectively communicate with key contacts. Like dominoes, we align our messages and target our audiences to achieve critical objectives. Each communication… each invitation… each interaction carefully placed to reach the desired result.

But one thing can cause even the most effective marketing strategy to topple: bad data. Without clean, correct and complete data, our messages can fall flat, our interactions become ineffective and our results erode.

Big Bad Data

These days, we keep hearing a lot of buzz about big data, but what we really need isn’t more data – it’s better data. When you consider the rate at which we are adding data and the speed at which it degrades, the impact can be compounded.

By 2025 the International Data Corporation (IDC) estimates that worldwide data will grow 61% to 175 zettabytes. If you are curious about what a zettabyte may be, it’s a number with a lot of zeros – 21 zeros in fact: 1,000,000,000,000,000,000,000. That’s a lot of data.

Another piece of the puzzle, additional research indicates that up to 30% of our key data degrades each year as people get hired, fired, promoted and change jobs; move and change addresses; get married and divorced; retire and even die. At the same time businesses are opening and closing; merging and being acquired; moving and relocating.

Without our constant attention, all this can add up to a huge pile of data that is missing or mistaken, duplicative or dated, incorrect or incomplete. Plus, each piece of data is often connected to many more pieces, so if just one is flawed, all of our marketing and business development plans can fall like… well, dominoes.

Table Stakes – The Costs of Bad Data

All of this bad data can be quite costly. Respected researchers say that poor data quality may be costing an average organization as much as $14.2 million per year, and that the annual U.S. economic cost of bad data may exceed $3 trillion dollars. According to the Harvard Business Review, one reason that bad data costs so much is that decision makers, managers, knowledge workers, and others have to accommodate it in their work. As a result, our staff may be wasting up to 50% of their time hunting for data, trying to confirm data they don’t trust and finding and correcting data errors. Bad data can also slow employees down and hinders their achievements, ultimately leading to poor performance, frustration and turnover.

Key Players

As CRM and data quality consultants for professional services firms, we frequently hear frustration from firm leaders who complain that their marketing teams can’t even pull together a ‘simple’ list. To address this, let me suggest that anyone who thinks that quickly and easily pulling together a clean, complete and correct list of any sort in a professional services firm is ‘simple’ has likely never worked in the marketing department at one of these firms. In fact, there are a number of valid reasons that contact management can be challenging in this type of organization.

First, the contact management software and processes in professional services are built a bit backwards. In most organizations when new employees join, they are given access to an existing CRM database of contacts. But in a professional services firm, the CRM contacts actually come in from the professionals, with each partner providing their own personal list of contacts. Since many of these contacts are often known by others in the firm, duplicate contact creation can be a real conundrum.

Plus, busy professionals don’t always have the time to pay adequate attention to their address books because of other responsibilities. And the way that some CRM systems are set up, this means that dirty records flowing in could corrupt clean CRM contacts.

Additionally, in a professional services firm, time is money – literally, so any process that puts a drain on time can also be a financial drain. Consider the inefficiency of having multiple, or even hundreds, of professionals who bill time at hundreds, or upwards of $1000 an hour, regularly reviewing the same bad lists of the firm’s combined contacts. Additionally, bad lists lead to bounces, and if a firm’s bounce rate on a campaign becomes high enough, email from the firm can get labeled as spam, causing the firm to be blacklisted by key companies. Some eMarketing providers will also block access if a firm’s bounce rate is too high.

Piling It On

These data issues can be a particularly painful problem in professional services where, probably more than any other industry, being able to communicate effectively with Clients and prospects is essential. Firms have to be able to share information from and about their professionals in order to successfully communicate, market and develop business. Plus, professionals spend a lot of their valuable (billable) time writing and speaking, and if their messages don’t reach the right audiences, all this time (and money) is wasted.

And professional services firms don’t just rely on data for communications and invitations. They also use it to determine the best ways to market their firms… and identify opportunities for business development… and advise pricing strategies… inform hiring decisions… decide where to open offices… determine which practices to invest in and… well… most everything. More critical yet, data is used for decision-making. This means that if your data is flawed, so are your decisions.

Bad data can also cause other issues. If the professionals don’t trust the data, by default, they often won’t trust – or use – the technology. And while bad data is problematic enough on its own, consider what happens when we throw in (literally) multiple data sets by integrating other software or systems. For instance, a number of well-intentioned firms have executed well-thought-out CRM integration strategies only to later find out that the data in the other systems was in worse shape than the data in the CRM.

Pieces of the Puzzle

Bad data in professional services firms often transcends the CRM. Time and billing data can often be the worst. In many firms, the information in the financial systems is often entered haphazardly by assistants whose primary objective is to get a Client number or open a matter as quickly as possible so the billing can begin. While this hasty business process is understandable, it also represents a missed opportunity to enter relevant data about Clients such as their company information, key contacts and industries.

Compensation systems can compound the problem with companies entered multiple times to facilitate sharing credit. For a firm’s top Clients, it’s not uncommon to see masses of multiple entries for the same organizations. Add to this that many companies have a number of subsidiaries and alternate addresses or offices. As a result, it can be a struggle just to get content to key contacts at companies with headquarters in one state but additional offices or facilities in other places.

While in the past, firms acquired CRM systems to do the blocking and tackling of marketing such as list management and relationship identification, now marketing departments are being asked to support the firm in a number of new efforts such as alternative pricing and project management. Many firms are also being required to respond to an ever-increasing number of RFPs and to efficiently track information on activities such as events and sponsorships to show real return on marketing and technology investments.

To be able to respond to these new requests, marketing and business development teams have to be able to rely on their data. Preparing a pitch requires tapping into the expansive experience of our firms, practices and professionals. Promoting the firm’s pricing strategies requires an understanding of past profitable (and unprofitable) matters. All of this requires accurate data. But often matter or engagement closing and cost evaluation practices are informal at best, when they exist at all. Perhaps if there were processes put in place to gather relevant data, it would be easier to come up with pricing alternatives that are preferable to, or more creative than, billable hours minus a percentage.

It’s also impossible to identify how many business development opportunities we are missing out on due to data discrepancies. How can we determine where to cross-sell if we can’t even get a view into the work we are doing for Clients in order to see where they may be underserved currently? Even more painful, how much time and effort are wasted on pitches that the firm should never have responded to in the first place because they haven’t tracked data related to response costs or win/loss rates?

Disconnected Data

Consider another, more insidious data issue: some of the information we would love to have access to –

information that could really improve our marketing and business development efforts – resides in repositories that can be incredibly hard to tap into: the heads of our professionals. Information about key business development interactions and activities is often missing because our business developers are too busy – or not motivated or required – to share it.

Similarly, we struggle with spreadsheets. Don’t discount this particular data dilemma (the struggle is real!) Even in firms with the best contact management systems and processes, it’s not uncommon to find the professionals (and yes, even the marketers) entering diverse data into disconnected spreadsheets. A global firm that we recently performed a CRM Success Assessment for had no fewer than 20 spreadsheets where they were tracking everything from sponsorships to referrals to experience to opportunities. It doesn’t matter what the name of the software is … it’s next to impossible to ‘Excel’ at anything when you can’t even figure out where all your data is kept!

Mastering the Game

We could talk all day about the giant puzzle created by dirty data. But to master the data quality domino game, it’s more important to focus on stopping the data dominoes from falling and blocking future issues so you don’t get buried under a pile of bad data. Based on more than a decade of experience in working together with hundreds of top firms on CRM and data quality projects, here are some top tips to help you identify data quality issues and prevent future problems.

  1. Assess. Start by stepping back from the never-ending data deluge and take a minute to assess the mess and come up with answers to a few important questions that can help you to scope the situation:
    • How much bad data do we have?
    • Where is it located?
    • How did it get there?
    • Who is in charge of it?
    • What is it costing us?
    • What is the best way to clean it up?
    • Who will assist with cleanup?
    • How long will the cleanup take?
    • How much will the cleanup cost?
  1. Plan. In determin the best way to clean the data, it’s helpful to start with a cost-benefit assessment to help determine the best way to proceed. For instance, if you have a significant amount of bad data, it may be more efficient to start with an automated data cleaning and appending option. This type of technology can help to improve and enhance a large amount of data in a quick and cost-effective manner. But it’s important to note that an old adage can also apply to data quality projects: you can have quick, cheap or good – pick any two. So, after any automated data quality process, always take time to analyze the results because they usually won’t be perfect. But also remember that sometimes perfect can be the enemy of good. Perfection can take a great deal of time and money to achieve, and for some data sets, good may actually be good enough.
  2. Data Stewarding: If you still have a lot of bad data remaining that is crucially important, such as top client information, or if you happen to work in an organization that can be a little fanatical about the quality of their data (you know who they are), then you will want to move forward with manual data stewarding to research, clean and append missing information to the remaining records. Additionally, in organizations that need good reliable data going forward (you know who you are… all of you), ongoing data stewarding will often be essential.
  3. Prevention: Once you are comfortable with the quality of your data set, the next step is to prevent future data problems. Too many firms perform a mighty (and mighty expensive) cleanup effort only to then stop and take a ‘breather.’ But maintaining good contacts has to be an ongoing priority, especially if you don’t want to repeat the whole painful process over again every few years.
  4. People and Processes: To prevent bad data in the future, take the time to talk to the individuals who are the contributors to and custodians of existing data sources. Of course, this includes your system users. Communicate with them about the importance of good data quality and train them to help prevent future problems. Also be sure to assess existing processes and procedures to determine how the current data domino situation occurred and stop the cycle from repeating.
  5. Data Sources: Identify any other sources of bad data that are compounding your contact problems. For instance, if there are existing integrations that are creating duplicates or overriding good data with bad, you may need to determine how valuable that data may be. If it’s critical, find a way to work with other departments to find a solution.
  6. Styles and Standards: To standardize data entry, create data style guidelines and a data entry manual to keep data consistent and prevent new duplicates from being created. Not only will this documentation help to make your data stewarding more efficient, when accompanied with the right training and communication, it can also help system users understand how important their role is in the process.
  7. Technology: Use software to make data quality more efficient and effective. One tool that can be particularly effective is an Enterprise Relationship Management (ERM) system. This software is important not only because it can create current contacts from the signature blocks of emails, which is critical because a lot of busy professionals no longer take the time to add contacts records to address books, but it can also be used to validate and fill in missing information on existing records, which means less time spent researching records.

Slow and Steady

While it’s easy to become overwhelmed by the domino effect of dirty data, what’s important is to put the problems in perspective. Don’t try to focus on the whole puzzle at once.  Instead, work on data and projects that can yield real return on investments of time and technology. Based on our experience working together with hundreds of top professional services firms, we recommend:

  • Consider starting with the most relevant records such as cleaning the contacts for current Clients. Begin with a manageable project such as the top 100 to 500. If you are rolling out or trying to encourage adoption for a particular group of users, focus on their key contacts before training.
  • Research bounced emails. These are key contacts you want to reach but who may have changed roles or locations. Instead of simply removing them from lists, vet bounces after each campaign. Better yet, regularly run your lists through an automated data process to identify and update bad emails before sending a campaign to ensure that information actually reaches your targets in a timely manner.
  • Review frequently used lists to ensure that your communications and invitations are reaching the right recipients. Too often lists become out-of-date or incomplete leading to missed opportunities. Make list hygiene a regular practice and enlist assistance from the assistants if possible.
  • Add key company information or industry data to relevant contacts such as prospects. That way you can segment and target them for relevant communications and invitations to events they may be interested in.
  • Finally, focus on one-off projects that are time-sensitive. For instance, an upcoming event often provides a good opportunity to get users engaged in cleanup efforts, particularly if the event is important to them.

A Winning Strategy

It’s also important to understand that because data does degrade so rapidly, data cleaning can’t be a one-time project or an initiative. Every day that you decide to take a data quality break, the bad data starts piling up again… one piece at a time. Then, before you know it, you’ll be losing that data domino game again.



At CLIENTSFirst, our team of almost 100 consultants and data quality professionals has spent more than a decade helping hundreds of professional services firms assess and address their data quality challenges. If you need help with strategies for winning the data quality domino game, feel free to contact us at 404-249-9914 or We are always happy to share information, ideas and best practices for success.

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