The Domino Effect of Bad Data and How to Fix It
Tuesday, October 10, 2023

Marketing 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 becomes 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 1,000,000,000,000,000,000,000. Yes, there are 21 zeros – meaning 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.

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. 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. In addition, 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 slows employees down, wastes their time and hinders their achievements, ultimately leading to poor performance, frustration and turnover.

As CRM and data quality consultants for professional services firms, we frequently hear frustration from firm leaders who say 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 a professional services marketing department. 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 a new employee joins, 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 contacts. Since many of these contacts may be known by others in the firm, duplicate contact creation becomes 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 sometimes dirty records flowing in can 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 combined firm contacts. 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 are even known to block access if a firm’s bounce rate is too high.

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, identify opportunities for business development, 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 technology 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. A number of well-intentioned firms have executed well-thought-out CRM integration strategies only to later find that the data in the other systems were in worse shape than the data in the CRM.

It’s Not Just a CRM Problem

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 matter open 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, 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 may have a number of subsidiaries and alternate addresses or offices. As a result, it can be a struggle just to get content out to key contacts at companies with headquarters in one state but additional locations in other places.

Pieces of the Puzzle

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. Some 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. To promote 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 the billable hour 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 identify 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 win/loss rates?

Disconnected Data

Consider another, more insidious data issue: some of the information we would love to have access to – 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 – 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 marketers) entering diverse data into disconnected spreadsheets. A global firm for which we recently performed a CRM Success Assessment had more 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 out 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?
  2. Plan: In determining 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 also applies 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 will not be perfect. But also remember that sometimes perfect can be the enemy of good. For some data sets, good may actually be good enough.
  3. Data Stewarding: If you still have a lot of bad data remaining that is crucially important, like 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), ongoing data stewarding will often be essential.
  4. 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.

Gaining the Upper Hand

Here are a few more top tips to help you gain the upper hand with dirty data and prevent future contact collapses. 

  1. People and Processes: To prevent ongoing bad data, take the time to talk to the individuals who are the custodians of existing data sources. Communicate to them the importance of 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. Of course, this includes your systems users.
  2. Data Sources: You may also need to 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 valuable, find a way to work with other departments to find a solution.
  3. 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 introduced. 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.
  4. 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 not only can create current contacts from the signature blocks of emails, which is important because a lot of busy professionals no longer take the time to create and add contacts records to address books, but it can also be used to validate or 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 it 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 these steps:

  • Start with your most relevant records like current client companies. Begin with a manageable data cleansing project for the top 100 to 500 along with associated key people.
  • Research bounced emails. These are likely contacts that you want to reach but who may have changed roles or locations. Vet bounces after each campaign, or better yet, regularly run your lists through an automated data process to identify bad emails before sending a campaign to ensure that information actually reaches your targets in a timely manner.
  • Review frequently used lists to ensure your communications and invitations are reaching the right recipients. Too often lists become outdated or incomplete, leading to missed opportunities. Make list hygiene a regular practice and enlist assistance from the assistants if possible.
  • Finally, tackle 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.

Data quality is akin to a living ecosystem, constantly evolving and requiring ongoing attention. Rather than viewing data cleaning as a singular event or temporary initiative, it should be perceived as a continuous process. Each day without active data quality management, inconsistencies and inaccuracies can creep in, bit by bit. Just as a garden overgrown with weeds, if left unattended, your data can become overwhelmed with errors. To stay ahead in the data game, regular maintenance is imperative.

 

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