Data Quality Management: Avoiding Rookie Errors
November 17, 2022

Today, there is widespread agreement that data is the lifeblood of any organization. To quote research, 85% of organizations see data as one of the most valuable assets to their organization.

This statistic isn’t surprising. Data offers the potential to transform business, and organizations of all sizes today are investing in chief data officers, data scientists, business intelligence technology, analytics tools, and such. In the legal sector too, just in the last few years, we have seen more and more firms appoint chief data officers to adopt a data-driven approach to business—for everything from cost reduction, efficiency optimization, and governance improvement to business development (BD) and innovation.

Here comes the caveat: This is only possible with good quality data. A vital characteristic of good data quality? Accuracy.

Getting data right the first time

So, ensuring that data is accurate at all times must be a commercial imperative for organizations that adopt a data-driven approach to business.

It is well documented that there is a business cost to using poor or inaccurate data. Gartner estimates that poor data quality is responsible for an average of $15 million per year in losses. Interestingly, nearly 60% of those surveyed don’t know how much bad data costs their business because they don’t measure it in the first place.

The findings are similar from other industry sources, too. IBM has estimated that bad data costs the U.S. economy around $3.1 trillion each year. Additional research from Experian also finds that bad data has a direct impact on the bottom line of 88% of American companies, with the average company losing around 12% of its total revenue. This is despite the fact that organizations are adopting business tools and new technologies such as AI and automation to gather data.

And the impact of sub-standard data isn’t just financial; the damage to reputation and brand is potentially far greater and for the long term. If we look at the professional services sector specifically, in the current primarily digital, hybrid, and remote working business environment, email marketing has grown in importance as a vehicle to generate and nurture leads, but there is still little evidence of success from these initiatives in isolation. Ill-targeted campaigns can have the opposite impact to their original objective of new business and growth.

Rookie errors to avoid

So, how can firms ensure a clean, accurate, and high-quality contact database? It’s worth highlighting that firms sometimes focus on the size of the database, but for marketing and BD purposes, less is more. The emphasis must be on quality and prioritization of contacts to ensure the best chance of success.

Fundamental to this objective is the establishment of best practice data quality management processes in the CRM system, a piece of technology that most professional services firms already have deployed in some form. Here are top tips and, potentially, some rookie errors to avoid:

  • 100% reliance on automation – Today’s technology is advanced, and indeed most business systems offer a high level of automation to help manage administrative and repetitive tasks. That said, in any activity, there is always a “high-value” element where human judgement is essential. Despite the investment in technology in this area over the years, the human supervision element is still the most accurate and valuable to make judgements on data accuracy.Take artificial intelligence (AI). At last, the over-hype of the technology has been found out. Once positioned as a magic bullet, today there is recognition that AI must be applied purposefully and thoughtfully. Also, the success or failure of an AI project almost entirely hinges on the quality of data that is fed to the AI engine. Consequently, there are teams of data quality managers, analysts, engineers, and data stewards whose primary job is to find and cleanse bad data and fix data errors across business systems and data repositories for accuracy and consistency. So, human involvement and judgement is crucial to ensuring data integrity. The same mindset is needed for automation of data management.
  • Single source of data – There isn’t just one way to gather data. So, don’t rely on one source for marketing, BD, and CRM data. Take LinkedIn as a source for new business prospecting data, for example. There’s no doubt that it is by far the most widely used professional network today but completely relying on it is almost certainly going to compromise data quality for marketing and BD activity. Think about it—do people always keep their profiles up to date? How many people actually have their contact details included? Astonishing as it may sound, there are many senior executives who don’t have a LinkedIn profile even today.Rather, establish processes that gather, assimilate, and combine data from various data sources. Ensure a well-rounded and good quality database that includes lawyers’ own contacts in Outlook® and firm databases within the various business systems (e.g., practice management).
  • Prioritize your data – With firms in 2022 dealing with more data as their services and products, geographic reach, and client pursuits expand, there must be a priority mechanism in place that increases focus for data accuracy to support specific growth initiatives and campaigns. If there is increased focus in, say, the oil and gas sector for a firm, then prioritize and add more weighting to contact data in this space to help support the efforts.
  • Complete records – Does your contact data have “white spaces”; i.e., missing job titles of key clients and prospects—or even worse—incorrect or outdated job titles? This situation increases the likelihood of sending the wrong type of information and communications to contacts. Done repeatedly, not only will campaigns be counterproductive, but the inaccuracy of data will also erode confidence in the firm’s CRM system.
  • Stale data – Typically, data remains current only for a short period of time. Contacts move jobs, and phone numbers, emails, and business titles change. Therefore, ensuring data accuracy and integrity is a continuous process. Today, modern CRM systems offer the capability to ensure data accuracy out-of-the-box. For instance, if the firm hasn’t been in touch with a contact within a defined period of time and there are no relationships assigned to the client in question, the CRM system alerts enable the firm to take the necessary action, which could be rekindling the relationship or even deciding that the contact is no longer relevant to the business, and whether the record in question should be removed from the system.
  • Data compliance – The negative impact of non-compliance with the GDPR and numerous country-specific data regulations is real. Busy lawyers don’t always record information in a way that ensures contact information is held in compliance with the regulation. Set the data capture thresholds appropriately in your CRM system so that contact data from lawyers is captured and used to update records in a timely manner.Likewise, a key element of GDPR compliance is the ability to apply individuals’ “right to be forgotten,” if a contact requests it. To manage this process, modern CRM systems deliver against such requests through an embedded compliance tool that suppresses data to remove all relevant details about the contacts but also prevents the data from being added back into the CRM system by another user. This ensures that the contacts in question are automatically removed from marketing and BD target/distribution lists in the CRM. The reputational damage of a compliance breach is far greater than a financial penalty.

In essence, ensure a balance between automation and human involvement in your data management processes. Eliminating human judgement entirely may be folly; rather, use human involvement strategically. Far too often, and especially in professional services firms, data stewards are considered as ticket handlers. It is an old-school viewpoint and it shows that the role of a data steward is poorly understood. Firms that strategically use data stewards say their campaigns have higher open rates and negligible bounce-backs, their contact records are up to date, and there are minimal duplicate contacts and “suspect” emails in the system. This trust in the data helps to build momentum for BD initiatives, enabling the team to roll out more targeted and nuanced campaigns.

It’s far more cost-effective to prevent poor-quality data from entering the organization than to gather data aimlessly and haphazardly—and then attempt to cleanse it for usability. This prevention, combined with realistic human oversight, ensures a strategic and proactive approach to data management, underpinned by timely course correction. It is essential to deriving value from data—and has been proven across technologies. After all, there’s a reason that organizations all over the world are employing data teams for technology-driven projects.

Fiona Jackson

Fiona Jackson

Fiona Jackson has spent over 15 years implementing and working with InterAction in professional services firms, including legal and accountancy. In these in-house roles, supported by InterAction, she managed marketing communications, devised and implemented business development strategies as well as trained and mentored fee earners. She worked closely with internal clients to understand their business processes end-to end and guided them in utilising the 'intelligence' ...

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