All companies have some degree of data quality issues, and the measures they are taking to mitigate them are not enough.
Be it lean startups or multinational enterprises, true, proper maintenance of data quality gets swept under the rug. The initial efforts put in place early-on aren’t matched with best practices in the long run. Managers may be tempted to leverage existing CRM platforms tools to try and meet the data cleansing needs – not knowing that these solutions fall sorely short of any kind of proper record cleansing. While budgets are emptied in favor of customer acquisition and advertising spend, these half-hearted attempts at cleaning house leave valuable customer records collecting dust, hidden in plain sight.
Utilizing CRM solutions for data hygiene can have its benefits if one understands their limitations. While valuable for managing the customer pipeline, overall CRM and other marketing platforms fall short of what data scientists consider true record cleansing, deduplication, and record matching. In other words, the necessary building blocks to true data quality maintenance.
So why do they do it? Here are some reasons we have heard about why businesses have stuck their heads in the proverbial data quality sand:
1. “Our data quality practices are just fine – our employees just need to be more diligent about following them.”
It’s arguably one of the most important rules of thumb when it comes to a company’s data – if it’s not accessible, it’s not helping your bottom line. Some companies silo off access to their valuable data from their own employees, so when it comes to practicing safe and effective habits when handling that data, employees are more likely to make mistakes that can be costly down the line.
If your department or company’s data quality practices are only hindering initiatives instead of sparking ideas, consider if your “best” practices are really the best for your team. Look to where data is mismatched and duplicated to help trace the source of the error for ongoing fixes.
2. “We already have an expensive marketing and CRM platform, we have everything we need.”
Stakeholders often mistake data quality tools for contact management platforms and think that data quality tools are inherent in existing applications or are a modular function that can be added on. It may be hard to believe that expensive and sophisticated CRM or ERP tools don’t inherently account for data quality. While customizing or extending existing ERP applications may take you part of the way, we are constantly talking to companies that have used up valuable time, funds, and resources trying to squeeze a sufficient data quality solution out of one of their other software tools and it rarely goes well.
3. “We don’t have the manpower to devote data quality initiatives right now”
Postponing on properly cleaning and updating your data can cost more in time, money, and resources in the long run. Even a regular audit of your data won’t keep inaccurate data from entering your system. Consider nicknames, multiple legitimate addresses, and variations on foreign spellings just to mention a few. Cleansing and wrangling data is an integral part of maintaining a clean CRM, but point-of-entry implementations are integral to keep duplicate records out. Without preventative measures to check dirty data at the door, that freshly-cleaned data will quickly decay to the tune of 30% per year.
4. “Nobody cares about data quality.”
Unfortunately, when it comes to advocating for data quality, there is often only one lone voice on the team, advocating for something that no one else really seems to care about. The key is to find the people that get it. They are usually in the trenches, trying to work with the data or struggling to keep up with the maintenance. They may not be empowered to change any systems to resolve the data quality issues and may not even realize the extent of the issues, but they definitely care because it impacts their ability to do their job.
5. “We plan on working on better data processes – just not right now.”
Businesses may recognize the importance of data quality but just can’t think about it until after some other major implementation, such as a data migration, integration, or warehousing project. It’s hard to know where data quality fits into the equation and when and how that tool should be implemented but it’s a safe bet to say that the time for data quality is before records move to a new environment. Put another way: garbage in = garbage out. Unfortunately for these companies, the unfamiliarity of a new system or process compounds the challenge of cleansing data errors that have migrated from the old system.
6. “Our department can’t justify investing in a data quality solution right now.”
One of the biggest challenges we hear about in our industry is the struggle to justify a data quality initiative with an ROI that is difficult to quantify. However, just because you can’t capture the cost of bad data in a single number doesn’t mean that it’s not affecting your bottom line. When it comes to building a case towards the importance of clean, accurate data, remember: the true cost comes in doing nothing.
Bottom Line – For data quality initiatives, the time is now.
When it comes to keeping up with the fast-moving pace of technology, companies quickly become complacent, adopting the “good enough” approach that ultimately halts innovation and growth. Blockbuster Video, Kodak, Toys R Us, Nokia, Atari, Motorola were all once gigantic and thriving, yet despite their size and once-powerful positions, their inability to keep up with the technology of today – and the data that stems from it – resulted in their downfall.
The good news is that the answer is clearly within reach, and it starts with taking a look at the state of your data. The rate at which companies breathe in and out data is growing rapidly, yet so are the regulations and restrictions behind that data. Keep your company one step ahead by championing for data quality initiatives now – before it’s too late.