Big data is all the rage these days. Businesses are frantically investing millions in demographics, psychographics, behavioral, lifestyle, and household data of their customers, all in the bid to deliver futuristic, hyper-personalized experiences. But big data is inherently ambiguous, unstructured, and inconsistent. Worse, it’s obtained from multiple sources and companies are lacking the necessary solutions to consolidate this data to get a complete customer 360 view – which is the foundation of any customer-centric initiative.

To truly understand your customer, you need to make sense of customer data, but when your records are of poor quality and are not linked across your business systems, your personalization & digital transformation initiatives risk failure.

What is Record Linkage and Why it Matters?

Businesses have internal and external data sources. Internal data sources are the data stored in your CRM, sales platform, customer service platform, and more. External data sources are third-party data sources such as data from vendors and suppliers, from third-party data companies, or from social media platforms. Data-driven companies attempt to fuse internal and external data to get a complete overview of their customer, which is used for analytics, business intelligence, marketing, and exclusive promotions.

Record linkage is the process of using data matching technologies to merge these records because it’s a nightmare to use traditional methods to connect the dots between millions of complex records. You would need to make use of advanced, ML-based technologies to match multiple data sources and consolidate it to form a single source of truth.

For instance, an e-commerce retailer would want psychographic and demographic data to understand their buyer’s purchasing habits and use that information to set their pricing model. Similarly, a bank would want to integrate third-party data from partners and vendors to offer exclusive promotions or services. They will need access to a consolidated record that holds all this information in order to understand their customer better and deliver targeted campaigns at several points of the customer journey.

Having a consolidated view of your audience enables a deeper insight into their purchase behavior, their pain points, and their expectations from your business. It goes without saying how this positively benefits your ROI and brand image.

That said, as simple as record linkage sounds, it’s extremely complicated in practice.

What are the Key Challenges Preventing Companies from Linking their Data?

With terabytes of data streaming in on a daily basis, companies are being overwhelmed with the sheer management of this data.

Some are struggling just to make sense of their internal data before they can even invest in big data. Some are trying to find efficient ways to fuse external and internal data while maintaining data integrity and accuracy.

1. Internal data sources are hardly consolidated: An enterprise may make use of several platforms, each dedicated to the requirements of its specific department. For instance, marketing may use Hubspot, customer service may use Jira, IT may use Oracle Fusion – all holding varying information of the same customer. In day-to-day operations, this setup may work fine; after all, most companies prefer multiple sources of truth instead of a centralized, single source of truth. The problem arises at the time when the company needs to acquire in-depth customer analytics or when it needs to compile business intelligence reports to make critical growth decisions.

At this point, the disparity in data leads to disjointed, inaccurate customer views. Your teams will request data from multiple departments, exporting from one, and importing into another to create business reports and pie charts! All this to and fro of data results in human errors which further degrades data quality. Assuming your teams won’t be deduplicating or fixing deeper data quality issues, you’ve just been handed a report that is flawed. The consequences of flawed reports? Plenty. The costs. High!

Here’s a simple example of disparate data. Multiple systems operated by multiple users storing information in multiple ways!

Disparate data systems. Image source: Data Ladder

Pertinent to mention at this point, before you think of investing in big data or infusing external sources with internal sources, you have to make sure your internal data sources are linked across your business systems and error-free…..which leads to our second challenge:

2. Poor Data Quality is Grossly Overlooked: Does your customer data look like this?

Poor customer data. Image source: Data Ladder

Would you believe it if I told you this problem is grossly overlooked and undermined? Managers would seldom want to invest time in correcting this data and employees make fixes as and when needed. It’s not uncommon to see sales or marketing reps doing manual fixes directly in the CRM to pull out reports. The lack of data quality practices results in data that is unfit for data matching and record linkage (the process of combining your lists).

Imagine if you had to transfer from one CRM to another or if you had to move to the cloud or a new ERP system. You would have to spend months fixing this data before it can be fit for migration. This is why 38% of migration projects fail.

3. Inability to Process External Data: Acquiring more data is easy, but more data isn’t the solution to customer-centric initiatives – usable, accurate, reliable data is. External data is hardly quality data. Rife with inconsistencies, coupled with a rapid data decay rate, external data is unreliable. It needs to be cleansed, parsed, processed before it can be connected with internal data sources. Most companies, however, don’t have the capacity to process external data in real-time and use data lakes as data dumpsites, keeping the data for later use. Problem is, external data decays rapidly.

The longer you delay data processing, the more rapid its decay rate. Take for instance your customers’ firmographic data. People change jobs frequently. Customer A who was working at Company A no longer has the same email address nor holds the same title. When you want to include Customer A in your mail list for a certain activity, it’s invalid. Considering you have ten thousand records and if the rate of decay is 10% per month, you’re losing a 1,000 potential customers in one month alone!

It’s safe to say if you don’t consolidate your data and ensure its quality while doing so, you’ll have a hard time stepping into a digital future that demands playing and winning with data.

ML-Enabled Customer Data Matching Technologies as the Key Factor in Accurate Customer Record Linkage

Your teams can no longer afford to spend days and months exporting data from enterprise platforms to Excel sheets and making fixes on the go. You also cannot afford to hire expensive in-house data analyst teams to manually sort data. You certainly cannot expect your IT team and business team to constantly move data to and fro for every report or analytics project.

Therefore, the match for big data is not more human resources, but ML-enabled technologies, governed and operated by business users.

ML-enabled data matching technologies make use of a combination of fuzzy algorithms (top-in-line solutions even have proprietary algorithms) to match million of records, returning highly accurate matches. But data matching is not only what these solutions offer. Because you need clean, accurate data to link records, these solutions also come equipped with data cleansing processes – meaning you can clean dirty data by using built-in business rules (for instance turning all abbreviations like NY, NYC to New York), dedupe data (removing duplicate instances of one record) and finally clear the data of any inconsistency.

This saves a company quite literally months of effort. Linking records is not as challenging as ensuring the consistency and quality of records before linking. If you don’t have access to clean data, the matching will return results with high false positives and false negatives – beating the purpose of accurate record linking.

Customer data guides all customer-centric objectives, but only if the data is of high-quality and is accurately linked to give businesses a much-needed overview of their customers. In an age when customers demand businesses to meet their expectations even before they realize it, you cannot afford to miss out on opportunities caused by poor, disparate, dirty data.