Why the marketing data ecosystem needs to up its game on certification and transparency

Josh Raper, VP of Marketing at Affinity Answers, is a 15-year advertising veteran with a diverse background and significant experience in business and strategic marketing across digital and traditional experiences. Josh has led major global and national campaigns for McDonald's, Allstate, Kellogg's, InBev, Esurance, among others.

Most industries have voluntary certifying bodies that attest to the quality of their members.

For example, in the investing world, a chartered financial analyst (CFA) is someone who has been certified by the CFA Institute after having completed three rigorous examinations testing their investing and financial acumen.

Though taking the CFA Exam is voluntary, it has become the benchmark against which most investment professionals are measured, and its selectivity — fewer than one in five candidates pass all three levels — is a true indicator of quality.

Yet, when it comes to data, marketers traditionally haven’t had the same sort of rigorous standards against which to measure their data quality. Like a retail investor blindly trusting the claims of redditors on WallStreetBets, brands must simply trust that the audience data they purchase to power their marketing and ad campaigns will lead to optimal results. This approach isn’t good enough for long term marketing success.

Today, the marketing data ecosystem is starting to undergo similar quality standards that are essential to fostering trust and transparency in what has been an opaque field to date. Here’s an overview of what’s needed and why marketers should care.

Step 1: All-in support of data certification

Data quality is a frequent conversation topic among marketers. In September 2020, the term reached its highest popularity on Google trends since July of 2005. As marketing continues to push towards personalisation, and, more importantly, as privacy regulation continues to evolve, marketers need assurance in the quality and compliance of their third-party data.

So where do we start?

The first step is establishing mutually agreed upon industry standards for data quality. This would be the equivalent of setting the curriculum and test format of the CFA Exam.

However, when purchasing third-party data, buyers traditionally place huge amounts of trust in providers without getting that same transparent and standardised format of information. There are many considerations when purchasing data: how and from where was it sourced? How recent is it? How was it modeled? Is it authentic or riddled with info generated by bots?

Buyers expect their data to be high quality, but most don’t have standardised measures to assess this. As data regulation conversations continue, many third-party vendors are proactively seeking certification from reputable auditors in order to guarantee high-level data quality. Neutronian, for example, has built their framework around the preceding questions in order to provide the assurances data buyers are seeking.

Step 2: Disentangle data quality and data efficacy

Outside of third-party certification, organisations need to understand the differences between data quality and data efficacy. Data quality is mostly focused on parameters such as accuracy, validity, and consistency across platforms. It can be summed up like this: is my data privacy compliant and is it usable?

Data efficacy, on the other hand, is mostly focused on whether that data has a direct application to your business objectives. It asks “how good is your data at achieving what it’s supposed to?” The main distinction between the two, then, is usability vs. usefulness.

You must have data quality to have efficacy, but you can have wonderful data quality that doesn’t really help you improve the customer experience if it isn’t aimed at the right target.

Next, consider master data management to ensure that you are creating a trusted and authoritative view of customers that can be easily and seamlessly tracked across platforms such as Salesforce, Netsuite, Creatio, or other CRM platforms.

For instance, different platforms might have slight variations on a customer’s name that limit your ability to bring those records together in a single view. Master data management is a framework that can unite these disparate records and give you that data quality and efficacy to make insights actionable.

Why does this matter for marketers in 2021 and beyond?

This is important on many levels. Just as there are countless potential negative impacts on taking the financial and investing advice from someone who doesn’t have a real background in the field, the same holds true for using bad data. The impacts of bad data can include anything from a decrease in customer satisfaction and retention to distorted campaign success metrics. In this hyper competitive market, brands can’t afford to get it wrong.

Additionally, as privacy regulation shifts, it will become increasingly important to ensure that the audience data your organisation is purchasing from providers and marketplaces is compliant and certified. The investment world realised the importance of certifying its professionals to maintain trust and transparency, but we must apply that lesson to the world of data.

Commiting to data providers who have taken the steps towards certification is a great first step to ensure that you have good data quality. However, it’s up to your organization to determine your data efficacy. You’ll need to test the data over time to see if it’s really moving the needle with your target audiences.

Interested in hearing leading global brands discuss subjects like this in person?

Find out more about Digital Marketing World Forum (#DMWF) Europe, London, North America, and Singapore.  

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