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Most industries have voluntary certification bodies that attest to the quality of their members. For example, in the investment world, a financial analyst (CFA) is someone who was certified by the CFA Institute after completing three rigorous exams that test their financial and investment acumen.

Although taking the CFA exam is voluntary, it became 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.

However, when it comes to data, marketers traditionally did not have the same kind of rigorous standards by which to measure the quality of their data. Brands simply have to trust that the audience data they buy to drive their advertising and marketing campaigns will lead to optimal results. This approach is not good enough for long-term marketing success.

Today, the marketing data ecosystem is beginning to be held to similar quality standards that are essential to foster trust and transparency in what was an opaque field to date. The question is what is needed and why marketers should care.

Full support of data certification

Data quality is a frequent topic of conversation among marketers. In September 2020, the term reached its highest popularity in Google trends since July 2005. As marketing continues to move toward personalization and, more importantly, as privacy regulation continues to evolve, there is a need for certainty in the quality and compliance of your data.

The first step is to establish mutually agreed industry standards for data quality. However, when buying data from third parties, buyers traditionally place a great deal of trust in suppliers without getting the same transparent and standardized reporting format. There are many considerations when buying data: how and from where was it sourced? How recent is it? How was it modeled? Is it authentic or is it riddled with bot-generated information?

Buyers expect their data to be of high quality, but most do not have standardized measures to assess this. As conversations about data regulation continue, many third-party vendors are proactively seeking certification from accredited auditors to ensure high-level data quality. 

Understanding data quality and effectiveness.

Data quality focuses primarily on parameters such as accuracy, validity and consistency across platforms. It can be summarized as follows: is my data privacy compliant and usable?  Data effectiveness, on the other hand, focuses primarily on whether that data has direct application to your business objectives. The question is “how good is the data at accomplishing what it is supposed to do?”. The main distinction between the two, then, is usability versus utility.

You must have data quality to have effectiveness, but you can have excellent data quality that doesn’t really help improve the customer experience if it’s not targeted correctly. Master data management should be considered to make sure you are creating a trusted, authoritative view of customers that can be easily and seamlessly tracked in platforms like Salesforce, Netsuite, Creatio or other CRM platforms.

Just as there are countless potential negative impacts of receiving financial and investment advice from someone who has no real experience in the field, the same goes for using incorrect data. The impacts of this type of data can include everything from decreased customer satisfaction and retention to skewed campaign success metrics. In this hyper-competitive marketplace, brands can’t afford to get it wrong.

In addition, as privacy regulation changes, it will become increasingly important to ensure that the audience data an organization buys from vendors and marketplaces is compliant and certified. 

Engaging with data providers that took the steps toward certification is a great first step in making sure you have good quality data. However, it is up to the organization to determine the effectiveness of their data. They should be tested over time to see if they are really moving the needle with the target audience.


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