How enterprises can use third party data for their data strategy

By now, it’s a given that enterprises need data to succeed, or even to survive. Data is what tells you what features to prioritise for the next update, what messaging has the greatest impact on your audience, even who your audience really is.

One recent government-led study reported that 99% of businesses perceive data as important to their success. You just have to wonder why only 90% of them have implemented a data strategy or data-related initiatives.

But to achieve true data-based decision making, businesses need insights from a whole range of data, and third party data is rapidly going out of fashion. For example, Google is still determined to phase out third-party cookies, even though it recently agreed to delay their demise until 2023.

But while first and second party data is crucial, they can’t cover everything. For example, your users aren’t likely to be as honest in their feedback to you as they are on an independent review site. B2B businesses might have no direct connection with their end users and have to rely on market research. IoT data from connected devices gives you a more accurate understanding of product use and drawbacks than any customer satisfaction response.

It’s clear that third party data deserves to be part of your data strategy. The only question is what you should be doing to include it and draw on it as fully as possible.

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Ensure data quality

The insights you derive from any datasets are only ever as reliable as the data itself. There are typically many more opportunities for errors and duplicates to creep into third party data, because you don’t always have control over the manner in which it’s gathered and it’s more divorced from your internal data networks.

As a result, it’s even more important to ensure that it’s cleaned and verified carefully and responsibly. Pre-processing raw data manually can be challenging enough when you’re talking only about your own proprietary data; once you move on to third party data, the difficulty level becomes stratospheric.

Automated data pre-processing is thus vital for ensuring that your data is trustworthy. It’s worth it to invest time and energy in building a pipeline that uses machine learning (ML) algorithms. ML automated processes can spot duplications, errors, and gaps in datasets far faster and more accurately than humans can, raising the quality of your data.

Connect all your datasets

Every kind of data is valuable in and of itself, but the more data you can gather together, the richer and more reliable your insights will be. It’s important to integrate third party data, no matter where it’s coming from, with first and second party data too. It’s like the difference between seeing user reviews on G2, and being able to correlate them with the amount each account spends per transaction.

That’s why it’s increasingly important to move all your data management to the cloud. It can seem challenging to choose between different offerings, like weighing up Snowflake vs. Redshift, but it makes a big difference to your analytics results.

When all your data is in a single cloud-based repository, it helps break down silos between data sets, sources, and types, so you can integrate them all into a “single source of truth”. This way, your analytics tools can draw on a full set of data, plus when your data is fragmented, it’s easier for elements to be missing or mistaken without you realising.

Guarantee data security

Today’s consumers are far more aware of the risks of data theft and identity fraud, which makes them a lot more nervous about sharing data. It can be challenging to get users to agree to third party cookies or data sharing, so you’ll need to work hard to overcome their anxieties.

If for no other reason, it’s important to tighten your data security and data privacy levels. If you receive data from distributors or partners, they need to know that you won’t sell their customers’ data or carelessly expose it to hackers. Your security profile needs to be high, especially around your data storage and analytics, and your compliance with various data confidentiality regulations has to be watertight.

Among other things, cloud data storage can help in this regard. When your data is stored in the cloud, stakeholders won’t need to download anything to private servers or devices which could be vulnerable to attack. Cloud analytics tools also remove the need to physically migrate data to the server that hosts analytics, further reducing your attack surface.

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Third party data is here to stay

Although third party cookies are being phased out and more platforms might follow Apple’s example by requiring users to opt in to data collection, third party data is always going to add value to enterprise insights and decision-making. As time goes by, it’s likely to only get more important to remove silos to data, ensure your data is kept safe and secure, and raise the bar for data quality, so that you can make the most of third party data insights in your data strategy.

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