When it’s time to automate DataOps, do you build or buy?

In our last few posts, we’ve explored the four steps to implementing DataOps: identify KPIs, create foundational data, transform foundational data, and automate. But now you’re ready to automate, the million-dollar question is this: should you build out your own automated reporting infrastructure, or buy?

Regardless of your approach, there are a variety of factors to consider. First, you‘ll need to understand the data requirements of your business and the talent you have supporting it. Like most technical challenges that require significant engineering expertise, digital-first enterprises face a fundamental ‘build vs. buy’ decision based on the following factors:

• Data use: the volume of data and the frequency of reporting (i.e. queries) you intend to run are important factors that will help you pick the right solution
• Budget: in addition to in-house staff, it‘s important to keep ongoing maintenance costs in mind from both a personnel and licensing perspective
• Talent: some companies are fortunate enough to have sophisticated data analysts on staff, while others may not have a deep technical team on which to rely. These highly specialized professionals can be expensive to hire, and challenging to retain
• Urgency: because of your current technical infrastructure and the capabilities of your competitors, you may need to move more quickly

That said, the build vs. buy debate comes into sharp focus with DIY, especially when it comes to re-sharding large datasets, because it requires such specialized expertise to create and manage.

If you‘re contemplating the DIY approach, you need to consider whether big data is a core competency you need to build within your organization, or whether data analysis based on the use of such data is what you really want. If it‘s the latter, you‘ll probably want to buy opposed to build, to keep your technical experts focused on analysis of data and creating solutions on top of that data that will add unique value to your business.

Foundational data – the basis for digital insight

Deriving business metrics from raw data in a bottom-up manner is one of the most powerful tools available to any digital-first company. This can only be achieved by transforming raw data into foundational data – an intermediate step between the unstructured and the insightful.

Foundational data is the canonical representation of data in its purest form. As an example, consider impression delivery data from GAM. Pulling manual GAM reports may provide the data needed to understand campaign delivery for a specific set of customers or key-values. But if you want to look at a different slice of data or time-frame, you need a different report, which means someone on your team needs to manually configure certain dimensions, time frames, geographies, and so on. This means you never have a “single source of truth” to slice and dice, explore, or connect to other data. In other words, you never have the basic building blocks that consistently tell the same story in the same language.

Now, what if it was possible to pull down a complete repository of your GAM data, for every line item – and across all geographies, properties, and time frames – and then clean it up so that names, IDs and key-values are consistent across all datasets?

That valuable transformation of raw data is foundational data.

Ask yourself the following: “Is it more efficient for me to build a new internal team with expensive and highly specific expertise to manage data, or will my existing team deliver better business results by relying on proven technology from an established vendor instead?”

In working with one of our international publisher customers, we found that while they could handle basic data, they realized it didn‘t make sense to build a large internal team dedicated to basic data management when they could be investing the same resources in creating innovative advertising products.

At the end of the day, the business objective is to maximize client results and increase CPMs, and by implementing a ready-made data automation platform, all this becomes possible.