Data Automation

How to deploy DataOps: Step 3 – Transform foundational data to create distinct KPIs

Switchboard Feb 24

How to deploy data ops
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    In our last post, we explored Step 2 of the four steps to realizing the benefits of Data Ops – creating foundational data.

    Now that you have foundational data (i.e. you’ve marshaled the raw data from various systems and vendors), you need to be able to quickly and accurately access the data according to the KPIs that you defined at the outset. This takes a few steps.

    In many cases, crucial KPIs must be derived from different data sources with unique schemas and formats. To fully realize how the overall value of your data is greater than the sum of your individual channels, each data source must be prepared based on its unique properties.

    DataOps automates many styles of data preparation into a single process that transforms raw ingredients from any data source to create foundational data. This powerful type of data can then be shaped into KPIs that power better business decision-making. The diagram below illustrates the gap foundational data fills to help you move from data to intelligence.

    Bridging the gap between raw data and KPIs

    It’s important to remember that even a single KPI can require blending multiple sources. To understand this better, let‘s look at a KPI from Step 1 as an example: Sell-Through Rates. Let‘s say the Revenue Operations team needs to pinpoint where the highest and lowest Sell-Through Rates occur across multiple properties. These metrics could be derived by analyzing delivery logs from the ad server. But what if the client demands only viewable impressions? Deriving the answer requires combining delivery data with impression data from the viewability measurement system.

    The kind of data blending described above quickly gets unwieldy without a structured approach. Foundational data is a layer of data derived from raw data. The focus is on modeling canonical “base metrics”, such as Impression Delivery, Viewability and Revenue. These base metrics can then be used to derive more complex KPIs, for example, Viewable Sell-Through Rates, or eCPM.

    There are huge benefits to the foundational data approach. It enables you to establish common data standards between multiple teams across the organization. It also encourages efficient reuse of data for different analyses and reports, meaning your entire organization starts to benefit through more timely and accurate communication, reduced errors, and ultimately, better decision-making.

    You may have already created a data science team to establish foundational data, which will lay you in good stead for building a robust, strategic data asset across the organization. But while this team can help to combine raw data and analyze the KPIs, even the most skilled data engineers lose valuable time deriving a KPI from scratch. When business teams come to depend on this data, they will inevitably demand fast and reliable reporting of these metrics, again, through data automation. And to do that, you‘ll need to make sure you pick the right tools.

    If you need help unifying your first or second-party data, we can help. Contact us to learn how.

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