Data Automation

The Risk of Manual Reporting

Switchboard Mar 9

Can you afford the risk
Table of Contents

    Manually compiling data has a number of disadvantages:

    • The data is subject to human error;
    • It is expensive to hire data engineers and data scientists to aggregate the data in a way that is useful to business teams;
    • It isn’t scalable as your organization grows;
    • and frankly, it’s boring work.

    Data scientists and data engineers can, and should, be engaged in more strategic tasks than endlessly massaging spreadsheets. There is another risk that is sometimes overlooked when it comes to manually aggregating data: and that is timeliness.

    Becoming a data-driven enterprise is a growing necessity in today’s business environment. If you are making decisions without data, you are at a competitive disadvantage as data provides critical insights to inform your strategy and tactics.

    As companies become increasingly data-centric, supervisors and c-suite executives will expect regular, timely, and accurate updates to assess performance. The trouble is, a large proportion of enterprises are still battling with manual spreadsheets.

    Let’s assume your CMO sets a weekly meeting in which they ask: “How are each of our campaigns performing?” This question is easy to ask, but deceptively difficult to accurately answer without modern DataOps. If you’re still compiling, normalizing, and contextualizing your data manually, you are likely providing an answer that describes the picture from weeks ago… and certainly not today or yesterday.

    If your data engineers are busy manipulating spreadsheets, they are processing data significantly slower than an automated solution. And that means you are making real-time decisions using potentially obsolete data.

    For example, one of our customers’ main data sources was from Amazon. They needed to know in real-time how their Amazon campaign was performing so they could make adjustments and reallocate resources to maximize efficiency and optimize ROI. Before embracing automation, processing Amazon’s data took anywhere from a week to two months. Week-old data is bad data, and frankly, two-month-old data is worthless. By using old data, they ran the risk of charging too much. In fact, that same customer wasted $100k in one week on a marketing campaign because they were targeting the wrong market segment due to using old data.

    Once they embraced automation, they were able to use accurate, validated, and timely data to make informed decisions, adjust quickly, and avoid wasting significant resources. I don’t know about you, but I’d be pretty upset if I lost $100k in one week due to a needless error.

    Real-time data delivery through automation is essential to gleaning critical insights and making required adjustments at the speed of business. Otherwise, you risk leaving money on the table.

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

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