The Risks Of Being Too Data-Driven

In business today, data is everything. And proudly proclaiming that your organization is “data-driven” is not just a given, it’s a mandate. Firstly, data helps inform the decisions you make leading to your strategy development and gives substance to its abstraction. Second, metrics provide direction for strategy activation, helping you optimize your execution.

But what if I told you that you could be too data-driven?

Because there are real risks to that strategy that can be harmful for your marketing and your business.

Data can be unclean -- or worse, misleading or meaningless: Unfortunately, companies don’t spend a lot of time cleansing -- or corroborating -- their data. But data can be filled with errors or holes. Spending time and resources on bad data is not just a waste of time, but a danger to your brand.

We’ve all worked on insights from a campaign and then found issues with tagging and data collection. Activating on bad data obviously won’t produce the results you’re looking for.



And many “data-driven” organizations are in reality focused on meaningless or vanity data, with no real connection to in-market success.

I once worked on a lead-generation campaign to launch a new car that set a goal of 100,000 leads. We then tracked and optimized relentlessly against cost-per-lead -- a meaningless metric, as it turned out. While the campaign was successful in hitting the lead goal, we found that a huge percentage of leads were low-quality.

Implication: Confirm the value of your data by checking both the validity and the relevance of each metric/KPI.

Analysis-paralysis: The best organizations use data as a tool, helping to guide strategic direction and confirm hypotheses.

However, data overload can paralyze some organizations. They may attempt to “boil the ocean” to try and make sense of it all, slowing down decision-making and action. Or they become so overly reliant on data that they constantly await the next batch. These organizations end up letting data make decisions for them, versus using data as an input.

Implication: Use your data as an aid to experience and judgment -- and recognize that getting started trumps inactivity.

Context-less data/lack of analysis: Some teams are so busy collecting data, they’re not analyzing or deriving insights from it. They talk about “dashboards,” but aren’t adding layers of context, business, or creativity to yield actionable insights. Without any added business or consumer context, data alone can lead to worse decisions than having none at all.

It’s also important that you’re not just looking at evaluative measures. Knowing that a landing page or an email effort, say, is performing above or below a threshold is important, but not helpful. Make sure you’re also measuring diagnostic data: data points that help understand the “why” behind performance.

Implication: Make sure you add context and qualitative aspects to your data for strategic insights and implications.

Metrics replace your strategy: Often teams suffer from “surrogation": when they focus so intently on the metrics they've identified to represent their strategy, they lose sight of the original strategy itself. For example, when a company has a goal of having “world-class creative,” and uses a quantitative creative testing score to determine what “world-class” is. In no time at all, the entire creative process can become more about creating work that passes the copy test than the higher goal of developing breakthrough communications.

Implication: Ensure your data strategy is focused on measuring progress toward a strategic goal.

I’ve heard that a better approach to being “data-driven” is being “data-informed” -- the happy medium between leveraging experience and instinct only, versus being completely reliant on data. Whatever it’s called, this is the key -- using data in conjunction with experience, instinct, and business strategy. Ensure you are making decisions based on data, but within a larger context of market, users, goals, and vision.

2 comments about "The Risks Of Being Too Data-Driven".
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  1. John Grono from GAP Research, December 21, 2021 at 10:26 p.m.

    Spot on Michael.

    In my experience over the years 'cleaning' the data is probably the most important (and time consuming) chore.   Adding data is also a challenge because it may convolute, contradict or confirm previous findings, and that applies to adding longitudinal data to the existing data set or adding new data souces to the data pool.

  2. Michael Baer from TechCXO, December 22, 2021 at 8:52 a.m.

    Thanks, John. And totally agree - combining data from different sources can be very challenging for the reasons you say, plus the difficulty often faced just getting the different sources and streams to even communicate with each other. The simple act of collecting your own first-party data presents lots of challenges... 

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