Data Disconnect: The Magic of Making Poor Decisions, Even Faster

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If you’re like most organizations, your data has become almost entirely decoupled from your broader strategy. This isn’t a revelation, it isn’t controversial: around 60% of companies are still basing decisions on “gut feel” rather than the veritable wealth of information at their disposal.

And you know, for much of our history, that hasn’t been a problem. Our instincts have magically guided us to swift and easy currents because we have lived within systems both localized and predictable.

Unfortunately, today, we have the twin eddies of globalized economics and technological complexity to contend with. And when data isn’t collected properly, when it isn’t analyzed with rigor, it simply leads us down the wrong path quite a lot faster.

Your life on a new planet

Congratulations! You’ve been selected: You will be one of the first to venture to a new Earth-like planet, exploring terrain new to man and mission. On your very first journey, you encounter an expansive gorge — 16 feet wide at its slimmest, 200 feet deep at its shallowest. But you’ve encountered similar situations before. Just as you are about to shamble across a clumsily made rope bridge, you turn to see your companions bounding straight across.

The gravity, you see, is much lower here.

We are conditioned to make predictions reliant on our past beliefs. And that works just fine — as long as those past beliefs remain true. But here, in a world driven by rapidly accelerating technology and unprecedented shifts in consumer patterns, we may as well be on an alien terrain. Our instinctive predictions break down.

To make the right decisions, we need to rely on our data.

The grand narrative, underneath

At its core, data tells a story. It tells the story of what is really happening, absent preconceptions, without the taint of instinctive belief — albeit, this data still has to be normalized, analyzed, and understood.

In technology, and in marketing, this story is quite frequently divergent from what we want to believe — rom what we instinctively believe — because the world is changing quite fast.

Still yet, most modern enterprises do not drive their decisions with data — even if they kind of think they do. Either they are not tracking all the data they need, they’re not chasing the right metrics, or they are analyzing those metrics incorrectly.

The “why” of it isn’t complicated: It comes down to acquisition, alignment, and, well, attitude.

• Data is never properly acquired. Data collection, management, and analysis is its own discipline — its own science. In many organizations, departments self-report their own data without any normalization, while company-wide data is collected by a web services team, marketing team, or externalized agency. The more complex an organization’s technological stack becomes, the harder it becomes to extract, normalize, and analyze data with any reliability. Because organizations don’t prioritize data management, these initiatives tend to bounce from group to group, task force to task force, until the initiatives are dropped.
• Data isn’t correctly aligned. There is a reason that data science is a discipline: because it isn’t easy to extract information from raw data streams. It’s easy to make assumptions based on raw data that could be correlative rather than causative — and, furthermore, it’s alluring to track metrics that tend to “go up” rather than the metrics that are strictly valuable. If an organization is collecting its marketing metrics through its marketing agency, that agency has a vested interest in tracking the metrics that it knows well, such as raw traffic — it doesn’t have to be intentional; it can merely be the case that these are the metrics the agency is most comfortable tracking. However, these metrics may not be the metrics that actually lead to commitment or engagement.
• Data is simply ignored. Finally, everyone — you and I — is vulnerable to simply ignoring data when it doesn’t present the information that we expect. When the data tells us what we want it to tell us — when it validates us — we want to believe it. When the data tells us something we don’t want (or, worse, don’t understand), we are tempted to ignore it. After all, it’s just numbers. Attitude matters regarding data: a data-driven world must prioritize data first. And that doesn’t mean we explicitly believe the data — it means that if the data returns something we don’t expect, we need to understand why it did that, and we need to chase those answers until we are satisfied.

We all want to believe that we understand the world. And most of us certainly don’t want to stare at a dense wall of code trying to imagine the woman in the red dress — when we are told a story, we want that story to be accessible and digestible.

We want it to make sense.

But the pursuit of that accessible story is exactly what makes us vulnerable to our deepest biases.

Data as an afterthought — and a conclusion

Perhaps that’s all a bit uncharitable. If you’re reading this article, you do know that data is important — and you probably don’t ignore it. But that only brings us to another point: the great disconnect between an organization and its data is usually a problem of process, not people.

• First, for the most part, we treat data as an afterthought. It’s a post-mortem. Once a campaign is finished, the data is compiled and sent off. Where does that data go? Is that data ever used? Only if there is a process of feedback and continuous improvement. If there are no processes to reflect upon this data and act with agility, the data serves no ultimate purpose except for archival.
• Secondly, perhaps more damagingly, data is often used as its own foregone conclusion — rather than analyzing the why and the how of what happened, the data is used to determine success. Is the department bringing in leads? Are those leads converting to sales? What is the average value of those sales? There is now an emotionality, a viability, to this data: It is now being used to justify the very existence of the reporting team.

Even in data-forward companies, both these factors can lead to long-term data irrelevance. By treating data as an afterthought — and as a conclusion — data is not properly looped into a continuous improvement process.

Calling the data doctor

Maybe that all sounds a little bleak. Fantastically, there’s a really easy solution: fostering a data-driven culture begins with acknowledging data as its own entity.

Too frequently, data becomes a tertiary task isolated within operations, marketing, or sales. Rather than being the focus of any department, it becomes decentralized and decoupled. It bounces between internal teams, is managed and measured by cross-functional teams, and is never allowed to deliver value.

Creating data-driven strategies necessitates both a deeper and broader understanding of data collection and analysis — and that is a discipline that is growing in complexity day by day. But it’s also no longer optional: the world has become far too complex — and, for the most part, we now find ourselves on alien terrain.