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The Hardest Part of Analytics Isn’t Analysis. It’s Data

In this special guest feature, Matin Movassate, CEO and cofounder of Heap, suggests that as data scientists and business leaders alike fixate on the great potential of technologies like machine learning and AI, we risk losing sight of what’s most important: the data itself. After all, fancy visualizations and predictive analytics don’t matter without the right data powering them. Matin wished he had Heap during his previous stints at Facebook, Google, and Mozilla. He studied CS at Stanford and enjoys climbing and poker.

From advanced BI tooling to machine learning and artificial intelligence, modern businesses have more ways than ever to slice and dice their data. As data scientists and business leaders alike fixate on the great potential of these new technologies, we risk losing sight of what’s most important: the data itself. After all, fancy visualizations and predictive analytics don’t matter without the right data powering them.

Every single business needs to prioritize collecting and structuring their underlying data over the analysis they use to understand it. Here’s why:

Data will be ingrained in every part of how we do business

Companies have just begun to grasp not only the complexity of data, but also the depth of its relationship with their own employees. All business roles and levels need to make good decisions, and the best decisions are made with user data. Thus, every department – not just the data science team – should have access to that information, from product to customer service to sales.

It’s no longer enough to just review topline metrics at a monthly all-hands meeting. Organizations must infuse data-driven processes into their decision-making. Take a modern marketing team, for example. Marketers today have a multitude of rich data sources at their disposal, especially with the explosion of smartphones, tablets, social media platforms and digital touchpoints through which a brand can interact with its audience. If all of this data is collected into a central place, it opens up powerful new ways of understanding long-term customer behavior. Other departments like sales, product, and customer success similarly have access to an unprecedented amount of data.

Every bit of data contributes to the bigger picture

As data plays a bigger role across every department and level, businesses must consider all of its data as a growing collection of opportunities. Every dataset – CRM, CMS, ERP, marketing software – contains a multitude of possible insights. Findings that seem insignificant now might matter a great deal down the road. It’s impossible to know upfront what data matters, so businesses need to collect as much of it as they can. This lets companies retroactively unearth insights, even if their priorities or market conditions change.

Insights are only as good as the underlying data

Data quality is king. Bad data leads to bad results. If you base your decisions on incomplete data, it becomes harder to trust the results, and it ultimately erodes confidence in a data-driven culture. Clean, complete, and correct data is necessary for generating actionable insights.

We saw this with the 2016 presidential election. Most predictions were based on national and state-level polling results conducted over the phone. But phone surveys are especially susceptible to nonresponse bias, which itself varies wildly from state to state. This affects the forecast for the Electoral College more than the overall popular vote, yet the Electoral College is what wins elections. The result? Skewed data producing the wrong prediction.

Machine learning has received a great deal of hype, and for good reason. But it cannot live up to its bold potential unless it’s informed by a strong foundation: clean, complete data produced by an organization that ingrains data into its culture. The term “data-driven” has been around for years, but in today’s fast-paced and increasingly digital economy, it will need to become a cultural mandate for companies everywhere.

 

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