How To Make Your Company More Data-Driven

Big Data

How To Make Your Company More Data-Driven

“We are a data-driven organization.”

“Unlocking our data is key to helping our customers succeed.”

“We make data-driven decisions.”

“Data is the new oil.”

Whether it’s a startup or a large, established business, you’ve probably heard a manager declare something similar to the above about how data is becoming one of the most valuable assets in the company. The view is that data will make your decisions far more superior, transforming your business for the better.

Unsurprisingly, like many sayings such as these, they tend to point to an ideal without the instructions on how to get there. It’s much easier to say your company wants data-driven everything than it is to create the culture and supporting infrastructure needed to turn these sayings into actual business benefits.

So, how does a company build itself into becoming the most data-driven organization in their industry?

It’s a difficult task that requires many changes, but it is both possible and imperative in today’s competitive market. As a fast-growing startup that specializes in cloud data analytics, my company helps companies become more data-driven with open source technologies. Having led multiple startups through various stages of growth and having worked closely with customers, I’ve learned that companies need to not only ask questions of their data, but to also ask questions about which data. Answering both of these sets of questions enables data-driven decision-making across the whole company — in every department.

The value of data grows as data gets faster and analysis becomes more granular.

Previously, companies regularly looked at data on a quarterly and yearly basis. That was partly due to a slower pace of growth and a less competitive environment than in today’s internet era. Now, our data systems are able to collect far more granular amounts of data and do so in near-real-time. In order to innovate quickly and course correct, analysis is happening more frequently — and oftentimes simultaneously — across real-time and archival systems. 

Like calculating interest on a daily basis, daily data analysis can lead to a compounding effect on the value of your data. By knowing what’s happening across your key performance indicators, your course corrections (and learnings) can compound into a hockey stick of growth. And it requires people to have the know-how and willpower to get that granular.

On the other hand, you have to be willing to let go of data if it’s not leading you to make better decisions. Have hypotheses and then move on if they don’t bear out.

Data user experience matters, and data latency is the hidden killer of innovation.

Many companies now design the user experience in their products and services. This has proven to reduce the friction that would otherwise impede customers from realizing the full value of them. Similarly, a data user has experienced too, and it’s usually not a good one! Most data systems are very complex and disjointed and have a range of access patterns based on a myriad of compliance and security requirements. As a result, a data user’s needs are usually low on the priority list.

For example, we worked with a company that needed an analyst to know roughly where different data lived in order to use it. If the user wasn’t aware of a silo of data located in a table on a particular system, there was no opportunity to gain insight from that data.

Additionally, the issue of data latency (i.e., the time from when a user has questions to the time they are actually answered), compounds the frustrating data user experience. Today’s systems require a lot of preparation and copying of data, which tends to add a lot of time to the whole data analytics journey. The result is that data users don’t bother asking certain questions and, thus, limit their insights. That’s like only using a set of 26 World Book Encyclopedia volumes instead of the World Wide Web.

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How To Make Your Company More Data-Driven