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Analyzing customer data successfully

Is your business making these common data analysis mistakes?

4 min read

Marketing Strategy



Most businesses understand that their data is no longer the true asset. Rather, it’s how they look at their data that brings value. Data can guide strategy, make predictions and lead to concrete results. However, this all comes with one big caveat: it only works if you’re analyzing it correctly. Reports indicate that businesses spend up to 80 percent of their time wrangling data and only 20 percent actually analyzing it. Businesses understand that they need analysis and insights from their data, but they continue to make the same mistakes when it comes to working with it.

Are you in that group making the most common data analysis mistakes? Ask yourself these three questions to improve your analysis and rapidly turn data into actionable insights.

Is your data behind an IT wall?

For business leaders without a tech background, it seems natural to pass off data analysis to your tech department. Traditional data analysis methods require complicated models and complex integration techniques after all. However, by allowing data to live behind a wall of tech, you’re beholding yourself to the IT department every time you want to ask a question of your data. Business leaders want answers and insights at the speed of business, not months after they need them. 

Instead, democratize your data and make it accessible so that insights are instantly available despite one’s tech skills. This can be done by seeking out less traditional data analysis and integration tools. For example, at FactGem, we create reliable data analysis models and quickly integrate data securely to let you garner immediate business insights without the need for code.

Is your data in silos?

Data is frequently distributed across application and business functional silos, often due to the use of the traditional data collection and management tools mentioned above. When data is fragmented, important connections are difficult to spot. For example, we recently worked with a Fortune 500 retail company that wanted a clear picture of its primary shopper. The company had data from a variety of sources with info like demographics, shopping habits, etc., but without breaking these data points out of their silos, they were unable to form one complete consumer archetype.

Don’t settle for pouring over spreadsheets, staring and comparing, trying to make correlations out of separated data. Look for tools that can merge and integrate your data to make drawing conclusions quick, easy and impactful. Applications that rapidly integrate data should allow you to only integrate the sources and attributes that you need, as you need them, and also have the ability to seamlessly integrate more data later without requiring you to constantly remodel or reload your data.

Are your data sources rigid and inflexible?

When you rely on traditional collection and analyzation tools, you create rigid data. For example, with traditional tools, once a data model is created, inflexible pipelines are created to move data from its source to the data model you analyze for insights. Creating these pipelines for each data source take some hefty programming skills, and a lot of time to implement, update, and maintain. Want to add a new data source? Time to build a new pipeline, slowing down your quest to garner new insights.

Adaptation is the name of the game in business, and business leaders need their data to keep up. Instead of models that require you to map out all of the data you think you’ll need upfront and punish you for changing it down the line, look for data collection tools that can adapt in real time to accommodate your needs. Start with the models that already exist, and rapidly connect these for insights and reporting on a unified view of data.

Data analysis is tricky, but it doesn’t have to be impossible. By prioritizing data democratization, integration and flexibility, you’ll quickly gain more impactful insights from your data. 

Megan Browing Kvamme is the founder of several women-owned businesses, including the technology startup FactGem, where she is CEO.