Rocking The Bottom Line: Top 3 Tips For Data Analysis And Decision Making

Data Analysis

You have a company. Every day, via multiple channels, data pours in: customer names, SEO stats, sales forecasts, click-through logs, total conversions. Somewhere in the data, you are sure, lie answers to those questions that wake you in the middle of the night. Somewhere, there are clues to the next big opportunity, the next hot trend, the key factor to differentiate from your competition. Somewhere. But how do you tease that one golden thread from the mass of information on your servers? How do you make the data work for you?

1. Ask The Right Questions

To get the most out of their data, companies need to clearly define the objectives before beginning any kind of analysis analysis. Specific questions help teams focus on the right data, saving time and money. Instead of broad topics, such as, “How can I create more revenue?”, ask specific questions that data can help answer. For instance, start by asking how you could approve your funnel? Or start by tracking where potential customers stop during the purchase process and create questions to analyze the reasons.

2. Gather Data Now To Avoid Data Wrangling

Gathering the right data is as crucial as asking the right questions. For smaller businesses or start-ups, data collection should begin on day one. Jack Dorsey, co-creator and founder of Twitter, shared this learning with TechCrunch. “For the first two years of Twitter’s life, we were flying blind… we’re basing everything on intuition instead of having a good balance between intuition and data… so the first thing I wrote for Square is an admin dashboard. We have a very strong discipline to log everything and measure everything.”

While it is important to log often and early, companies need to focus on the data that can provide genuine value. Earlier this month the New York Times published an article, “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights” which details the unglamorous aspects of data science. They say that data scientists, “spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.”

Even though we have access to more data than ever before, there is still a bottleneck that cannot be solved completely by algorithms. A huge part of the problem is that data sourced from different channels – including documents, the web, and various databases – often comes in different formats. An algorithm needs information in a single format so it can understand the data before it provides insights. Humans, particularly data scientists, are left on cleanup duty, leaving less time for analysis.

3. Set Measurable Goals For Decision Making

After you have your question and your data, then comes the hard part: decision making. Ensure that your decisions are aligned with the company’s mission and vision, even if the data are contradictory. Set measurable goals to be sure that you are on the right track.

Who Is Rocking Data Analysis & Decision Making?

A terrific example of data analysis and decision making came from Walmart, in 2004, regarding orders for emergency merchandise in preparation for Hurricane Frances. Executives wanted to know which merchandise they should stock before the storm. Their analysts mined records of past purchases from other Walmart stores under similar conditions, sorting a terabyte of customer history to decide which goods to send to Florida (quantitative data).

It turns out that, in times of natural disasters, Americans turn to strawberry Pop-Tarts and beer. Linda M. Dillion, Walmart’s CIO at the time, explained that, “by predicting what’s going to happen, instead of waiting for it to happen..trucks filled with toaster pastries and six-packs were soon speeding down Interstate 95 toward Walmarts in the path of Frances. Most of the products that were stocked for the storm sold quickly.” Walmart’s analysts not only kept Floridians pleasantly buzzed on beer and Pop-Tarts during the storm, but also created profits by anticipating demand.

Disney is also a company to watch when it comes to data analysis. Disney has introudced a Magic Band. Similar to a FitBit, this piece of hardware enables travelers to purchase food and merchandise and unlock hotel doors. This data in turn provides Disney with key data regarding the habits of its park visitors allowing the company to “transform the experience of waiting in line, and more effectively manage park flow. From streamlining the delivery of food to your table at a park restaurant to assessing the need for placement of water stations.”

Is your company using data in a smart way? Let us know in the comments.

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Kalie Moore currently handles inbound marketing and PR for datapine. datapine is a highly scalable SaaS tool that revolutionizes database analysis by making even complex functionalities available to non-technical persons. From the heart of Berlin, datapine shows customers around the world how to make better business decisions faster. Kalie also writes at Berlin Startup Girl, a blog detailing the ins and outs of the Berlin scene and other international startup ecosystems.

  • Kalie, thanks for the article! Very interesting examples. Data analysis is really a powerful mechanism to make your business successful. I saw that you work at Datapine – a tool for data visualization. Just wanted to introduce you a similar tool which helps to get quick data insights- Skyvia Query. It supports popular CRMs, accounting, marketing, e-commerce and other systems. You are able to access data, create queries simply by drag and dropping and build tables and charts for data analysis (or export the results if nedded). Here it is: https://skyvia.com/query/