Doing More With Data
By David Peters, CPA, CFP, CLU, CPCU
Perhaps there is no bigger buzzword right now in the business world than data. It seems like every company is trying to utilize data, capture data, or become data driven.
It seems like every conversation that I have with a CFO, no matter what industry he/she is in, begins the same way: “If we could just get our hands on the data, I know we could be better. I just don’t know where to start.”
So how can we do more with data? Before we talk about systems, programs, and the technical side, we need to think about our own mindset concerning data. Too easily we get overwhelmed by all the data that is out there. We see statistics about how many views YouTube gets in a day, or how many likes Facebook gets, and immediately our heads start to spin. First, we need to learn that good data analysis doesn’t depend on capturing all the data that is out there. It merely involves capturing the right data. We need to learn that we can’t capture everything – and nor do we need to. It is impossible to have every relevant piece of data for our business available instantaneously. Often times as CFO’s, we try to think of everything ahead of time. We can’t do it on this one. Customers change. Businesses change. Our world changes. We can’t think of everything, so we need to give that up.
Think more practically. Think to yourself what data would really move the needle for your business. What does your data wish list look like? Whenever I have talked about data analytics in front of a large group of people, I have often asked them that question. I ask them to think in specifics – not just generalizations. For example, one might say “I wish I knew what my customers are thinking before they purchase my product.” This may be true, but that’s too general and vague to do any good. Unless our customers will allow us to put tracking devices on them or their computers, we are never going to get that data!
Conversely, if one says, “I want to know what website page the customer was looking at before they purchased my product,” that may be possible! If I am the CFO of an insurance company and I know that a large percentage of my customers were reading an article about how much coverage one needs before they purchase an auto insurance policy, that tells me something about my customer. Does it tell me exactly what they are thinking before they bought a policy? Not exactly. However, it does give me some indication that my customers are concerned about selecting the right coverage levels. I might respond with more email blasts or blog articles about how to calculate coverage needs. These types of insights are only possible if you start with data wish lists that address specific business problems though.
Once you have your wish list together, you should start thinking about the types of data that you have available to address those items. Data can be broken down in several different ways. For example, one way to think about data is internal versus external data. Internal data comes from our company’s systems. For example, information about how long it takes a customer to pay may come from our accounts receivable system – which would make it internal data. Information about the average number of days that accounts receivable stay open for the industry may come from external reports. Another way to split the data that is available to us is streaming versus static data. Streaming data may include things like video content, while static content consists of key data points imported from reports at set time periods. For example, the daily sales report might be static data. Finally, you could also contrast structured data and unstructured data. Structured data might be something like a numerical ranking given by a customer on your service level. Unstructured data might be customer feedback in the comments section of a survey.
None of these data types are inherently better or worse than others. Understanding the data types that are available though, can help you make better decisions on the types of data you want. For example, streaming data is often more timely than static data. However, it may also be more costly or difficult to capture. While many companies can capture static data in an Excel file, fewer companies are able to capture video as it is happening in their own systems. Similarly, structured data is easier to analyze than unstructured data. While you can calculate average customer ratings based on numerical survey data, you can’t really calculate means or variances on the comments section. However, you may get deeper insight into how a customer experienced your service based on unstructured data. The best data type really depends on the question you are trying to answer.
The key to doing more with data begins with understanding what data you need and what data you could obtain. Don’t worry about all the data that is out there. Think only about what data would help your business. Thinking about data in this manner allows you to see data for what it should be – a means to solving business problems.