Turning Data into Information (Part 2)

How Data is Collected

Data collection used to be fairly simple. You wrote down the numbers that you had to and someone would throw it in a spreadsheet, turn it into a nice colorful graph, and present it to the decision makers. Today data can be collected in a continuous stream from a variety of sources and the risk becomes collecting the wrong data rather than not collecting enough data.

Some ways to collect data include:

  • Census: Obtains data from every member of a population (usually not practical because of time and cost)
  • Sample Surveys: Obtains data from a subset (or sample) of a population. Check out Google Forms or SurveyMonkey for a fast and affordable platform to collect and analyze survey data.
  • Administrative Data: Data collected from day-to-day operations. QuickBooks and other accounting software is the most common place to collect administrative data.
  • Tracer Studies: Combines a regular survey, in-depth discussion, and one-on-one interviews
  • Experiments: Controlled study intended to determine cause-and-effect relationships (AB testing is most common).
  • Observational Studies: An uncontrolled observation intended to determine cause-and-effect
  • Questionnaires: Gathers data through a series of questions
  • Interviews: Conversation between two or more people where questions are asked by interviewer and answered by interviewee
  • Focus Groups: A group of people is asked about their attitudes toward something of relevance
  • Case Studies: Offers a complete picture of what happened and why (used with other data)

I addition to the above methods, there is a plethora of technology that can assist you in collecting data. While some of the tools can be expensive, others are part of day-to-day operations and can be used to harvest valuable data. Some examples of these tools include:

  • Google Analytics: We mentioned this in the topic of “Why Should I Collect Data” but it is worth mentioning again. Google Analytics can track a visitor’s location, device, landing page, and behavior while on the site. In addition, you can see where the visitor came from (Google, Bing, Yelp, etc.). This can help you to see where to spend those pay-per-click marketing dollars.
  • Analytic Devices: There is a variety of hardware options that can be useful in collecting data. Analytic surveillance systems can read license plates, count objects that pass through the device’s field of view, and some can provide heat maps of heavily traveled areas in a brick-and-mortar business. Card access systems for offices can give insight into employee attendance and visitor traffic. Spectroscopic analyzers can be useful in manufacturing and raw material processing in addition to collecting data. Lastly, powerful phone systems used to be reserved for large businesses with deep pockets, but modern phone systems allow small businesses to harvest data from employees and customers. Phone calls can be recorded, transcribed, and analyzed for keywords, call duration, and quality of resolution.
  • Analytic Software: There are quite a few software programs that can collect data from employee interactions. This can help you determine the most productive members of your workforce. While Intuit’s QuickBooks is king in the small business accounting department (and can provide a surfeit of data), there are other options for small businesses to manage invoicing, income, expenses and payroll. FreshBooks offers a cloud alternative that provides beautiful and easy-to-understand reports for a fraction of the cost of QuickBooks Online.
  • CRMs: Your sales force probably has more data than you know what to do with. Collecting this data into a unified location can give you the means to look at the data holistically. A CRM can be as simple as a spreadsheet or highly complex. Some of the best CRMs offer marketing automation, sales force management, contact center automation, and geolocation technology. These tools can be in the closet or the cloud. com, Base (rated 2017 Best CRM by PC Magazine), Microsoft Dynamics 365, SAP and Oracle CX are popular and powerful options. Affordable alternatives include HubSpot, ZoHo, and Insightly. Alternatively, if you have a decent IT department, an SQL database can provide you with a platform for a customized CRM solution.

(Read More: Forbes’ 30 Terrific Tools for Small Business)

Knowing the question that you want to answer determines which of these methods will result in the most reliable and usable data. Once you know which methods would produce the desired data the question of time and money becomes the focus of data collection.

Turning Data into Information

Now that we have discussed the ways to collect data the question becomes what to do with so much raw data? Data analysis is the process of interpreting the meaning of the data we have collected, organized, and displayed in the form of a table, bar chart, line graph, or other representation. Through analysis, the data can become information, which is the processed, organized, and structured data that has been given context as to make it useful. The way in which you analyze the data is reliant on what question you are trying to answer or what process you are trying to improve. There are a variety of tools that can be used to turn raw data into information. (Data vs. Information)

When examining the tools that are available there is always the careful dance between risk and reward. Some of the tools for data analysis can be quite expensive, but you are getting what you pay for. If you believe that you will collect and analyze data then utilize the information derived from it, then some of these tools will pay for themselves within a matter of months. If you are hesitant at the amount of data you can collect and use or you are just not sure what you are trying to accomplish, less expensive tools can still provide you with useful insights.

Excel is a standard tool for analyzing quantitative data. Most of us have worked with some sort of quantitative data inside of Excel. The graphing tools, conditional formatting options, pivot tables, data validation, and seemingly endless support resources makes it one of the most popular tools for data collection and analysis. Check out Excel Easy’s Data Analysis Tutorial, Analytics Vidhya’s Simple Yet Powerful Excel Tricks for Analyzing Data, or enroll in edX’s Analyzing and Visualizing Data with Excel online course.

Qlik offers a powerful line of analytic tools and (bonus) the most basic software is free for personal and business use and offers quick and easy data loading, drag-and-drop dashboard creation, and data storytelling to share insights.

Mathworks, makers of MATLAB and Simulink software charge a pretty penny for their MATLAB product, but certain modules can be purchased for lesser amounts. If you are serious about data collection and analysis, then it may be worth checking out.

If you have a large dataset and a small budget, Trifacta may be an excellent solution. The open-source software can offer chart recommendations, inbuilt algorithms, analysis insights, and can generate reports in no time.

In 2016, RapidMiner was recognized as a leader in Gartner’s Magic Quadrant for Advanced Analytics. The software is free for up to 10,000 data rows but beyond that the price jumps to a costly $2,500 per year.

I’ve mentioned Google Analytics, but that is certainly not the end of Google’s usefulness in the analytic industry. Google Sheets is an excellent alternative to Excel for those operating on a shoestring budget. The free tool is cloud-based, easily shareable, and has many of the same features as Excel. A bonus is that if you know your JavaScript, you can make it jump through some pretty impressive hoops. An even better bonus is that if you don’t know a lick about code, there is an entire community of contributors that have built some pretty awesome add-ons like the ability to map addresses from the spreadsheet, integration into Google Forms, and the ability to turn your spreadsheet into an app. (See more at 50 Google Sheets Add-Ons to Supercharge Your Spreadsheets)

The last analytic tool I will tout is your own IT department. Earlier in the article, I mentioned an SQL database and I’d like to briefly revisit that topic. SQL stands for “Structured Query Language” and is used to communicate with databases. The language has been around since the 1970s and is the most common way of accessing information within a database (which is simply an organized collection of data). Why? Well, first of all, it is easy to understand, can be used with massive amounts of data, and it is easy to replicate and audit queries. No need to bore you with the nitty-gritty details (today), but you need to know that you can use queries such as LIKE, IN, BETWEEN, AND, OR, and NOT to sort through huge amounts of data. This can give you a highly-customized platform to store and analyze data.

Outside of the software, what can you do to turn data into intelligence?

Why not do some experiments? For example, you’ve noticed that about half the visitors to your website fall off on the index page. Implementing AB testing can be a powerful way to determine what is more likely to result in a conversion. You can use Google Analytics to implement a random AB test of whether a red button or green button result in more clicks. Google Analytics will provide you with information on which color resulted in the most clicks. After a period of time, Google will provide you with an easy-to-understand report as to which performed better.

Conclusion

Collecting and analyzing data is not the genie in the bottle. Imagine that each piece of data is a seed. Some of these seeds can grow into information, others may become useless weeds, and still others may result in absolutely nothing. It requires some careful thought, strategic planning, and a special commitment in order to utilize data successfully. It requires that someone be willing to make a plan, implement it, and troubleshoot it until it works. Once you’ve got your system in place, you can start harvesting the information. While computers can assess a dozen variable a second, it takes a human mind to derive usefulness from data. To realize the benefits of this information, you need to utilize it in the decision-making process. Once you’ve done that you’ll begin to see the return on your investment.