10 Questions that Should be Answered Before Every Data Analysis Project: Part 1

10 Questions that Should be Answered Before Every Data Analysis Project: Part 1

by Jenny on June 16, 2015 Comments Off on 10 Questions that Should be Answered Before Every Data Analysis Project: Part 1

You’ve got a ton of data…a never-ending stream of data…data, data, data. But, unfortunately, that’s all it is: a bunch of data. In order to turn all this data into actionable information, you need to do some data analysis. Before you start a data analysis project, there are 10 questions that you really ought to address.

Today I’m going to discuss the first five of these questions; my next post will delve into the rest.

  1. What exactly are you trying to accomplish with this data analysis project?
    Do you want to measure performance or are you hoping to gain fresh insights? For example, perhaps your company puts on three different free seminars as a way to introduce prospective customers to your firm. You have a great deal of data on past seminar attendees. Now you want to use this data to answer some important questions, such as…

    • Do these seminars actually bring your company more business?
    • What’s the best day and time for scheduling events – both in terms of overall attendance and conversion?
    • Do attendees come just once, or do they come back to attend one of your other seminars?
    • Does the type of venue (i.e. community room, hotel, restaurant, office building conference room, etc.) impact attendance or conversion?
  1. How does this project fit in with other company initiatives?
    While it might be “nice” to analyze your data, in order to create an initiative that gets momentum and is treated as a priority you need to define the business case for the project. How could analyzing this data potentially impact your business? How might not analyzing the data impact your business? How much are you losing or overspending by not having the results of this analysis readily available? What else is going on that this project could impact?
  1. What data analysis systems do you currently have, and why aren’t they working?
    You’ve got raw data. Can the system that gives you all this raw data also analyze it for you? Or, if it is providing some level of analysis already, in what ways is it not giving you what you need?For example, you may be using Google Analytics to obtain basic information about your website’s performance. This system gives you lots of nice charts and graphs regarding page visitors, length of average visit, etc. But this is all aggregate data. What you really need is information about individual site visitors, and Google Analytics does not provide this.Or perhaps your raw data is in one of your internal systems. In this case, you should consider whether it’s better to have someone do the data analysis outside of this system, or if you can make some changes so that the system will automatically do the desired analysis for you.

10 Questions that Should be Answered Before Every Data Analysis Project- Part 1

  1. What will success look like?At the end of the day, what do you want to be able to say or do as a result of this data analysis project?Going back to the example of the company that puts on free seminars, success for this firm might be gaining the ability to know when and where to schedule seminars in order to maximize product sales.For another organization, “success” might mean being able to pull up a dashboard every Monday morning to see where the previous week’s clients came from.
  2. How will the data be used?
    Many people fail to think about this issue ahead of time, and then end up with a data analysis system that does not meet their needs. How often do you need the results to be updated? Is this a one-time project or an ongoing effort? If it’s ongoing, will you be running a monthly report so you can see what’s happening, a daily report that will impact how you staff your call center, or an ad-hoc report when certain situations occur? Will everyone involved have access to all of the results, or is there specific data that some will see that will be hidden from others?

Conclusion

These first five questions will help you determine how, in an ideal world, your data analysis project should be structured. Watch for my article on the other five questions, which address issues related to people and resources.

Need some professional help getting your data analysis project off the ground? Give us a call – this is one of our specialties. We’re here for you!

Read Part 2 Here

Jenny

Jenny Dinnen is President of Sales and Marketing at MacKenzie Corporation. Driven to maximize customer's value and exceed expectations, Jenny carries a can-do attitude wherever she goes. She maintains open communication channels with both her clients and her staff to ensure all goals and objectives are being met in an expeditious manner. Jenny is a big-picture thinker who leads MacKenzie in developing strategies for growth while maintaining a focus on the core services that have made the company a success. Basically, when something needs to get done, go see Jenny. Before joining MacKenzie, Jenny worked at HD Supply as a Marketing Manager and Household Auto Finance in their marketing department. Jenny received her undergrad degree in Marketing from the University of Colorado (Boulder) and her MBA from the University of Redlands.

Jenny10 Questions that Should be Answered Before Every Data Analysis Project: Part 1