Over the last decade we have seen the world of big data dramatically evolve. Prior to 2006, the technology did not exist to make sense of big data. It was too expensive to store all of the data a company generated, and there was not enough computing power to make sense of the information regardless of what it cost anyway.
However, after Amazon launched Amazon Web Services (AWS), there opened previously unimaginable opportunities for data storage and computing power at an affordable cost. With all these new computing possibilities we have evolved the tools to manage any query, but we are still not addressing the hardest problem in data: asking the right questions.
This is why we have put together a list of the right questions to ask, ensuring your company’s data success:
1. What exactly do you want to find out?
First things first, what are your goals and what decision-making processes will they facilitate? Consider in your organization what outcome from a data analysis would deem it a success? Start with these introductory data analysis questions, and let them be the basis for where to begin your data analysis. These initial, basic questions can create a guideline for the specific data insights you would like uncovered down the line.
2. What standard KPIs will you use that can help?
Once your organization has gone through the process of determining exactly what it is you want to be able to discern from your data, the next step is to be able to figure out key performance indicators (or KPIs). KPIs allow a company to determine the successes and failures of their various initiatives. For example, let’s say you want to see which one of your marketing campaigns last quarter did the best. “Did the best” is really too vague to be useful. This is where KPIs come in. How do you measure this success? Is it the campaign that drove in the most revenue? Or the campaign with the most profit? Is it the campaign with the greatest ROI? Or the one that gave you the cheapest email subscribers?
All of these KPIs can be valid choices. You just need to pick the right one and have company-wide agreement that this KPI is your standard of measurement.
3. Where will your data come from?
Once you have determined what questions you want your data to answer and how to measure those results, the next step in your process is to determine the source of the data you will be analyzing. Be open-minded about your data sources in this step and consider sources from all departments in your company including sales, finance, marketing, consumer relations, IT, and beyond. Data sources might include CRM data, data from Google Analytics, financial data, or data from social media platforms like Facebook or LinkedIn. The important thing is to make sure that whatever data source you choose, the information is relevant to your answers from questions 1 and 2 above.
4. How can you ensure data quality?
About a year ago, we put together a blog post, “Do You Trust Your Data?” In this post we outlined a number of ways to ensure data quality including knowing the source, having proper training and performing accurate evaluations. However, the best way to ensure quality data is to make sure you have “cleaned” your data set. This is important because it gives your organization an opportunity to discard incorrect or outdated information. It is also a good time to add more fields to your data set to make it complete and useful.
Ultimately, there is no doubt that poor data quality leads to poor data analysis. When you are working towards an analytics-driven organization, it is important to ensure that the quality of the data you collected under question 3 is of a high caliber.
In conclusion
No matter where your company is regarding its data infrastructure, it is never too soon to start looking for new connections in your data. You never know exactly what the results will be, but trusting your data to reveal the right questions is the best way to move forward.
If you are unsure of how to start or continue with your data analysis projects, reach out to Treehouse Technology Group. We excel at helping companies collect, manage and understand their data.
