Have you ever been in a conversation where people are using the terms “data analysis” and “data analytics” interchangeably? Do you then wonder if they are really talking about the same thing? And does it really matter?
To give some context, while people tend to use these terms interchangeable, there is a difference between the two. Data analysis refers to the process of examining in close detail the components of a given data set. Data analytics is a broader term referring to a discipline that encompasses the complete management of data – including collecting, cleaning, organizing, storing, governing, and analyzing data. Putting it as simply as possible, data analysis is a process, whereas data analytics is an overarching discipline (which includes data analysis as a necessary subcomponent).
So now that we understand the fundamental difference between data analysis and data analytics, is the first question we should wonder during a conversation around data analysis and data analytics be “are they really talking about the same thing?”, maybe, but is it the most important question?….In my opinion, No! The real question should be focused around if you have the data. Data analysis and data analytics differ in their approach to data, but data is a necessity for both.
I have been in countless analytic deployment project kick-off meetings where it is just assumed that there is data present and so the first step is to define the types of patterns/issues we want to look for in the data. Ideas are thrown out there, the goals of the project are established, and everyone leaves the meeting feeling great about what was accomplished and excited about the results they can expect. The project is then handed to the technical team for review. While the goals of the project are all well and good, the question the technical team poses is how are connecting to the data? The answer is usually the location that the data resides (i.e. in this data base or in that BAS, etc.). That again is great information, but it doesn’t answer the question of how we are CONNECTING to the data. Connectivity is the critical link to data collection, and data collection is the critical link both data analysis and data analytics.
Just to make sure you are following along, let’s go back to being in a conversation around data analysis and data analytics where the terms are being used interchangeably. Should we be wondering if the terms are being used appropriately?….No, you should stop all conversations and ask “how do you plan on providing access to your data?”. Data access is the first key element of the implementation process for any analytic deployment. By answering the following questions, you can identify the source(s) of the data to be analyzed: Where is the data located? How will you connect to the data? Is it live data via a connector, or batched data from csv files or connection to an existing database. The goal is to identify the available data, where it is, how you will connect and what format it is in.
Data is a corporate asset and empowering companies to seek and make good fact-based decisions that drive better outcomes. Connecting to it; collecting it, storing it, ensuring its integrity; analyzing it, and using it to make business decisions and develop strategy.
By Kylie Devey, MBA, M.Ed, CSPO
Director of Operations, BuildingFit