The importance of setting up a good strategy 🙂
In recent years and in the future, the use of data has gone up significantly. Here in Data Analytics Ireland we will look to explain the concept, please share or link to this article so others can see its contents.
The reason for this is that the digital economy has taken off for several reasons:
(A) Automation of tasks has become easier.
(B) Less use of paper, becoming a greener economy.
(C) Technology improvements have meant that storage and processing make the process of delivering services easier.
As a result of all this:
(A) A large amount of data is captured.
(B) Consumers habits about how they use a service or the information they look for now has a digital footprint.
(C) Now once a consumer has used a service ( whether purchased or not), the ability to understand their habits can be captured to deliver them better:
- The services they want.
- The products they want.
- Quicker turnaround time.
How can this help with all the data that is captured and stored?
Let’s understand what Data Analytics is doing!
So in this article, we have already outlined what the background is as to how the industry has evolved to where it is now.
As outlined, all information traditionally would not have been stored in a format that was not easily accessible.
Now, with Data Analytics, the work can be broken down as follows:
Step 1 – Data Capture
To understand what you want to analyse, a data analyst will work with their technical colleagues to ensure that the correct data is captured.
Once the completeness and accuracy are fulfilled, your data quality will become less of an issue.
Step 2 – Analysing
Once you have all your data saved, then the next step is to understand the data. Understanding your data can happen in several ways:
(A) You create visual charts of it; this allows the viewer of the information to get an initial view of the information without looking at the underlying data. Sometimes this will show patterns in data or clusters.
(B) Use statistics to see if they can explain the data. This could show information such as how data is correlated or otherwise. Also, probabilities could be calculated to show what outcomes might happen in the future.
(C) Data analysts might also need to understand how to build a machine learning model to use complex algorithms, to explain the data better, sometimes patterns that are not immediately understood can be unearthed and investigated further. There are two approaches commonly used for this, namely supervised and unsupervised machine learning; this link will explain thoroughly what they are about.
Step3 – Presenting
Now that all the steps have happened to have the data you want, as a decision-maker will it be feasible to look over rows and columns of data, and get a sense of what it all means? An emphatic NO is an answer, but this is where some of the data analytics visualisation tools come in!
Visually presenting data points, very quickly allows a viewer of the information to come to a decision quickly, and the tools that are outlined below will help with that process. These are a handful that will allow the data to be sliced and diced, there are many more out there, but they all allow data to be drilled down into and get to a real understanding what is going on.
Step4 – Decision Making
So after all this analysing, there need to be decisions made. The output of any data analytics work is to:
(A) Have the data in the correct format.
(B) In a place that it can be accessed and reviewed.
(C) Relevant to when the decision needs to be made
From the outset, as part of the work of performing the data analytics, an assessment needs to be made as to how often a decision will need to be made, with what data and when.
At this point, the decision-makers should have a set of data ready for them to look over and reliably make a decision based on what they have in front of them, if they can’t make a decision then possibly, steps 1-3 should be reviewed and revisited.
Often what happens in the data analytics world, is what information was required to make a change needs to be updated, improved upon, or additional data needs to be added on.
It is the job in the data analytics teams to source that data and or change how they present it, to now reflect the decision that needs to be made.