Data Analytics is the process of collecting and examining data, the purpose of this is to find trends and patterns within the data. This data could be used to unveil new information that can enable companies to make more-informed business decisions.
Using Data Analytics allows for a better-informed business model, the information gained from this allows people involved in the business to make business-related decisions for improving workplace efficiency, designing move effective marketing strategies and gaining better insight into the ever-changing market.
Data Analytics is different from just looking at and presenting information for upper management. Data Analytics gives everyone a more in-depth way of understanding the information gained from the Data, allowing them to make decisions with a better understanding of what effects their decisions may cause and what effects their previous decisions have had before.
Data can be split between quantitative and qualitative analysis.
Quantitative Analysis looks at numerical data which is used in a statistical approach.
Qualitative Analysis looks at non-numerical data which shows more detailed data which gives a more in-depth outlook to the information processed.
Exploratory Data Analysis (EDA) has the aim of finding new information from data held within the system, this is a more predictive type of data analysis used to make future decisions.
Confirmatory Data Analysis (CDA) is used to draw a conclusion on past data confirming whether it was correct or not.
Most companies will use some form of data analysis, even if it's just market research, but, whats the use of know the external terrain if you don't know the internal terrain? Use analytics to understand company internals better!
Analysis can reveal things no one knew was happening, quite simply because no one was looking. Using predictive and prescriptive analysis opens the doors to looking ahead.
Data analysis is a tool to be used by businesses to better direct them. Using the right analysis companies can both save money, and better identify future opportunities.
The Data Analysis timeline represents the stages of a process in which Data Analytics can be carried out:
In The Future
In The Past
This is the analysis of the reports given from the descriptive stage this gives an understanding of “Why did it happen?” Root cause, corrective action should be drawn from here.
Live data is actively analysed, so, if any problems were to arise it would allow for an almost immediate response to any new information that may arise.
Here the analysis is looking towards the future with the aim of looking to find “What will happen?” Alogrithms need to be used based on previous trends & "Knowns"
Finally, here the analysis is looks towards possible fixes or improvements using the data collected from previous events or put simply “What should I do?”
This is the reporting stage for things that have already happened and gives the people involved an understanding of “What happened?”
Industries using Data Analysis
All Industries can benefit from Data Analysis with the many ways it can be used and implemented but a few prime examples include:
Healthcare and Pharmaceutical
Fraud Detection - Large numbers of claims are easily sorted through which can help weed out things like fake prescriptions and false claims easily
Detecting Diseases - Using previous patient data to spot symptoms early on
Customer insight – Allows customers to understand the providers they are with or intend to join and can directly influence their desire to switch
Better operational performance – Improving performance and predicting failures which could cause distribution problems
Optimizing web user experience – By looking at user trends web analytics aims to change the content each user finds in response to previous searches.
Optimizing corporate marketing – Again from user trends, this aims to show whether advertisements are effectively placed and designed.