25 Mar How Splunk Can Be Beneficial For Big Data Analytics In An Organization
DATA DATA DATA!!! From large multinational conglomerates to measly small start-ups, data is everything. Be it an IT start-up, a retail store or even football, data is everything. Back in 2014, the sole reason Germany won the football world cup is because of data analytics.
Data analytics is where we try to gain useful insights from the data stored in the database or the data warehouse. It helps to achieve the goals of the organization. Let us see how Splunk can be beneficial for data analytics.
How big data analytics can be used to set up a sustainable business model?
Let’s take the example of retail store business.
Like every other business, this business also faces highs and lows. Let’s see how an analytical tool can help us get deep insights into the business model so that a decision can be made before major setbacks. Here, in this example, we use a superstore market that has multiple branches across multiple cities in Myanmar.
They used Splunk to know,
· Which city has the highest profit?
· Which product is selling with a profit, based on the season?
· Which product has the least amount of sales?
· Which gender buys more products?
· What kind of transaction is being done the most?
· Which product has the highest profit margin?
· Which product has the least profit margin?
With all the above information we can create a sustainable business model.
“The above image shows the data of a retail store with multiple branches. Abbreviations’ are given below.”
S.P – Selling Price
Cogs – Actual price without tax
C.P – Cost Price
We can index the data into Splunk, create a dashboard and try to get meaningful insights out of it. This will further promote the growth of the organization to new heights.
Solution provided by Avotrix to one of its retail clients along with its dashboard.
Let us take the following panel for example.
The above image is a pie chart showing the product-wise profit concerning the branch.
From the above image, we can get an inference that, in branch B, the electronic accessories and food & beverages have the least amount of profit as compared to other products. Based on this new insight, we can increase the profit by offering better deals or discounts along with increasing the numbers. Let us focus on another panel.
The above panel shows the profit concerning product and gender. From the image, we can conclude that females shop more than males. So from the inference, we can target the female audience as they spend more on these products comparatively.
From the above panel, we can conclude that branch B has the lowest rating and that needs to be increased compared to the ratings of branches A and C.
From the above panel, we can conclude that across 3 branches, health and beauty have the least number of products and we can add certain offers on these to increase the sales. On the contrary, electronic accessories are the highest sold.
From the above panel, we can conclude that across 3 cities, Yangon has the highest profit and Mandalay has the least profit.
From the above panel, we can conclude that people are mostly using e-wallets compared to credit cards. From this insight, we can add offers towards customers using e-wallets thereby increasing the number of sales.
Data science is seen as a new field that is transforming and taking every industry to new heights. If you are a person who is interested in this field and want to take courses such as big data analytics, contact CyberChasse.com
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