Analytics in Retail – a short introduction
Data has become the game changer of the market. Today’s possibilities in technology are amazing – and still rapidly evolving. With that, the ability to adapt faster than others becomes a survival challenge in Retail. Digital transformation as the integration of digital technology in almost all areas of business is a must for Retailer. Why is that? The pace of competition has dramatically changed and digitisation has raised powerful rivals from outside the industry.
These are facts everybody is aware of, but “how to solve” this challenge is a complete different topic.
To understand how the traditional backend systems can be refined with “the cloud” really hurts: Lot’s of possibilities! Technology nowadays is amazing. Personally, I’m persuaded already today, we can do everything we want to do with data. The challenge here is, that we are lost in possibilities and have to relearn to “dream”.
Here an example, I found while giving an introduction to SAP Analytics Cloud to an CIO:
Imagine, you’ll get the possibility to build a house. You are allowed to use every material you would like to use: Italian marble, German oak, high-quality steel etc. However, you have never seen a house before. So how can you decide, if you need a wooden hut, a skyscraper, a family home or an apartment home? You can’t, since you have no experience at all and you have first to find out the differences between all kinds of houses to be able to choose the best one for you (needs, budget, etc. included).
In my opinion, that is exactly the way it is today. We are lost in possibilities. That’s why I decided to start a blog series to share my own “Retail Analytics” experience with the community. But first: Who am I?
As a physicist, I joined SAP a decade ago in the field of Business Intelligence (Warehousing). Given the fact, that in some of my projects at university, I needed multivariate data analysis, my interpretation of the term “business intelligence” was quite different to reality. I imagined fancy mathematical algorithms fed by huge datasets gaining insights a human being never could have found out without maths. (Thus, my interpretation was, what is called “Data Science” nowadays). Quite soon, I realised, that at that time, “Intelligence” is more or less to be able to propagate huge data sets to a frontend in the way of having a good performance and giving analytical insides into business.
Back to the topic: analytics. Do we still need analytics, if data science is the new hype? That’s easy to answer. Of course, we need it. Analytics is the beginning of your journey to understand data. And without understanding data, you can’t do business and you can’t even do data science. But nevertheless, we have to admit, that getting data ready for analytics has changed dramatically.
Everybody in the SAP community knows, that there are so many systems offering analytical possibilities for Retailers: S/4 HANA and it’s Embedded Analytics, SAP Customer Activity Repository with all kinds of POS transactions, C/4 Suite with valuable customer information and not to forget BW with its purpose being designed for analytics. And there it is again: Lost in possibilities!
These are facts everybody is aware of, but “how to solve” this challenge is a complete different topic.
To understand how the traditional backend systems can be refined with “the cloud” really hurts: Lot’s of possibilities! Technology nowadays is amazing. Personally, I’m persuaded already today, we can do everything we want to do with data. The challenge here is, that we are lost in possibilities and have to relearn to “dream”.
Here an example, I found while giving an introduction to SAP Analytics Cloud to an CIO:
Imagine, you’ll get the possibility to build a house. You are allowed to use every material you would like to use: Italian marble, German oak, high-quality steel etc. However, you have never seen a house before. So how can you decide, if you need a wooden hut, a skyscraper, a family home or an apartment home? You can’t, since you have no experience at all and you have first to find out the differences between all kinds of houses to be able to choose the best one for you (needs, budget, etc. included).
In my opinion, that is exactly the way it is today. We are lost in possibilities. That’s why I decided to start a blog series to share my own “Retail Analytics” experience with the community. But first: Who am I?
As a physicist, I joined SAP a decade ago in the field of Business Intelligence (Warehousing). Given the fact, that in some of my projects at university, I needed multivariate data analysis, my interpretation of the term “business intelligence” was quite different to reality. I imagined fancy mathematical algorithms fed by huge datasets gaining insights a human being never could have found out without maths. (Thus, my interpretation was, what is called “Data Science” nowadays). Quite soon, I realised, that at that time, “Intelligence” is more or less to be able to propagate huge data sets to a frontend in the way of having a good performance and giving analytical insides into business.
Back to the topic: analytics. Do we still need analytics, if data science is the new hype? That’s easy to answer. Of course, we need it. Analytics is the beginning of your journey to understand data. And without understanding data, you can’t do business and you can’t even do data science. But nevertheless, we have to admit, that getting data ready for analytics has changed dramatically.
Everybody in the SAP community knows, that there are so many systems offering analytical possibilities for Retailers: S/4 HANA and it’s Embedded Analytics, SAP Customer Activity Repository with all kinds of POS transactions, C/4 Suite with valuable customer information and not to forget BW with its purpose being designed for analytics. And there it is again: Lost in possibilities!
********************************ROHINI KUMAR*********************************
Please support my work : sap.rohini988@gmail.com LinkedIn Link : https://www.linkedin.com/in/rohini-kumar-14647a13a/
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