Ep. 1: Turning Data into Business Value | By The Data Alchemist

Data is everywhere in almost every different form, but how much does it really add value to us and the business ? Beyond collecting the data, how wisely we use it ? A question that keeps us wondering whenever we see a pile of data collected in some or other form.

This can be quoted as the paradox of "Rich vs Poor" as the organizations continues to create or duplicate a wealth of data across multiple platforms and mediums, but being poor in contextualizing it as Information, to reap the maximum benefit out of it.

Why Data Alone Isn’t Enough !!

There is an interesting "data" story behind the Ford Edsel car, it was reported that Ford has spent millions to gather customer preference data and the analysis ended up with a decision to manufacture this luxury family sedan. But in spite of this, it became one of the biggest product failure from the manufacture as the customers wasn't preferring to buy this model and resulted in a net loss of $350M. So, what could have went wrong here ? The data, no isn't bad, The methodology - no, it is proven technique, then ? It is the interpretation and application of data over the organization's business value and strategy.

What could be a possible framework that turns Data into Business Impact ?

There is no specific or proven framework to implement this but based on the key pre-requisites to a business valued based data-driven solution, I have framed the below representation and naming it as "Data-Driven Business Value Lifecycle".

Each of the steps are self-explanatory on its own, so not going into the details of my thought-process but happy to discuss if you have anything specific to chat about ??

Key pointers to check before starting any Data & AI project

It is essential to assess before we get into any investment around analytics project. Some foundational questions to check,

? What business problem we are trying to solve ?

? What improvements we foresee with Data & AI inclusion ?

? What is the expected Return on Investment (RoI) ? Can it be measured ?

? What if we don't go with this project ? What risk or loss we anticipate ?

Once we see optimism over the answers to the above questions, we can start assessing other areas including Data Readiness, Resource Availability, Governance model, Long term strategy and many more.

DeepBrew - AI Engine of Starbucks

Starbucks has Deep Brew, an AI platform that analyzes customer purchase history, preferences, and real-time data to create customized drink recommendations via the Starbucks app. If a customer drinks Cold brew during Summer, it recommends seasonal flavored Cold brew options in the App. It not just stops with recommendation, helps in dynamic pricing based on demand and hooks customers with customized promotions and offers. Also, it is been said that it does analyses the historical weather data to keep the inventory appropriately. For example, recommends to hold more cold brew inventory during hot summer time. The possible business impacts would be increased customer base, customer loyalty, increased sales and revenues, optimized operations and much more.

Final Thoughts

Analytics Project: Investment >>> Technology

Data: Good >>> A Lot

RoI: Adoption >>> Performance

Any data is only as valuable as the decisions it drives.

Leaving you with a question, "What’s one way you can start using data smarter today?" Interested to hear your thoughts !

Until next time, keep turning data into insights! ??