Big Data

What is Digital Transformation?

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) June 12, 2017

For a phrase that’s being thrown around a lot recently, what does “Digital Transformation” really mean? When someone says that they want to digitally transform their business, what does one really mean, why do they want to do it, and should they approach this “digital transformation” process?

First off, let’s start with a definition. If we don’t know what we are trying to achieve, then how do we know how to get there? Or to quote the famous Greek philosopher Yogi Berra: “If you don’t know where you are going, you’ll end up someplace else.”

In a recent blog “How To Achieve Digital Transformation,” I stated with the following definition of Digital Transformation:

“The coupling of granular, real-time data (e.g., smartphones, connected devices, smart appliances, wearables, mobile commerce, video surveillance) with modern technologies (e.g., cloud native apps, big data architectures, hyper-converged technologies, artificial intelligence, blockchain) to enhance products, processes, and business-decision making with customer, product and operational insights.”

However, 45 words are too many. Good definitions should be 20 words or less. If it takes longer than one breath to explain something, go back to the drawing board!

So here is my updated definition that I will likely continue to refine:

Digital Transformation is application of digital capabilities to processes, products, and assets to improve efficiency, enhance customer value, manage risk, and uncover new monetization opportunities.

Dang it! 25 words. I’m going to need some help (maybe I don’t count the first two words “Digital Transformation”).

But to make this Digital Transformation definition clear and actionable, we also need to define “digital capabilities,” which we’ll define as:

Digital capabilities are: electronic, scientific, data-driven, quantified, instrumented, measured, mathematic, calculated and/or automated.

While it’s very useful to have a definition, how about we highlight the value of digital transformation by illustrating the difference between a traditional organization and one that has been digitally transformed? So let’s consider a hypothetical case study comparing two companies in the Grocery industry – a traditional Grocer and a “Digitally Transformed” Grocer – to see what the differences might look like.

Digital Transformation:  Grocery Chain Case Study

Let’s create an example that most everyone can relate to – the Grocery business. Everyone has to buy groceries, and there is certainly plenty of business model disruption and customer disintermediation happening in the Grocery industry! So let’s compare a traditional Grocery chain with a digitally transformed Grocery chain.

Note: all the information for this exercise has been pulled from public sources, and I have provided links to those public sources.

 

Grocery Chain #1:
Traditional Business Model
“Our goal is to be the first choice for those customers who have the opportunity to shop locally”
Grocery Chain #2:
Digital Business Model
“To be Earth’s most customer-centric company, where customers can find and discover anything they might want to buy online, at get those items quickly, at the lowest possible prices.”
Customer Engagement Foundation Captures customer purchase data via in-store customer loyalty card Couples customer in-store, web and mobile purchase data with web and mobile browsing, social media activity, consumer comments and in-store and warehouse sensors to uncover actionable customer, product and operational behavioral insights
Customer Experience Minimize check out time while minimizing cashier and operating expenses

Grocery checkout resembles a breadline

Ending self-checkout experiment due to increased shrinkage costs

Leverages web apps, mobile devices, e-commerce with online shopping carts, QR codes, , and in-store IOT devices to simplify customers’ shopping experience

Patented One-click web and mobile order

Smart convenience stores without checkouts and cashiers

Introduction of “Smart” shopping carts

Marketing Leverage mass marketing programs tied to Packaged Goods Manufacturers 13 4-week cycle promotion calendars

The Costly Bargain of Trade Promotion

Ineffective Promotions Are Dragging Down Top CPG Brands

 

Leverage prescriptive analytics to create customer-specific recommendations and promotional offers based upon customer’s behavioral profile, product preferences, and shopping history

“Customers who bought this item also bought” recommendation engine

3 Marketing Lessons From Amazon’s Web Strategy

Personalized Marketing – Marketing one to one – A glimpse on Amazon’s strategy

Product Stocking / Merchandising Uses pre-planned Package Goods Manufacturers promotions and planograms calendar to stock stores

Planograms Among Reasons Why In-Store Merchandising Process Broken and Outdated

Retailers suffer the high cost of overstocks and out-of-stocks

 

 

 

Predicts and estimates what products and quantities customers are likely to buy based upon past and “similar customer” purchase patterns, upcoming events, and stocks those products as close to the physical customer as possible

Amazon Prime: Bigger, More Powerful, More Profitable than Anyone Imagined

A Prime misunderstanding: explaining Amazon Prime’s success

Amazon Prime Stock picking algorithms based on disk access methods

Delivery Exploring home delivery and curbside pickup (which still makes customer responsible for product pick up) options with mixed results; vast majority of product sales still in-store

Supermarkets pick up the pace with curbside pickup

Leveraging home delivery ecosystem, consolidated shipping orders, and delivery scheduling, but aggressively exploring own options to control costs, improve customer satisfaction and glean more operational insights

Amazon creating own over-night delivery service

Amazon Same-day delivery

Creating Amazon PrimeAir for drone home delivery

Supply Chain / Inventory Management

 

Managing inventory across a complex network of independent and third-party manufacturers, suppliers and distribution centers

Vendor Managed Inventory improves collaboration, but Retailers giving up control of certain aspects of their business

Collaborative Planning, Forecasting, and Replenishment (CPFR) limitations

 

Leveraging customer product behavioral data to re-write supply chain and inventory management processes

6 Ways Amazon Is Changing Supply Chain Management

 

 

Pricing Maintain “Everyday Low Pricing” strategies with heavily-managed centralized pricing decisions

Everyday Low Pricing May Not Be the Best Strategy for Supermarkets

 

Dynamic pricing based upon monitoring current market and competitor data

Uses algorithms that scour the web and constantly change pricing

 

Partner Ecosystem Increasing local content (produce, products) in the physical stores

Ripe for Grocers: The Local Food Movement

 

 

Creates marketplaces to increase profits but more importantly provides continuous source of market data

Generates nearly 50% of profits through an open marketplace

Exploits Affiliate Marketing to drive market presence and build out partner ecosystem

 

Table 1:  Hypothetical Grocery Industry Digital Transformation Case Study

Digital Transformation Case Study Summary

While this assessment is certainly not thorough, I think one can start to see the dramatic differences between a traditional business model that mainly relies on a physical presence to be successful, versus a digitally transformed business model that employs these new capabilities – powered by customer, product, and operational insights – to enhance customer value, physical facilities, products and processes.

So how does this digital transformation affect the customer and the business? From a customer perspective, digital transformation can create a more seamless, more prescriptive customer engagement that yields the following benefits:

  • Easier for the customer to find the products that they want, especially those at the “long end of the tail
  • Easier for the customer to comparison shop and get recommendations from others to ensure that they are getting the product that they want at a competitive price
  • Easier for the customer to get their products and schedule delivery (same day delivery, drone delivery)
  • Easier for customers to share their shopping experiences and get timely resolution to any issues or problems

And there are significant benefits to the retailer, including:

  • Superior customer insights via more detailed and more diverse customer engagement data that can lead to optimized planning, procurement, logistics, inventory management, merchandising, marketing, sales and support processes
  • Improved customer lifecycle management (from acquisition, to maturation, to retention, to advocacy)
  • Reduced inventory costs through predictive product, market, and seasonal predictive analytics and real-time data feeds
  • Reduced security risks via a more thorough instrumentation of the entire order-to-cash cycle
  • Less product spoilage and shrinkage with customer, product and operational predictive insights
  • Superior business flexibility with the ability to identify shifting customer demands and predict the resulting customer, product and operational impacts

I would love to have the cycles to do this sort of Case Study across more industries, but I do have a day job that pays the mortgage (and Starbucks and Chipotle visits!!). The most exciting part of these new digital transformation discussions that I’ve been having with customers is that these outcomes are all now possible, and, in fact, are coming to fruition (that is, with a little guidance J).

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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