Fortune 500 and the Art of Execution

The Fortune 500 Companies 2018 rankings came out last week, and browsing the list, the following random thoughts struck me about the list and the technology industry:

• Walmart - you can be in a very, very traditional brick-and-mortar business (yes, they have been making inroads into e-commerce, but for the most part, Walmart is a traditional retailer), but as long as you keep doing things well, you can be in the top 10. Not only that, you can be the top-ranked company by revenues for a sixth year in a row. In this case, you can be numero uno, with annual revenues that top five-hundred billion dollars - $500 billion, be more than twice the size of the second-ranked company (Exxon-Mobile is ranked second, with annual revenues of $244B), and also employ the most employees (2.3 million).
• Apple - you can be a mass-market luxury brand (yes, that is a contradiction in terms), sell only a handful of products (its Mac, iPhone, and iPad product lines bring in 79% of its revenues) and be in the top 10 - ranked fourth. You will also get to make the profits of any company - $48 billion. You also get to be the most highly valued company - at $922 billion.
• Amazon - you can sell almost everything under the sun, sell it almost all online (its foray into physical stores and its acquisition of Whole Foods notwithstanding), employ the second-most employers of any company in America, be a $100 billion plus company, yet grow revenues by more than thirty per-cent (to $177 billion), and crack the top 10 - ranked eighth. You also get to be the second-most highly valued company on earth, at $765 billion.
• Netflix: you do only one thing: in this case, streaming video content on-demand and producing your own content, almost triple your profits (199% jump year-on-year), not be in the top 200, and yet deliver the best 10-year returns to shareholders (48%, annualized!) 
• The top five most valuable companies on the list are all technology companies - Apple, Amazon, Alphabet (the parent company of Google), Microsoft, and Facebook.

Bottom line? What is common across all these companies is a relentless focus on execution. Execution - a simple lesson to learn, yet incredibly difficult to practice. Flipkart, the Indian e-commerce giant in which Walmart (press release) bought a 77% stake for $16 billion, valuing the company at $22 billion, learned that the hard way, when it lost focus in its fight against Amazon.

Further suggested reading:

1. Why Flipkart seems to be losing focus
2. Flipkart vs Amazon: Beware the Whispering Death
3. Battle in India for e-commerce market leadership is no longer between just Amazon and Flipkart - in March, 2018, this was particularly prescient.
4. 
   

This is an expanded version of my LinkedIn post.

© 2018, Abhinav Agarwal. All rights reserved.

Big Data Introduction - Workshop

Our focus was clear - this was a level 101 class, for IT professionals in Bangalore who had heard of Big Data, were interested in Big Data, but were unsure how and where to dig their toe in the world of analytics and Big Data. A one-day workshop - with a mix of slides, white-boarding, case-study, a small game, and a mini-project - we felt, was the ideal vehicle for getting people to wrap their minds around the fundamental concepts of Big Data.

On a pleasant Saturday morning in January, Prakash Kadham and I conducted a one-day workshop, "Introduction to Big Data & Analytics". As the name suggests, it was a breadth-oriented introduction to the world of Big Data and the landscape of technologies, tools, platforms, distributions, and business use-cases in the brave new world of big data.

We started out by talking about the need for analytics in general, the kinds of questions analytics - also known as business intelligence sometimes - is supposed answer, and how most analytics platforms used to look like at the beginning of the decade. We then moved to what changed this decade, and the growth of data volumes, the velocity of data generation, and the increasing variety of data that rendered traditional means of data ingestion and analysis inadequate.

A fun game with cards turned out to be an ideal way to introduce the participants to the concepts behind MapReduce, the fundamental paradigm behind the processing and ingestion of massive amounts of data. After all the slides and illustrations of MapReduce, we threw in a curve-ball to the participants by telling them that some companies, like Google, had started to move away from MapReduce since it was deemed unsuitable for data volumes greater than petabyte!

The proliferation of Apache projects in almost every sphere of the Hadoop ecosystem meant that there are many, many choices for the big data engineer to choose from. Just on the subject of data ingestion, there is Apache Flume, Apache Sqoop, Apache Kafka, Apache Samza, Apache NiFi, and many others. Or take databases, where you have columnar, noSQL, document-oriented, graph databases to choose from, each optimized for slightly different use-cases - Hbase (the granddaddy of of noSQL databases), Cassandra (that took birth at Facebook), MongoDB (most suited for documents), Neo4j (a graph database), and so on.

Working through a case-study helps bring theory closer to practice, and the participants got to work on just that - two case-studies, one in the retail segment and the other in healthcare. Coming off the slides and lectures, the participants dove into the case-studies with enthusiasm and high-decibel interactions among all the participants.

The day passed off fast enough and we ended the day with a small visualization exercise, using the popular tool, Tableau. At the end of the long but productive day, the participants had one last task to complete - fill out a feedback form, which contained six objective questions and three free-form ones. It was hugely gratifying that all but one filled out the questionnaire. After the group photo and the workshop was formally over, Prakash and I took a look at the survey questionnaire that the participants had filled out, and did a quick, back-of-the-envelope NPS (Net Promoter Score) calculation. We rechecked our calculations and found we had managed an NPS of 100!

The suggestions we received have been most useful, and we are now working to incorporate the suggestions in the workshop. Among the suggestions was for us to hold a more advanced, Level 200, workshop. That remains our second goal!

Thank you to all the participants who took time out to spend an entire Saturday with us, for their active and enthusiastic participation, and to the valuable feedback they shared with us! A most encouraging start to 2018!

This post was first published on LinkedIn on Feb 5, 2018.
© 2018, Abhinav Agarwal.