Building a World-Class Technology Platform Brand

July 30th, 2013 1 comment

“A picture is worth a thousand words; a brand is worth a million” - Anonymous.

A great technology platform brand is hard to find. I walked through an expo hall of a technology conference the other day, I found 28 technology platforms I didn’t knew existed. They may be great technology platforms, but they’re not great brands.

The technology platform not only provides a foundation for internal teams to innovate quickly but also provides external stakeholders to build innovative solutions on top. It not only provides an abstraction and hides underlying layers of complexity (hardware, software, data) but also provides an easy self-service interface so others can build value-added platforms on top of it. It provides valuable insights and personalized user experiences to each customer by absorbing real-world data and analyzing it with other disparate data sets. The platform is the nervous system of the technology company and the data it manages is the life blood.

Today, most successful technology companies have a platform that it manages. This technology platform is core to the company’s strategy and helps drive its technology vision. The company’s ability to refine and invent quickly to customer’s requests, experiment with new business initiatives and effectively scale to reach the global audience not only determines its success and its position in the technology industry but also influences the cool-ness factor that entices new technical talent to join the company.

Hence investing in building a technology platform brand is extremely important these days.

What does it take to build a world-class technology platform today? What are the important ingredients of a world-class technology platform and strategies to create a world-class technology brand?

First, you have to hire great people who can build awesome products. There are no compromises on this front. These are prerequisites to build high-quality highly-available platforms that solves real problems or enhances everyday life. If your platform sucks, and you are not doing anything about it and nobody is using it, there is no platform.  No matter how hard you try, you cannot build a great technology brand. That’s obvious. In this blog post, I am assuming that you have smart people in your team and they are building awesome innovative products and services that customers love. There are still a few more things you need to do in order to create a world-class technology platform brand:

  1. Evangelism
    Evangelism is based on Passion; Passion is based on belief. We start believing when we realize that what we are evangelizing really changes peoples’ lives. Every “high-octane” employee in a technology company has a potential to become an evangelist for the company! Real evangelism is contagious and it spreads with every “gospel”. This is the key ingredient of the recipe!
  2. The Art of Story-telling
    Stories are data with a soul. Evangelize by telling stories – stories that discusses benefits (and not features) of the platform - stories that captures and capitalizes on emerging forward-looking trends in the market, stories that highlights how individuals and businesses can improve productivity, reduce costs, increase speed and performance or whatever you are evangelizing. When you believe in your platform and when you master the art of story-telling, you will start making an impact and build a brand perception that matches the product reality. See Building Powerful Web Applications : a Love Story.
  3. Two-way Amplifier
    Every approved spokesperson in the company needs to be a two-way amplifier. Amplify the company’s vision and the ‘Voice of the CEO’ outside the company and amplify the ‘Voice of the Customer’ inside the company. Providing structured feedback about products (new feature requests, complaints, enhancements) to people is as important as telling the world about your technology platform. Being an evangelist, AWS sent me to talk but I was there to listen and learn. After every trip, detailed trip report goes to every senior exec and service owner of the company. Internal evangelism (and getting everybody on the same page) is as important as external evangelism.
  4. Maintain a Regular Cadence in the Conference Circuit
    Content is King; Content Distribution is the Queen. Its important to be out there, on field, talking to fans, customers, partners and competitors. Conferences are best place to network. Sometimes the “Hallway Track” is more influential and effective than the session tracks. Keeping a regular cadence of your platform’s latest innovation fresh in the minds of people who regularly attend key technical conferences is important. Share intelligent growth stats and metrics in your presentations that demonstrates the scale of your platform.
  5. Invest in the Ecosystem
    None of us is strong than all of us. Partners and community accelerate the value of your platform. Just like eating your veggies AND doing exercise both are important to stay healthy, investing in your platform (scale, new features, ease of use) AND investing in your ecosystem are important to build a successful brand. Not integrating with other technology platforms is not optional. It is essential to integrate with all Social Platforms (Facebook, LinkedIn, Twitter, …), Mobile platforms (iOS, Android, WindowsPhone), Cloud platforms (AWS, Azure, Google,…) and provide out-of-the-box integrated SDKs in all language platforms (Ruby, PHP, Java, .NET,  Python, …) .
  6. Reach the global audience quickly
    In the world in which all of us have a short attention span and need updates in 140 characters or less, we need to communicate our idea quickly and convey our message in concise and precise manner. Our world is getting flatter everyday, and hence we have to think global in everything we do. Short succinct videos targeted at a global audience is one great strategy to reach the masses quickly.

A great technology brand tells a story of how it improves lives, transforms the way you do business today and solves real problems. It takes time to build a great brand and hence its important to invest in your technology brand early in the process and above data points are few ideas that can help you build a world-class technology platform brand.

– Jinesh

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Big Data Cloud Trends – Cloud Accelerates Big Data Analytics : Part 3 of 3

January 29th, 2012 No comments

In the previous posts, I discussed the importance of Big Data and its relationship with cloud computing. In this post, I will discuss how you can leverage the cloud for your Big Data.

To get the most of your Big Data in the cloud and maximize your Return on Analytics (ROA), here are some simple ideas that will help you leverage the full potential of the cloud:

Enhance Your Data

Having good data is always better than having lots of data. Incorrect or inconsistent data might lead to skewed results. For example, when you have to analyze data from hundreds of different disparate sources, inconsistency in structure and format of datasets often lead to biased insight, especially when the data is not transposed or transformed into a common format. In order to get good accurate and consistent data, it’s important to enhance it. Enhancing data typically refers to cleansing, validating, normalizing, deducing and collating the data.

Performing “data hygiene” and cleaning up massive amounts of data is not a trivial task especially when you have disparate sources and large amounts of data collected at different time intervals. One can only achieve a certain percentage of “data enhancement” programmatically through scripts and programs (like running ETL jobs). However, for more enhanced accuracy, you will quickly realize that you need a human eye to validate, normalize or collate the data. Enhancements like identifying contextual similarities between columns (geo code vs. street address) or normalizing catalogs can be done by humans better than computers. You can get access to massive human workforce and split your big job into short tasks like validating the suggestion by computer program, normalizing datasets, mapping data elements, providing meta-data, cleaning up irrelevant data, transcribing audio/video files using the cloud.

Point Your Data Source to the Cloud

When your philosophy is to collect as much data as possible and measure everything, you will need massive storage capacity. When you have to collect and store massive amounts of data, the cloud makes it easy. Cloud storage is scalable, durable, reliable, highly available and most importantly, it’s inexpensive to store and can often be free to upload. Instead of moving data in periodic batches, you can point your source of data (your web clients, log generators, and so on) to the cloud, which brings the data closer to the compute resources for analysis.

Moreover, storing data in the cloud allows you to easily share and collaborate with both your partners and the consumers of your data, because they too can leverage compute and storage resources by the hour. They can analyze and extract relevant information from your data.

Analyze your Data in Parallel using the Elastic Supercomputer

The Open Source Hadoop and its ecosystem of tools bring massively parallel computing to mainstream developers. Hadoop enables the ad-hoc full-scan queries that we discussed earlier. It enables developers to break away from pre-optimized data warehouses and do exploratory analytics.

Hadoop in the cloud gives any developer or business the power to do analytics without the capital outlay. Today, you can spawn a Hadoop cluster in the cloud within minutes on the latest high performance computing hardware and network without making a capital investment to purchase the hardware. You have the ability to expand and shrink a running cluster and decide how soon they need their answers. With some cloud pricing models, you can bid for unused capacity at even lower prices and lower your costs even more.

Companies are realizing that analytics and processing massive datasets in parallel is sweet spot for the cloud and that the cloud doesn’t limit their choice of analytical functionality. You are not limited to using Hadoop; you can run Open MPI or any other commercial tools on your dataset and get better insight. You have the power to choose from a range of different analytics software – The Big Data Stacks – or even use a combination of open source and commercial tools on the same dataset to get the insight you desire and evolve analytics over time. Moreover, the cloud is elastic and cost-efficient, while at the same time offers a range of price and performance alternatives that can be tailored to how soon we need an answer. It’s a truly elastic supercomputer.

Access Aggregated Data in Real-Time with a 2-Tier Processing Model

Delivering business insight whenever you need it across massive amounts of data is not easy. To optimize results, many companies leverage a 2-tier processing model. First, they use a Batch Tier to analyze massive datasets in parallel, and store the aggregated data in separate data store (Query Tier). This pattern has two advantages. First, it leverages the power of the cloud by analyzing the batch in parallel, which greatly reduces processing time. Second, by storing pre-computed data in a NoSQL scalable data store (like Apache Cassandra, HBase, Amazon SimpleDB, Amazon DynamoDB, MongoDB, Riak etc.), continuous querying of the aggregated data is possible. Since the data is automatically indexed on input, it can be queried in near real time. This is especially useful when you want to visualize Big Data.


The cloud accelerates Big Data analytics. It gives enormous power to the Data Scientist – a new and emerging discipline – to work with Big Data, without limits. Since the cost of experimentation is low in the cloud, they can experiment often and respond to complex business questions quickly. The cloud makes it easy to absorb Big Data datasets and process them in parallel, and offers a variety of Big Data Stacks to choose from. Customers can choose to apply the technology most appropriate for their needs.

Most importantly, moving your Big Data to the cloud allows you to gain more insight from the data quickly and at a price point unmatched by older technologies.  The cloud provides instant scalability and elasticity and lets you focus on analytics instead of infrastructure. It enhances your ability and capability to ask interesting questions about your data and get meaningful answers. This changes the game.

- Jin

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Big Data Cloud Trends – Data-First Philosophy and the Cloud: Part 2 of 3

January 20th, 2012 No comments

In the previous post, I discussed my views regarding importance of Big Data. In this post, I will discuss what has changed and why Big Data matters today.

The Big Data Phenomenon is not new. For ages, companies have collected, stored and analyzed massive amounts of data. So why there is so much buzz around Big Data today?

In the old days, a Senior executive from a large company might know what questions he is going to ask (for example, “I want nightly sales reports of every product”) and the questions drove the data model which in turn drove which data to collect and where and how to store raw data. As a result, the schema (structure and organization of data) was optimized to answer a given set of questions. The problem with this approach is that it became prohibitively expensive to answer ad-hoc questions. Each new question submitted to a standard data warehouse required a full scan of the data in order to answer them, as there were no pre-computed indices available. This limited the algorithms that could be used to analyze the data from those tables. When market dynamics changed, executives were not able to get answers to new different questions because either raw data was not preserved or queries ran so long that deep exploration wasn’t possible in a reasonable time frame or there was not enough computing capacity available that would perform these full scans at reasonable time intervals.

Nowadays, with increased competitive pressure and changing market conditions, the philosophy towards data has changed. Instead of asking to answer a particular question, the senior executive wants to say “Measure everything and collect as much data as possible” without worrying about what questions to ask. In order to make this work, it is vital to collect the data in all phases and aspects of a project and then apply the right algorithms, analytical technology stacks and tools against that data in order to gain business insight. This new shift towards the “data first” philosophy has changed how we store and analyze data.

Public and Private Big Data Remix

There are two types of Big Data: public (data that is generated and created by an organization or community and available to everybody) andprivate (data that is generated and consumed within the organization). Companies have found that when they analyze public and private datasets together, they learn far more than when they analyze those data sets separately.  For example, when one can analyze e-commerce sales data (private data) for a given city with that city’s demographics, large events, and festivals (public data), we can obtain better valuable insights like how that city’s purchasing patterns will change as it prepares for those events and festivals. Likewise, when we “mash up” historical flight data with real time weather (forecast) data, we can predict the probability of our flight getting delayed few hours before the airlines themselves know it.

Hence, Big Data is not just about gaining insight from internal private datasets but about analyzing disparate datasets and exploring new dimensions of analysis.

Enter Cloud Computing

Deriving value from this Big Data requires massive computing and storage resources and emergence and proliferation of Cloud Computing has accelerated this phenomenon.

Cloud computing ensures that our ability to analyze data and to extract business intelligence is not limited by capacity or computing power. The cloud gives us access to virtually limitless capacity, on-demand. In doing so, it lowers total cost, maximizes revenue and gets the work done faster at scale

Elasticity, the ability to grow or shrink compute and storage capacity on demand in the cloud, is the fundamental property of cloud computing that drives the cost benefits. While provisioning the infrastructure for data warehouses that are tuned to answer regularly asked questions (like generating the nightly sales report) can be easy to predict, the analytics to discover new trends and correlations in the data (and in other public datasets) is an activity that requires an unpredictable amount of compute cycles and storage.  For example, to process Big Data in a traditional on-premise set up, businesses have to provision for the maximum power they might need at some point in the future. To process Big Data in the cloud, businesses can expand and contract their infrastructure resources depending on how much they need at the present moment.

Cloud computing empowers businesses to quickly leverage their data and derive valuable insights from it. They no longer have to wait for weeks or months to procure, acquire and setup physical servers and storage.  With cloud computing, businesses can roll out hundreds or thousands of servers in hours versus months and analyze the data faster than their competitors. The cloud helps businesses realize the value of their data that they already own and convert it to a competitive advantage.

In the next post, I will discuss some emerging Big Data Cloud Trends…..

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Big Data Cloud Trends – Data as a Core Asset : Part 1 of 3

January 19th, 2012 No comments

Today, the core of any successful company is the data it manages and its ability to effectively model, analyze and process that data quickly – almost in real time – so that it can make the right decision faster and rise to the top.

In fact, data as a core business asset has risen to the same level of importance to a business as its people and its capital. Today, because of the time and energy businesses give to understanding the behavioral implications of their data, scenarios like the following are an everyday occurrence:

  • An e-Commerce company collects and analyzes user behavior through clickstream logs in order to understand purchase patterns. The analysis drives future product presentation models and web site features.
  • A social network analyzes the information it collects about its users. By carefully analyzing their stated likes, dislikes, social and demographic data, it can provide personalized ads to each user.
  • A large retail chain analyzes historical inventory data to stock up goods at the right place and right time.
  • A Pharmaceutical company correlates subsequent product purchasing patterns from adverse drug reactions.

Twenty years ago, none of the previous scenarios were possible for any but the largest of businesses that could afford to invest time, resources and capital.

Nowadays, decisions are based on the data and not on political or personal preferences. Hence, capturing data and distilling knowledge from that data has become even more important. And if businesses can collect, shape, and analyze data faster than others, it becomes their competitive advantage as they can make decisions faster. Since the size of data is ever-increasing and businesses compete on their ability to analyze the data, the importance of collecting, storing, analyzing and visualizing massive amounts of data has increased dramatically.

Imagine what your business could achieve if it could provide personalized and customized experiences of every product(s) to every single customer based on their preferences, moods and market conditions!

Enter Big Data

Big Data is all about storing, processing, analyzing, sharing, distributing and visualizing massive amounts of data so that companies can distill knowledge from it, derive valuable business insights from that knowledge, and make better business decisions, all as quickly as possible.

Every company is sitting on a big pile of data. With every click, swipe, pinch, tap, like, tweet, check-in, share, API call, phone call, they are generating data. Moreover, with the increasing number of consumers and devices that act as “sensors”, the growth of data is not going to slow down any time soon. If there was a way to analyze, decode and derive patterns and trends from this “Big Data”, it could be significant competitive advantage that could prove extremely useful to the company.

Big Data is not just about data itself but it is ability and capability to derive business insights from this data.

In the next part, I will discuss what has changed and why there is so much buzz around Big Data these days

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What Steve Jobs Taught me?

October 5th, 2011 No comments

I never met Steve jobs but, like millions in this world, always had the pleasure and privilege to listen and learn from him. Steve defined an era, a culture and a style that many of us will cherish for years to come. There are number of things Steve has taught me, few of them are:

  1. Always Challenge the Status quo: If you don’t see things right, try to fix it. If you can’t, try harder (Apple’s comeback)
  2. Believe in your products and that you can change the world with persistence (Pixar, iPod, iPhone, iPad)
  3. User Experience and Design Matters: Make products, systems, things, processes that are easier to use and that they look good even though they are complex products (Simple designs of swipe, pinch, tap)
  4. Create a brand that people love; Present with elegance: This is not just applicable to speeches and presentations, but is applicable in everything we do. For example, if you are cooking daily food, be a chef and decorate it. (Steve’s presentations)
I am not saying I have nailed all these things. These are things I will cherish from iSteve!
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How Teaching Should Evolve in this New Decade – My Thoughts

January 16th, 2011 4 comments

I always wonder how should teaching evolve in this new decade. The processes, methods, techniques that our teachers used when we were kids in school might need a facelift for this new digital age. The times have changed and our teaching methods should also change with time. Today’s “digital kid” is far more connected to the world than we were. He has far more access to resources (the WWW) and gadgets (“Superphones”) than we had. So I ask to myself, if I were to rebuild and architect the new education and teaching system, what would I do?

  1. Teaching where there  is no right answer
    When I was in school, I remember, the kid who raises the hand first or tells the correct answer to teacher’s question, wins. Teacher delivers lectures, students are expected to read and often remember the answers/outcomes and teacher then tests the students on the topic. There is always one right answer to the question. If the student answers that question, he passes. If he does not, he fails. I think,  teaching should be such where there is no right answer. All the answers are right. For example, If I was teaching history or geography, I would ask my students to design an infographic that discusses the culture, currency, values, achievements, leaders of that country or have them record a small video with timeline or write an essay, or enact a drama or create a movie. Invest in time so that they become more creative. By doing this, there is no one right answer. There is no fear.
  2. Teaching where there is no grading by teachers (Student’s decide themselves and realize)
    I rememeber when I was in school (back in India), getting grades, getting into a good school, getting merit-based scholarships were rattling in my mind. I was afraid of the competition. Fear was driving me instead of passion. Teachers graded the exams and students compare themselves among their friends and feel miserable, if they get less grades. Following the above example, in the new education and teaching system, I would let student decide which infographic was better than other one by letting them analyze among themselves. Failure through realization is best form of learning. Unless they realize what they have done wrong, they will not learn. By doing this, students themselves realize and understand why the the other student’s infographic (art, design, color etc) was better than their own and they will learn to be better next time. Using the collective power of students and their votes, opinions to ”grade” other students, we not only teach them power of the ”crowds” but also encourage creativity. I believe it will also foster innovation.
  3. Teaching to help students find their own passion
    One of the fundamental pillars of the new education system, IMO, should be help students find their passion. Passion is something you believe in. Passion gives you internal energy to excel. Passion should be driving factor of what students want to do and not what market is demanding or even parental/peer pressure. If you find passion, success will automatically follow.  But sometimes, students don’t know what their passion is or what they like to do. Hence the new teaching and education system should invest in helping kids find their passion and also help them believe in what they love to do. For example, exposing the kid for 1 month to different environments like art, engineering, politics, medical fields and measuring the excitement. Highly recommend reading The Element: How finding passion changes everything. Passions change over time. Hence we have to also teach students to believe in their new passion.
  4. Teaching with minimal textbooks and exams
    When I was a kid, we were given few textbooks for a subject/course and we knew that exam is going to have questions from that textbook. Hence we mugged up every single page and paragraph of the textbook not clearly understanding its goal. The goal and purpose of having a textbook is to provide a timeline, guideline and schedule along with information to learn a particular topic holistically. Exams that designed and confined to the Textbooks are not a great way to learn a topic. For example, if I am teaching a topic on say Applied Physics or Math, I would like use a textbook so it helps me cover all the relevant topic of Physics and Math. But the exams, if at all I agree to the concepts of exams, will be outside of the bounds of textbook and something more real-world like measuring friction between tire and road. Exams are not the only way to test students. There are nice innovative ways like seeing them perform in project that involves Math.
  5. Teaching with more Projects and Experiments than lectures
    Over time, I have grown to understand that the best way to learn a particular technology or a topic is to do a small experimental project with a small goal. For example, if I am learning to code in Java, I will not listen to a lecture or start with Chapter No. 1 of Java for Dummies book. Instead, I will think of small project, and surf the internet for tutorials and dive right in to what I want to get built. Same goes with any topic in the world. If I want to learn about global warming, I will work on creating a small video about causes of global warming. When you involve kids in a project and encourage them to learn a particular topic, they not only learn that topic but also learn teamwork and how to collaborate. Teaching is a byproduct of project. Experimental goal-based projects give you a mission and deadline that will drive enthusiasm. Students may not learn the all aspects of the topic but they will learn “How to learn”, which is very important.
  6. Learning to be more fun and playful
    Projects, Games, Contests, Experiments make teaching and learning more fun. Teachers in the new education system should be more creative and should spend time on designing innovative games and projects for students instead of rating and grading exams. Leverage Facebook, Twitter, Youtube, Myspace and adopting the newest and latest techno-phenomenon that is popular among the kids to teach them about a topic. Using the latest tools and gadgets to our advantage, we can make learning fun. We can teach a topic over a video conferencing session or have an iPad game that teaches them about Calculus.

I agree these ideas might not be feasible today or might not be applicable to every level from Kindergarten to 12th grade and beyond. But, I strongly believe these ideas will make teaching and learning more fun. What do you think?

– Jinesh

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Mark Zuckerberg On Innovation

October 2nd, 2009 No comments

Great lessons to learn from Mark -


In summary, Mark talks about three main things:

  • Stay Focus on company main mission:
    Allow people to share more information about themselves, let them control it, help them stay connected to the people they care about.
  • Move fast:
    Join the company, learn and build something quickly, launch it, iterate, get feedback quickly and “break everything”
  • Don’t be afraid of being Bold:
    Never build something great by doing something the same way others have done it.

These are good lessons for any startup. I love it.


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Hello world!

July 6th, 2009 No comments

Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!

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