Have you ever spent an afternoon in the backyard, maybe grilling or enjoying a game of croquet, when suddenly you notice that everything goes quiet? The air seems still and calm — even the birds stop singing and quickly return to their nests.
After a few minutes, you feel a change in the air, and suddenly a line of clouds ominously appears on the horizon — clouds with a look that tells you they aren’t fooling around. You quickly dash in the house and narrowly miss the first fat raindrops that fall right before the downpour. At this moment, you might stop and ask yourself, “Why was it so calm and peaceful right before the storm hit?” -How Stuff Works
The last 5 years, with the onset of Hadoop, Cloud, and Mobile was merely the calm before the storm. There is a new modern technology stack, a data revolution, and the onslaught of machine learning that will shape the storm to come over the next decade.
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The mobile supply chain has wreaked havoc on the traditional technology stack. With the advent of high volume chips, screens, storage, etc. it has become cost effective to move away from a vertically integrated architecture, to one that is much more flexible and dynamic. We have evolved to a 6 layer Next Generation Technology Stack:
Layer 1: There are 2 aspects to layer 1: a) the repositories and b) the data itself. The repositories include the new breed of flexible and fluid data layers, ranging from Hadoop to Cassandra to other NoSQL data stores. Very flexible, adaptable, and tuned to modern internet and mobile applications. This also includes databases, data warehouses, and mainframes; said another way, anything that stores data of strategic and operational relevance. Within the repositories, the data itself creates a competitive moat, and offers strategic advantage when used appropriately.
Layer 2: A highly performant processing layer, which enables access to all data in a unified way, and easily incorporates machine learning and produces real-time insights. This is why I have called Spark the Analytics Operating System.
Layer 3: Machine learning, on a corpus of strategically relevant data, is the new competitive moat for an enterprise. This layer automates the application of analytics and delivers real-time insights for business impact. It’s the holy grail that has never quite been found in most organizations.
Layer 4: A unified application layer, which provides seamless access to analytical models, data, and insights. This is the glue that enables most business users to leverage and understand data-rich applications.
Layer 5: The easiest way to democratize access to data in an organization is to give users something elegant and insightful. Vertical and horizontal applications, built for a specific purpose serve this role in an organization.
Layer 6: The number of people connected to the Internet has surged from 400 million in 1999 to 3 billion last year. The number of connected devices is estimated at 50 billion by 2020. These are all access points for the Next Generation Technology Stack.
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In Big Data Revolution, I dissected 3 Business Models for the Data Era. In summary, there are 3 dominant business models that I see emerging:
Data as a competitive advantage: While this is somewhat incremental in its approach, it is evident that data can be utilized and applied to create a competitive advantage in a business. For example, an investment bank, with all the traditional processes of a bank, can gain significant advantage by applying advanced analytics and data science to problems like risk management. While it may not change the core functions or processes of the bank, it enables the bank to perform them better, thereby creating a market advantage.
Data as improvement to existing products or services: This class of business model plugs data into existing offerings, effectively differentiating them in the market. It may not provide competitive advantage (but it could), although it certainly differentiates the capabilities of the company. A simple example could be a real estate firm that utilizes local data to better target potential customers and match them to vacancies. This is a step beyond the data that would come from the Multiple Listing Service (MLS). Hence, it improves the services that the real estate firm can provide.
Data as the product: This class of business is a step beyond utilizing data for competitive advantage or plugging data into existing products. In this case, data is the asset or product to be monetized. An example of this would be Dun & Bradstreet, which has been known as the definitive source of business-related data for years.
Since my work on business models was published, my thinking has evolved a bit. While I think each of those business models is still valid, I am less certain that any of them on their own will create a distinctive competitive advantage. Instead, I believe that the value is where the software meets the data, and access is democratized. Said another way, it’s hard to create value by only looking at one layer of the Next Generation Technology Stack.
Enter The Weather Company…
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Last week, we announced our intention to acquire The Weather Company. The media reaction has ranged from, “IBM is buying a TV station?” (we are not), to “IBM is buying the clouds”, to “IBM is entering the data business.” Some of the reactions are wrong, others are humorous, and some are overly simplistic. The reality is that IBM has just made a significant move in defining and leading in the Next Generation Technology Stack. This interview captures it well. Let’s look at this in terms of each layer:
Layer 1: IBM has long been a leader in Layer 1, around all types of repositories. From Netezza to DB2 to the mainframe to Informix to Cloudant to BigInsights to enterprise content; most of the worlds valuable enterprise data is stored in IBM technology. With The Weather Company, we now have a rich set of data assets. The Weather Company can decompose what is happening on earth into over 3 billion elements. And, its not just weather data. In an increasingly mobile world, location matters.
Layer 2: IBM is the enterprise leader in Spark. Through a variety of partnerships like Databricks and Typesafe, we are a key part of this blossoming community.
Layer 3: Through our open source contributions to Machine Learning, our rich portfolio of Analytical models, and the worlds greatest Cognitive system (Watson), IBM can provide applications (Layer 5) and insights that are unmatched. Just think how powerful Watson becomes when it understands location and environment, as well as everything else it already knows.
Layer 4: The Weather Company has an internet-scale high volume platform for IoT. It can seamlessly be extended for other sources of data and “can ingest data at a very high volume in fractions of a second that will be an engine that feeds Watson”.
Layer 5: IBM has a rich set of industry applications and solutions across Commerce, Watson, and countless other areas. The Weather Company applications and websites handle 65 billion unique accesses on weather and related data per day. This is scale that is unmatched.
Layer 6: The Weather Company mobile application has 66 million unique visitors a month and connective tissue to tap into the 50 billion connected devices that are emerging.
In summary, this is much more than weather data. Overnight, IBM has become the leader in the Next Generation Technology Stack. It is the basis for extension into financial services, automotive, telematics, healthcare, and every other industry being transformed by data.
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It’s always calm before the storm hits. Sometimes, in the moment, you don’t even recognize the calm for what it is. My guess is that most people have not considered that last 5 years the calm before the storm. But, it was.
| Reference: | The Calm Before the Storm from our JCG partner Rob Thomas at the Rob’s Blog blog. |
