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The cutting edge platforms for today will be the commoditized platforms of tomorrow. As the technology matures and evolves, the previous generation of technology becomes easier to build and deploy enabling a rush of vendors to capitalize on it by making it accessible to the largest possible customer base. This puts enterprises in the nontechnology sectors in an awkward position. Often not ready to consume the latest and greatest technology due to parts of their stack unable to leverage new technology and requiring upgrade to and deployment of the stepping stone technology, these enterprises have to choose between vendor lock-in in a multi-year software and service contract or risk building and implementing a version of the older technology in-house. Business Drivers of Infrastructure-as-a-Service The biggest risk in building technology platforms in-house is the risk of commoditization. The argument played out with the debate over internal vs. public clouds. Initially, enterprises were hesitant to leverage public clouds with several of them opting to build internal, private clouds. Building a cloud is hard. Operating and maintaining a cloud is even harder. Ensuring that the cloud is running on and leveraging the best in class technology requires dedication to the cause. This is often missing in non-technology enterprises by design given they are driven by different and separate business drivers and considerations. A cloud service provider is motivated to ensure the best in class service and technology because that drives revenue for them. An enterprise whose main business is not offering cloud or software services will not be motivated by the same drivers and thus there will be an inherent difference in their approach and success with building and delivering an internal cloud. Business Drivers for Platform-as-a-Service The same argument (public vs private clouds) applies to platforms. Building the best in class platforms that offer the ability to develop cuttin

Enterprises across the board have a lot of untapped potential in their data. The data is not only relevant and useful within the enterprise but can be a valuable source of insights for the enterprise partners and customers. In some cases, the value of this data can be high that partners and customers are willing to pay extra to get access to this information at a certain fidelity, freshness or scope. Enterprises that do not have a clear and coherent monetization strategy are leaving money on the table. In addition, they stand to lose customers to competitors who gain the first movers advantage by addressing this market need. The Value of Data The first step in determining a monetization strategy is an audit of the enterprise data assets and a determination of the customers who are interesting and willing to pay a premium for access to this data. The Value of Data is proportional to the following: Freshness The more "fresh" a dataset is higher its value typically. This is because there is an advantage in the early visibility provided by first access to new information. 'Freshness' is defined the latency between the creation of data and the delivery of the data to the consumer. Consumers of data will pay a premium for fresh data if it fits into their decision and action strategy. Fidelity Higher the "fidelity" of data i.e. how much detail a particular data point carries also increases the value of the data in the eyes of the data consumer. Higher fidelity data offers more information and detail enabling the consumer to design highly valuable analysis that leverages the additional details offering a deeper insight into the situation at the present or historically. Raw The more "raw" a data set, higher its value as it can support a much larger set of analysis scenarios that a processed data set could support. Data sets that are aggregated, sampled, filtered or transformed can have a lower value as they can severely limit the type of analysis. Raw

One of the biggest hurdles to Digital Transformation and Digital Innovation is the organization's inertia and tendency to optimize for self-preservation. Self-preservation can exist in the enterprise at the individual, team, divisional or the organizational level and can have a devastating impact on the organization's ability to innovate and grow. Self-preservation is not a new phenomenon however, it is more deadly for an enterprise now than ever. This is because the speed of technology change has increased geometrically. In the past, self-preservation would automatically get corrected as the technology was generally learned and adopted slowly with enough time for the enterprise to become aware of the change and implement it. However, the rate of technology change has magnified tremendously and the enterprises no longer have the luxury to take their time with the change. Inaction risks getting left behind and other competitors who leverage and change faster stand to capture the largest market shares and customer mind share. Self preservation is the tendency of the enterprise to ignore, undermine or postpone the adoption and integration of new technology in the enterprise to avoid a change in the status quo across technology, products, services and most importantly, day to day operations and organizational structure. Self-preservation can lead to what is termed as "politics" in an org, it can stifle innovation and innovative individuals & teams and it can favor business driver stagnation over risk taking. 5 Signs of Self-Preservation The following are signs of self-preservation Highlighting the Journey of Innovation as Failure Adversarial teams and individuals within an enterprise who are interested in self-preservation often go out of their way to highlight the tough, risky journey of true innovation as a failure citing the cost and the time being taken to address the real problems in a truly innovative manner. While the individuals and teams trying to

Sentiment, Sentiment Analysis, Sentiment Tracking have become a hot topic with multiple 'AI' startups focussing on providing sentiment driven insights to enterprises. The number of such startups points to the potential that enterprises see in Sentiment analysis and the impact it has on how the enterprise plans, operates, executes and delivers value. Sentiment Analysis is process of extracting sentiment (emotion or feelings) captured in signals that are embedded in various types of media such as print, text, audio, video, images etc. For example, if a reporter submits a report on a particular enterprise, the sentiment embedded in the article can point to how excited, worried, upbeat or impassive they might are about the enterprise. This sentiment can be then used by the enterprise to understand the perception about the enterprise that the external market carries and whether that perception is improving, degrading or staying unchanged. This insight can be used by the enterprise to improve their go to market plans, change their PR strategy or even go deeper and change their product strategy. Sentiment Analysis Is Not New The tracking, measurement and use of Sentiment is not a new scenario. Enterprises have been leveraging the output of sentiment analysis for a long time. User surveys, focus groups, market research, customer interviews etc. are all examples of generating data to perform and track sentiment. Similarly, influencer marketing through association with influencers or events or organizations with a certain perception or sentiment associated with them is a common technique to improve the enterprise's own sentiment. Sentiment Analysis and strategizing based on the analysis is a common and required function for any enterprise. Sentiment Analysis Using Artificial Intelligence With the advent of Artificial Intelligence (AI), enterprises now have another technique in their kitty to understand how they are perceived in the market and how that perception i

Modern, RESTful APIs are not considered standard, table stakes and expected out of any new project, effort, application, system, service or product. It has become so normal to talk about developer interfaces, developer adoption, application development and innovation in the same breath as APIs that a distinct effort to build APIs for a new product or service seems out of place and abnormal. APIs are the defacto standard of app development. So where do we go from here? We believe that APIs are about to enter the second growth spurt. APIs will evolve from not just interfaces and integration enablers into the rockets that propel enterprises towards innovation and market dominance. Here are three key trajectories that will lead the next API evolution and revolution. Innovation - Starts, and Ends with APIs All modern technologies such as Artificial Intelligence, Machine Learning, ChatBots, Analytics, BlockChain etc. begin and end their stories with APIs. APIs are what enables the communication between front-end user interfaces and the backend technology services. All new machine learning capabilities offered out of the big four tech companies have seen the light of day through APIs. Intent & Sentiment extraction, Topics, Categories, Summarization, Image Recognition, Entity Extraction etc. are all capabilities powered by Machine Learning, Natural Language Processing that is ultimately being delivered as APIs to application developers. Similarly, ChatBots are typically designed to get the user entered text, use an intent API to determine intent and then use a service API to respond to the user conversationally or with a service. Clouds - Multi-Cloud, Hybrid Cloud As the big three cloud providers grow their market share and attempt to attract attention, increasingly, enterprises need to think about how they minimize their risk by building in the flexibility to switch their cloud provider if and when they need. In addition, hybrid architectures or a cloud migration

For many enterprises, transforming into a digital organization is a very big priority. Digitization is more than a passing fad; instead it almost is a precursor to survival in the next decade. Analog mechanisms of running businesses are no longer sustainable nor likely to give confidence to customers, employees, stakeholders and shareholders. Measuring Digital A digital organization is characterized by the following Time to Customer Insight The Time to Customer Insight in a digital organization is the time it takes to collect, process, analyze information to determine the health of a customer, their satisfaction with current products and services, their unmet, possibly unstated needs and the impact that external market events might have on the customer. Time to Reaction Time to Reaction is the time taken to react to a customer insight through the introduction of a new product/service to solve an existing or a new problem or through better packaging of existing solutions to address otherwise existing problems. Time to Market Time to Market is the time taken to bring a new capability, product or service to market often as reaction to a customer or market insight or feedback Time to Iteration Time to Iteration is the time taken to solicit, gather, process, analyze customer feedback and effect a change in existing products or services or bring new products and services to market to address the customer feedback. Digital Organizations Digital organizations are characterized with minimal Time to Customer Insight, Time to Reaction, Time to Market, Time to Iteration and a constant effort and investment into further optimizing and minimizing these metrics. Digital organizations focus on the flow of information through the organization and use of the information to generate and deliver more value for the customers. Key Characteristics of Digital Organizations Instrumentation of Interfaces, Products, Systems, Applications, Processes A digital organization ensures

One of the top reasons for digital innovation and transformation failures can be summarized as the enterprise trying to reinvent the wheel. This is the tendency of the enterprise to attempt to create technology, platforms, and applications that have already been implemented, scaled, optimized and almost perfected. This tendency almost always ends up a failed one as it does not create any net new value for the enterprise but comes with a massive opportunity cost as the enterprise spends crucial resources on reinventing the wheel than innovating for the customer. How The Wheel Is Reinvented Nontechnology enterprises can get trapped in a reinvent state where they conclude that homegrown technology is the only path towards customer and business value. This in itself is not entirely false however the type of enterprise and their decision-making process along with the capabilities they have in house have a very large impact on the success of the strategy.eRaaadada Reinventing the wheel happens when a non-tech enterprise discovers a technology trend towards much later in the hype cycle almost towards the end when the technology is hitting the mass market and decides to recreate or reinvent its own version of the technology. This is often done with the assumption that with some investment, the enterprise can have a home grown version of technology or platform that is designed specifically for its needs and is thus a better fit. However, enterprises assume that the state of the technology will remain constant and while they are attempting to home grow a version that can match the current state of the art. In reality, the state of the art shifts and the enterprise is not able to bridge the gap. Who Reinvents The Wheel Typically, technology teams often decide to go down the path of reinvention when they are allowed to make technology upgrade or technology creation decisions without business KPIs and cost constraints i.e. clear success criteria with fixed cost and clear ti

A pitfall in the path to innovation and disruption is the lop sided focus on "creation" and not enough focus on "consumption". Creation is the process of creating platforms, products, and solutions where as consumption focusses on ensuring that the created artifacts deliver the intended value. Too often enterprises get caught up in "creation" or enabling "creation" and lose sight of the fact that without consumption, anything they create is bound to be a failure. Creation & Consumption can not be Sequenced A misconception that often exists is that "if we build it, they will come" i.e creating the product and solution is enough and its existence will automatically lead to consumption and value generation for the customer. However, creation and consumption cannot be sequenced i.e. made a focus sequentially. Creation and consumption only succeed when they go hand in hand where tight, iterative loops ensure that the creation is informed by consumption trends and feedback and that consumption is also leveraging the latest creations. What Does A Lop-Sided Focus Look Like The top 3 signs of this lop-sided focus are as follows Focus on Building Platforms When the focus of the enterprise is building platforms and when customer value is only created when a developer leverages the platform to build a customer facing application, it often means that the effort invested in the platform has no ROI. When the focus is on platforms, application investment suffers and the platform builders get sidetracked with platform KPIs as opposed to business value KPIs. The platform builders might only focus on "Developer" satisfaction when ultimately, customer satisfaction matters for the business. Focus on Tools, Not on Solutions Another sign of lop-sided investment is a focus on tooling at the expense of solutions. Enterprise teams can often go overboard with building large libraries of tools and using the adoption of the tool as a metric of success. However, simil

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