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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

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

Digital Transformation is a huge topic now. Most software vendors have adapted their products to include the Digital Transformation message. Transforming into a digital company is no longer a choice and it is clear that the companies that transform faster can develop a competitive advantage that will serve them for years while the companies that do not will slowly fade into irrelevance. Digital Transformation is the change in an enterprise that makes it acquire and deploy the best in class technology assets and systematically removing analog or manual touch points, handoffs, processes and decision making. It is the ability of the enterprise to understand, assimilate and leverage the latest technology trends such as cloud, mobile, APIs, AI, Blockchain, ChatBots, Analytics, IoT etc.

Enterprises have to constantly decide, at every step in their digital journey, should they build or buy. This question often is posed as a critical, do or die decision and the answer varies on a case by case basis. Building can be expensive, take longer but offers future proofing and more dependability whereas buying offers a faster time to market, less risk and accountability forced through contractual terms. However, a key point often overlooked is the cost of integration. Integration can be required at multiple levels. Vendor Applications Vendor applications typically require a two-way connection between the enterprise systems and the vendor application. The application requires incoming data and information from somewhere in the enterprise technology stack and an output stream of information back into the enterprise at one or more points in the stack or workflow. Vendor Platforms Vendor provided platforms typically have similar integration requirements as Vendor applications requiring an incoming data & information connection and an outgoing information connection into the enterprise process, workflow, platform or product. Application-To-Application Application to Application integrations where an application needs to be connected to another application to either provide data or signals to enable the downstream application to create value can be seemingly deceptive. Application-To-Application integration costs can grow at O(n^2) as potentially, worst case, each application could be connected with every other application. Enterprise Stack Fragmentation The problem of integration is exacerbated by the fragmentation of the enterprise at the organization level. This problem is also known as "Shadow IT" is driven by superficially differing needs of multiple lines of businesses in an enterprise. Shadow IT typically leads to multiple instances of similar technology stacks that cause data, compute and information to be silo'd. Stack fragmentation and its

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

To survive in the current competitive times, organizations must transform their cloud strategies to deliver actionable data in real-time and exceed customer expectations.