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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 most common misconceptions out there is the belief that technology equates innovation. Innovation is the creation of new value through a better solution for a problem that either does a better job in solving the problem or does so in a manner that the solution is accessible in a larger set of circumstances by a larger number of people. On the other hand, technology is simply the tooling that holds the promise of new solutions but by itself, is meaningless. Enterprises can become enamored by the promise and hype about technology and go down long, complex journeys, invest millions in upgrading technology and still come out empty on the other side because they built technology for technology's sake. Even in well intention boards and C-suite, industry peer pressure and hype around technologies can force action that ultimately leads to massive investments in people, software, technology, and vendors but does not yield the ROI promised by the technology. Leadership The problem can be accentuated when not enough due diligence is done on the applicability of the technology to the enterprise given the current state i.e. the point in time when the technology is being introduced, the customer's propensity to accept the technology-driven solution i.e. are the users ready to embrace, adopt, learn and utilize new solutions and burning problems that necessitate the adoption of new technology to better solve the problem. When the timing of new technology introduction is gotten wrong, it almost always fails to deliver on its promised ROI. Leaders need to rise above the hype and peer pressure and ensure that they understand, first and foremost, the burning problems that plague their customers or make the bedrock of their future strategy. Next, leaders need to ensure and validate that the technology in mind can actually be used to solve the problem through rapid prototyping and minimal investment. Once customer feedback on the prototype has been validated, then only

Technology Congestion is a not a recent phenomenon but the urgency around Digital Innovation and Digital Transformation has brought it front and center. Technology Congestion is a point in the Digital Journey where multiple technology initiatives, executed in parallel become entangled with each in a state where none of the initiatives, hampered by inter dependencies, prioritization, and cost, is able to complete, make progress and deliver business value. Modern Experiences Require Multiple Technologies Building a consumer driven, customer centric experience that truly delights and moves business KPIs requires several technologies to come together in almost a magical experience. This means that not on boarding and deploying multiple technologies is not an option or possibility. Enterprises have to build competencies in multiple technologies (and they have multiple strategic options to do so) and this can be a daunting task. Managing Technology Dependencies Often, an app-centric methodology requires a complete focus on the user and customer's experience. Delivering that experience can requires technologies that leverage each other or are inter-dependent on each other. Inter-dependencies can be sequential i.e. Technology A is required to be installed and operational before Technology B can be initialized. Inter-dependencies can also be matrixed i.e. a service X might require service Y to be complete and Service Y requires Technology B. Inter-dependencies can also be circular where System M feeds information into System N and System N, in turn, provides feedback to enable System M to iterate and improve. Innovation To A Screeching Halt Technology congestion can stall innovation. Sorting out dependencies can delay innovation and new product development and cause the enterprise to become anti-app-centric. The net impact is lost time and energy in technology installation and deployment with less than ideal focus and attention on customer value and user experience.

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

Blockchain are certain to disrupt almost all industries fundamentally. Though there are technical issues, the idea of utilizing a blockchain to prove ownership, to prevent double spending and to establish trust and transactions in an otherwise trust-deficient world, is gaining excitement and acceptance. More and more enterprises are getting curious about the blockchain and are willing to start investing in learning, prototyping, and building on top of the blockchain technology. Starting out with the blockchain requires investment in a building the domain expertise, establishing the identity, establishing the infrastructure and deciding the blockchain that the enterprise wants to work with. Among these decisions is the question: Should the enterprise choose a public blockchain or a private blockchain. Both have advantages and disadvantages and the question can often come down to what are the short term goals and potential applications that the enterprise wants to build on the blockchain. Public BlockChains The default option when considering blockchains is the public blockchain. This is the blockchain that is truly decentralized, leverageable for any type of transaction and in the case of the Ethereum blockchain, offers SmartContract authoring capability that makes the blockchain very attractive for building contracts that reflect the needs of the real world. However, for an enterprise deciding between public or private contracts, there are some considerations that require attention. Speed and Scalability Public blockchains tend to be slower as they are limited by the number of transactions that can be verified every second and confirmed every 10 minutes. There are several efforts underway to make blockchains faster and more scalable however those will take time to get implemented. Speed and scalability will continue to be an issue and contracts and applications that require instant or near real time execution will suffer from this lack of speed and scale. Se