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A lot has been said about agility and the need for enterprises looking to innovate and disrupt to build agility. Agility, at the same time, also gets confused with the process of scrum. Small and large teams get enamored with the idea of scrum and mistake the process with the state of being agile. This is often more detrimental to the enterprise and can often create more process and not enough real agility. What is Agility Agility is the efficiency with which an enterprise executes and delivers on its objectives and goals. Agility is the ability to react to changes in goals, feedback from customers and shifts in strategy. Agility, from the outside, looks like a predictable stream of value delivered by the enterprise that matches and exceeds the needs of the customer. Organizational agility requires agility at multiple levels within the enterprise to drive the insights that can channel and align the efforts of the entire organization by leveraging data and information to make quick and informed decisions. Business Agility Business and customer-facing employees need to achieve "Business Agility". This is the ability of these employees to react to business critical in real time if needed and have access to the latest information at any decision point. Business Agility enables users to reduce the latency or lag between a need in the market or of the customers and when they are able to service the need. Decision Agility Analysts and data scientists creating the insights to drive decisions require "Decision Agility" i.e the ability to easily discover, leverage and use data for analytics and insights through any and multiple tools and channels. Analysts and data scientists need to produce insights that reduce the time and effort required to convert data into information and insights that are required to drive key decisions and actions. Development Agility Application developers and data engineers need the ability to easily generate, collect, access and deli

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

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Digital Transformation is tough, even for seasoned technologists. This is because it is a transformation of an organization at its core. Everything from culture, technology, ideation, development, integration, delivery, and support needs to fundamentally shift to be more customer-centric, service driven, automation first and experimental in nature. No wonder that a lot of organizations take a long time and a lot of investment to see ROI from their digital transformations. Here are 5 signs that your digital transformation might be in trouble. Culture mistrusts the core digital transformation team You are spawning new initiatives before completing previous ones Decisions are top down with low accountability at the leaf nodes You tend to focus on technology stacks with little focus on customer value Inter-organization politics stifles cross-organization scenarios Culture mistrusts the core digital transformation team It is almost impossible to make an entire organization aware and participate in digital transformation at the same time. There are exceptions but in our experience, starting out with a core digital transformation team is a much better strategy than otherwise. This team should be enabled to attack a limited set of important and business relevant problems, build cutting-edge solutions and use them as examples to train and evangelize digital transformation strategies to the rest of the organization. However, the more entrenched an organization in the old way of doing things, the harder they might this central team. Resistance can be active and passive such as refusal to share data or provide the relevant context of the problem. An organization that does not set up the early crusaders for success almost always has a much harder time showing value from their digital transformation activities. You are spawning new initiatives before completing previous ones Executing on a digital transformation strategy is much harder than defining the strategy especially fo

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.

Tips & Tricks for Efficiently Tracking Lean Projects Recently, we wrote about a couple of problems facing many Lean practitioners. These were problems that hindered their learning process and often caused overconfidence. Amongst the advice to help combat these problems arose the need for detailed tracking and note taking with regards to practice and progress. Being able to accurately track not only the effects but also the process of a Lean project is critical to ongoing success, and is a key to stimulating growth amongst not only the receiving end of your project objectives, but also amongst the employees and Lean practitioners themselves. To start off, let's take a look at the current state of Lean project tracking for many Lean practitioners. A recent LinkedIn discussion posed the very question of project tracking, and sought to understand a few different distinct metrics. In addition to measuring the effectiveness of any program when it comes to completion, tracking can also be about measuring the scope of your projects, so that you can accurately project what the effects will be before you actually reach the stage at which you expect them. Part of this "scope" involves tracking exactly who is involved with your project, who is affected, and how they are progressing in their respective tasks. Where we are now First of all, it's important to evaluate the current methods that are prevalent in Lean record keeping. The LinkedIn discussion starter, self-identified as Ian R., mentions in his opening post that, when he last posed the question about a year ago, the consensus was that most practitioners were simply using excel spreadsheets for their tracking needs. While there's nothing wrong with relying on Excel for the basics, other users were quick to offer up some slicker alternatives, signaling a sharp (and welcomed, in our book) departure from some of the more basic methods. Unsurprisingly, there exist several specialist software applications whose n

Transforming into a tech company has become top of mind for executives in all major industries. It is clear that modern technology will fundamentally alter what and how business is done in every domain, sector, and industry. This has led to a call to arms in every enterprise to understand how they can transform into a tech company. The Tech Company Magic Tech companies have fine-tuned the art of bring new digital products and services to the market, quickly, efficiently and effectively and understanding customer feedback to iterate and improve. This capability makes them incredibly agile and leads to faster experimentation that is cheaper and involves less risk. In turn, this enables them to bring new capabilities to the market and even if all do not succeed or get traction, a few do and that drives innovation, customer satisfaction, and growth. From the outside, tech companies appear to be massive juggernauts that are unstoppable and able to crush everything in their path. The 'Non-Tech' Technology has been leveraged in every sector and industry, however, it has almost always been treated as a means to an end, something that is required but never the real value driver for the customer. This has led to the typical organizational structure in enterprises into "Business", "Operations" and "Information Technology". The "Business" arm generates value for customers, the "Operations" team carries out the requirements of the Business team and the "Information Technology" team provides the systems (databases, network and compute) required to "keep the lights on" for the Operations and Business Teams" This structure served enterprises well in the last decades as customers did not have an alternative to directly working with the enterprise and this fortified the value supply chain and also established a hierarchy of sorts within the enterprise where the business looked down upon operations who looked down upon technology. The purpose of

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