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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

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

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.

Digital Transformation has become a necessity for enterprises in every vertical, sector and industry. Software is indeed eating the world and there is no industry that cannot ignore the burning necessity of transforming to a software driven organization. However, the road to digital transformation is long, unpaved and full of dangers. It is a road that most enterprises that are embarking on it have never encountered. These organizations realize that their ultimate survival depends on navigating this road but are hesitant, unsure and scared because this transformation requires competencies, culture and an approach that is alien and unknown. Though, traditional IT departments have always been part of such organizations, they have always been looked upon as plumbing that ultimately is only relevant in the background to keep the lights on. Faced with impending doom, organizations have no choice but to rethink their IT. This is not simply an initiative in the IT team. CEOs and CFOs need to rethink what and how software can and will disrupt their companies. Business and IT need to come together to have a joint software driven experience strategy and needs to be prioritized by the CEO and funded, for the long term by the CFO. Do you need to think about a Digital Transformation strategy? If you answer YES to any of the questions below, you should. Are your teams, data, and systems fragmented? Are your key processes fragmented, manual? (for example, Onboarding, Decision Making, Incident Management, Support) Is your data of low-quality data (customer profile, transactions, glossary, documents) Is your regulatory compliance inconsistent and more a matter of luck than planning? Could your customer relationship, lifecycle, performance management be better? Could the information flow in your organization be matured? Do you have a weak understanding of internal and external events and how they impact your business?

The quality or the lack thereof can be a huge contributing factor to a fractured and sluggish digital journey where ROI is hard to achieve and results come in short supply. The quality of data has a direct impact on the ability of the enterprise to be aware of relevant events, its reaction time, the decision time and its action time. A clear and concerted effort is required to measure and improve the data quality to drive better decisions and actions. Common Quality Issues The following are the most common quality issues Comprehensiveness Comprehensiveness quality issues refer to key attributes or data points missing from the data collected by the enterprise. This can occur when the data producing systems or the data delivery networks have glitches or malfunction or are incorrectly configured to miss entire rows of data or attributes of the data. Integrity Integrity quality issues refer to the corruption of the values of key attributes to contain unidentifiable or unreadable data. When key attributes are empty or null when they are by design, not allowed to be empty/null or when an attribute contains a value that does not meet the specifications of the type of the attribute for example, a string column contains an integer or a timestamp column contains a string not parse-able into a timestamp. Integrity of data is important before data can be included in the data set to drive analysis, decisions and actions. Sampling Sampling quality issues refer to the inclusion or exclusion of a certain percentage of the records in a data set with the assumption that the remainder records are good, representative sample of the original data set. Bad or inaccurate sampling can lead to a distorted view of reality and that can lead to bad decisions. In addition, sampling itself can make the data set inappropriate for certain types of analysis that require the entire dat set to be utilized for training. Filtering An upstream filtering scheme can end up removing too many or

Digital transformation helps businesses to keep themselves updated with the emerging customer demands. It uses digital technologies that transform traditional and non-digital business processes and services into fully digital ones.

Are you AI-First? By editor Posted October 13, 2018 In Artificial Intelligence, Digital Strategy, Technology & Design 0 An AI-First company is an enterprise that has transformed to believe and understand the incredible and disruptive potential of Artificial Intelligence. Such an enterprise not only sees the value but can see the destructive impact of being left behind. An AI-First Company understands that it might not know all the answers but realizes that it needs to seek out a path forward with AI or risk being marginalized. Key Characteristics of an AI-First Company A-First companies might not be any different from their previous form but think and act differently. Here are some key characteristics of such companies. Approach to Problems and Planning An AI-First company evolves its approach to problems. AI-First companies realize that determining the existence of a problem and selecting the most consequential problems is a function of data and analytics. An AI-First company invests in building predictive mechanisms that can signal current or upcoming problems including the severity and priority of these problems. Building these predictive mechanisms becomes the first step in determining how and when to prepare for problems or upcoming issues. In addition, these companies leverage news and information that is generated inside and outside the enterprise as it is generated and ensure that their employees and customers have access to the insights embedded in the news and information. Approach to Products and Product Development An AI-First company understands how a prediction or classification could help them deliver a better solution to a problem faced by their customers and how their existing products can be adapted or new products created that change behavior based on the predictions and classifications. Enterprises that understand the power of AI ensure that data scientists come part of the core product ideation and development team with a heavy infl

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

7 Steps To Transform Your Physical Event Into A Virtual Event

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

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.

One of the key milestones on the Digital Journey starting with a Digital Strategy, Digital Transformation and then sustaining on Digital Innovation is the point where the enterprise reaches a point of data maturity powered by a single, organization wide, consistent version of the truth including the state of the customers and the state of the business and the state of the employees. This point is critical as it becomes the launchpad for several, forward looking initiatives including Artificial Intelligence, ChatBots, Blockchain etc. "Complete" A Complete version of the truth ensures that the following criteria is met: Entity Pivot The key entities that need to be tracked to generate a complete, comprehensive version of the truth are the following Employee Employees, regardless of customer facing or not, need to be understood including where they excel vs. struggle and where their struggle impact the customer experience. Key information about employees that should be tracked is what the employees are working on, how productive they are and how often they introduce delay and errors in business processes. Business Business visibility requires that the enterprise be able to track key metrics such as customer lifetime value, customer attribution, customer acquisition cost and customer satisfaction. In addition, the stage of the customer ranging from prospect to commit to paying customer to abandoned needs to be tracked. In addition, the customer's quality of service and experience needs to be tracked and understood. Customer The most critical of the three is the understanding of the customer. Customer KPIs have a direct impact on and are completely impacted by the Business and Employee KPIs. It is extremely important to understand how customers are searching for, discovering, learning, understanding, using and continuing to use the product and services delivered by the enterprise. In addition, it is important to understand what capabilities drive what kin

The new Short Circuit Software version is now available. TORONTO, ON - ARCAD Inc. is helping create a safer working environment for repair service people and electrical maintenance workers who service electrical systems by providing on-line and PC based software for short circuit and arc flash hazard analysis. The arc flash software will help a business meet OSHA, NFPA 70D, CSA Z462 regulations and code requirements. SCA V1.0 (Short-Circuit-Analytic) software performs available fault currents calculations in three phase electric power systems. The program will take into consideration electrical parameters of the power supply as well as the power distribution system including cables, bus ducts, transformers, utility, motors, generators, etc. The software will automatically convert the entire system into a unique unit from which the short circuit current at each point is calculated. Because the process is simple and efficient, it will save a business money and time. Short Circuit Analytic Capabilities are as follows: Computes contributions from generators and motors Prints out multi-page single line diagrams Calculates available 3-phase short circuit currents within your power distribution system Saves calculation results and equipment data Compliments ARCAD software for arc flash hazard analysis and labeling Should you encounter any problem installing or running SCA V1.0, contact http://www.arcadvisor.com and they will assist you in identifying and resolving the problem.

Much has and needs to be said about an enterprise's AI strategy. Artificial Intelligence or AI is considered a fundamentally disruptive technology similar to the steam engine, electricity etc, a technology that will be pervasive and absolute in its impact on the world and its inhabitants. The ability to find hidden patterns to predict the future or detect a behavior has massive implications across the world, in every industry, sector, and domain. When faced with this realization, enterprise's can find themselves stuck, paralyzed and unsure about how to proceed. The field of AI is decades old already and the early success stories have been practicing AI for multiple years already with the tech industry leading the way. How can an enterprise that has no experience and competency in this area let alone lead the technology or even leverage it appropriately to drive business value? When developing the AI strategy, two ideas are paramount. First, this a fundamentally disruptive technology and the enterprise will need to establish it as a core competency for the foreseeable future. Not doing so will not be an option. Second, a long-term plan to success is superseded by the need to drive quick wins and small successes not only to build confidence but use real-world experience to develop and hone that skill. The Short-Term AI Strategy The short-term AI strategy should focus on driving immediate business value through enhanced customer experiences that leverage any field of AI be it machine learning, deep learning, natural language processing etc. Driving the usage and deployment of AI in front of an end user making them smarter, productive and better informed can pay rich dividends by not only helping the enterprise can real-world experience, but it can also give a perception boost to the company as being innovative and cutting edge. However, most importantly, this can highlight and promote the success and potential of AI in the enterprise and encourage a snowball eff

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