Other

Enterprises across the world are struggling to make sense of their growing volumes of data. The Big Data market is expected to become $56 billion in revenue in 2020. Data science is helping enterprises integrate data from across the organization and uncover actionable insights to improve business decision-making. From improving forecasting accuracy to understanding customers better, detecting fraud in time to driving prescriptive analytics – the use cases of data science in the enterprise are many. However, the success of your data science initiative is a lot more than the amount of data you collect or the tools you use to process that data. It all depends on your approach towards data discovery and data preparation – the two most critical phases of any data science initiative. Why data discovery matters When it comes to driving value from data science efforts, every person in the organization – whether a business owner, analyst, software developer, or program manager – needs to be able to read, understand, and glean value from all the information that’s coming in. Data discovery is the process by which organizations can detect patterns in data with the aid of advanced analytics, thus enabling consolidation of all business information. By applying skills in data relationships, data modeling, and guided advanced analytics functions, data discovery helps to reveal patterns and get all the insight that the data has to offer. Using interactive data visualization techniques, decision-makers can view information at a glance and understand major trends as well as spot outliers – in an instant. Because data is presented in charts and graphs on one page against being buried in tables spanning multiple pages, it makes it far easier for people to absorb and act on data. Data discovery makes it easy for everyday users to make sense of data and find answers to questions without relying on the IT department to set up complex data environments. It makes it easy for them to handle a high volume and variety of data while greatly reducing time-to-insight. This ability to analyze patterns and trends within data sets can help businesses meet business goals, ensure success, remain relevant in the digital era, and gain a competitive edge. Data discovery also paves the way for more sophisticated and pattern-oriented data analysis, helping organizations to overcome the challenge of providing ready-to-use statistical functions to business users and deliver proper outcomes – without having to write a single line of code. It goes beyond mere monitoring of organizational performance and extends capabilities to optimizing business processes and fueling new business models. Why data preparation matters Given the various types, formats, and volume of data that is accumulated across enterprises today, preparing or pre-processing data to improve reliability, consistency, and accuracy of data is critical. By consolidating, cleansing, and transforming data, organizations can more easily connect to one or many different data sources while cleaning, reformatting, or restructuring dirty data. Since such pre-processing of data is extremely time-consuming, data preparation greatly reduces the time it takes to discover insights. Since organizations end up collecting a lot of data – all of which isn’t needed for data analysis – it can skew the model for predictive analysis. Data preparation helps in effectively managing the volume, velocity, and variety of data. By merging relevant datasets into a new data set, filtering, cleansing, and aggregating it into the right format, data preparation eliminates faulty, irrelevant, or erroneous data, enriches it further, and improves the accuracy of data science initiatives. Using analytical or traditional extract, transform, and load (ETL) tools, organizations can effectively integrate a variety of data sources and cleanse and transform it by adhering to data standards. This can only help organizations not only unravel insights faster, but it can also help them drive value sooner – across the enterprise. Generate real business value In today’s digital age, the right business decisions are the consequence of analyzing the right data. Therefore, all business users need to be able to access and make sense of the data they’re handling. Given the volume and variety of data being assimilated, applying data science techniques to all of this data is not only a time-consuming process but the analysis results that follow also have a high chance of being incorrect. Focusing on data discovery and data preparations is extremely important to efficiently integrate data from various sources and cleanse and transform it to generate real business value. These phases not only enable data-driven decision-making but also help in enhancing business outcomes while propelling intelligent business strategies. Linkedin X-twitter Facebook

A lot has been spoken about the explosion of data in the world. As we generate the zettabytes of data, big data and analytics have cemented their place in the enterprise. Today, most successful and forward-thinking organizations are banking on big data and analytics to power their decision making. Being data-driven is the only way to drive the enterprise because, in today’s environment where VUCA reigns supreme, there is no place for guesswork. The evolution of the CIO’s role Amongst other things in the organization, the rise of big data and analytics has influenced the role of the CIO. The CIO was once considered a functional unit head, who made the promise of rapid delivery. Over the years, this role progressed further, and we saw the CIO become more of a strategic partner who enabled business convergence in the wake of technological evolution and adoption. Today, the CIO’s role has evolved a little bit more and become more centered around business transformation. The CIO is almost like the change agent who ensures that all IT functions that support the business work smoothly to effect transformational change. To implement transformational change, you need information and intelligence –something that you can get only with data and analytics. As with any area of IT, there are strategic considerations involved in selecting a big data and analytics strategy for an organization. Here are some guidelines for CIO’s to follow that help in designing robust big data and analytics strategies that lead to organizational success. Identify potential business outcomes Having a big data and analytics strategy is great. But what are its use cases? Where do you want it to deliver value first? We must remember that a goal without a plan is just a wish. For great results, a big data and analytics strategy begins with identifying business outcomes where data has the potential to generate business value. For example, if an organization wants to increase its customer share of wallet because customer loyalty is no longer enough, then the focus of the data initiative has to be on the data that influences the customers potential to purchase products. The CIO, thus, has to be aligned with organizational and business goals to map the data strategy to these for productive outcomes. Democratize data The CIO is also the promulgator of business-led analytics. While IT data infrastructure remains IT’s responsibility, the capacity to use data, and the ideas to best use data comes from the frontlines and the business users. The business users, along with the analytics and data science teams, have to be enabled to become more data-driven in their decision-making. For this, it is important to democratize data and use advanced analytics platforms that allow business users to build and manage data flows and gain access to powerful data visualizations, create predictive models, and streamline and automate forecasting processes. The CIO has to make the right technology choices that enable business users to become more data driven. Model collaboration The objective of big data and analytics is also to enable collaboration to drive transformation and lead to successful business outcomes. When designing big data and analytics strategies, CIOs have to not only promote collaboration between technology teams but also between the business and the technology. The big data and analytics strategy has to be tied closely to the business teams and what outcomes they want to achieve. The technology, tools, and platform selection have to support this and help them build useful information networks. Technology choices here would involve looking at the frameworks the platform uses and identify the ease with which users can access the right data and build data sets, irrespective of their technological expertise. It also involves ensuring that the platform offers a range of ready-to-use or easy-to-build algorithms so that business users can create data models that serve business purposes and generate business value. Generate support for transformation IT has become the backbone of the enterprise of today. As digital becomes an organization’s priority, CIOs have to design their big data and analytics strategies so that they can achieve a business impact. For this, the CIO has to ensure buy-in and generate support amongst business leaders across the organization and make technology choices that help in fostering true partnering relationships based on common goals. This involves scrutinizing technology decisions beyond the cost and high-level strategy parameters and evaluate those from a business point of view. This can be done by determining the data and analytics capabilities required to fulfill the organization’s business strategy. Pursuant to this, the CIO has to take a deep dive into technical assessments, system, and data platform compatibilities, and vendor capabilities to make the right decisions. Technology capabilities One of the greatest advantages of big data and analytics is that it helps organizations move from insight to foresight. However, this involves making the right technology decisions and leveraging the right data platform that enables not just analysts and data scientists to make use of data but also enables the non-technical business user to become data driven. Technology decisions would involve looking at platforms that help business users overcome the challenges that emerge when identifying and categorizing large data volumes, especially test data. Platforms that support faster data visualization turnaround, allow flexibility to gain insights into any subject area, and provide a consistent user experience aid the success of big data and analytics strategies. Technologies that help business users play with data without technical skills pave the way for becoming a data-driven organization and ensure the success of data strategies. Along with this, the CIO also has to evaluate the extensions and apps and accelerators the platform can provide. Extensions involve evaluating parameters such as the capability to manage large volumes of data, the capability to do complex processing, in-memory storage, and security. The Apps and Accelerators for elements such as fraud detection, risk identification, demand forecasting, predictive intelligence, recommendation engines, etc., which make the process of becoming data-driven in decision making faster and more

The race to win over the best data scientists is getting fiercer each day. Reports show a 29% increase in demand for the role of data scientists year over year and a 344% increase since 2013. We find the demand for data scientists becoming more insatiable as organizations move forward in their digitization journeys and begin to leverage the power of data to fuel their business decisions and improve competitiveness. Just as organizations are accelerating their hunt to scope out and recruit ace data scientists, some want to leverage the opportunity in the data science landscape and become data scientists. Owing to this need, we have witnessed the rise of several data science programs, some more coveted than the other. The Harvard Business Analytics Program is one such program that has managed to generate much interest in those wanting to ace their journey as data scientists. Great, comprehensive, thorough, with a cross-disciplinary curriculum, this analytics program promises to propel data scientists to become data leaders. Those who can attend this program are sure to benefit from it. However, what about those who cannot partake in this program? What can these people do, the ones who cannot venture through the hallowed portals of Harvard Business to open the door to the promised land of data science? Data science and technical competence – look online I’m not going to delve into the details of what educational background you need to start your journey as a data scientist. You have Google for that. Along with the basic technical education, data scientists wanting to become the new ninjas have to look at honing multiple skills needed for the job – statistical analysis, programming skills, data management, data wrangling, machine learning, data intuition, etc. are important areas that where you’ll need in-depth knowledge. You also need a good knowledge of analytical tools, (R is probably one such platform that fits like a glove in the data science narrative), and strong coding skills (knowledge of Python will help here) are also needed. Having experience in the Hadoop platform also is an important skill set to have as a data scientist. In fact, according to a study, “3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.”  That is a long laundry list, and it keeps getting more comprehensive and a great way to home these skills are to look online for comprehensive data science courses that cover all this and more. Along with this, it also helps to look at specific areas of improvement to further your journey as a data scientist in a more personalized fashion. For example, knowledge of machine learning concepts such as neural networks, reinforcement learning, adversarial learning, etc. can be of great use when you want to stand out as a data scientist since many data scientists do not have this skill. Improving proficiency in SQL is also immensely helpful even in the NoSQL landscape as SQL is designed specifically to help data scientists to access, communicate, and work with data. Knowing the concise commands saves time and reduces the volume of programming needed to run difficult queries. Knowledge of the Apache Spark platform other than Hadoop, for example, can be another area to upskill as the former makes it easier to run complicated algorithms faster and supports data scientists handle more complex unstructured data sets. Get Hacking – don’t doubt the Hackathon experience If you thought Hackathons should be attended only when you are on the prowl to land a great new job, then think again. Hackathons are great for both tech-enthusiasts and newbies to check their skill levels, learn new things, and score a hands-on experience in problem-solving real-life business problems. Want to know what the latest and trending thing is? Go to a Hackathon. Hackathons like MachineHack, TechGig, Kaggle, etc. are great places for data science enthusiasts to elevate their skills by participating in challenges set on real-world problems. Hackathons are also great to identify how and where your skills need updating since the data science industry is dynamic and prone to change. All in all, these challenges are a great place to learn by working on real-world problems, upskill yourself, and showcase and build expertise. Work on real-world projects You cannot learn everything there is to learn about data science in a class because data science is all about solving real-world problems. Data science is both a science and an art – data scientists have to wrestle with the large data volume, create models, and provide statistical analysis. This is the science part of it. The art in data science lies in the data scientists’ capability to understand the business problem to design the right solution and communicate the data results in a language that non-data scientists can understand. Data science is more than just building a data model – it is about understanding a business problem and then building a data model to play around with that data and glean intelligent insights. If you don’t have the opportunity to work on ‘real-world’ projects, you can always self-start with your own data science project. Find a data set that interests you, ask and try to answer questions around it, write out the results, and then rinse and repeat to develop real-world experience. Or you could look at organizations looking for help in their data science initiatives to further your learning. Data science is a niche field. It is also a democratic field that allows those with skills, knowledge, and curiosity to thrive. So just like how data scientists find the solution to every problem, finding out how to hone your data science skills to be the coveted data scientists doesn’t have to be difficult only because Harvard is not within hands reach.

With a new regulatory alert being issued every 7 minutes, growing compliance regulations are challenging banking institutions in a variety of ways. Changing customer behavior, and the constant evolution of technology is compelling them to change how compliance is approached. Ensuring compliance with a rising number of government and industry regulations can be hard-hitting and put a strain on the already drained resources. While traditional compliance models were effective for an era where simple enforcement was sufficient, today, they offer a limited understanding of business operations and underlying risk exposures. With the risk of regulatory sanction, reputation and financial loss, due to a failure to observe compliance obligations becoming extremely far-reaching, those who adapt best are the ones to enjoy a distinct competitive advantage. As each new industry regulation and its associated deadline causes a massive influx of new data that has to be stored and analyzed, garnering insights rapidly becomes vital for optimizing processes and pinpointing any potential problems areas. With compliance costing businesses $5.47 million annually and non-compliance $14 million, analytics is enabling organizations to keep pace and avoid the risk of costly non-compliance. It is helping banking organizations to stay ahead of compliance requirements, and better anticipate and respond to change. Here’s how analytics can help with banking compliance: Unearth reporting insights: Institutional banking clients, as well as regulatory auditors, constantly demand banks to reveal risk and possible exposure scenarios. Real-time analytics is a critical aspect here that allows banks to handle high volumes of data and unearth insights that meet the growing compliance needs. Using analytics, organizations can collect and distribute necessary compliance data to deliver reporting insights that are required throughout the enterprise, and meet regulatory requirements with ease. Improve risk control: Since non-compliance can result in substantial losses, analytics can help scale up the computational power of risk management. Decision-makers can ask more complex questions and get more accurate answers faster while developing new business strategies. Analytics-aided techniques can produce more accurate regulatory reports and deliver them more quickly. Since the need to pre-aggregate data is eliminated, risk managers are in a better position to understand the nuances in data, reduce fraud losses, and improve risk control across the enterprise. Enhance productivity: As banks need to be always ready to provide regulators with a quick response to regulatory stress tests, analytics plays a big role in making processes faster and more effective. Using advanced analytics, organizations can achieve faster and more accurate responses to regulatory requests and give teams analytics-driven decision support. Banks can use analytics to understand compliance levels across the enterprise, identify avenues that fare poorly, and take measures to enhance productivity and save money. Drive agility: With thousands of new regulatory requirements being ushered in every year, manually managing compliance activities is a fruitless undertaking. Manual compliance efforts are not only cumbersome and tedious, but they are also extremely prone to error. This increases the degree of risk and limits a company’s ability to meet growing regulatory requirements. Analytics allows organizations to better manage risk and compliance obligations; by aggregating data that’s needed from across the business, analytics paves the way for greater reporting accuracy and efficiency. Using analytics, organizations can respond quickly to the evolving regulatory landscape, and drive agility. Lower costs: With massive legacy and personnel costs going towards regulatory and financial reconciliation, firms have a pressing need to comply at a lower total cost of ownership. Since regulations and the market environment greatly hamper banks’ abilities to just throw money at the problem, analytics helps drive improved metrics and reporting through automation. Banks can transform raw data for cognitive and analytic processing, meet regulatory needs at a fraction of the costs, and drive higher efficiency. Effectively manage compliance Banking and other financial services companies have to contend with a variety of industry regulations and compliance requirements. As the time and cost of regulatory compliance and reporting vastly increases with every new regulation, keeping up is a great cause for additional stress – especially at a time when new competition and increasing customer demands is creeping from the sides. Advanced analytics is enabling the banking industry to become smarter in managing the myriad challenges it faces – by offering compliance officers enterprise-wide intelligence, analytics can help avoid financial non-compliance and stay a step ahead. Analytics-backed solutions are enabling banks to not only manage the increasing cost of compliance, but also the risk of non-compliance – both monetary and reputational.

Data Science – one of the fasted-growing fields around the world has taken companies across various verticals by storm. It has become an integral part of the working of many enterprises – across finance, retail, healthcare, insurance, gaming, entertainment, news, etc. Don’t believe us? Let us take a look at some real-world applications of data science – All the search engines, including Google, Bing, Yahoo, etc. use data science algorithms to deliver the best results – quickly. Recommendation engines – yes, the ones that suggest you the next product to buy or the next movie to watch – are all powered by data science. Buzzfeed uses the power of data science for headline optimization Banks use data science and machine learning algorithms to make better lending decisions, serve their customers more efficiently, and ensure customer satisfaction. Insurance companies use machine learning and predictive models to detect fraud. This helps them save time and costs in pinpointing the claims and also detect fraudulent claims. Airline companies including Southwest Airlines, Alaska Airlines use data science to predict flight delays, drive customer loyalty programs, and ensure customer satisfaction. Fast food chains use big data to change their menu features based on the drive-through lanes – if the line is longer, the menu shows the items that can be quickly prepared and served. When the queue is shorter, the menu features other high-margin food items MGM, one of the most famous casinos in Las Vegas, uses data science and data analytics tools technologies to make the gaming experience better for customers, constantly measure their performance, and make better business decisions. The voice-based-assistants on your smartphones – yes, your own Siri and Cortana – are all powered by data science. The dynamic pricing that you experience while booking an airline ticket or tickets for your favorite show is the result of number-crunching and analysis done in the background based on demand, supply, competitor pricing, etc. At Rubics Labs, we have created a data science platform that enables business users to be the data scientists, ask relevant questions to their data, and get actionable insights. We have made data science easy and simple! Check out www.rubicsape.com to experience the power of Data Science.

In today’s connected world, data is easier to find, store and use. Think of all the data or information that are available for enterprises from mobile (clicks, visits, interactions), internal systems, social media, email, text, and so on. All the data collected is unique and come from a different set of demographics, region, and devices. This provides opportunities to enterprises for delivering a one-of-a-kind experience to their potential customers. It also enables them to make informed decisions with the predictive power of analytics, maximize their ROI, and achieve business success. Data Science is the area of expertise that helps organizations in making sense of the data and make the most of it. It is evident that the impact of data science is profound. But on the flipside, there are certain preconceived notions about data science, which can be a roadblock. That said, let’s look at some of the common myths about data science, which we refuse to believe. Data Science is Easy Data science is anything but easy. While there are no standard set of tools and techniques to master it, you will not even find a fixed educational standard or certifying bodies that can pave the path for a guaranteed successful career as a data scientist. The skills need to be acquired, and although certain tools and technologies can assist you, the learning should always go on. For instance, Hadoop may be one part of your data science arsenal, but you will still need to keep acquiring new skills and gain more knowledge as data science and analytics evolve. This takes us to the next point. Data Science = Only for Data Scientists IBM predicts that the demand for data scientists is going to soar 28% by 2020. What does this tell us? Well, to begin with, it presents an acute shortage of workforce needed to handle ever-increasing data. This gap needs to be bridged by training the right professionals, who may not be trained in data science yet and should aim for the right set of skills and perform specific tasks. Termed as citizen data scientists, these professionals don’t have an advanced degree in the field but can surely master the technology over a period. Hire them to perform some simple as well as moderately complex analytical tasks. They will play a supporting role to your existing data scientists and at the same time, bring their unique skills and expertise to the table. A Degree in Data Science Will Make You A Data Scientist   Another faux pas in data science is the belief that by earning a degree in data science will make you a data scientist automatically. Yes, you can get a master’s degree with a curriculum that includes course materials related directly or indirectly to data science. But that won’t necessarily propel you towards becoming a professional in the field. You need to work in the field, handle different projects and work on real data to get hands-on experience. The real world is different than textbook learnings, and therefore, it may take some time before you step into the shoes of a data scientist. It is a gradual process that needs consistent efforts. Learning a Tool is Learning Data Science  More often than not, learning a tool is confused with becoming an analyst or a data scientist. But the ground reality is entirely different. Knowing how to use a tool will make you a programmer or the expert of that technology, but it does not necessarily make you a data scientist. This is because you will also need to master several other nuances such as domain knowledge, statistical understanding, modeling, etc. that are crucial in handling big chunks of data. This doesn’t negate the need to learn tools though. But yes, you need to strike a balance between learning tools and gaining the right skills to become a data scientist in the real sense of the word. Data Science is Only for Big Companies Another myth on our list is that data science is meant for big firms just because it requires expensive hardware, software, and prior expertise. On the contrary, what big enterprises need to look forward to is hiring smart people who have a natural knack for data science and provide them with the appropriate easy to use data science platforms. Such platforms can allow them to apply data science in various processes or at various steps. Therefore, instead of focusing on sophisticated resources and spending all your budget on the same, recruit talented data scientists who can leverage available data. Data Science Is Only for Specific Industries Data science is open for all industries, and the roles of a data scientist are never limited to one particular domain or niche. Yes, there are some industries such as retail, banking, or transportation that need data science and analytics presented differently. But overall, it is a commonplace in every industry that exists today. If you want to hire a specialist in your industry, you need to take a pick from the data scientists, who already have many years of experience and expertise in the same domain. Calling It a Day These are some of the common beliefs (albeit false) related to data science. How many of them were you staunchly following till now? Have any more such wrong notions that need to be added to the list? Bring them on. We’re all ears!

By 2020, 36% of the workforce will comprise of Millennials – these are the human resources who are born after the baby boomer era. The Millennials and Gen Z are the digital natives. They are not only extremely comfortable with technology but also expect their workplace to be digitally advanced too. With this new workforce, comes the need for a modern workplace. What fostered productivity earlier has been replaced by newer motivation and driving forces coupled with new ways of collaboration and working. The digital workplace has become an inherent part of the organization. Legend has it that Steve Jobs was extremely fixated on collaboration between the various teams, and hence, he facilitated the build of a concourse to enable various teams to meet and exchange ideas. A similar principle is being further augmented by next-gen technologies, which are also increasing interconnectedness in the workplace. Enterprises today are implementing next-gen solutions to optimize day-to-day operational costs. This brings us to the first point about connected workplaces. Interconnectedness: It is no more just collaboration but intelligent interconnectedness. Whether it is a factory floor or a corporate office, connectedness is being redefined with AI and IoT. Smartphones have converted themselves into mini offices and at the click of a button, can turn themselves into a virtual conference room. On the shop floor, a person sitting in the control room has a clear view of the health of the various equipment as well as the whereabouts of people working in various areas.  It is helping is fostering greater collaboration, transparency in operations, and also workforce safety. Smart Buildings: Another example is the conversion of office buildings into smart buildings. The operational cost of keeping an office running is fairly substantial for most enterprises. Star Trek fans would know about the concept of running the spaceship in grey mode. This essentially means that when the starship has fuel constraints can only operate those machines and components which are crucial to keeping the spaceship flying. IoT and AI can enable the offices to run on the proverbial grey mode during the off-peak hours. The lights and other power equipment are getting sensor controlled. This is contributing heavily to power saving (leading to cost-saving), the safety of the workers, and of course, helping companies become more environmentally friendly. Automation Leading to Productivity Enhancement: Communication, as stated earlier, is being abetted by AI and IoT. For instance, if your employees need a software upgrade or want to get a damaged ID card replaces, it can be automated with the help of AI/ML. One can raise self-service requests and get most of their issues closed. This allows the operations staff to work on more value-add things and better communicate with employees on the things that really matter. On another note, have you ever noticed how the files that we use, the names of the people that we most communicate with pop up automatically? There is smart text analytics with the help of which most of the communication gets flagged so as we do not miss those important emails. Workplace Safety: In industries such as oil and gas, worker and ecological safety is one of the primary areas of concern. No one wants a rerun of the Deepwater horizon episode. The ID card of the employees, as well as their families, are IoT enabled with a microchip. The sole motive of that chip is to give a real-time location update about all the people on the site. This helps the companies optimize their safety effort in case of a disaster. With the help of the tracking device, the relevant stakeholders can know where their human resources are and in what priority they need to be rescued. Employee Engagement: Now let us come to the most awaited piece, how can AI drive employee interaction and training. HR analytics is already a part of the interactions between the HR and the employee. Various data points are tracked to gauge employee engagement levels within the company. This gives the HR leaders a view into the churn predictability. This input can help organizations retain their most prized employees. Similarly, AI can also identify upskilling opportunities. AI/ML-based identification and formulation of training modules to reskill and upskill resources have become a critical part of the use cases. We are sure that companies will explore this extensively going forward. Having said all that, one must realize that we are at a cusp of change, and these technologies are still maturing. The market is responding with a lukewarm approach to the adoption of AI and IoT in the workplace. But as and when it gathers traction, this will start playing a pivotal role in generating employee productivity.   

While there are several definitions of “digital leader”, the one which resonates with my thinking is “A digital leader is a senior leader who promotes long-term digital transformation through not only technology, but a combination of strong strategy, culture, structure, and collaboration”. Digital transformation is high on the corporate agenda of organizations across all levels and industry verticals. Millennials are the largest living generation today, and they form a large portion of the workforce. This digital-native workforce generation is extremely comfortable with the latest tools and technologies and expects their leaders also to be tech-savvy. Today’s leaders, therefore, not only have to have a firm grasp on technology but also need to have the vision and innovative mindset to bring a fresh mindset to the table. At Rubiscape, we work with several thought-leading CXOs who are leading the digital transformation change in their organizations. I have been fortunate to interact with several of those. And apart from their solid technical understanding, I believe there are some very peculiar traits that differentiate them from others. They have the vision When it comes to leading digital transformation, vision is everything. Digital leaders understand that digital transformation is an opportunity for them to create a competitive differentiator, to come up with new business models, to create stellar customer experiences, and to innovate. They are equipped to carve a holistic digital strategy to ensure business growth and lead disruption. They strike a fine balance between legacy systems and innovation etching the digital era Digital leaders have a broad understanding to delve into balancing the organization’s strategies. The pace at which technology is mitigating has necessitated the need to stand by the same in order to strive for success. And digital leaders know exactly when to strike the rod hot. Digital winners are ones who expertly leverage diversity in the workforce while optimizing their current business strategy. They understand the importance of building the right team The most effective way to boost a company’s performance in the digital age is by hiring the right talent. Efficient digital leaders seek expertise to enhance the firm’s overall efficiency. This does not simply suggest hiring the right professional but mapping the young digital enthusiasts with the existing experienced resources. Thought-leading digital leaders gather the best from the market to create a mix of a technologist with the digital-savvy leaders. At the same time, they understand the importance of giving the team creative freedom and space. They allow their teams to fail and learn. They are experimental and embrace risks Making mistakes is unavoidable and digital leaders understand that. True that the digital world is mostly unpredictable and plagued by risks, influential digital leaders steer in an environment of sequential work culture to alter the same to parallel working. They inspire others to be creative, take risks,  introduce new policies, try new technologies, introduce a new method of working – all while acknowledging that failing is still an option. They thrive in uncertainties Digital leaders believe that chaos only creates a path for a better future. They strive to delve in uncertainties and see failures as the way to learn and succeed. They believe that digital does not justify staying on the safe side by simply adopting technology but induce innovation in all situations to see an exponential growth of the organization. They thrive in ambiguity while seeking clarity Until the twentieth century, the strategy opted by all organizations worldwide worked to achieve clarity. However, the growing pace of the digital economy has forced today’s leaders to delve in a tough and complex situation. This leads the adoption of ambiguity by all the digital leaders. Digital leaders seamlessly function in an ambiguous situation by acknowledging that they will learn on their way and constant change is the name of the game. They are collaborative Today’s digital age demands strong collaboration between various departments within an organization. Knowledge sharing is paramount and sharing of information and insights across departments is the need of the hour. Thankfully, today technology is available to enable such collaboration. Effective digital leaders work towards bringing in the cultural change to foster collaboration through an open environment. In fact, they themselves take a lot of efforts to involve teams in decisions, elicit ideas, and discuss issues to come up with possible solutions. Digital leaders are the mentors, the influencers, and possess contagious enthusiasm. They understand the power of technology and the importance of building a team that can transform ideas into reality. They emphasize on creating the right culture – the culture of innovation, the culture of collaboration,  and the culture of risk-taking. To be very honest, I think all the leaders will need to be digital leaders in the very near future – how about we all start prepping up now?

Let me begin with a story. Virgin Atlantic took on the project to improve the design of their reclining chairs on their airplanes. Many stalwart designers were roped in. Many designs were presented. But none of the designs was a hit. Then on the horizon came a young designer, Joe Ferry. Ferry was already working at Virgin in the capacity of a designer. He asked if he could give this project a go and was, in return, given a free hand. And then the herringbone-configured Virgin private sleeper suites were born. Had Virgin Founder, Richard Branson not given Ferry this creative freedom, would Virgin be ahead of the pack with millions of happy fliers? All Branson did here was that he gave an intrapreneur the opportunity he/she needs. Intrapreneurs, much like entrepreneurs, are a breed of achievers who drive the organization, explore unexpected directions and give a business an interesting new dimension that spurs growth…but they do so from within the organization. As I look around and see the constantly shape-shifting market dynamics and growing challenges, it becomes clear that only the innovative will survive. We have to constantly work towards building intrapreneurs within our organizations. Why am I such a big fan of intrapreneurship? Here is a basic laundry list The millennials are here – how will you retain them? We can’t ignore the fact that very soon 75% of the workforce is going to comprise of millennials. What does this have to do with intrapreneurship you ask? How’s this for a reason – research shows that millennials are the true entrepreneur generation. When the past generations dreamed of perks, the millennials are focused on doing more, delivering more value, and being their own boss. The game of hiring and retaining top millennial talent has never before been so savage. High salaries no longer guarantee loyalty. Free food, on-site barbershops, and swimming pools are losing their charm. But if we want to get the attention of this generation, we have to enable their entrepreneurial spirit. ‘Entrepreneurship’ is the buzzword for the decade – and intrapreneurship helps millennials navigate challenges and drive value. With this, they no longer see themselves as cogs in the machine doing the same monotonous work every day. The Entrepreneurial Zeitgeist – how will you establish this in your organization? Silicon Valley made entrepreneurship the popular kid on the block. Now everyone wants to be an entrepreneur. However, fact and feeling are two different things. While most of us want to be entrepreneurs, studies show that only 7% actually end up being successful ones. However, if we begin to promote intrapreneurship within our organization, we get to identify who our high-potential employees are and also the future leaders of the company. If we think about it closely, would Steve Jobs have been able to build Apple into the giant that it is today Steve Wozniak? When we talk about growth, we have to account for pieces of the business that aren’t doing so well or ones that could do better and then reach out to people with entrepreneurial who can think creatively and come up with innovative solutions. And intrapreneurship enables this. Thinking outside the box- are you just talking the talk and not walking the walk? I also love intrapreneurship because it gives an organization the capability to think outside the box. Innovation might be a term that might be groaning under its overuse; the fact remains that it is one key differentiator that separates a successful business from an unsuccessful one. Organizations need to stay agile in an environment where technology and automation are changing the rules of engagement. Unless we are creative, we will lose market share or will be pushed out into the oblivion. We only have to look at established companies like Kodak and Nokia to understand the price we might have to pay if we don’t take innovation and creative thinking seriously. Promoting intrapreneurship within an organization builds an entrepreneurial culture within the organization. This is extremely relevant today as organizational culture demands that we enable forward and creative thinkers who not only help organizations keep up with their growth path but also helps the organization become a leader. Google, for example, is famous for its ‘20% time’ policy which encourages their employees to spend to spend 20% of their time working on projects that they think will benefit Google. This policy has boosted entrepreneurial thinking. It has also helped Google remain one of the most innovative companies in the world. Intrapreneurship is a tool – An intrapreneurial culture builds the bottom line It is logical to assume that all companies want to stay competitive and to improve their bottom line. Intrapreneurship is a great tool to give employees opportunities to encourage them to come up with new ideas that will help the business. We need new ideas, but along with these new ideas, we need new ways to solve existing business problems. Employees are the best resources to understand the problems that plague the company. So it only makes sense to give them avenues to solve these issues and identify new areas of growth. By building an intrapreneurial culture, organizations can set a precedent of growth by giving employees opportunities to bring their ideas into fruition without them having to leave their jobs and risk their livelihood. The positive impact on the bottom line becomes a consequence of these actions. Organizations today and those of the future have to be agile, creative, and also open to the world. Those who leverage the entrepreneurial talent of their employees at all levels will be able to achieve this because the role of human resources has become critical in meandering organizational growth expectations. Given the status quo, promoting intrapreneurship injects speed and flexibility in an established organization that could previously be imagined only in a start-up environment. And with the complex dynamics at play, it is only a matter of time before intrapreneurship becomes the new normal for all forward-thinking organizations.

Women’s Day is here and we are back to the conversation regarding women’s representation in the corporate world. It’s strange that in the 21st century we need a special ‘day’ to recognize the contribution of women in our lives…both personal and professional. At some level, I feel, that we should not need to have a special day dedicated to women…a special day dedicated to recognizing her contribution, her mettle, and her achievements. We should not need a special day to truly appreciate the women in our lives and across the world. But at the same time, I feel that we still need a Women’s Day because, despite all that they have achieved, women still remain grossly underrepresented in the corporate world, still struggle to bridge the pay gap, and have to work harder to shatter the glass ceiling. In the technology space, for many companies celebrating equality and talent has become a CEO level issue. There are several examples of tech leaders emphasizing the importance of equality in the tech space whether it is in terms of equality of pay or choosing women for higher and better opportunities and bigger responsibilities. In today’s marketplace ‘equality’ has got business value. So this would mean that the participation of women in the workplace, especially tech is on the incline right? Well statistics say otherwise. Surprisingly, or rather unsurprisingly, only 5% of leadership positions are held by women in the tech world. Reports also state that more than two-thirds of US startups have no women on their board of directors. A recent report by the World Economic Forum states that the global gender gap will take over a hundred years to close going at the current rate of change! To a reasonable mind, these stats seem ridiculous…almost unreal. Why would we be looking away from a  workforce that is highly skilled and efficient only because of their gender? Fact is that in the US alone over 1.4 million jobs will open up by 2020 and the US will have only 29% of qualified graduates to fill these roles. Out of this, only 3% will be women. Strange? Well, sometimes the truth is indeed stranger than fiction. Being a part of an organization where over 35% of the workforce constitutes of women, I very strongly feel that today tech companies are in need of strong leaders. Women Leaders. Simply because, if history has taught us anything it is that women, when enabled and empowered, are capable of reaching any height of success. I think at some level it is downright insulting if I use gender as a yardstick to suggest what an individual can or cannot accomplish. If we look around, women have already proved that they are excellent administrators, team leaders, exceptional workers, innovators…the list goes on. So why look at justifying their roles or their mettle because of their gender? Let’s look at some basic facts that are relevant in today’s context and are not guided by some medieval thought emerging from the dark ages as to why there should be more women leaders in tech companies. To begin with, unless you have diversity in your organization you will be only getting a myopic view of the world. Today women are active participants in the consumerization landscape. It only makes sense to have a set of eyes that will guide you on what this demographic wants because they know what they want. A study at Development Dimensions International (DDI), the global leadership development consultancy, states, “Encouraging gender diversity in leadership ranks leads to more diversity of thought prompting improved problem solving and increased business benefits. Organizations with women in at least 30 percent of leadership roles are 12 times more likely to be in the top 20 percent of financial performers.” And more pertinently, by not encouraging gender diversity in the tech space, all you are doing is alienating a vast, trained, skilled, efficient, and eager workforce. That’s not the best business strategy, is it? I strongly feel that its high time that the doors to this exclusive ‘Boys Club’ that exists in technology companies open its doors wide to women not because of anything else but because of their ‘talent’. It is high time that hiring in organizations is done because of ‘talent’. It is also high time that we stop looking at women as soft, nurturing souls and recognize that the same nurturing person can be an equally powerful and impactful leader. Let’s start hiring for job roles on the basis of qualifications of merit and stop looking for lame excuses as to why a woman would not suit that role. We have entered the age when machines are communicating with one other and with us. For most, that is progress. But is it really progress when we still don’t recognize the achievements of half of the world population despite her having proved herself, in the harshest and most toxic environment? I believe that real progress will only happen when we take a conscious decision to unanimously hire and promote on the basis of merit and not gender. Until then, we will continue to need a Women’s Day.