Author name: Rubiscape Team

Data Science has (yet again) topped LinkedIn’s list of the most promising jobs. It has been at the top of Glassdoor’s Best Jobs in America list for the past three years. Gartner predicts that 90% of the large companies will have Chief Data Officers by 2019! Just a couple of years ago, it was a just a buzzword. Fast forward today, it is something that has become a priority for most of the CXOs – data science certainly has seen a rapid transformation. It has moved far beyond “is this necessary for business” skeptic stage – several organizations have already laid strong foundations for projects to make use of AI, IoT, ML to enhance customer experience and achieve operational excellence. While companies understand the value of data and are collecting a massive amount of structured and unstructured data, they lack the talent, tools, and expertise to make sense from that data. It is no surprise that most companies are able to analyze only 12% of their data. Data science is the driver which can help companies make sense of data. Data scientists are the people who help companies in cleaning and organizing the data, visualizing the impact of the data, and turn raw information into reliable, usable, and meaningful insights for data-driven real-time decision-making. Benefits of Data Science The immense value of data-driven decision-making is clear to organizations. With data science, companies can Proactively mitigate risks through identification of patterns and by taking in time actions Better understand the customers and take initiatives to drive up customer experience and engagement Improve forecasting through timely analysis of past performance and market behavior Innovate better and faster innovate – create real-time solutions that address the current challenges Gain unique insights for business expansion Drive up creativity through knowledge gathered from various data sources Enable data-driven decision-making at the micro-level within the organization – thereby achieving significant impact on the bottom line Quickly adapt to changes thereby improving efficiency and boosting performance Data Democratization – The Challenges With the benefits of data science being ample clear, enabling data-driven decision-making across all levels of the organizations through data democratization is high on the agenda of company CXOs.  Data literacy is the top priority as companies have started to realize that data at the fingertips of many can be transformational for organizations. However, becoming a data-driven enterprise is easier said than done – Most of the companies lack the proper understanding of data science and its real impact Many companies are not clear about the investments in technology, people, and processes Enterprises are often not clear about the exact business scope and the required investments for that Companies are worried about data security and privacy A majority of the companies do not have data science expertise – however, they do have domain and business experts who can derive meaning from data Hello Citizen Data Scientists Considering the dearth of data science talent, lack of expertise in hiring the right skills, organizations are realizing that they need to utilize the expertise and domain knowledge of their internal team of experts to derive the maximum benefits from data. The new concept of “Citizen Data Scientists” has started emerging. Citizen Data Scientists are the business experts who may not necessarily understand the technicalities of stats, math, and machine learning, but they understand that importance of data and how it can be leveraged for decision-making. They are the ones who are leading the data-driven initiatives in modern enterprises. The Revolutionary Rubics Platform To make the citizen data scientists successful, they need to be enabled and empowered with the right tools – tools that will allow them make the most of their subject matter expertise and not burden them with technicalities, math, science, or data modeling etc. Rubics is the revolutionary data science platform that helps business leaders apply the Right Models to the Right Data and easily identify patterns in massive volumes of data – enabling them to predict outcomes easily and achieve the desired business outcomes. Enterprises derive great value from their data – because, using Rubics, anyone who can ask sophisticated questions can derive actionable insights from their data! Rubics allows the creation of compelling visualizations and easy-to-read reports and dashboards for easy exploration and real-time decision-making. This extremely easy to use platform enables connecting and processing a massive amount of data at your fingertips – without the need to even download any software or connect to any database! With Rubics, data-driven decision-making becomes a reality – decision makers get reliable, data-driven answers at the click of a button. Rubics makes data science a phenomenon for everyone. Rubics is an ideal platform for data scientists, visualizers, data enthusiasts, business analysts, and also the decision makers. Try it for free – today at www.rubicsape.com Linkedin X-twitter Facebook

Today, every business is a digital business. The evolution of digital businesses demands business leaders to make a leap towards a newer view of data and analytics. As the world moves deeper into the age of technologies like AI, ML, Blockchain, IoT, etc. what data science and analytics trends should businesses be most aware of? Given that data science and analytics are projected to become critical to any business strategy, what does this mean for businesses looking for growth in 2025? Here are 7 data science and analytics trends that will be dominant in 2025. Data Mesh A data mesh is an innovative architectural paradigm. It embraces the ubiquity of data in the organization by utilizing a domain-oriented, self-serve design. As against conventional monolithic data infrastructures that handle the usage, storage, transformation, and output of data in a single central data lake, a data mesh facilitates distributed domain-specific data consumers and takes ‘data-as-a-product,’ with every domain taking care of their own data pipelines. Data mesh will take the industry by storm in 2025 because it provides a solution to the shortcomings of data lakes by enabling greater autonomy and flexibility for data owners, allowing greater data experimentation and innovation while reducing the burden on data teams to field the requirements of every data consumer through one pipeline. Increasing Value of Data & Analytics with Data Marketplaces and Exchanges According to Accenture, by 2030, over 1 million businesses will monetize their data assets, and over 12 exabytes of data will be transacted each day. Additionally, the data marketplace will unlock more than USD 3.6 Trillion in value. In the coming years, large organizations will either become sellers or buyers of data through formal data marketplaces. Data marketplaces and exchanges are surfacing as both products and platforms across private and public sectors. They enable the contribution of and access to critical data assets powering a wide array of global data for initiatives such as climate change, wildlife protection, or other public health, social issues. Today, individuals and IoT powered devices generate exponentially more data than ever before. If leveraged appropriately, this will revolutionize the impact of data and analytics and spur completely new data-based innovations. It will also generate new sources of value and revenue via data monetization for businesses that wouldn’t otherwise have a chance to contribute or access unique datasets. Data Democratization Data democratization means that everyone in the organization has access to data, and there are no gatekeepers that could create a bottleneck at the gateway to the data. The objective is to have everyone utilize data at any time to make insightful decisions with no constraints to access or understanding. The capability to instantly access and comprehend data translates to quicker decision-making, which further translates into more agile teams and business model innovations. These teams will have a competitive edge over slower data-stingy organizations. When businesses allow data access to everyone across all levels, it empowers individuals with ownership and responsibility to leverage data in their decision-making. Bye Bye Dashboards. Hello Data Storytelling Modern data analytics platforms fail most of the frontline workforce – because insights are not contextualized, easily consumable, or actionable. Businesspeople are still clueless to know which insights to act upon. Businesses expect everyone to be data-driven, not just the analysts or data scientists working in the company. But the tools that work exceptionally well for data analysts and scientists are not extendable. These are too complex for salespeople, customer success people, and almost every other non-technical employee. Consequently, automated data stories with additional consumerized experiences are foreseen to replace visual, point-and-click authoring, and exploration. The transition to in-context data stories will transform how and where users interact with analytic insight, and the most relevant insights will stream to users based on their context, role, or use. Continuous Intelligence Today, the digital revolution demands speed, regardless of data complexity. Businesses want to see all the data immediately and continuously. They do not want to get trapped with an IT-established dashboard with rigid drill paths that restrict their capability to instantaneously answer critical questions. Businesses that are driven by revenue growth and capitalizing on the digital revolution recognize that today’s analytics cannot tolerate a punctuated analytic pipeline. There have always been multiple ways to carry out analytics fast by employing various tools and tricks. However, analytics was always disconnected by separate modules, separate tasks, and independent teams with dedicated skills. But this robs time from what matters most today – timely nonstop actionable information from all the data. Continuous intelligence is about frictionless cycle time to draw constant business value from all data. It’s an innovative machine-driven approach to analytics that enables businesses to access all the data quickly and accelerate the analysis businesses require, regardless of how off the beaten track it is, irrespective of how many data sources there are, or how enormous the volumes are. DataOps DataOps is a new system for data management. It incorporates development, DevOps, and statistical process controls and employs it in Data Analytics. It fosters collaboration, automation, and continuous innovation of data in a data-powered environment. DataOps has been mainly aimed at advanced data models. It plays an indispensable role in building best practices throughout a function. Leveraging automation and agile approaches, DataOps builds best practices that allow businesses to deliver value to a range of stakeholders via continuous production. DataOps enables automation and brings speed and agility to the data pipeline process. Before the data is implemented, data scientists must create data pipelines, test them, and change them. By implementing DataOps best practices, businesses can have a continuous stream of data flowing in the pipeline. This unlocks one of the most critical benefits of DataOps, that is, the potential to gain real-time insights. Gaining real-time insights from the data shortens the time it takes to transform raw data into actionable business information. Additionally, DataOps helps enhance data quality via version control, continuous development, and continuous integration. Practical Blockchain for Data and Analytics The promise of blockchain

As consumers become the center of every business product and service, just providing an interface is not enough. Today, it should also add value and build long-lasting relationships with them. In my opinion, the consumer-facing internet would not have been possible without big data and analytics. Big data is enabling businesses to better interact with consumers, improve satisfaction and retention, and bring in more revenue. Here’s how big data and analytics plays a major role in meeting the high expectations of today’s digital customer – both in terms of usability and seamless, multi-channel access. Context sensitivity One of the biggest mistakes many businesses make is to assume that customers would take out time to sift through large amounts of content to get what they’re looking for. However, as customers get more and more tech-savvy, they expect interfaces to be as intuitive as possible – delivering the right content at the right time. Big data and analytics help build context-sensitive user interfaces that automatically detect a user’s intent, deliver the right number of options needed on the screen at any given point and reduce the number of clicks or swipes required to carry out any given operation. With context-sensitive user interfaces using big data and analytics, businesses can provide their users with UIs that can adapt as much as possible to customer needs and helps in guiding them through the right pieces of content. Zomato uses big data and analytics for a variety of tasks: from homepage customization to user personalization, intuitive search, among others. Since most customers have a tough time deciding what to order and from where, Zomato showcases context-sensitive options based on the user’s preference for specific cuisines, establishment types, locations, and price bands or what is most popular or new or exclusive at a given location and time. Such suggestions eliminate user effort, simplify the decision-making process while simultaneously reducing anxiety and shaping intent. By applying analytics to every interaction, Zomato is making the most of the countless opportunities to connect with customers, earn trust, and build value. Personalization Customer experience is all about offering relevant data in the quickest way possible. If you get the data right, you can apply big data and analytics and shape the overall customer experience. Personalization tools are great for showing people items they will like, but are unlikely to discover by themselves. They improve the overall experience by offering relevant items at the right time and on the right page. As Steve Jobs once said, “a lot of times, people don’t know what they want until you show it to them.” By analyzing massive amounts of data, big data enables companies to learn about user preferences and offer personalized suggestions. With a market value of $151 billion as of May 2018, Netflix leverages big data and analytics to a large extent for the purpose of content discovery; by highlighting as much of its content library as possible, Netflix has been able to increase viewership and lower churn. Tuned for extreme categorization, Netflix can offer content to the exact people who would be interested in them. With global expansion, Netflix has now done away with the region as well as language-based preferences. As the user base increases, more data gets generated that strengthens the algorithm and results in more insights into user behavior. The end result? Efficient content discovery and extreme personalization that is enabling it to achieve its global ambitions. Recommendation Engines With the growing amount of information on the Internet and with a substantial rise in the number of users, it is becoming really challenging for consumers to get to information that interests them and increasingly important for companies to offer information according to their tastes and preferences. Big data and analytics power modern recommendation engines that make use of algorithms and data to recommend the most relevant content to a particular user. By 2020, 51% of consumers will expect companies to anticipate their needs and make relevant suggestions before they make contact. Amazon Prime’s recommendation engine is a major driver for Amazon’s stunning revenue growth and successfully integrates recommendations across the buying experience – from product discovery to checkout. The retail giant’s big data and analytics algorithms take cues from just a few elements, namely users’ purchase history, items in their shopping cart and items they’ve viewed, rated and liked. Amazon displays the most relevant products in real-time and delivers a personalized shopping experience. Recommendations are especially important in e-commerce, as rare, obscure items that are not very popular and don’t drive a lot of revenue end up being overlooked. Using big data and analysis, Amazon is able to recommend these items to shoppers and enhance the potential of ROI on slow-moving inventory. By enhancing their online store’s user experience with personalized recommendations, Amazon is able to drive better product discovery and improve sales. Gain Customer Loyalty As companies struggle to improve their time-to-market deliveries, they end up delivering products and services in haste. However, such products don’t generally scale and deliver the performance users expect. Since today’s modern applications are inherently used by many people, offering personalized user experiences is more than essential. 72% of consumers expect companies to understand their needs and expectations. Customer success today goes beyond offering solutions in unique moments of need. Despite the millions of dollars which businesses spend on improving customer experience, there is a huge gap that puts a strain on customer service channels as well as on customer relationships. To deepen customer engagement and stay ahead in this constantly evolving business environment, businesses need to dig deeper and serve customers in each individual moment. Consumer-facing internet is quickly becoming a great way for businesses to connect and communicate with customers. By creating an experience that will help increase engagement and satisfaction, businesses can gain customer loyalty and achieve more sales and revenue. Linkedin X-twitter Facebook

When digital and information processes are advancing at a breakneck speed, companies have to come up with strategies to stay on the top of the game. Finally, for all of us, delivering the right components to the end users is the ultimate goal. Hence, a strategic partnership which gives expertise mileage to the participating companies paves way for a smoother long term success. This is exactly what we have achieved as one of the new business partners of SAP. Today, I am honoured to be selected on the SAP Partner Advisory Board. It is a feather in the cap – to become a significant member of a market leader in business applications. For long we have managed to create a name for ourselves in the area of Data Sciences and Big Data Analytics. We have been involved in SAP-based Navigator Analytics Framework and also offer customer-centric SAP solutions. Now, with this privileged appointment as one of the eleven representatives chosen from over 600 SAP partners in their network in India, what I intend to do is to reach out to the consumers with our niche expertise. This partnership is different from a mere reseller-ship. SAP looks at us for our solutions in the areas of Big Data Analytics, IoT, and Mobility. It’s a value-added partnership, combining SAP’s market position with our compelling technology capabilities. We definitely aim to achieve competitiveness by pushing the boundaries of co-innovation. That brings us to what exactly do I mean by co-innovation. This concept is the need of the hour to ensure a strong and loyal customer base. Apart from strengthening the foothold in the digital market space, co-innovation allows companies to highlight their domain specific expertise. Which is precisely what SAP is allowing Rubiscape to do. Instead of getting someone to simply further sell their product, SAP was looking for a company to share knowledge and inputs to deal with end customer needs and market requirements. Since we already have a SAP-based Navigator Analytics Framework, which we further intend to develop into a Cloud and Mobility enabled Data Science and Analytics Platform, it will help the end users realise the power of a SAP platform with the help of our service value creation. Along with this offering, our SAP BI practice has for long had an excellent track record and successful end-to-end customer engagements. This experience, I believe, will come handy in furthering growing customer needs. In turn, we are able to provide valuable inputs to the partnering company SAP, thus, contributing considerably to their product development and roadmap. I sincerely feel that this could go on to become a perfect example of what excellent co-innovation partnerships do. They help leverage knowledge sharing capabilities and resources to mutually work on each other’s positives, eventually making decision-making process a breeze for their valuable customers. I am equally happy to share that ours is the only company of all the others SAP has partnered with to create the Advisory Board, to have an exclusive focus on Data Analytics. We believe in customer-driven engagement models and have special proficiency in Business Analytics and have already managed to create a name for ourselves in the area of IoT and Mobility apart from Mobile Apps, Frameworks and Accelerators, among many other offerings. Thus, as a valued partner, Rubiscape is able to bring to the table the latest knowledge and cutting-edge expertise to take SAP to the next level. It is just a matter of time before a productive partnership such as this one will prove that it is bound to take care of customers across all strata and industry segments. Linkedin X-twitter Facebook

Services and Product based companies and their business strategies is a much discussed topic in any management class. However, there is not enough justice done to the topic of pivoting a Service Company to a Product Company and vice versa. This change requires a paradigm shift in how a business is planned &  operated and require transformational changes ranging from re-defining business strategy, the organisational right-structure, relevant infrastructure and policies, and most importantly, the required mind-set as how the new business / teams operate throughout the Product Life Cycles, especially the go to market strategies. This is a fundamental shift and the first step is a change in the strategy. A services company typically sells intangible goods which is the skill of its people. Customer Relationship Management forms the centric part and service-based-solutions are built and delivered to meet the specific client needs. A product company, on the other hand, spends a significant amount of time in ideating the market need to solve a problem in a unique way and building a standardised product which will be widely distributed to the target markets  and beyond. The offering  follows a typical Product Cycle of introduction-growth-maturity-decline. A product company’s forte lies in the market research, innovation, development, production, and mass distribution of its product. There is generally a clearly defined specifications, features, performance and value outputs that a product can deliver for individual clients. This major change in the outlook of a company requires full commitment and the teams need to be re-organised and repurposed or new talent needs to be hired. The radical change can only be effected if implemented in a top-down approach. There are examples where despite having a great product, it did not gain traction because of lack of alignment within the organisation and the key people did not have enough clarity on how to bring about the change. A successful transformation from a service centric business into a Product Company requires a solid change in how all the departments function within the company. A services based company’s sales force is many times localised and specialised within the parameters of region/geography, technology offered, and client base, since service companies often specialise in their niche expertise. This is because they need to have a thorough understanding of the client’s business they cater to so that they can provide solutions and add value to the client. On the contrary, the sales force for product companies is working with a fairly standardised offering. Apart from the in-depth knowledge of the product they are selling, they also need to be equipped with a deep understanding of its application areas and target markets. The deepest ramifications are perhaps to the operations and structure of development teams within the company. Service based companies have deep technical expertise pertaining to the client’s business which they cater to and they build custom tailor-made solutions depending on the client needs. There is no requirement to conduct market research or identify necessities and solutions for service companies as the clients already define the problem and the expected solution. This development process is quite different in product development. A typical product development lifecycle starts from market research, product conceptualisation, design and prototyping, and then moves on to development, testing, and deployment. Often there is some level of post sales maintenance and support involved. The services company would need to setup specific organisational silos responsible for each of these functions because, failure at any stage comes at high development cost in terms of time delays and resources. The pre-market and post-market strategies is an entirely different ball game as well, which service companies typically do not have to deal with. The pre-market strategy requires tedious and thorough market research to identify problems and market needs and map them to the company’s development strengths. This analysis factors in everything from gauging customer perception and experience (UI, UX), economic impact such as margin, scalability, development overheads to marketing impact of the brand image, product perception, and viability. Service companies skip these steps and focus solely on how they can add more value to the current client business. The post market strategy deals with required support and maintenance, marketing and promotion, and future releases and improvements. Such companies typically have defined engagements with the client and upon delivering the promised solutions transition and move to the next engagement. Thus, a very robust organisational structure needs to be implemented when a company moves from a service-based offering to a product-based offering. Often times it might be necessary to hire employees or managers who have experience in managing product development life cycles to ensure that the company can hit the ground running. There are well-researched frameworks that can be leveraged for ensuring product success. Most times service companies fail to adapt and continue to operate in their existing work culture which leads to inefficiencies in development. When a company pivots and reinvents its market approach it requires strong leadership and full commitment in adapting/changing and aligning all the work streams to a product-centric orientation. The entire fabric of the company from the management and developers to HR, sales force, and support teams need to be realigned for an effective transformation.

Data is the new oil. All of us agree that big data is, well, a big business. Worldwide, businesses are struggling to deal with the mountains of data they have at their fingertips and derive actionable insights from that data. Businesses have come to realize that more than just gathering data, it is important to know what to do with that data. To make use of this data and optimize the business outcomes and to leverage it to meet the business goals, it requires specialized skills and expertise. Businesses do understand that they need data professionals and they are increasingly looking for data scientists to help them make sense of this data. However, there is a huge mismatch between the demand and supply. According to Gartner, in the USA alone, there would be a shortage of 100,000 data scientists by 2020 How can this gap be quickly filled? I feel that the role of data scientist has a lot of similarities with the role of business analyst. Let us see if and how business analyst can climb up the ladder and be in demand as data scientists – Business Analyst Vs Data Scientist A company relies on its business analysts to gain business insights by interpreting and analyzing data and predicting trends-related aspects which help in making critical business decisions. Business analysts also focus on end-to-end automation to eliminate manual intervention and optimizing business process flows which can increase the productivity and turnaround time for an efficient and successful end result. They also recommend systems changes needed to optimize an organization’s overall execution. Data scientists, on the other hand, specialize and purely rely on data which is further broken down to simpler facts and figures by using tools such as statistical calculations, big data technology, and subject matter expertise. They use data comparison algorithms and methodologies to identify and determine potential competitors or resolve day-to-day business issues. Business analysts often work on preconceived notions or judgments related to the factors that help drive the businesses. Data scientists, whereas; have had an edge over business analysts, as they leverage data related algorithms which provide accuracy and also use mathematical, statistical, and fact-based predictions. As organizations are proactively defining new initiatives and campaigns to evaluate the existing strategy on how big data can help to transform their businesses, the role of business analyst is slowly but certainly widening into a major role. Learning The Tricks of the Trade Business analysts have some definite advantages if they decide to become data scientists. Business analysts often have domain expertise and industry knowledge which is extremely useful for data analysis. They are, in their role, familiar with data analysis. Apart from this, they also have the ability to translate complex information into a more understandable form. In order to start the transition from a business analyst role, the first step is getting well versed with technology and programming. For instance – starting from the basic understanding of the Structured Query Language (SQL) and later moving to more advanced big data technologies like NoSQL, MPP databases, and Hadoop. The next logical step is gaining knowledge in algorithms such as recommendation engines, K Means Clustering, Linear and Logistic regression, Time series analysis, text analysis, decision trees, and NLP. In order to effectively implement big data techniques, there are a variety of tools such as Pentaho business analytics, Talend Open studio, Tableau desktop and server, Mahout, and Splunk, to name a few. A mastery over these tools will definitely provide a cutting edge when it comes to building the skills sets for a data scientist role. Apart from the technical skills, data scientists need to be expert at math and statistics. So it is a good idea to learn statistics or brush up the knowledge. It is also important to understand machine learning – what it means, how it works and the real world applications of that. In my opinion, the huge demand for data scientists is a boon for business analysts. The role of a data scientist can be a natural transition if business analysts start to delve deeper into the data and bridge the data relationship across several systems within an organization. The traditional business analyst relies on mere experience and know-how of the business whereas; data-driven decisions are proving to be more accurate and precise. Business analysts have a scope to ride the wave of the big data transformation and stay relevant. Besides, the use of analytic tools has made it simpler for business analysts to perform the duties of a data scientist. In any case, organizations are now on the lookout for business analysts equipped with the intelligence of knowing the right analytic tools, big data technology, and machine learning rather than simply relying on business analysts to predict the future of a business.  So if you are a business analyst then you have a lot to learn to stay relevant but the good news is, there are various data science programs which can help you retool to stay competitive. At Rubiscape, we are actively looking for Data Scientists and Business Analysts with a data science mindset. If you think you fit the bill – drop me a message.

Intellectual property refers to the creativity of the mind. It can be in several forms like providing innovative ideas, new inventions, or artistic work. Interestingly, the concept of intellectual property was first officially acknowledged in 1883 during the Paris Convention for the Protection of Industrial Property and the Berne Convention in 1886 for the Protection of Literary and Artistic Works. Over the years, innovation became a buzzword of our time. Companies always look for their brand name to be synonymous with the word – innovation. They strive to build products or ideas that no one has built or launched in the market before. When it comes to product development, today, outsourced product development has become the common norm for organizations around the world. The reasons for this are obvious: It helps in improving overall project efficiency and decreasing the time to market. It gives an opportunity to the companies to leverage the experience of the vendor and build better products. It allows the company stakeholders to concentrate and focus their time and efforts on company’s core business areas, instead of worrying about the technical aspects. At the same time, it optimizes cost savings by reducing the extra overheads and unforeseen expenses. At Rubsicape, we follow a highly differentiated approach to product development – At Rubsicape, the product development strategy involves developing new products, services, or ideas for new as well as existing markets. This strategy is driven by continuous research and development, brainstorming of new concepts as well as the ongoing assessment of customer and market requirements. Our aim is to deliver the right products at the right costs, combined with an experienced marketing support, and help our customers to stay competitive. We believe in keeping up with latest trends in the market, remaining on top of emerging technologies. This allows us to offer solutions that are customised for individual customers. This is a unique value that Rubsicape boasts of as part of its product development outsourcing capability. Another important factor which Rubsicape strongly relies on is its technology strategy. There is a dedicated effort to ensure that the latest technology is being adapted in a way that it does not become obsolete in the near future. People, processes, and technology are all important aspects that need to be considered for a successful end product. A ‘people challenged‘ organization could face issues such as the staff having outdated skills or lack of knowledge of new business process methodologies or architecture, design, and implementation skills. At the same time, following a rigid, process-laden approach might lead to ‘process challenge‘ that often leads to delays in product delivery. Then ‘technology challenges‘ include implementing technology options that are not in sync with the organization’s goals or do not use the latest technology concepts. Rubsicape has the right blend and balance of people, processes, and technology that ensure successful adaptation of its innovation drivers and eventually helps to build a robust product. We have a wide spectrum of technology expertise in the latest technology trends like IoT (Internet of Things), smart analytics, cloud computing, and product development. Most importantly, Rubsicape believes in building long term relationships with its customers. This has led to creating an EDC or Extended Development Center which is nothing but a dedicated development and innovations lab as part of its offshore delivery center. With the concept of EDC, dedicated resources, and space with access restrictions are allocated depending on the needs of the customer. It includes added features like complete IT security, remote access, and a virtual development environment. The EDC also ensures complete IP security for every customer. Thus, the core idea of building this model is to materialize solid partnerships with the customers. In today’s crowded professional services space, it is not easy to find a unique positioning. While it could be convenient (and, therefore, tempting) to fall back on safe ideas, I believe that to ensure a winning hand, you need to have a differentiated value. That’s exactly what we are trying to build at Rubsicape!

It’s all about numbers now. Data rules our lives 24*7 and there’s no getting away from it. It has altered the way economies run, how businesses make decisions, and what choices even the lay population opts for. The data-decision making tools help one and all in making well, better-informed decisions, and get the best out of available resources. So how really has our day-to-day life changed thanks to this newly found number-crunching technique? While most of us largely tend to believe that data-driven transformation has only affected business processes, in reality, it has also made a huge impact on our personal lives as well. Let me give you an example. I see so many of us these days invariably having a wearable device – fitness tracking ones such as FitBit, Runtastic, then smart watches like those of Apple Watch and the Pebble watch. These devices track data to provide us with some of the most accurate, personalized information about our daily activities. Furthermore, there are other data-driven tracking apps such as AmpStrip which looks like a band-aid and if you stick it to your chest, it can monitor your heart rate and send the information to your smartphone. Have you also checked the Headspace app? It apparently scores high on reducing stress, improving the user’s mood, and reducing depression, basically falling under the mindfulness app category. There are also numerous apps which help us track and manage the number of calories we consume through the day so that we can stay on our weight management goals. That is what data-driven technology has given us – simply making any kind of choice an informed decision. People are tracking, measuring, sharing and displaying everything from sleep, exercise, location, productivity, food, mindfulness, or even mood. Data analytics has shown a positive impact across multiple areas for individuals – we now have a choice of opting for personalized treatments, we can have tailor-made courses delivered at their doorstep for further education, or we just expect the right products to be suggested to us on online sites based on our preferences. I think a few things have contributed to this – one, sensors have become smaller and more accessible. Secondly, the proliferation of mobile devices – everyone is carrying these high computing devices with them all the time. And thirdly, social media has made sharing and collaboration extremely convenient and fun! Data-driven companies have become valuable Industry sectors such as manufacturing, telecom, and healthcare are some of the biggest beneficiaries of data analytics. Businesses which have gone the data-driven way are doing great especially in terms of staying ahead of the competition. Technology is paving the way for innovation in IoT and digital preferences of consumers. Data-driven analytics simplifies the humongous amount of information that is generated in the course of business. I believe that this has resulted in improved performances of businesses as well as individuals. According to the MIT Center for Digital Business, businesses which opt for data-driven decision making have 4% higher productivity rates and 6% higher profits. A recent study by S&P Capital IQ titled “Top 10 Companies with Highest Market Capitalization Worldwide” found that 6 of the 10 companies are data-driven. The top ranking one is Apple with $741 billion market capitalization followed by Alphabet with $585 billion and Microsoft with $505 billion. Interestingly, Microsoft, in 2011, had a market capitalization of $218 billion. From there in April 2017 it has jumped to $505 billion. There are also new entrants such as the Alibaba Group with $278 billion, this year. That says a lot about how these companies have reaped benefits by turning to data-driven decision management. Is data truly everything? While I agree that being data-driven is how things need to be to progress, I also must admit that there are certain times when we should still rely on instinct and emotion to make a final decision. The human brain and mind are enough programmed to discern the information received. Thus, sometimes maybe the data might not be enough to give a complete picture or a report might be biased on how it is collected. At such times, it is important to consider the analysis completely from all viewpoints before arriving at a conclusion. On that note, I recollect a lovely line from the book Blink, The Power of Thinking Without Thinking by Malcolm Gladwell. He says, “Our world requires that decisions be sourced and footnoted, and if we say how we feel, we must also be prepared to elaborate on why we feel that way. We need to respect the fact that it is possible to know without knowing why we know and accept that – sometimes – we are better off that way”. Something to think about, right?

When you look at a successful business what is the first thing you tend to notice – is it the leader or the team? Of course, it has to be the leader who must have worked hard to grow the business. But is it possible to do so single-handedly? Actually not. The leader must also have a reliable team to be able to realize his dreams. So then are great leaders made by great teams or are strong teams the result of a great leader? Here is my take- From A Leader’s Perspective The primary role of a leader is to drive the entire team towards one single goal. This goal translates the daily running of a business. To further expand this, for a business to run, there are several aspects which need attention. For instance, the leader has to create a presence in products and services, finances, markets, administration, operations etc. Obviously, it is impossible to look into all these things by yourself. Hence, only when the leader has a team which can co-manage certain responsibilities will it be possible to completely grow. Great leaders create great organizations – these leaders are able to unleash the best from the team and align their strengths towards achieving the set objectives. As a leader, it is expected that the person will be strongly motivated and will extend that same feeling to the employees. If the business is blessed to have an equally inspired team, the leader finds is very easy to explain the significance of the company goals. What a leader can achieve largely depends on the team spirit as well. A leader should be able to influence others and build trust among the team members. It is the leader’s responsibility to define the team’s purpose and align those with the organization’s plans so that everyone works cohesively towards the strategic goals. As leadership specialist Tricia Naddaff, president of Management Research Group says, “When teams interact, they create a new, stronger entity“. I believe companies such as motor giant Ford and even Google, for that matter, are excellent examples of what can be achieved if the leader has a fine team to work with. From A Team’s Perspective The primary role of a team is to put in efforts to successfully achieve the set targets – short-term as well as long-term. Since they would have someone to guide them regarding the dos and don’ts, I feel a team has to be proactive and lend all possible support. They have to be committed towards their assigned tasks, because, by doing so, will eventually attain their own personal growth – if the company progresses, so will their personal equity. It also would be a good learning experience to better your own capabilities. Things like problem-solving ability, decision making, training for result-oriented performance are just some of the positives a leader is actually teaching the team. The more a team can learn from its leader, the more benefits it can enjoy. At the same time, it is important to remember for team members to help each other out and not just stand by the leader for collective growth. There are several examples of companies which have done well thanks to a competent leadership. J. R. D. Tata, Dhirubhai Ambani, are some such prominent leaders who not only achieved growth of their respective businesses but also managed to help their employees rise. There is also Mark Zuckerberg who provided able leadership. He trusted even the newly hired staff members and made them a key part of the company’s plans. In an ideal scenario, leaders should develop their teams to take ownership and eventually be able to let them independently lead a task or a project. Without a team, there is no leadership to prove. Yet, teamwork occurs when there’s someone to cohesively bring all the members together. Simply because having one better than the other does not help in achieving set goals. As research scholar Dr. J. Richard Hackman has identified, “the presence of five conditions – real team, compelling direction, enabling structure, supportive context, and competent coaching” are vital for a team’s effectiveness, or in short, a company’s success. In my experience, it is a cyclic process. While leaders take up new challenges and initiatives, it is the team which executes those. When the team takes up challenges and delivers on those, the leader starts exploring newer challenges. I can relate this with the leadership model described in Indian mythology – Brahma (creation or innovation), Vishnu (execution, implementation life cycle), Mahesh (destruction or upgrade/change). An effective leader’s role is to be the Brahma and Mahesh and to align the team of Vishnus who can effectively collaborate with the Brahma. Hence, I feel that when we discuss whether a leader achieves success because of a team or if a team is defined by its leader, it is worthy to note that for a business to click and expand, it needs a combination of both – a great leader as well as a great team complement each other. It is all about empowering each other for like-minded goals.

The explosion of the Internet of Things is expanding to completely change life as we know it. Smart cities, smart manufacturing facilities, smart homes to smart farms, the IoT market is growing exponentially. According to Gartner estimates, we can expect to have over 20 billion connected devices by 2020. However, as this device explosion is increasing the internet architecture also has to mature to support the coming IoT revolution and assuage the growing concerns of security and management that come from so many devices getting connected to the internet. As an increasing number of devices get connected to the servers, the servers become overloaded and can become vulnerable to cyber attacks. Clearly, the IoT needs a higher degree of security so that the demands of the IoT can be met. The what and why of Blockchain Many IoT devices are designed to be small and unobtrusive. Identifying them and pulling these out of circulation can be a mammoth challenge if these are compromised by a botnet or an injected vulnerability. Blockchain, a technology that has created a lot of hype and excitement in the technology community, is being proposed as a possible solution to tackle the security challenges that emerge with the rise of connected devices and IoT. Blockchain consists of an encrypted computer filing system that creates records that are tamper proof and real-time. Its encryption capabilities enable security and anonymity and allow for mutual consensus verification that translates into collective network updates and consequently presents accurate datasets at all given times without the need of a central governing authority. How do Blockchain and IoT work? IoT expert and lecturer at San Jose State University, Ahmed Banafa states, “Blockchain is promising for IoT security for the same reasons it works for cryptocurrency: It provides assurances that data is legitimate, and the process that introduces new data is well-defined.” IoT security is a big topic of discussion because of the sheer volume of data that flows between these connected devices and the embedded processors. It is because of this IoT security needs to be comprised of complex authentication, control, and security layers and provide an infrastructure that helps in managing these devices and control data access more accurately and seamlessly. The security framework, thus, has to also consider including layers that eliminate unauthorized devices and cut out bad or hacked devices from the IoT network. What Blockchain does is that it provides a platform for IoT to stay reliably interconnected and avoid threats that can plague the central server and create a secure network that helps enterprises manage physical operations remotely (for example in manufacturing) without having to depend on centralized cloud servers. Why combine IoT and Blockchain? It seems that the advantages of combining IoT and Blockchain in today’s environment are really quite simple. Blockchain, by nature, is transparent. So the Blockchain records can easily identify any data inconsistencies, leaks or breaks as these activities can be tracked and analyzed by the authorized personnel. Blockchain employs the use of encryption and distributed storage. The machines securely record the details of each and all transactions that take place without any human oversight. Given that the ‘write-access’ to the Blockchain is held by the machines, overwriting records with inaccurate information is downright impossible. Blockchain makes the IoT network more secure since it allows for the creation of agreements that can only be executed when the required conditions are fulfilled. Blockchain also mean adding another layer of security to the overall IoT environment so that malicious actors cannot access the volumes of data being exchanged between the several devices on the connected network. The challenges of Blockchain and IoT As with any new technology, there are some challenges in implementing Blockchain in IoT as well. Enterprises looking at Blockchain need to take into consideration computational costs as data mining in IoT is computationally intensive while most of IoT devices are resource restricted. IoT applications demand low latency but, mining of blocks can be time-consuming. Enabling fast transaction speeds and verification processes are presently a limiting factor in the IoT and Blockchain equation. One of the biggest advantages of Blockchain, that of decentralization can prove to be a hurdle for enterprises. The move to this decentralized network is essential for Blockchain to work and this shift can lead to integration issues. As the IoT gains more traction both in personal lives and in the enterprise, it is becoming increasingly clear there will be the need of a facilitator to support this connected economy- a facilitator that places no single individual in charge, creates a network that is equal and secure for interactions, and levels the playing field…and Blockchain ticks all these checkboxes. There might be some issues that we still need to resolve with Blockchain, but as of now, this facilitator is Blockchain. Checkout Rubiscape.com.