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The concept of Open Innovation fascinates me. Fundamentally, it is a very decentralized, distributed, and more participatory approach to innovation and that’s where the beauty lies. I have always been a believer of co-innovation. Today, when there is so much knowledge which exists in the world, it is impossible for one individual or one company (no matter how big or small it is) to innovate effectively on its own. Being in the technology business, like me, many of you must have heard the term open source and I think open innovation rides on that.  The Open Source phenomenon began much before than open innovation and was developed independently. The main purpose of open source is to encourage greater participation of the community of developers who can contribute to making improvements to the source code to develop the project. The result is a better quality software with multiple perspectives from technology experts. Open Innovation, on the other hand, is more about creating a greater pool of perspectives from a diverse community to get ideas. Essentially both focus on getting superior quality results by encouraging participation. Here are some thoughts and views on open source and open innovation and the key differences that exist between them: Open Source and Open Innovation- how do they differ from each other? Open Source software is available for free to be used by the users and organizations with the purpose of using it and modifying it, according to their needs and requirements. The software may be tweaked and improved by developers around the world which may result in a higher quality product. Open Innovation provides unrestricted access to ideas, products, designs that may be used by a number of individuals in different sectors for a wide range of purposes. The model of Open Innovation is based on the premise that businesses can gain benefit from the bi-directional flow of ideas and innovations coming from outside or within their companies. However, there are some key differences between both these concepts – Open Source offers a framework for intellectual property policy along with economic exchange. On the other hand, open innovation still leaves this aspect unanswered. The scope of Open Source is limited to software development or making improvements. Whereas; open innovation is used much widely for problem-solving, improving current products, or even in research projects. Open source promotes collaboration and sharing – here, the collaborative culture inspires people to work together across the boundaries of the organization. Open innovation lays emphasis on a similar value, but the collaboration is more transactional or contractual in nature. Why I Support Open Innovation System? As I had outlined in my vision for a knowledge society, knowledge triggers innovation and the open innovation system offers many benefits to foster knowledge sharing – Provides networking opportunities Open Innovation helps in collaborating and engaging with smart people outside the organization to assist in problem-solving. Thus, there are more minds that work together to tackle a challenging situation and find a viable solution together. It provides a great opportunity to engage with partners and users who share similar interests and work towards a common goal. Reduces costs of development With open innovation, there are greater possibilities of finding companies who already have readymade products and technologies or entrepreneurs with innovative ideas that can help in cutting down the time and cost involved in R&D for other enterprises. There is access to readymade products and companies need not have to worry about building it from scratch that helps them to focus their efforts towards improving the products and make those superior. Promotes cultural diversity Most of the breakthrough innovations are a result of collaborating beyond the boundaries of the organization. Diversity because of the cognitive differences that come with different organizational cultures helps in avoiding groupthink. People from the same organizations share similar thoughts on how things need to be done compared to those coming from other organizations. Greater emphasis on learning Open innovation networks can be a useful way for learning about new concepts and facing challenges more effectively in the organization. This eliminates the need to set up many high risk and high resource internal projects for the purpose of learning as there is a chance to learn from other people’s mistakes in the network. There are greater possibilities of getting different points of view that can provide better insights and result in better learning outcomes. Open Source and Open Innovation – Successful Use Cases GE’s open innovation initiative GE started this effort using the open innovation system with their Ecoimagination challenges where they came up with an ecosystem of partners in the form of VC’s to get some innovative ideas on issues related to smart grid and healthcare. They also ensured that these ideas were implemented using GE and its smart network. GE experimented with many new things with regards to innovation and even partnered with Local Motors for starting their new initiative for co-creating a new concept in home appliances. FirstBuild, which is their initiative in creating an online and physical community is targeted at designing, engineering, developing and selling a whole new generation of a range of home appliances. GE has embraced open innovation successfully and believes that it’s almost impossible for enterprises to have all the best ideas and collaborating with experts and entrepreneurs who share a common passion for solving the most pressing issues in the world is the best approach. LinkedIn shows how open source can be used in a smart way LinkedIn has been successful in delivering the industry’s most awesome open source software – Cruise Control load balancing platform for Apache Kafka. Cruise Control did not have a real community but it was mainly developed to be used for LinkedIn. LinkedIn made sure that it created Cruise Control in such a manner which would translate beyond its needs to make it generalizable and extensible. There is a lot of effort involved to open source code such as Cruise Control and it is the open

Innovation is the big buzzword with most of the software development organisations. At Rubsicape, we try to put a lot of emphasis and focus to drive this concept and make it an integral part of the company’s day to day activities and value structure. Innovation is an idea or concept that can be transformed into reality and applied for practical use, which can eventually contribute to the success and growth of the organisation. It is an important catalyst for an organisation to adapt to the ever changing and dynamic market. Being innovative does not always mean the need to come up with new inventions but it can also mean changing the existing strategies, mode of operations, business model, etc. to deliver better products or services. Rubsicape follows a culture which fosters innovation. To achieve this, we follow these basic set of guidelines within the organisation. This blog is an attempt to share with you all our learnings in the process. Innovation Workshops At regular intervals, we organise innovation workshops for our teams. The idea of having a dedicated workshop ensures that we all get time, apart from our day to day tasks to collaborate, brainstorm, and work towards our innovative ideas. As a part of the workshops, specific teams are created with employees from different teams and departments. This not only helps to build a camaraderie within the organisation but also generates ideas which are out of the box, build trust, and prevents conflicts or disagreements among peers. Also, the workshops are conducted as a contest where the top innovations are selected and brought into practice. To encourage participation, rewards and company wide recognition are published for the winning team. At times, some of these workshops are conducted offsite to create a more relaxed environment for the teams and help their minds to wander in search of the ultimate idea. Making it a part of everyday activity Although Rubsicape focuses on dedicated workshops it does not limit the creativity of its employees to occasions like the above. Employees are continuously encouraged to share their innovations with each other as a part of their day to day work, meetings, and also coffee breaks.  Simple targets are set to come up with one idea per week for each team. The brightest ideas are then selected, nurtured, and taken to the next step for implementation. At an individual level, we set personal goals for each employee to think creatively and differently and help them achieve those. Accepting Failure We do not believe in building a culture which inhibits people from taking risks or making mistakes while coming up with innovative ideas. Failure is inevitable and if criticised, it often brings down the morale of the employees. Not a single great idea or innovation is done right the first time. Learning from mistakes paves the way for more innovate thoughts and brighter ideas for the next time. If an innovative idea is not feasible then the best option is to understand the root cause and develop alternate approaches. Rubsicape’s belief in nurturing innovation also stresses on having an open culture where employees can regularly freely interact with their supervisors or the management team irrespective of their designations or positions. Training It is not rational for an organisation to consider each and every employee to be sharp and inclined towards innovation. As a part of the on-boarding program at Rubsicape, specific training related to innovations is provided to the employees. In the long run, the Return on Investment is high, both for the organisation as well as the employee at a personal level. Ideas Bank We have a well-defined process in place to ensure that innovate ideas are captured and thoroughly documented; kept at a centralised location which is accessible to every employee. This helps us to check how a similar issue or problem was dealt with in the past or what approach was suggested or implemented in order to fix it. Also, by having a repository, it acts as a source of reference for the employees. They get a broader view and a better perspective to study the innovations in detail which have been already documented. Innovation can help organisations to deliver better and produce exciting products and services. In today’s highly dynamic and volatile market, there is no future for run-of-the-mill products. There is no option but to keep evolving by using innovation to ignite the spark. Linkedin X-twitter Facebook

Innovation is the big buzzword with most of the software development organisations. At Inteliment, we try to put a lot of emphasis and focus to drive this concept and make it an integral part of the company’s day to day activities and value structure. Innovation is an idea or concept that can be transformed into reality and applied for practical use, which can eventually contribute to the success and growth of the organisation. It is an important catalyst for an organisation to adapt to the ever changing and dynamic market. Being innovative does not always mean the need to come up with new inventions but it can also mean changing the existing strategies, mode of operations, business model, etc. to deliver better products or services. Inteliment follows a culture which fosters innovation. To achieve this, we follow these basic set of guidelines within the organisation. This blog is an attempt to share with you all our learnings in the process. Innovation Workshops At regular intervals, we organise innovation workshops for our teams. The idea of having a dedicated workshop ensures that we all get time, apart from our day to day tasks to collaborate, brainstorm, and work towards our innovative ideas. As a part of the workshops, specific teams are created with employees from different teams and departments. This not only helps to build a camaraderie within the organisation but also generates ideas which are out of the box, build trust, and prevents conflicts or disagreements among peers. Also, the workshops are conducted as a contest where the top innovations are selected and brought into practice. To encourage participation, rewards and company wide recognition are published for the winning team. At times, some of these workshops are conducted offsite to create a more relaxed environment for the teams and help their minds to wander in search of the ultimate idea. Making it a part of everyday activity Although Inteliment focuses on dedicated workshops it does not limit the creativity of its employees to occasions like the above. Employees are continuously encouraged to share their innovations with each other as a part of their day to day work, meetings, and also coffee breaks.  Simple targets are set to come up with one idea per week for each team. The brightest ideas are then selected, nurtured, and taken to the next step for implementation. At an individual level, we set personal goals for each employee to think creatively and differently and help them achieve those. Accepting Failure We do not believe in building a culture which inhibits people from taking risks or making mistakes while coming up with innovative ideas. Failure is inevitable and if criticised, it often brings down the morale of the employees. Not a single great idea or innovation is done right the first time. Learning from mistakes paves the way for more innovate thoughts and brighter ideas for the next time. If an innovative idea is not feasible then the best option is to understand the root cause and develop alternate approaches. Inteliment’s belief in nurturing innovation also stresses on having an open culture where employees can regularly freely interact with their supervisors or the management team irrespective of their designations or positions. Training It is not rational for an organisation to consider each and every employee to be sharp and inclined towards innovation. As a part of the on-boarding program at Inteliment, specific training related to innovations is provided to the employees. In the long run, the Return on Investment is high, both for the organisation as well as the employee at a personal level. Ideas Bank We have a well-defined process in place to ensure that innovate ideas are captured and thoroughly documented; kept at a centralised location which is accessible to every employee. This helps us to check how a similar issue or problem was dealt with in the past or what approach was suggested or implemented in order to fix it. Also, by having a repository, it acts as a source of reference for the employees. They get a broader view and a better perspective to study the innovations in detail which have been already documented. Innovation can help organisations to deliver better and produce exciting products and services. In today’s highly dynamic and volatile market, there is no future for run-of-the-mill products. There is no option but to keep evolving by using innovation to ignite the spark.

With the buzz around IoT and the tangible business impact organizations are seeing through their IoT investments, it is no surprise that the Internet of Connected “Things” is expected to reach 50 Billion by 2020. Especially in sectors like manufacturing, healthcare, and automotive, IoT devices are seeing a deeper penetration. It is projected that by 2025, the global worth of IoT tech will be $6.2 trillion! Most enterprises are already investing in mobility and big data as their two key priorities and these areas broadly fall under the umbrella of IoT. Let’s take a look at how these three technologies intersect to create a connected future – Mobility With a plethora of smart devices and sensors being connected, there is a need for ‘something’ that tracks, controls, and monitors these devices. Today, mobile apps are playing that role leading to a rapid evolution of this industry. These apps serve as a bridge between IoT objects and provide a seamless, comprehensive experience with extreme ease of use. Apart from that, through an integrated mobility strategy, organizations are able to foster collaboration, connectivity, and communication within their enterprise – which, in turn, leads to improved service delivery, reduced operational costs, enhanced customer experience, increased productivity, efficient management of remote workforce and field service, and thereby leading to competitive advantage and business RoI. Data Analytics Billions of connected devices generate a humongous amount of data at a very rapid pace. This data, if not analyzed and used in real-time, is of no use. The real value of big data analytics is to process this wealth of information and create actionable insights to trigger automated tasks. Analytics is helping businesses to go beyond “gut-feeling” and move towards an accelerated, accurate, and real-time decision-making. By taking smarter decisions based on data, enterprises are able to drive revenue growth, drive customer loyalty, prevent downtimes through predictive analysis, and drive business model innovations through valuable insights on customer preferences. Internet of Things (IoT) IoT is fundamentally transforming the way information and data are created and exchanged. This has a tremendous impact on many business operations and how businesses interact with their customers. Several business areas such as inventory tracking and management, field service, machine operations and maintenance, customer engagement, marketing, and also disaster planning and recovery have seen a massive transformation with the introduction of IoT. IoT is helping organizations in their business transformation initiatives by generating new revenue streams, optimizing business processes, reducing times, facilitating faster decision-making, creating better customer experiences, and improving operational efficiencies. Getting it all Together – Automotive Industry is at the Forefront of this Revolution One of the many sectors which have significantly benefited through the convergence of Big Data, IoT and Mobility is the automotive sector. Several automotive giants like GE, Tesla, BMW, Volkswagen, Toyota, etc. have invested heavily in these technologies and are also seeing tangible business results through that. General Motors, for example, uses big data for creating 360-degree customer profiling and uses that information for more accurate sales predictions. It also uses Geographic Information Systems and data analytics to help dealers in boosting their performance. By gathering detailed customer-specific information, GM focuses on highly personalized and targeted marketing. Even a 100-year-old company like FORD is calling itself a technology company now and has invested aggressively in IoT, data analytics, and Smart Cars. It has very ambitious plans for launching its autonomous vehicles in the near future. Automotive giants have also started building new ecosystems to support this transformation of self-driving cars and expanded connectivity. They are partnering with technology companies to create a coherent and seamless experience for their consumers. Very soon, the vehicles on the road will be very different from what we see today. They will not only be self-driving but those Will provide assistance to the occupants in the vehicle Will be able to maintain themselves through self-diagnosis, optimization, and self-healing capabilities Will fit better into the broader ecosystem – to provide guidance based on traffic conditions or weather conditions or to seamlessly connect with the smart home appliances Today, we have entered the era of “shared mobility” which is phenomenally transforming the modern transportation. Here, a range of technologies like mobility, IoT, and big data is powering a “sharing economy” to make transportation cost-effective and efficient. The road ahead is fascinating, and automotive giants have already shifted gears! I am excited to be a part of this transformation. What about you?

Here’s something for trivia. It is Kevin Ashton, a British technology pioneer, who has invented the term Internet of Things or IoT.  I mention this because lately everyone is excited about IoT and very keen on implementing it for all possible business scenarios. From connected cars, smart homes, to smart kitchens, IoT is set to transform our lives and businesses. According to GE estimates, the convergence of machines, data, and analytics will become a $200 billion global industry over the next three years. Like every new technology, there are some successes and some failures with IoT too. However, I firmly believe that the failure of IoT (in whichever scenarios it has happened), is not because of the technology but because of other factors such as lack of understanding of the technology, lack of expertise, lack of collaboration between IT and business, and most importantly, lack of good quality data. A majority of the businesses fail to scrutinize the data they have at hand – the core on which it functions. Unless and until the data gathered is of superior quality, it would not be wise to expect IoT to bear 100% results. Let me explain why. How IoT projects fail I was recently reading a survey by Cisco which found that as much as 60% of the IoT projects fail at POC stage itself! I think that the main reason for this could be that businesses are not adequately equipped to handle the transition to an IoT system, followed by having staff which is not at all trained to manage the new operations. Every department needs to be in sync with how the IoT technology functions so that the data generated can be smoothly exchanged for eventual outcomes. Thus, along with the integration of devices if proper training of personnel is not carried out then the lack of ground work causes failure at the very implementation phase. The other issues are with security and privacy of data – errors in transmission and data storage, improper usage of data, and so on. The most important one of all, however, is poor data quality. According to Gartner, 40% of all business initiatives fail to achieve their targeted benefits due to substandard data quality. Two of the biggest examples of faulty data sources would be the 2016 US elections and the Brexit results. In both the scenarios, the predictions went completely wrong thanks to the way data was collected and its analysis. A little about data quality Hence, having the right data is important. It basically includes good quality data, sufficient quantity of data, and reliable sources, to name a few aspects. Relevant and accurate data which is consistent across data sources proves to be helpful to make maximum use of resources – again, something which is essential for a newly begun project to take off. Simple things like preventing viruses from affecting your devices ensure that the data stays usable for a longer period of time. Confirming that there are no manufacturing defects is also crucial so that the generated data is free of any discrepancies, in case it needs to be further replicated. In other words, if we need quality data, we need quality devices as well. Following which, to further ensure thorough quality, businesses also need to check for accuracy of data and its source. On these lines, I feel investing in solid data management systems is a good idea especially to take care of data integration. It will provide a business with proper tools and techniques for the benefit of its employees, as well as build better data assets. Managing services and improving on accountability will also become easier with sound data at hand.     Businesses undoubtedly need to work on quality because only then can they sustain the rat-race. For instance, in 2015, some 15 billion objects were connected to the Internet. However, by 2020, 200 billion objects are predicted to be connected to the Internet. Data has the capacity to provide unique insights and is a powerful tool for business intelligence and analytics. The only thing to remember is that it needs to stay clear of any errors and has to bear the ability to bring in results. IoT has a massive potential and is something which is opening up a lot of opportunities for businesses by automating tasks and exploring machine learning. It is allowing interconnectivity of decisions and, thus, benefiting everyone who is associated with it at each step. This also means that it will continue to create more and more information or data in the coming years to completely change the way processes happen at the moment.

Digital Transformation has emerged as one of the top priorities of the enterprise as a whole. While 2018 has been more focused on optimizing existing processes and operations, 2019 aims to leverage technology to create new business models that will take the enterprises’ digital transformation journey to fruition. Some of the main focus areas promise to be security and an increased focus to improve customer experience by leveraging technology. However, if 2018 has taught us anything it is that while Digital Transformation is technology-focused it is more about an organizational change that includes business and people as much as the tech at play. As we are entering 2019, here’s a look at some technology trends that I feel will drive digital transformation in the coming year. Blockchain Deloitte predicts that Blockchain will soon overtake other technologies such as cloud computing, IoT and data analytics in the VC investment race. Given that digital transformation focuses heavily on customer experience, technologies like Blockchain significantly impact the speed at which elevated customer experiences can be delivered. Blockchain will mature considerably in the coming year owing to the data preservation, security and networking capabilities of this technology. With Blockchain, enterprises can circumvent traditional cybersecurity barriers and support the information sharing needs of the new digital enterprise of today. Blockchain gives enterprises the structure that they need to overcome and mitigate the threats that arise with the digitally-charged enterprise that is more connected and integrated. Marco Lansiti and Karim R. Lakhani in a Harvard Business Review article aptly state that Blockchain “has the potential to become the system of record for all transactions. If that happens, the economy will once again undergo a radical shift, as new, blockchain-based sources of influence and control emerge.” IoT IoT will be one of the trends spearheading business transformation in 2019. It is expected that IoT is heading for wider adoption in the year ahead owing to the strong gains in business efficiency and charged up innovation and business profitability. Enterprises are jumping on the IoT train to create smart workplaces for greater productivity and efficiency. Research shows that 78% of businesses say that IoT introduction has improved their IT team efficiency. The industrial sector has shown a great affinity towards this technology with six out of ten respondents already having implemented IoT. Next-gen IoT platforms are giving enterprises the capability to merge new data sources with traditional ones, provide more precise data inputs, examine data in real-time and consequently help businesses with the capability to gather new data correlations, analyze important data and question institutional thinking. Artificial Intelligence and Machine Learning Digital Transformation thrives on data. And while data analytics has proved its merit to the enterprise in their digital transformation journey, it’s time to supercharge it. As data becomes more strategic to organizations to make intelligent decisions about services, products, employees etc. the need for better, faster and smarter analytics is pushing the enterprise towards AI and Machine Learning. AI and Machine learning will gain a stronghold in 2019 as the technologies that hold the key to solve pressing business problems, drive data-driven decisions, enhance customer experience, optimize and automate processes. The transition to an API-based economy will also be driven with AI as analytics become more pervasive in the enterprise ecosystem in their digital transformation journey. In the ensuing year, we can expect enterprises to progressively weave AI all through their innovation stacks to weed out a portion of the experimentation that CIO’s feel compelled to undertake in their digital transformation initiatives. The Rise and Rise of Real-Time Edge Analytics As the connected ecosystem proliferates in 2019 as organizations continue on their digital transformation journey, the spending on real-time analytics is only going to rise. In the coming year, we can expect to see wider adoption of real-time edge analytics to find co-relations within internal and external data. We can expect a greater push to move from batch to stream data processing to get real-time actionable insights. This also becomes more relevant in the digital transformation context as the consumer lies in the heart of all digital transformation initiatives. We can also expect to see an increased AI-cloud interdependency with most leading cloud giants pursuing an AI-lock in approach by providing open source AI-related services to reduce complexity and the burden on IT departments. While 2019 looks promising for digital transformation, enterprises need to ensure that they contain their technical debt, and strategically organize the posture of enterprise data so that the data is not inconsistent, fragmented, duplicated, and siloed. We need to focus on loosening parochial data ownership and adopt disruptive emerging technology to support digital transformation efforts to usher in the enterprise of tomorrow.

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

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 2021? Here are 7 data science and analytics trends that will be dominant in 2021. 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 2021 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