Author name: Rubiscape Team

2020 is a year that will always be etched in the history of mankind as a year of change, evolution, and global catastrophe like none other. The COVID-19 pandemic challenged the existing systems and processes across the world and not just healthcare. From full-time remote working to distance learning, the world has realized that if not for technology, surviving a pandemic would not be easy. Challenges post the pandemic One of the biggest challenges now that we have sailed through the first wave and setback of COVID-19 is flattening the curve, preventing it from spiking, and eventually dropping it. As the world reopens slowly, as it should, the healthcare systems can breathe a sigh of relief, but only if the outbreak remains in control. Preparing for a second wave (which has been already observed in a few geographies) as well as being future-ready for such health crisis will be on the agenda of decision-makers across the globe. As the world crusades towards gearing up for a life of normalcy after the pandemic dies down, we have to also apprehend the fact that we need to be better prepared for such situations or worse in the future. The good news is, with technology and its impactful implementation, we can. Science is the only forward-looking solution that can help in mitigating such health emergencies in the future. Right since the outbreak of novel coronavirus in December 2019, there have been more than 2000 research papers describing the health impacts of coronavirus, possible treatments and vaccinations as well as its global repercussions. AI to the rescue against the pandemic Artificial Intelligence seems to be the most prevalent and conceivable solution for defeating this pandemic and the next. Currently, AI models are being widely adopted to fetch insights from several scientific results to discover a new line of treatments and speeding up symptom-based diagnosis in areas that lack aggressive testing systems. AI seems to be immensely helpful in mining the research papers and comprehending useful data points and information. The AI-powered chatbots are being used as essential communication tools in telehealth, making patient care accessible while maintaining social distancing. And while experts find newer ways to combat the current pandemic with the help of AI, it is safe to say that AI will help the world in ways more than one to be future proof. Let’s see how: Spotting the outbreak before it spreads with predictive analysis Artificial intelligence is rapidly being used to analyze patterns of the virus and in future as well, and NLP (natural language processing) algorithms will be monumental in analyzing search trends, press reports, social media announcements and public health warnings issued by local, national as well as international governing bodies. With the help of AI, a probable outbreak can be detected before it spreads to length. In fact, even during the COVID-19 outbreak in December 2019, the first reports of a possible pandemic came from several AI-driven outbreak risk detection software. This predictive analysis can help in mapping air travel data to affected countries and help in quick segregation of possible carriers of infection. AI-driven predictive modeling can help in mapping the path of the spread of the virus, alerting hospitals and health authorities to start preventive measures sooner. Personalized predictive analysis can help in deriving each individual’s probability of contracting and surviving an infection, which can further help in determining the time taken to develop herd immunity while ensuring lower death rates. Improved diagnosis and medical care One of the biggest lessons learned in the COVID-19 pandemic is the importance of quick and accurate diagnosis. With a limited number of testing kits and longer testing processes over vague and common symptoms that can appear with or without being infected by the coronavirus, the world has suffered. However, with Artificial Intelligence, machine-learning led models can come to the rescue in tracking human errors in diagnosis, picking traces of possible infections from medical imaging without even opting for regressive testing. Eventually, AI is estimated to detect COVID-19 using image recognition without going for a full-blown testing process, which is expensive as well as latent. Healthcare workers are already making extensive use of AI-powered systems to detect and monitor the disease, and in a world post-COVID-19, this will be rapidly adopted for patient care to reduce the risk of frontline workers. Resource allocation using ML Optimizing healthcare resources and frontline healthcare workers for crisis management during a pandemic is extremely important. Machine learning can help in resource allocation by forecasting patient volumes, connecting this data to the hospital staffing to ensure adequate amounts of resources are allocated. Ambulances and other emergency care services can be strategically placed around areas that have a higher probability of needing them. Development of drugs and vaccines Not having a line of tailor-made drugs or vaccination has been one of the most problematic reasons that have come in the way of curbing the spread of COVID-19. Artificial Intelligence is helping in identifying drugs that can prove effective in the cure of COVID-19 using generative design algorithms. This can further aid in speeding up the process of vaccine discovery. To Summarize… The world is slowly and gradually accepting the new reality, but the anxiety prevails. As governments lift the lockdown and the restrictions, AI-powered medical care is sure to garner more interest in getting over the current crisis as well as preparing for the next.

Today’s digitally-savvy and modern enterprises, across a wide range of industries, have become data-driven. From businesses across healthcare or retail to technology, data is the new oil. Only those organizations which leverage the power of right data are able to create a competitive advantage for themselves. With new government resolutions and regulatory regimes, businesses are forced to pay more attention to risk and compliance analytics to prevent unethical business decisions and practices. Compliance and risk are the integral parts of any organization and the absence of either can result in distrust, loss of potential customers and reputational damage to the organization. In light of this, businesses have started putting together an organized plan to leverage the structured and unstructured data and harness its power to effectively monitor threats, frauds and comply with the rules. The use of data and technological advances in the analytics solution space help organizations reduce the chances of violating the government compliance norms. Apart from this, a risk-aware culture in the organization works as the backbone for the proper functioning of the departments. According to a survey by OCEG, more than 84% of the companies agree to the fact that using analytics in GRC would benefit their company in the long-run. Some of the key benefits of Risk and Compliance Analytics include – Effective Risk Management – Monitoring of the complete risk lifecycle through visualizations for risk-based pricing, fraud detection, line-assignment, credit-risk modeling, loss forecasting, foreclosure prediction, risk-based pricing and event modeling Compliance management – Maintenance of compliance and effective business processes while reducing risks in areas such as environment, green technology, and International Trade Compliance Fraud Management – Uncovering of new trends, fraudulent schemes, and scenarios Audit Management – Managing the internal audits more effectively by aligning them closer to the business Analytics, undoubtedly, is the future of risk and compliance. Some of the advanced analytics techniques that are widely used to adhere to the regulatory norms include – Early detection of new fraud and risk associated with the organization In-depth analysis of the text to detect problems in the written documents Visual analytics to display the right information in front of the different stakeholders and regulatory bodies Monitoring to keep a track and mitigate the damage due to known compliance and fraud risk Raising of risk-based alerts for taking better business decisions The compliance teams within organizations need to constantly navigate the challenges of complexity and norm changes around compliance. The changing regulatory pressures over a wide variety of subject areas and the changing regularity environment keeps the compliance teams on their toes. Technology has been the backbone of helping organizations with their risk and compliance management. There is no one-size-fits-all compliance management solution for all the organizations – the selection of the solution depends completely on the compliance needs of the organization, the budgets, and the available skills. Having said that, here are a few things which businesses must consider while selecting the solution- Functional Coverage Checking the functional coverage of the solution is important to understand whether the solution matches the requirements and business goals of the organization. Also, organizations need to check whether the solution covers some of the specific functions essential for compliance and risk management of various departments of the particular business. Integrations The technological solution should provide a holistic approach to data gathering and analysis. It should provide a centralized ecosystem which gathers the data from various source systems and offers a one-stop solution for analysis of that data. Flexibility and Adaptability Considering that the regulatory norms keep changing on a frequent basis, the technological solution should be flexible enough to adapt to such constant changes. The solution should be able to quickly capture the new changes without any impact on the legacy systems. Reusability The data gathered for risk and compliance is very valuable and can be useful for the overall integrated analytics. The solution should be able to reuse this data so that minimal incremental time and effort are spent on data acquisition on a regular basis. Ease of Use Another key aspect before selecting the compliance analytics solution is the degree of user-friendliness. It’s important to know the predictive text input, the total number of entries required to operate and the level of customization of reports. Customization of reports is an essential feature to look for because it helps you alter the analytics depending upon the variables in the picture. Compliance and risk analytics helps in preventing corporate scandals, fraud and even civil and criminal liability of the company. It also enhances a company’s image in the public eye as a self-policing company that is responsible and worthy of shareholders’ and debt-holders’ capital. Today, every organization needs to take risk and compliance analytics seriously because it is the only possible way of identifying and addressing issues, which allows the company to avoid potential fraud, scandal, and even criminal behavior. Linkedin X-twitter Facebook

Data is the asset everyone is frantically looking to leverage. Worldwide, businesses have access to data that is continuously piling up in their storehouses, waiting to be drawn into useful insights. Through their experience, businesses are coming to realize that gathering data is no more a challenge but doing something with it is. Leveraging the right data at the right place and utilizing it for business growth is the key to success. Therefore, companies are looking for data science experts to help them make sense from their data. As the traditional decision-making process is evolving, the fields of business analysis and data science are expanding as frontiers in harnessing data expansion. For small businesses, these roles might seem overlapping. However, for companies who have more comprehensive data storage, processing, and analytics avenues, it makes sense to differentiate the responsibilities of a business analyst from those of a data scientist. Opportunities and the Skill Division Between the Two The role of the business analyst is to liaison between IT and business stakeholders. They need to be involved in finding answers to the demanding questions to optimize value for money. Whereas, on a broad level, the responsibilities of a data scientist span finding out new insights, solving complex data problems, and revealing data that can help maximize the potential of a business. Arriving at conclusions through data-driven strategies and eliminating guesswork is the job of a data scientist. Data science holds a lot of promise for businesses to transform holistically in the digital age. But according to a study by McKinsey, the results of many data science initiatives have been disappointing. In spite of investments in analytical tools, available capabilities, and bringing in data science skills, organizations have only been able to derive a fraction of the true potential. The biggest hurdle in extracting value has been attracting top talent to work on organizational goals. Most companies need data scientists who are well-versed with domain and business knowledge alongside data science expertise. While a data scientist needs in-depth know-how of all the latest tools, statistics, programming, and coding, they could do with a surface-level understanding of the latest algorithms in artificial intelligence and machine learning. On the other hand, a business analyst needs to be comfortable in assessing organization-level changes, defining new requirements, and developing business cases. Domain and business understanding are critical for succeeding in the business analyst role. This is precisely why it is desirable for business analysts to cross over into data science – a field with more promising career prospects that could use a business analyst’s experience. Crossing the Divide from Business Analyst to Data Scientist In switching over to a data scientist role, a business analyst can continue to leverage skills such as organizational and business knowledge, an analytical perspective to problem-solving, and secure communication. But now they need to acquire technical competencies in five core areas: Business Statistics Machine Learning Programming Mathematics Part of the job of a data scientist is to follow a pattern. It includes – Understanding business needs by decomposing a problem and looking closely at its various use cases. Bringing out the perspective of multiple stakeholders depending on the problem and structuring issues into sub-tasks to build solutions using data mining techniques. Understanding the various data assets and assessing their value to the given problem. Then, using techniques such as descriptive analytics and visualization, measure the quality and usability of data. Strategize additional investments into gathering the right kind of data to facilitate organizational growth. Data preparation, which includes data enriching techniques, to improve the overall leverage from data. Data cleaning and organizing can be considered sub-tasks for this step. Data modeling is where all the previous pieces come together with data mining jobs. The parameters of models are calibrated to bring about optimal business solutions. Here, the in-depth understanding of a data scientist plays a crucial role in achieving positive business outcomes. It also includes evaluating the effectiveness of the solution by validating it against business objectives and reviewing the process for future iterations and deploying the results of data mining efforts to the user along with A/B tests. Comprehending these work patterns and putting all the pieces together helps a BA cross over into their role as a data scientist. It’s the Right Time to Move from Business Analysts to Data Science Role Since we are witnessing a proliferation in demand for data scientists, it only makes sense for business analysts to want to shift into the new and trending career prospect. As computing power gets feasible and accessible, companies might look for a business analyst who has technical proficiency in dealing with their data. This is a chance for business analysts to ride the way they are so close to and stay relevant to the market. Since data science is a lucrative and ever-expanding domain, business analysts can use their experience and expertise to gain the upper hand in securing the ideal position in a top organization. A Business Analyst only needs to build upon their existing skillset, and so the transition to data science can be a natural move for them in advancing their careers. If a business analyst can get good at crunching numbers, they can look like the perfect employee to the most esteemed organization. A data scientist’s skills are invariably useful to organizations of all shapes and sizes across industries. We are always looking for skilled data scientists to be a part of our growing team and help us solve complex business challenges for our global clientele. If you think you fit the bill, we would love to hear from you.

The demand for data scientists is booming and will only increase especially as data-driven decision-making becomes organizational mainstays, and new-age technologies and solutions reach maturity. Data is indeed the new soil…a fertile ground that is non-rivalrous, non-depleting, regenerative, and almost unlimited and holds the promise of great outcomes. As the importance of data grows in the organizational narrative, so does the role of the data scientists. Enough has been said about the job of the data scientist being the sexiest job of the 21st century. The requirement for data scientists and analysts is expected to grow from 364,000 openings to 2,720,000. Demand is outrunning supply in the data scientist universe. It is hardly a wonder that a career in data science is becoming increasingly lucrative. To be employable as a data scientist, apart from the theoretical knowledge, one needs to build a strong portfolio of projects, showcase some experience in solving real-world problems, and demonstrate working knowledge. With the COVID-19 pandemic, online learning has taken over classroom training. Digital universities are the future of learning. For emerging fields like data science, the training programs need to be innovatively designed. Educational institutes aiming to offer data science universities need to consider the following aspects in their data science digital universities – Course Content and Tools Infrastructure The course content has to be of primary focus and should bridge the gap between academics and industry by having a solution-based approach. The end goal of the data science course should be to increase the technical vocabulary and also to help the data scientists develop their skills as solution-providers. Apart from the academic nuts and bolts, the data science course should be based on a design thinking model to help the future data scientists develop solutions ideas with a strong foundation of design. It is, therefore, extremely critical to provide access to the right data science tools and platforms. The course should provide the students access to an end-to-end Data Science Platform with Artificial Intelligence (AI) and Machine Learning (ML), Data Visualizations, and Data Apps. The tools ecosystems should be exhaustive and help future data scientists become better creative thinkers, improve storytelling, and enable faster innovation and value creation. Learning and certification paths should also cover different themes that range from machine learning, predictive analytics, robotics, cybersecurity, blockchain, IoT, Cloud, and SaaS and should allow the learners to work on real-world cases to drive better learning. Expert Assistance and Application-Driven Learning Approach Data science courses also have to be designed for working professionals and allow self-paced learning. Such courses should have a scalable design to accommodate the shifting needs of the students, organizations, and industries. It is important to provide access to a robust mentor network that is a coalition of business leaders across industries and government bodies and other institutions. These mentors can share their expertise with the students for their authentic development. Along with this, it is essential to offer practical experience of working on real-world data science cases under the guidance of industry experts and data scientists themselves. Providing access to experts helps learners understand how to take academic information and translate it into practical knowledge for solving real-world challenges. Doing this empowers the students by making them more application-driven in their learning approach and helps them extend their learning to make it more impactful and deliver measurable value. Multidimensional Program Structure The digital university should also offer Data Science-AI-ML Accelerator and should facilitate interactions between the students, professionals, and mentors for cross-pollination of ideas and information exchange. The course structure and platform should adopt a more democratic approach and should allow the users to employ their findings and ideas for research projects, prototyping projects as well as building MVPs. A digital university offering a marketplace to sell such solutions is an added bonus. Along with academic knowledge, data science courses should also focus on driving innovation challenges to enable beyond-the-classroom opportunities. These could span across a range of subjects and topics to allow students, academicians, and others to express their entrepreneurial spirit. Having an incubation system in place to enable the same becomes essential, especially when we want students to pursue entrepreneurship ideas. RubiVersity, the educational arm of Rubiscape, offers various facets of a digital university under one umbrella. By leveraging it, corporates and academic institutions drive innovation and create more entrepreneurs and data scientists. Want to know more? Let’s connect. Linkedin X-twitter Facebook

“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” –  Sir Arthur Conan Doyle, Sherlock Holmes It is amazing how even a century later the words from literature’s most famous detective continues to ring true. Data, after all, is becoming the lifeblood of organizations globally. Organizations are moving towards becoming more data-driven to optimize their assets and improve their growth opportunities. Given the massive volumes of data being generated, we are all willing to be led by data. But is generating the data enough? We all know the answer to that. NO. You might be busy generating data, but you also need to create roles within your organization to push you in the direction to becoming data-driven. Gartner estimates that “80% of organizations will initiate deliberate competency development in the field of data literacy, acknowledging their extreme deficiency.” And while there is enough talk about the technology aspect of data, we need to focus heavily on the ‘people’ who will be playing with this data. In other words, what are the different roles (Let’s put aside the role of the Chief Data Officer. That’s a given) that power a data-driven organization? Who are these people who will ‘speak’ to your data? Data Scientists What Hadoop is to Big Data, the Data Scientist is to a data-driven company. But while having a data scientist is essential to be data-driven, just hiring a data scientist does not make you a data-driven company. It’s just having a driver’s license doesn’t make you a Formula 1 racer. Or being a Formula 1 racer guarantee that you’ll be a good driver on open roads, right? So, what kind of a data scientist do you need? Your data scientist not only has to capably apply advanced statistical models to convert data into information but also should have the capability to make magic happen with data. They have to explore and experiment for improvising Machine Learning algorithms. They should be able to production-build models easily and be willing to creatively experiment with machine learning. These are the rock stars who will grow your business by transforming, processing, modeling structured and unstructured data. Data Artists Had, Charles Joseph Minard, the famous French engineer been alive today, I think he would have made a fantastic data artist. Just take a look at the visualization that he created for Napoleon’s 1812 Russian campaign. While the expedition ended in disaster, the map is considered one of the best statistical drawings created. The data artist, as the name suggests, is that person who presents the information generated by the data scientist in a manner that the user can understand it. Think beautiful and meaningful visualizations that make complicated information seems simple and implementable. These are the superheroes who bridge the gap between IT and business creating visualizations from obscure data that can be understood easily by business users and facilitate decision making. Data artists are part programmers, part visual artist, and part visualizers. They are the one who helps us analyze ‘what if’ scenarios that we face on a regular basis. As they need to work closely with data scientists, data literacy is key for these superstars. And along with this they also need to be clued into human educational psychology to understand visual processing capabilities. Data Enthusiasts If you want to be a data-driven organization, you have to cultivate your own set of data enthusiasts. If you look closely, you will find this breed of people existing in your organization. Who are these data enthusiasts? These set of people, much like Holmes, want to use data to come up with business strategies, design growth plans or offer recommendations. Because they like data as much as they do, these set of people are domain experts who usually have a working knowledge of data science and Machine Learning algorithms. They want to capitalize on their domain expertise and leverage data to build their own use cases. All they need is a data science platform that gives them exploratory and processing skills and learn and help them adapt to modeling techniques and build solutions Business Users We have written about the inevitable rise of the citizen data scientist as data embeds itself in the DNA of organizations globally. The Business User has a critical role to play in a data-powered organization. Why? Because for the organization to become data-driven every decision has to be backed by data. And it is the business user who drives this. But most business users are not data experts, right? How can they play with the data to derive useful business insights from the data themselves? And if they could, imagine the kind of business advantage that it would bring. Imagine your HR team playing with data to track, analyze and share candidate profiles to avoid bad hiring decisions. How about your marketing team becoming data-driven to customize marketing experiences and improve marketing outcomes? The uses cases there are many. In fact, I feel every department, every business user, benefits from using data to drive their decisions. And clearly, every department now needs its users to become citizen data scientists by giving them the capability to summarize, process, and visualize data by independently building and taking control of the machine learning outcomes. All you need to do is enable them with a platform that helps them do so. Business Analysts Given the growing deluge of data, the job of the business analysts gets more complicated. Today your business analysts need creative analysis capabilities given the plethora of data at their disposal. Think about it. Your KYC is no longer about the data that your customer fills in. It’s got social, behavioral, sentiment and all related customer data to consider as well. Business analysts have to get more creative in the manner they play with data. They have to make the data at their disposal work harder and work better. They need

For just about every industry, voice data is becoming an increasingly valuable asset. Voice data has become a resource for companies to discover game-changing insights and use them for growth and expansion. It is no surprise that the voice analytics market is set to hit the worth of $1.64 billion by 2025. For sectors ranging from healthcare to call centers, voice data is indicating how customers are interacted with, understood, and served. As customer experience is becoming a key brand differentiator, companies are leveraging all the data they have in an attempt to gain a competitive edge in the market. Voice analytics is now used to analyze recorded or live conversations using voice recognition tools and software. Then, these analytics can predict customer sentiment and behavior over phone calls, detect the quality of conversations, and gather deeper insights into customer needs and problems. While the technology is constantly evolving, there are several ways you can leverage it to gain a deeper, closer-to-reality version of what your customers value, and how they make purchases. Combining analytics from both speech and text can be game-changing for your business. How to Get the Most Out of Voice Analytics? To maximize ROI from voice analytics, here are some best practices that companies can follow: Identify gaps and goals – It’s best to start small. Measure how well your enterprise is performing with respect to some specific key performance indicators. After you have done that, define a clear outcome you want to gain from voice analytics. What is it that you want to happen as a result of voice insights? You can start by analyzing customer interactions and identifying the kind of questions and queries your agents struggle with. You could also dig deeper to find out what frustrates your buyers and makes them go bonkers with your reps. This is the opportunity window for you to leverage voice analytics. Determine KPIs – Each business has its set of unique KPIs that matter to their long-term growth. Speech analytics can help you track a host of KPIs such as customer satisfaction, the level of their anxiety, happiness, or confusion – by measuring their voice quality and fluctuations. You get to decide the KPIs that are important for your business. Decide on a handful of KPIs and then benchmark your current performance levels. Knowing where you stand at the beginning of the journey will help you understand the progress you’ve made. Track growth and performance on an ongoing basis to see how you are progressing. Onboard experts – No new software solution can be successfully implemented without the right team. A voice analytics software can be challenging to set up, implement, configure, and customize to your unique needs. You need to understand what data to gather, how to analyze it, and how to make the insights available for your team. Onboard voice analytics specialists who can lay the groundwork for your employees. This will ensure the time-to-value is shorter and you can see results from your investment sooner. Getting help from the right data and analytics experts who understand technology implementation can be your key decision, which will ultimately determine your success with voice analytics. Create a process – Established, tried, and tested processes work like the oil in a voice analytics machine. Without a specific process, things can go haywire pretty soon. Review your data usage, data gathering, and insight extraction procedures before putting them into implementation. Determine how you plan on leveraging the insights you get from voice analytics, who can have access to what data, and how you can control authorization and security protocols. Creating a process that helps you gain maximum leverage out of analytics data is critical. Install 360-degree accountability by distributing responsibilities within the relevant team. Train the team – Help your employees learn about the new system before you roll it out in its entirety. Define what segments are critical to which department and create a structured training program to help them understand how voice analytics can help solve their pressing challenges. Enroll them into a post-launch training that helps them transition into the new and improved way of using data for their advantage. Make sure the new training program is also a part of the hiring process so that fresh employees can work and be productive from day one. Data democratization can be your priority to help non-tech staff leverage voice analytics and derive as much value from it as your specialists. Monitor the results – Discover the value voice analytics is bringing to your bottom line, customer engagement, satisfaction, and other KPIs. Reveal these findings to your core customer support teams to encourage their participation. Ask customer reps for their feedback on how voice analytics can work better for your enterprise and be ready to make changes to your strategy or implementation. Expand – Speech analytics can capture large volume and variety of data that might not be limited to one aspect of your business. After you have seen results with a small approach, expand voice analytics implementation to other focus areas within your enterprise. Enhancements in the bottom line, revenue, and customer experience can be compelling enough for you to want to expand base in other areas. Creating a positive customer experience can be the driver of profit for businesses in this digital era. Positive customer experiences go a long way in facilitating brand image and helping onboard more customers and retain existing ones. How Voice Analytics is Shaping Customer Experience Customer services and support continue to be integral to the overall customer experience. Voice analytics is extensively being used in developing customer services and advancing support. Combining voice analytics with big data techniques are allowing companies to analyze numerous calls at once. By doing so, they get a fair picture of the working of their customer service operations. By using the insights from voice analytics, companies can implement initiatives to reduce repeat calls and call duration. Analyzing recorded or live calls can help service

For national development, you have to focus on education development. Sustainable development cannot be achieved unless there is a substantial investment in human capital. A cursory glance at how education interacts with the economy reveals why certain economies flourish while others falter. The recently released New National Education Policy seems to have internalized this factor. It has brought about some stellar reforms to an aging education system that has been handed over from generation to generation as an inheritance from the days of the first Industrial Revolution. India’s education policy has received a much-needed revamp after three long decades. This is a welcome change, especially since the world has changed enormously over the past few years. Technology, something that is driving the world, hasn’t been a part of any school syllabus. Even it has been in the form of computer studies, it has been rudimentary, to say the least. Now, when the pace of technological progress has accelerated and technology becomes the center of every industry, it becomes imperative not only to make it a part of the education system but also to leverage it to drive the system. What I really liked about the new policy was that it took an honest look at what needs fixing in our education system. Having school-going kids myself, I was thrilled to see that the policy focuses on ‘education’ and places equal importance on fun and play in learning. These concepts have been a part of the coveted education systems of the western world typically. The policy takes the challenges presented in the curricular and pedagogical structure of our current education system head-on and promises to usher it into modern times. The policy surely is posed to be a game-changer, especially since it takes into account the most pressing issues of an ailing education system. Inclusiveness, accessibility, language barriers – these are very real challenges that impede many, especially the low economic and marginalized communities to move along the path of learning. The policy, in its clear and transparent manner, identifies these gaps and promises to build the bridge. Driving ‘learning agility’ This is perhaps the most relevant and the most important change that has been introduced by the policy. The basic tenets of the policy are designed around the principles that children not only need to learn but also need to learn ‘how to learn’. The rapid changes in the knowledge landscape have been the key motivator for this goal. I find this approach visionary since the Indian education system so far has looked away from its aging methodology. While the factory-like education system (a system introduced during the time of the first industrial revolution) worked for a long time, the purpose behind that education system was to create a more employable workforce to work in factories. Today we need to develop a more ‘thinking’ workforce since everything else is getting replaced by automation. We constantly seek people who can be on the path to continuous learning. This bent of mind can be achieved if children are taught not only what to learn but also how to learn. Technology finally gets its due When software is driving every possible business, it is a surprise that it has not been used to drive education until now. The 2020 education policy thankfully addresses this big black hole and promises to integrate technology into all levels of Indian schools. It is not only going to be used to enable learning for students, drive inclusive learning for specially-abled children, and foster multi-lingual capacity but will also be used to help teachers to estimate their needs and aid their professional development. Technology will now streamline education planning, administration, and management in schools and colleges going forward. ‘Technology as a subject’ to get future-ready The new Education Policy aims to drastically revamp the education system to drive the innovation capacity of future generations. India has been the intellectual capital of the world. With the changes and advancements brought about by the policy, I am sure that we shall be positioned as the innovation superpower as well. The use of school complexes to drive adult education after school hours, the focus on vocational studies, employment of quality modules to teach sign language and more signal the coming of progressive times in a dating education structure. The policy places great emphasis on building a digital infrastructure and digital content to drive alternative modes of education and increase access to quality education. The policy also proposes a dedicated unit for digital and online learning that will enable all this and develop applications, online courses, modules, and satellite-based TV channels to drive better learning outcomes. The focus on experiential learning and making all institutions multidisciplinary comes as a welcome step, especially since all aspects of society and business today lean in on being multidisciplinary as well. The blurring lines between liberal arts and sciences will facilitate new-age skills that go beyond silos created by a ‘tech only’ or an ‘arts only’ approach. Focus on 21st-century skills Coding has been called out as a 21st-century skill and will be a part of curriculums from grade 6. It will make the future generation ready with relevant skills to increase employability in the future. It will also bridge the huge gap between affordability and coding literacy. The focus of this policy on key concepts, ideas, and its applications and developing problem-solving skills promise a bright future. New-age technologies such as AI and analytics are built on the premise of logical thinking, problem-solving, and critical thinking. The inclusion of these programs will consequently lead to a more employable future generation since these skills will be essential for professional success in the coming decades. The focus on digital literacy, coding, and computational thinking via subjects such as Artificial Intelligence, Big Data Analysis, and Machine Learning will boost the cognitive abilities of the students giving them the tools they need to succeed in the age of the AI economy. The new Education Policy recognizes the need and importance

Every organization is moving towards a data-driven culture. Companies of all sizes have woken up to the fact that they can now collect store and analyze data fairly easily and economically. That apart, the huge volume of petabytes of data has become a strategic asset now. Earlier the ease of data management and analytics was absent, but with the advent of open source technologies that can manage, cleanse, and analyze data, companies are wanting to unearth the data treasure trove. Various stakeholders in the organization have different expectations and requirements from the data. In this blog, let’s take a look at various such data consumers and what they expect from their data and data tools. Decision Markers Let us take a top-down approach and start with the decision-makers who are right there at the top of the food chain. They majorly don’t have much time and need the data to be presented in a visual format to them. Their primary objective is to leverage data to improve productivity and gain significant competitive advantage by growing their business. There are many tools available in the market where data visualizer can feed the data, chose the algorithm, and get the data represented in a visual. Such a visual representation of data makes it extremely easy for executives to consume and take action. These business owners are subject matter experts (fondly called as Citizen Data Scientists) and not necessarily technology experts. They need access to easy-to-use tools that will allow them to slice and dice the data and get real-time insights to take quick business decisions. Data Scientists Next in the hierarchy are the data scientists. These are the people on the ground who validate the collected attributes, the quality and run the analytical tools on the data sets to come up with inferences and insights. Data scientists need to understand the business goals and provide predictive analytics to come up with consumable forecasts based on which organizations can take relevant actions and decisions. They will have to crunch the numbers and keep it aligned with the ask of the business. While data scientists also well-versed with various technologies and tools, they need access to the right tools that can help them quickly build models and derive real-time insights. Analytics Leaders Analytics Leaders are the champions who have the end to end knowledge of how the data science team and department should operate, and they provide course corrections wherever necessary. They have to look at the value that needs to be discovered and set the correct priorities. They need access to toolsets that can help them in the end to end extraction, modeling, and selection of algorithms for data analysis and visualizations. This makes it easier for the business folks as well as the analytics managers to define the priorities for data science initiatives based on business needs. Data Artists Once the data has been analyzed, it needs to be presented to the business stakeholders to help them take timely business decisions. The presentation needs to happen in the form of visual graphs and charts that can be easily consumed by the executives. They need access to efficient tools that can allow them to convert huge volumes of data into attractive, dynamic, efficient, and meaningful presentations. Statisticians Statisticians are another important key stakeholder in organizations. Their importance is pretty much self-explanatory. They bring the statistical knowledge which is required for fitting in the models and running the analysis. The other key stakeholders are the analysts, who know which data to be collated and scrubbed to be made analytics-ready. This is a complicated task as different levels of queries need to be performed on various internal and external sources. AI/ML Leaders Many organizations are now leveraging the power of artificial intelligence and machine learning to implement chatbots and recommendation engines. In that scenario, they take the help of AL/ML specialists. These specialists understand the nuances of AI/ML like REST APIs, SQL, etc.. They can also implement standard machine learning algorithms such as clustering, classification, and perform A/B testing, and build data pipelines. They need access to tools that will allow them to get the right data and use it appropriately in their models to refine those. Data Engineers Many organizations use data lakes on cloud and various enterprise-level data warehouses to support their analytics initiatives. Data Engineers are responsible for building enormous reservoirs for big data. They construct, develop, test, and also maintain architectures such as large-scale data processing systems to boost the performance of the databases. They need tools to help them easily learn and adapt to modeling techniques and build solutions. Considering the varying needs of different stakeholders, organizations often end up investing in multiple tools. It creates huge complexity in the IT stack which is difficult to maintain and is extremely costly. Organizations also need to then spend time and energy in training the various stakeholders on different tools and ensure that the different tools integrate and facilitate smooth data exchange without any issues. Rubiscape, a disruptive data science platform, aims to a provide solution to all these problems. This easy-to-use platform can be used by each one of the above-mentioned stakeholders to address their specific needs. Try it now!

While the field of AI was formally founded in 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, we can find traces of the AI conversation in the attempts of classical philosophers to describe human thinking as a symbolic system. Right from the Wizard of OZ (think the Tin Man) to Gulliver’s Travels (think the engine on the island of Laputa) to the demonstration of  the world’s first radio-controlled vessel by Nikola Tesla at Madison Square Garden, or Houdini’s  radio-controlled driverless car…the early days of Artificial Intelligence have been a topic full of intrigue. The Early Days Over the years, philosophers, scientists, and mathematicians assimilated the idea of Artificial Intelligence. It was in the 1950s when Alan Turing suggested that just like how humans process data for information and decision-making, machines could do the same. He discussed this idea in his paper Computing Machinery and Intelligence. However, it took time to establish the proof of concept and it was in the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) that an open-ended discussion on the technology took place and the term Artificial Intelligence was coined. In the initial years of AI, from 1957 to 1974 the conversation around AI picked up steam. Computers became more accessible and cheaper and could also retain more information. The applications, quite naturally, remained basic relegated to primarily the goals of problem-solving and the interpretation of spoken language. However, the high goals of natural language processing, self-recognition, and abstract thinking were still a long way off. This was mainly because of the lack of computational infrastructure and the incapability to process information fast. The Wonder Years The progress of AI continued at an intermediate phase there onwards. It was in 1979 that The Stanford Cart crossed a chaired room without any human intervention becoming the earliest example of the autonomous vehicle. In 1980 the Wabot 2, a musician humanoid robot that could communicate with people, play tunes and read musical notes was built in Waseda University in Japan. 1986 saw the first driverless car built at Bundeswehr University in Munich that could drive up to 55 mph on empty streets. The 80s saw a great deal of progress in AI with researchers building multi-layer neural networks and statistical approaches to language translation amongst others. Despite the absence of or limited government funding AI managed to thrive. The 1990s and early 2000s saw many landmark goals of AI being achieved. In 1997, IBM’s Deep Blue defeated the world chess champion and grandmaster Gary Kasparov. In the same year, Windows implemented speech recognition software, developed by Dragon Systems. Progress in AI was happening and happening fast. The reason behind the accelerated pace of AI maturity could be attributed to the fact that computer storage was no longer holding us back as it did 30 years back. With technologies such as the Cloud becoming mainstream and the power of big data analytics, AI had got its enablers of success. As data emerged as the new oil of the 21st century, enterprises realized that they needed faster analytical capabilities. Then came the time that faster analytics was not enough. We needed the algorithms analyzing this data to become faster and self-learn as well. And while it has taken us almost 70 years to identify the combination of factors that need to come together to move AI from a concept to a ubiquitous reality, the time is finally here. In 2008, a small Google App with speech recognition heralded a breakthrough for AI. Google has finally managed to lift the 80% accuracy of speech recognition to a ground-breaking 92%! In 2011, IBM’s Watson won the won the U.S. game show Jeopardy and this win was hailed as a major triumph for AI. Today, we have AI integrated into our daily life. Our smartphone assistants are powered by AI. AI is running our voice-based gadgets such as Alexa. AI is making facial recognition possible. Where We’re Heading And while all the progress seems to be on the consumer-centric front with new objects and new devices and self-driving cars, AI was wheedling its way into the enterprise and changing the way organizations function. Right from augmenting automation to changing business processes and impacting decision-making, the AI impact in the enterprise has been hard to ignore. Retail has been proactive in using AI-powered chatbots and has used the technology for demand mapping, customer intelligence, marketing, advertising, and campaign management, pricing, and promotion strategies. Finance and banking are using AI aggressively for fraud detection and management, risk management, trading, and financial advisory management. AI is also driving the automobile industry becoming the engine of value for automobile companies. Right from infotainment systems to adding cognitive capabilities in cars, predictive maintenance, geo-analytic capabilities, our cars are getting smarter and safer owing to AI. At the back end, AI is driving process optimization leading to better productivity and greater value. It is helping automobile manufacturers with predictive maintenance to reduce downtime, better product design capabilities, and less waste. It also optimized the supply chain. The healthcare sector is also leveraging AI heavily. With the Smart Hospital concept maturing fast, AI is the technology that will make the hospital environment safer, smarter and more optimized for better patient satisfaction and outcomes. From nursing assistants, connected control centers, automation systems for proactive alarm and outages monitoring, AI is acting like the spine of the Smart Hospital framework. With a growing sensor-driven environment, the greater proliferation of IoT and the greater maturing of AI technologies such as Machine Learning, Deep Learning, Neural Networks, Speech recognition, Text Analytics and Natural language processing (NLP) etc., AI is surely and steadily going to ingrain itself into the synapse of the entire enterprise architecture. This year we can see a growing confidence in this smart and predictive technology as it brings on the table the promise of transformational change.

Data science has become an integral part of the business strategy of most forward-thinking and successful organizations globally. As the data proliferation increases and data becomes the enabler of business success, the role of the data scientists rises to become the hottest job of the 21st century. However, data science is an evolving field. Its scope within the enterprise is constantly changing. It is now being used to solve complex problems, to build models that can accurately identify high-value customers and avenues and strategies to retain them. Enterprises are using their power to create highly effective product recommendation engines, identify process gaps and areas of process improvements, etc. As the scope of data science is changing, the toolkit that enables data scientists and organizations is evolving too. The open-source community has been very active in this space and has aided the democratization of data science. The open-source ecosystem offers a lot of scope for collaboration and contribution, and the fact that these tools are now reliable and no longer limiting provides immense value to enterprises. However, with the plethora of data science tools increasing, how can you determine which one is suited for you to help you become the Sherlock Holmes of data and help make data science faster, deeper, and more effective? Here’s a look at some key considerations. Programming language Assessing the dexterity of programming languages is one of the key parameters while assessing data science tools. Data science tools are of two kinds, one for those with programming language knowledge and one for business users. Python and R are two open-source programming languages that have been popular in the data science landscape and have been used for data collection, data exploration, data visualization, and data analysis. They boast of great packages and libraries and are well-suited to meet the data science needs of organizations of today. Alternately there are data science tools that do not need any programming capabilities and thus enable greater democratization of data science. These tools are user-friendly and assist organizations in creating their army of citizen data scientists out of business users. This approach also helps organizations become truly data-driven. Flexibility Apart from doing statistical analysis, data science tools also have to give you the flexibility to perform things like regression, component analysis, clustering, machine learning, etc. and should offer one or more of these methods. They should also provide the capability to create, test, and maintain Basic and Advanced Analysis and Models. For example, if you want to build a statistical model and want to uncover optimal parameter values and want to use likelihood functions and optimization techniques, then the tools should offer the flexibility to do so. In-depth information A core aspect and function of data science is to help build awareness in the face of uncertainty. This factor has to be a key consideration when selecting data science tools. While some tools might provide results but unless they provide insights into how and why those results are reached, it is of no use. This is because it impedes the capability of the data scientist to de-construct the methods and the model to gain a deeper understanding of the model and system itself. Then, if the model makes an error, it becomes a confounding exercise to diagnose the problem. The capability to see inside nearly every statistical method and result and even black-box machine learning methods in a user-friendly manner can deliver immense value to data science efforts. Open-source is good An open-source tool kit is something to look for in the data science tool evaluation kit. Open-source has a robust feedback loop, a big community to support and continuous improvements that help fix mistakes and issues in a timely manner. However, while evaluating, you must ensure that the tool is maintained by a reputable organization and that it has a strong and committed user base. It is also imperative to ensure that the tool has been running without any significant issues. While doing so, it is important to assess the feedback loop and proactive community support when it comes to mitigating toolkits. Many tools not only leverage the power of the open-source community but also have a dedicated team of experts to take care of any issues, challenges, and concerns. Does it provide extensions? Given the growing volumes of data and the speed at which data processing needs to happen to power data science, it makes sense to evaluate the kind of extensions that the tool offers. Big Data connectors, API kits for Social & Cloud Platforms, Sensor Gateways, Mobile Apps, etc. are some usual suspects. The tool also should have the capability to connect to cloud-based services to manage the large volumes of data, solve the complexity of processing, and improve in-memory storage and security. Assess the analytics angle Analytics is a key component of data science. Thus, evaluating the kind of analytics the tool provides becomes an important parameter. Tools that provide a rich library of visualizations and powerful interactions and have the capability to integrate complex data sets across varied business and analytical areas is essential. Other analytics capabilities, such as text analytics and predictive analytics, help in furthering data science capabilities and velocity. Looking for capabilities such as Linguistic, Statistical, NLP and Machine Learning techniques to Model & Structure textual data for analysis, visualization, and collaboration also make tool selection easier. Along with all this, it also makes sense to assess the integration capabilities of the tools. Data science tools should give you the flexibility to integrate functionalities, import data, and export results in generally accepted formats, further data science efforts. For example, if you want to integrate a statistical software method into a particular language, you should be able to do that. With the right data science tools in place, your data science team can capably balance time against the quality of results and ensure that insights and information are timely. After all, ‘time is money’.