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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’.

The journey of Artificial Intelligence or AI from the innovation labs and experimental R&D divisions of tech firms to the mainstream consumer market has been nothing short of phenomenal. For businesses, AI plays a transformative role in shaping the experiences they deliver for their customers as well as employees. In fact, the global AI market is expected to be worth over USD 190.61 Billion by 2025. AI is not just a driver of new experiences but has been at the forefront of adding new dimensions to technology solutions that have been around for a while and were facing their saturation levels. Today, let us explore one prime area where AI is bringing about a rapid paradigm change to the way the world perceives it. It is none other than the world of apps. From smartphones to tablet PCs and wearables, there are millions of apps available for consumers covering a wide spectrum of use cases, and delivering on-demand services right at their fingertips.  While new apps are always disrupting the market, it is to be noted that what drives the app industry forward is the ability of app makers to design and launch apps with intelligence embedded in them. In other words, the app market is growing considerably due to the increased use of AI in their core by developers. From retail to healthcare and fashion, consumers demand intelligent apps that help them navigate through their daily routines and challenges. Let us explore 5 key areas where AI adds the intelligence factor to intelligent apps. Personalization Did you know that nearly 80% of consumers are likely to do business with a brand if they offer personalized experiences? Personalization is one of the key pillars of building lasting customer relationships. Businesses of all sizes race to deliver high levels of personalized interactions to help their customers feel more engaged. The case with apps is no different. AI can drive personalization to a whole new level in services offered via apps. Just have a look at how online shopping apps like Amazon delivers personalized recommendations to shoppers, or Spotify suggests tracks to its subscribers from genres or artists they love more. Every bit of these actions in personalization is driven by AI algorithms that work behind the scenes. Security Today, consumers use apps for pretty much all their needs, and very often these needs include paying for services consumed via the apps or even banking apps that contain sensitive financial information. In addition to financial credentials, today’s apps collect a variety of data from their users for offering more personalized services. With private data and financial credentials being constantly exchanged, apps are a hot target for cybercrimes. It is said that one in every 36 mobile devices has high-risk apps installed in them. AI can bring in threat intelligence for apps and help businesses detect suspicious activity being facilitated through their consumer apps. AI can learn continuously to thwart newer threats based on behaviors it has studied in the past. Performance Most apps leverage far more memory and processing power of smartphones or devices where they are installed, while they are not in full use. AI can enable optimal utilization of resources to ensure that apps work as modular components and only necessary modules draw their required memory and computing power thereby enabling devices to last longer in their battery charge. It can learn how different components utilize device credentials and optimize clock cycles and CPU performance to provide the most suitable operating environment for the app while balancing workloads. Intelligent Search Most consumer apps offer interactive search options for users to navigate and find what they were looking for. Bringing AI into the picture takes this search experience to another level. For example, a shopping app can leverage AI to find a matching product from a photograph that the user’s smartphone clicks. If a user happens to see a print ad or mannequin spotting a dress that he or she likes, they can simply use the shopping app’s camera provision to click or scan the dress, and the AI system can quickly discover similar dresses from the shopping service’s inventory and lay down recommendations of accessories that go well with it. Enhanced IoT Capability With AI algorithms, apps can learn from data that a user’s device collects when it is connected to other smart devices or IoT sensors. Based on its learnings, the AI system can help the app improve the overall experience for the user by optimally matching environmental outcomes of the IoT sensors to the preferences the user usually makes in the app. For example, a smart home system can work autonomously with the help of an intelligent smartphone app that learns the behavior of the home user like the temperature he or she usually sets in each room, the music they love, the volume they usually set on televisions or music systems, the time they turn on the garden watering system and much more. AI opens the door for a wide array of possibilities for today’s business apps by adding intelligence to the mix. For businesses, they have the opportunity to deliver more awesome and engaging experiences for their customers and, in return, enjoy loyalty for the long run. The future belongs to intelligent apps and AI will be at the forefront to bring more intelligence into the app ecosystem.

A common trend among businesses in this day and age pertains to the ‘Speed’ of operation. They are invariably seeking new ways to automate business processes and finding unique and novel routes to implement tech solutions into their workflows. This inclination towards operational efficiencies and a greater focus on quality can be attributed to the constantly evolving market and immensely diverse audience. The emphasis is on implementing creative and innovative ideas without any hassle of having to invest in lengthy processes. And this is precisely what paves the way for buzz-phrases No-code and Low-code. Both these concepts work in favor of streamlining and altering processes with the use of Technology. In other words, Low code and No code present themselves as viable options for businesses by allowing the deployment of ideas quickly and efficiently – while eliminating the need for costly processes of coding and compiling software for internal use. That explains why start-ups, SMEs, and even classical-coding-loving Fortune 500 companies are looking to have Low-code and No-code platforms at their disposal. After all, it’s all about saving resources (cost + time) and warding off the possibility of human resources being occupied with mundane tasks. In that light, this article will attempt to draw a parallel between the needs of distinct-level organizations and shed light on the concept, prominence, value, and necessity of Low-code and No-code platforms. But first, let’s understand what they actually mean. What is Low-code development?   Low-code development is an approach that aims towards the rapid delivery of stable, reliable, and tested software applications without the need for proficient coding skills, diverse code libraries, compilers, and IDE’s. This makes it easier for non-technical users to create customized, tailor-made business solutions. Here’s how Low-code platforms work: They use a combination of drag and drop features, graphical interfaces, visual design tools, and simple business logic scripts, allowing the non-technical users to build applications with pre-built features commonly found in applications developed with traditional coding languages. A significant technical advantage is that they support several mainstream programming languages, including Java, .NET, PHP, and C#, with minimal switching costs. So, the developers or perhaps the business users can make transitions between languages per the requirements. What is No-code Development?   No-code development is an approach towards developing applications, allowing users without programming knowledge or experience access (or use) the system’s capabilities – without the need for any coding skills or tools. Prominent examples of such platforms are online website builders. Here’s how No-code platforms work: They use visual “Assistants” or wizards that guide the user through the process of making changes to an application. The process uses pre-built components and features from within the platform, as well as external solutions, allowing users to build applications without having to worry about coding. Why Start-ups and SMEs Need Low-code / No-code Data Platforms   There are constraints to working smoothly in a competitive environment. More often than not, businesses need to optimize their requirements to comply with the resources at their disposal. But what if the market demands more? Well, then that’s a catch worth addressing. You can’t leave out on growth, or can you? There is a growing consensus that enterprises need to be data-driven. But how do they inculcate the data culture within the organization unless they give the right tools in the hands of every business user? To that end, here are some considerations as to why start-ups and SMEs must invest in Low-code / No-code data platforms. Better Data Accessibility through Quick Development Let’s say as a sales leader, you want to get answers to questions like – where are your orders coming from? What is the average value of an order? Is there any seasonality? Do customers buy the product/ service after any specific marketing campaign? So far, you have relied on multiple excel sheets to get some of these answers. Sometimes, you rely on your gut feeling. But now, you want to get all this data on a centralized platform, but you don’t have the in-house capabilities to build it. Leveraging the potential of a Low-code data platform, in such a case, would provide you with literally everything at the expense of a mouse-click. Even your team members would be able to make use of wizards and assistants to develop required apps in a drag-and-drop fashion without having to learn coding, of course. This tends to simplify the overall process, swapping out the need for technical expertise for an easier-to-follow visual interface that helps in building an app in a snap. The Unparalleled Combination Cost-Efficiency and High-Grade Features No-code/ Low-code platforms also tend to be extremely cost-effective than traditional application development efforts. This is because they generally contain pre-built components within their platform, making app development or data analysis effortless. Most of the modern-day platforms are cloud-based, which means that they’re accessible from any device. The nature of cloud-based infrastructure also means that your app can be updated and made available at any time without the need for complex configuration. In today’s data-driven world, decisions need to be made based on real-time data and insights. Low-code/ no-code data platforms offer extremely cost-effective ways to make use of the data at disposal. Real-time, Data-Driven Decision-Making Suppose a company uses some internal tools and manual processes to track the performance of its marketing campaigns. Unfortunately, they’re completely outdated and inefficient. They do not offer any flexibility or real-time insights to the business decision-makers, which makes them more of a hindrance than an asset. Business leaders cannot make real-time decisions based on the campaign performance because it involves a lot of manual data processing. The crux here is that it’s hard to achieve greater productivity without technologies that simplify the work process and streamline the operations. Again, Low-code / No-code data platforms turn out to be saviors — this time owing to the principle on which they have been developed – speed and efficiency. For instance, for the above case, a marketing manager can use Low-Code/ No-Code data platform

Companies need Data Science now more than ever before as the data economy proliferates to usher in the new age – one where data is the driver of everything. The value that data brings to the table can no longer be ignored – competitive advantage, accelerated pace of innovation, successful new product development, increased efficiencies of people and processes, etc. are the obvious ones. It is but natural to assume that organizations globally will be working hard to improve their data capabilities. It is then hardly a surprise when we see the job of the data scientist being hailed as the sexiest job of the 21st century. According to a study by Figure Eight, 50% of employed data scientists are contacted once every week for new opportunities. 30% are contacted several times a week, and around 85% are contacted at least once a month. These numbers further illustrate the growing demand for data scientists. Just like the role of the data scientists is growing in prominence, it is also evolving as data science matures and industries change owing to the technology impact. So, given the general interest in mind, here’s a look at some of the key skills that an ace data scientist will possess in today’s data crazy world. Technical Skills – Statistics and Statistical Programming It’s redundant to mention that data scientists are highly educated. At least, the ace ones are. 88% have a master’s degree, and 46% have Ph.D.’s. It is natural to assume that ace data scientists have exceptional statistical knowledge and are excellent at Hypothesis Testing, Probability, Descriptive and Inferential Statistics. Having an intuitive understanding of business statistics is another feather in the data scientist’s cap. Their technical knowledge and skillset are expansive. Given that different businesses use different tools and languages in their workflow, ace data scientists have to have a strong core of technical skills that can be applied to many problems. In-depth knowledge of analytical tools is a given for the ace data scientists. The preferred one being R since R is designed to fulfill the needs of data science. While R does seem to have a steep learning curve, 43% of data scientists use it to solve the problems they encounter. Python is another programming language popular amongst great data scientists as it provides them with better insights as well as helps them correlate data from large sets of data. Having a great grasp of Python and its libraries is almost like a trademark of an ace data scientists Algorithm Knowledge Algorithms are data scientists’ playground. These people compete with things such as logistic regression, decision trees, neural networks, random forest, clustering, and the like. Having a great grasp of machine learning and advanced machine learning knowledge becomes quite imperative to participate, play, and win in this game. A deep understanding of different machine learning techniques such as supervised learning, unsupervised learning and reinforcement learning, and their subsequent algorithms is a hallmark of a good data scientist. Ace data scientists also have knowledge of neural networks as deep learning models. They know how to create deep learning models and understand how Convolutional Neural Networks, Recurrent Neural Networks, and RBM and Autoencoders work. Business understanding Did you expect a laundry list of the technical skillsets in this blog? Well, there are a few things that separate a good data scientist from one who is the rock star. This is one of the differentiators. Having good business knowledge and deep domain expertise helps them put the data to work. Data-driven problem solving includes understanding the salient features of the situation at hand, assessing how to frame the right questions to get the right answer, evaluating the approximations that make sense, and knowing which resource to approach at the right juncture of the analytics process. A data-driven approach to problem-solving comes with experience and is a venerable weapon in the data scientist’s toolkit. Having a good business understanding also hones their visualization skills and helps data scientists present their data in a visually appealing format. This helps them communicate better with their end-users as they can use the language of business as opposed to the language of IT Intellectual Curiosity Albert Einstein’s famous words, “I have no special talent. I am only passionately curious” are profoundly relevant in the narrative of a data scientist. Ace data scientists are curious beings and have an innate desire to acquire more knowledge. And ‘curiouser and curiouser’ must you get, much like Alice in Alice in Wonderland, to be an ace data scientist. Why? Because 80% of the time a data scientist’s job entails discovering and preparing data based on the asked questions. And it is curiosity that helps them play with the data, push it, wrangle it and twist it and turn it in multiple ways to get answers that we get ace data scientists. Communication Ace data scientists have to be ace storytellers because they have to fluently translate their technical findings in the language that the non-technical user, such as the sales and marketing teams, can comprehend. The ace data scientist enables non-technical teams with quantified insights and also understands their needs, problem areas, and desired outcomes to wrangle the data appropriately. Creating a storyline around the data helps in communicating the findings easily across the organization, making it easy for everyone to enjoy the fruits of data-driven decision making. Ace data scientists are all this and more. But above all, they are also team players. They understand that they have to work across teams and people to help develop strategies, create new products, launch better campaigns, drive more sales, or improve business processes. While we expect to see this upswing in demand for data scientists, we also feel that organizations have to give their workforce the power to glean intelligent insights from data and make data-driven decision making and organization-wide practice. For this, they need citizen data scientists, who are the everyday employees, and give them the capacity to convert data into insights

The last few years have cemented the importance of data in any business. Owing to the phenomenal contribution data makes to any business, it is hardly a surprise that data scientists are the new superheroes. Glassdoor ranks the job role of the Data Scientist as the ‘Hottest job of the 21st Century’ three years running.  The Deloitte Access Economics report highlights that 76% of companies plan to increase their spending on analytics capabilities over 2019 and 2020. They also forecast that by 2025, the data science professionals with a post-graduate degree will be earning an average $130,176 p.a. The role of the data scientist has shot to prominence owing to the growing importance of data. Data is now becoming part of organizational DNA becoming not only a part of the decision-making process but also of product development, identifying business value and new business propositions, and evaluating risks amongst other things. Data science prepares organizations to find more value in technology. And it is the data scientists who so far have been the enablers of the same. However, as we delve deeper into the tech and the data economy, one thing becomes clear – data science is not a ‘back office’ thing. It cannot exist in the form of horizontal diversity with the organization. As data becomes critical for every organizational function, the traditional role of the data scientist has to evolve. And while organizations will have to have their set of data scientists, they will also need their band of Citizen Data Scientists – the ones who will be using data to empower business decisions and contribute to the bottom line. Data Science – then and now Data Science is no longer a niche role. It does not belong to the hallowed portals of a select few organizations alone. From healthcare to eCommerce, every business and every industry has the need for data science and consequently data scientists. These data scientists have advanced training in math, statistics, and computer science. They have to have in-depth technical knowledge of languages such as R and Python to create robust data models. And what good is this knowledge if they do not have great domain expertise? It is the domain expertise that helps these data scientists manipulate the vast sea of data at hand to glean intelligent business intelligence and insights. The role of the data scientist   The data scientist models and rearranges the data with a visual front end. This is traditionally operated in the scripting interface. The current technology being favored here is R3. Along with this, data scientists also have to manipulate different tools built for specific purposes to get the desired answers from the data at hand. They have to work with Knowledge Discovery Datasets, conduct data exploration as well as data visualization. R and Python have been the favorable programming languages to fulfill the programming needs amongst the data scientists. This has been mostly because these languages are user-friendly and have a big support network amongst other benefits. Clearly, to develop robust data models and fulfill data exploration, visualization, analytics demands etc., the data scientist has to have deep programming and scripting knowledge to use the available tools. This also establishes how niche the role of the data scientist is. The growing need for ‘citizen data scientists’ Given the growing role of data in every organizational aspect, can the role of the data scientist remain as niche as it is presently? In my opinion, organizations now need the non-data scientists, basically, the business users to assume the capabilities of the data scientist. They are the actual users. They are the domain experts. They have the right questions. They know the business problems they need to solve. What they need is the capability to exploit the data at hand to drive their decision making. If empowered, any business user can become a citizen data scientist and apply the right data models to the right data to predict business outcomes. They need the flexibility and the bandwidth to connect with the database and create compelling visualizations, reports, and dashboards. They need the capability to play with the data they have and explore the myriad possibilities associated with that data to answer their own questions. No dependencies involved. Data science has to come to the ground level to create a data-driven organization and develop a data culture. All that organizations need to enable the same is a platform that allows Open Source, algorithms, computation, and business users work harmoniously. The trick lies in ensuring that organizations don’t have to change their processes. The integrated platform should come with high interoperability – one which has a host of pre-built functions, employs popular algorithms, provides toolsets to analyze structured and unstructured data and social data, provides static models for changing data sources, and can handle changing data sources and volumes. Rubiscape is such a platform that empowers everyone to become a data scientist. So yes, with such a tool, your business head could be your new data scientist. Or your manufacturing head or your programmer…anyone in the organization can become a citizen data scientist. And this brings data science to the ground level creating an organization that is ‘data-driven’ in the truest and the broadest sense of the term.

We live in an era where even the mixer grinder in the kitchen produces considerable data and shares it over the internet to a host of apps and platforms. More than 2.5 quintillion bytes of data are generated every single day according to estimates, which translates into an average of 1.7MB of data created every second by every human on the planet! So, what do we do with all this data? The answer is simple – use it to improve our lives in multiple areas. Now comes the question of how to achieve this feat? The solution lies in predictive analytics, which deals with predicting outcomes of scenarios using statistical modeling of historic data. This is one of the key pillars of artificial intelligence and machine learning. Globally the market for predictive analytics was around USD 7.32 Billion in 2019, and it is expected that this figure will reach a staggering USD 35.45 Billion by 2027. In the past, predictive analytics has been perceived as an experimental concept or one that had limited practical usage within a business environment. However, it has now transitioned into a mainstream business enabling capability that is sought out by firms of all sizes. On this note let’s have a detailed look at some of the top real-world use cases of predictive analytics witnessed today: Autonomous Maintenance in Manufacturing   Being heavily dependent on machines, the manufacturing sector constantly invests in keeping their hardware at optimum health to ensure their production commitments are always met. This is precisely the area where predictive analytics has witnessed its most crucial real-time application in the manufacturing sector. By continuously monitoring data from factory components, manufacturers can predict maintenance routines needed. For example, General Motors runs AI-based predictive analytics on images from cameras mounted on factory robots to estimate their failure rates based on signs of wear and tear of components. These may be difficult to be spotted by the human eye without a thorough inspection, but an AI-based system can analyze and predict the chances of failure instantaneously. By identifying such problems at an earlier stage, they will be able to replace the components in due time before it leads to unplanned stoppages or disruptions in the assembly line. Improving Critical Decisions in Healthcare   Nearly 60% of healthcare executives have said that their organization uses predictive analytics to improve patient care and streamline their operations. One of the biggest use cases for predictive analytics in recent times came in the wake of COVID-19 when several hospitals used predictive analytics to track the deterioration of patients in general wards and predicted when they would require ICU support. There have been instances where a hospital reduced serious events by over 35% with proactive monitoring and predictive warning systems. Patient deterioration is a key factor that needs constant attention and in times like these when the healthcare facilities are overwhelmed with patients requiring care, events, where a patient undergoing serious deterioration goes unnoticed by staff, is common. Autonomous monitoring and prediction of criticality ill thus become vital use cases for predictive analytics in healthcare. Better Sales in Retail   In the retail sector, the more you know about your customer, the better will you be able to sell products faster to them. Predictive analytics can be the game-changer in deriving preferences of your customers from the vast amounts of data that they have already shared with brands in their past interactions. One of the best examples of how predictive analytics enabled a retail brand to improve in-store sales was how a Harley Davidson dealership in New York increased its sales leads by 2930% using an AI-based marketing platform. By analyzing customer information from across their CRM, point of sales, and website inquiries, the platform was able to identify the most relevant audience to target with marketing campaigns. This target base was found to have more interest in talking to an in-store salesperson, and the dealership was soon flooded with customer inquiries. Transforming Supply Chains in eCommerce   The eCommerce boom witnessed ever since the early 2000s has created a large-scale impact on how product availability is managed across buyer geographies. Customers expect their shipments to arrive in the fastest possible time and without any damages or misplaced order items. This has created the need for a massive supply chain system for eCommerce companies who rely on different stakeholders in their supply chain to ensure that the product is made available to the customer in the fastest and most economical timeframe. Predictive analytics is bringing about a revolutionary shift in the eCommerce supply chain market by helping businesses anticipate demand and optimize their supplies accordingly to ensure customer satisfaction for every order. Take a look at Amazon which has rolled out a new predictive analytics-based supply chain initiative called anticipatory shipping. In this, wherein data from past orders is used to determine where customers are likely to have more demand for certain products soon and then shipping the products to warehouses or storage destinations closer to the demand areas even before the order is placed. In this way, when the actual order is placed, customers can get hold of their desired product in a matter of hours rather than waiting for days for delivery. This mode of optimization of the supply chain is extremely useful for eCommerce businesses that handle perishable goods like groceries, meat, dairy, and daily-use vegetables and fruits. Better Governance in Smart Cities   The digital transformation wave has hit the shores of civic administration as well. Across the world, smart cities are being envisaged by governments to help create a sustainable living ecosystem for residents while at the same time preserving resources through optimal utilization. Predictive analytics can set the stage for propelling smart cities into the next level and make lives easier for its residents. For example, the city of Beijing uses data from around 35 air quality monitoring stations in its boundaries to forecast the chances of smog. Using predictive analytics, the city officials

The wealth management industry has always been an embodiment of data crunching. During the periods of recessions, it has become more so as data can bring in the requisite foresightedness. Since then, owing to the growing regulatory requirements and the fast evolution of technologies, the industry has been in constant flux. That apart, the market dynamics are also changing. Due to the rise of the new world order post the 2009 crash, there has been a rise in the HNIs and UHNIs in the Asian tiger economies. The new clients come with their unique and diverse set of requirements. This puts additional pressure on the wealth managers. They need to tailor the products as per the new requirements and also search for newer commodities to invest in. Let us look at a few use cases where Data Science can play a major role in making wealth management more data-driven and risk-proof. Risk Management: This word is synonymous with the wealth management industry. Clients are advised to spend their savings on stocks and shares of other companies. The UHNIs and HNIs want their risks to be covered and also get ensured return on investment. Predictive analytics can study past trends and historical data to analyze how a particular stock/ share it going to behave in the future. This predicted future value can be an essential criterion for the buy or leave of the decision-making process. Compliance: As already stated, there has been a lot of compliance and regulatory pressure on the wealth management post the economic depression, to curb the anarchy that prevailed till then. That apart, there are other regulatory requirements such as GDPR to comply with. It would have been one painstaking process if the new tools and technologies for effective data management hadn’t been made available to them. These tools and technologies help in data masking and data anonymization. Workflow Management and Process Automation: Needless to say, many organizations are under tremendous pressure to cut down costs and reduce their operational overheads. The wealth management companies are no exception. They are also exploring Robotic Process Automation (RPA) to streamline their workflows and automate a majority of the redundant tasks. Of late, their use cases are further expanding beyond the rudimentary check clearance and helpdesk activities. RPA and NLP are being used to identify anomalies and also monitor the dashboards alerting the stakeholders whenever something goes beyond the marked threshold. This particular attribute is coming in handy in curbing fraudulent activities too. Segmentation and Targeting: The number the billionaires from the Asian tiger’s economies has been on the rise in recent years, and it will continue doing so. Data Science helps in the behavioral segmentation of the clients. Data can provide insights into the most probable targets and low hanging fruits. It can also help identify the customers who are most probably going to jump the ship. By segmentation and proper targeting of the clients, one can easily have targeted campaigns that will reduce the operational costs of the organizations. This becomes the direct feed for the algorithms which are being leveraged to improved sales productivity. Investment portfolios are also sometimes given the shape of a product, which is something akin to the mutual funds. Given the algorithms, the products are also tailored to suit the UHNIs, and HNIs needs better. Research: When the wealth managers walk into the office of a client, they need to have their research and homework handy. This needs a lot of effort. Even though they have access to data, it is lying discretely in a different repository. Data analytics makes it easier to ingest data into one common pool. From there, NLP-based research can let the wealth managers have a view of the data they have and do the needful analysis. The rise of data visualization tools in the market can help the wealth managers look into the data in a visual format, which further assists in their decision-making process. Asset management: It is the core of all the services provided by wealth management firms. Analytics can provide a view as to which ones are being profitable, which one needs to be let go of, and are their new products which can be added to the kitty. Data analytics, driven by the market sentiments and powered by forecasting engines, can also find out newer assets, which have been traditionally being ignored until now. Movies like the Big Shot have already exemplified how data analytics can help make wealth management more foolproof. There are other organizational areas like workforce management and customer analytics, where they are being utilized inside the organization. We have also seen that data science helps wealth management companies make the operations leaner while complying with the regulatory requirements. Are you ready for the data-driven transformation?

The spending on Artificial Intelligence is expected to reach $57.6 Bn by 2025. Additionally, the current adoption of fintech is estimated to be at 33 percent around the world. It’s no surprise that IoT devices, in conjunction with data-fueled AI systems, have the groundbreaking potential for all industries, including fintech. With everything getting digital and automated, the finance and banking sector is set to radically change by the combined effect of machine learning and the Internet of things. To gauge the scope of potential, let’s look at some interesting ways how ML and IoT are transforming the fintech space. Leveraging ML and IoT to Birth Possibilities Personalized Wealth Management According to JD Power’s 2018 Retail Banking Advice study, 78% of consumers want financial advice and guidance from their bank. However, only 28% of consumers feel they are getting the same. The survey also unearthed the matters that concern customers the most. It found that customers seek advice more commonly about investment, retirement, savings, and keeping track of expenses. In response to this growing trend, almost every other major bank is now using AI and IoT to personalize wealth management for its customers. Personalized wealth management includes services such as tailored retirement advice, products, and plans that fit in well into a customer’s financial portfolio and offer them value considering their current standing. By offering personalized recommendations for products and services, banks can improve overall revenue, business from individual customers, and profit margins – all the while providing a better experience to their customers. Fraud Detection + Cybersecurity Risk Detection ML tools can analyze existing fraudulent cases, detect common patterns among them, and evaluate whether a particular transaction exhibits specific characteristic. he banking industry is the most obvious target for online hacks and frauds. It is, therefore, tempting to look at emerging and arriving technologies to help avoid such associated risks. The financial gain for fintech companies to use ML and IoT in this direction is massive. According to a 2018 report by LexisNexis, for every dollar of fraud, companies have to spend $3.37 in resolving it and appeasing the customer. Banks can address issues such as these by developing programs that use machine learning and deep learning to identify the nature of each transaction before it finalizes. Another possibility for banks is to use ML to learn and identify a customer’s behavior and notify authorities when a customer exhibits a pattern unusual to her. The trick would be to do this intuitively and accurately, so as not to pose an inconvenience to a customer who is not acting fishy. Personalized Customer Experience Banks have remained cold and distant for a long time when other industries have known and reacted to the fact that customer convenience is superior, just like product quality or service delivery is. The average customer still operates in the dark at their bank and is least aware of the policies, terms, and conditions that their bank follows. ML and IoT, along with data analytics, can help create a more friendly atmosphere within banks and other financial institutions, delivering more delightful customer experience. For instance, banks are now looking at customer spending habits and buying behaviors to provide personalized suggestions and saving plans to them that they might have missed. ML and IoT can become the drivers of personalization within the fintech industry, leading banks to better customer engagement. Better Customer Service For customers, getting on the phone with bank personnel can lead to a lot of miscommunication and misunderstanding, often resulting in visiting the bank in person. When it comes to customer service, banks can use AI to automate several tasks, leading to a more efficient, faster, and productive service. According to a report by PwC titled, “Financial Services Technology: 2020 and Beyond”, self-service dashboards are the path to smarter services and smarter sales for banks and FIs, which also look alluring to customers. Several studies have shown that customers now like to take matters in their own hands rather than having to speak with a customer service agent. AI-powered customer service can help banks cut down costs and save man-hours. Wireless Payments, Security, and Authentication The Internet of Things can have a huge impact on how we interact with applications in the fintech domain. As such, wearables have the potential to transform cash withdrawal and payment by replacing traditional cards and smartphones with smart devices. Not only that, wearables and IoT can mean better security in fintech, as banks start to use wristbands and smartwatches to track the person’s heartbeat as a biometric authentication key. Solutions such as Kerv position themselves as the first contactless payment ring, allowing us to be optimistic about the possibilities of IoT and wearables in the fintech industry. According to Forrester Research, AI and IoT are the technologies that will provide an edge to fintech companies by 2025, allowing them a massive opportunity to grow and expand on the customer engagement front. With the growth in technology and the ever-changing demands of financial markets, the revolution was inevitable. Combining Artificial Intelligence, Machine Learning, and the Internet of Things in banking can prove crucial in attracting customers, retaining them, and offering them value coupled with stellar customer experiences. Unsure about the next move? Rubiscape helps fintech companies with technologies that add value to their digital transformation efforts. Get in touch with us to explore synergies. Linkedin X-twitter Facebook