Author name: Rubiscape Team

A we enter the era of data-intensive computing, terms such as data science, machine learning and artificial intelligence are used almost every day, often even interchangeably. In comparison to a few years ago, where the role of data scientists was limited to research and R&D purposes, today, they have become mainstream and this is creating a huge demand in the industry. And the scope of AI has evolved from simple problem solving to offering human-like reasoning capabilities. This evolution is mainly due to questions that are plaguing business leaders on a daily basis: how do we drive innovation within the company? How do we provide insightful, out-of-the-box data analytics and boost the productivity and efficiency of our workforce? How do we ensure a higher success rate of our products and services than our competitors? What’s causing the growth of AI Artificial intelligence is being adopted into the enterprise at a rapid pace, and adoption is likely to surge in the coming years. According to IDC, global spending on AI and cognitive technologies will hit $52.2 billion by 2021. Two factors are driving this growth: first, a vast amount of data is becoming available in more and more application areas, and second, affordable infrastructure like Cloud is enabling enterprises to store, retrieve, and share data, which was unthinkable, inaccessible, or even impossible a few years ago. The growth of IoT is only making AI more mainstream; when combined with sensors, real-time localization technologies, and near-field communication, AI is able to drive value like never before. This massive shift into digitalization is allowing businesses to unify knowledge from scientific research with vast amounts of multidisciplinary data, complete tasks based on a stipulated set of rules, and create business wisdom like never before. Why AI and data science need each other The massive amount of data being generated by organizations today presents a huge opportunity to identify, categorize, and unearth patterns applicable to each business.  AI, with its immense capability, can automate many tasks that Data Scientists and Data Engineers perform on a daily basis, including preparing and cleansing data, checking for correctness, identifying issues, making data available to teams, and building hundreds or thousands of variations of models. And although it can automate lower-level steps in data preparation and visualization, it cannot, by itself, truly understand what a specific set of dta means for an organization, its business and in the context of the industry it operates in. The learnings from problem-solving, deduction and pattern recognition from each area have to be conjoined with other areas, and this can be done only using Data Science. Here are two major reasons why Data Science and AI need to work together: Feeding the right data to get the right insights: Whether it is analyzing billions of transactions to detect fraud or forecasting consumer demand to plan for growth, since AI systems primarily perform human-like functions such as logical reasoning, and learning from experience, they have to be fed with the right computing power and data to empower them to think like humans. Hence, instead of inputting large amounts of unnecessary data into AI, data science helps extract knowledge or insights from large volumes of data and feeds only the relevant bits into AI systems. This enables businesses to find appropriate and meaningful information from huge pools of data faster and more efficiently. Making the right decisions from the insights unearthed: Although AI systems, through all the reasoning, provide insights into operations, business performance, customer behavior, and market trends, they also need Data Scientists to help decision-makers understand what the insights really mean. Insights, together with human judgment can enable organizations to make timely, efficient, data-driven decisions. By generating hundreds or thousands of variations of models with different prediction features, they can create iterative simulations to choose the best variation. Such a dynamic, multifaceted decision process, will make AI intelligent assistants to Data Scientists, allowing them to run more complex data simulations, make key business decisions, and respond to problems in real-time. Match made in heaven In the age of data overload, you need more than just a cool AI platform to drive smarter decisions; what you need is a solid team of data scientists who can successfully run teams and steer what-if scenarios before your competitors do. Considering the many benefits that AI and Data Science bring to the table, instead of only focusing on feeding systems with a consistent set of data to measure past performance and plan for the future, you need to focus on data science and use a combination of analytics and machine learning techniques to draw inferences and insights out of the massive sea of data. This will help solve higher order questions and enable you to derive far greater business value than you ever imagined. AI and Data Science – truly a match made in heaven!

The term ‘Future of work’ has been dominating all the discussions related to the way we will live and work in the future. It has particularly gained prominence during COVID-19. So, what is the future of work, and how is it different from the way we work today? Deloitte defines the future of work as “The growing adoption of artificial intelligence in the workplace, and the expansion of the workforce to include both on- and off-balance-sheet talent.” It essentially means that future workplaces will leverage more technologies such as AI, robotics, and automation to bring in a change in the way we work. McKinsey’s Global Institute Report states that more than 30% of the activities associated with the majority of the occupations in the US will be automated. Unlike traditional work, the future of work will be technology-driven, lack fixed hierarchy, and will have a workforce that will cut across different generations. However, technology will not be the only driver of the future of work. The only way employees can thrive in a fast-paced future of work is by adopting the following skillsets. Future Skills for Future of Work Critical Thinking Every company has its share of challenges and problems. Critical thinking enables employees to resolve them effectively. It encourages teams to identify the problems, come up with solutions, and implement them. Critical thinking focuses on problem-solving rather than arriving at unsubstantiated conclusions and judgments. To promote critical thinking in the workplace, employees will have to make it a part of their work. One way to begin is by focusing on smaller problems first. That will boost confidence and encourage employees to take up bigger issues. Creativity and Innovation What sets companies like Amazon or Netflix apart from their competitors is their ability to innovate continuously. Unlike traditional workplaces where the focus was on following procedures and working mechanically, the future of work is more flexible and creative. A workplace that fosters curiosity, creativity, and innovation can keep employees motivated and inspire them to design new strategies to stay ahead of the competition. Innovation and creativity cannot be acquired overnight. Employees have to take efforts to make it a part of their work. Companies need to create an environment that boosts these qualities. Employees should be able to brainstorm new strategies to ensure that innovation and creativity do not get side-lined in regular work. Initiatives such as intrapreneurship could be established to encourage employees to come up with innovative solutions to solve some of the pressing issues. Collaboration Collaboration helps employees across different locations, teams, and skillsets to come together to achieve a shared goal. It capitalizes on of each team member’s strengths, so they can think together as a single unit and come up with concrete solutions and ideas to solve problems. To cultivate collaboration, employees should work with an open mindset, encourage others to share ideas without any inhibitions, and cross-collaborate with members of different teams. A collaborative culture helps employees to stay motivated at work and grow as an individual. Collaborative tools can help employees to exchange ideas and work together irrespective of their location or time zones. Flexibility and Adaptability The future of work is no longer about sticking to a particular job until retirement. Employees need to don multiple hats, learn several new skills, and be willing to work in different roles throughout their careers. They should be willing to learn and adapt their working style to changing situations. They must learn to be out of their comfort zone, accept new challenges, and be flexible enough to start from scratch, if necessary. They must be willing to accept failures, learn from those, and move ahead quickly. A company with a flexible and adaptable culture will be more responsive to the changes in customer needs and cater to it quickly and efficiently. Leadership In traditional workplaces, leaders were more focused on the outcome of the task, bottom line profits, etc. They believed in a rigid hierarchy and impersonalized relationships with co-workers. Today’s leaders are more transparent, inclusive, and focus on building a strong team that encourages each other to achieve goals. Their relationship with their team exudes trust and confidence. To cultivate leadership skills, leaders will have to learn the art of listening actively rather than just give out orders. They must communicate clearly, be transparent, become a part of the team, and set an example by showing a willingness to improve daily. Proactive Business situations will constantly change, as proven by current pandemic, climatic changes, and the proliferation of technology. Proactive employees will stay a step ahead and be prepared to solve these problems. Instead of waiting to react to the challenges, they take pre-emptive measures to address those. To become proactive, employees must develop foresight, learn to anticipate problems, and be prepared to resolve it. They must acquire an analytical bend of mind to recognize common patterns that exist in the business and use them to solve future problems. They must avoid reacting impulsively during an unforeseen circumstance. Employees who are prepared for the unforeseen are the ones who can help companies tide over difficult challenges and keep it ahead of the competition. Accountability and Responsibility Every company desires to work with employees who take ownership of their work and its outcome as it makes the employees responsible and mindful of the way they work. A lack of accountability and responsibility in the workplace makes employees complacent, disengaged, and demotivated. The only way to deliver better work is by making each employee accountable for their job. Employees can define the goals they plan to achieve to stay focused. Managers can set key metrics to measure their accountability and accuracy of work. They can also encourage employees to use tools to assess their progress and improve the quality of their work. The future of work calls for efficiency and accuracy in work – both of which can be achieved only if employees take responsibility for their work. Technology may take some of our jobs,

The world around us is changing fast, and we need to understand the seasonality behind the way the world works. Let’s understand this with the help of some examples. Imagine analyzing rainfall data, predicting crop growth, or even the fluctuations in air ticket pricing during carnival to Rio. One can easily decipher that there is a certain amount of seasonality to these data points, and they go in cycles. These seasonal aspects also have a direct bearing on all the other facets like sales of related products. Even the stock market is susceptible to seasonal upheaval. These give rise to the relevance of the analysis of the time-series data. In simple terms, this means collecting data at a regular interval with the time stamp on it to understand how the data varies with changing time. This analysis helps in predicting whether the data is stationary, is there a seasonality, or are they autocorrelated. Suppose the metallurgy department wants to predict the amount of rainfall that is going to happen this year based on the historical data. This is called forecasting and is a bit more complicated than a regular modeling task. To do this, one needs to first visualize the time series, make it stationary, and find whether there is an autocorrelation or not. Based on that, the model that fits them the best needs to be decided and applied. Once all these have been completed, the predictions can be made, and rainfall can be forecasted with some accuracy. Models for Time Series Analytics Let us dive deeper into the various models which play a crucial role in Time Series analytics. The most popular ones are the ARIMA/SARIMA model, Seasonal Decomposition, exponential smoothing, and GARCH. The most popular method is the simple exponential smoothing method. The principle is to analyze the past observations on the basic weighted average, which decreases exponentially as we go back in time. In case the data shows some seasonality, then the whole equation needs to be broken down into the – the seasonal, trend, and the remainder component. The ARIMA/SARIMA model is the short form of Auto-Regressive Integrated Moving Average and Seasonal ARIMA. This model combines the linear combination of the past variable values along with the error values in the forecast. Another methodology is the GARCH methodology, which factors in the fact that the error terms will change over some time. Methods to follow in Time Series Analytics While the modeling process for time series analytics is extremely complicated, there are a couple of basic steps that need to be followed to make it simple. The first step is to identify the correct problem statement. For instance, the problem statement could be predicting stock prices. Stock prices of various companies vary over time and are the best fit for the time series analysis. Another example could be to understand electricity usage over a period or predict air quality – for all these cases we need to employ Time Series Analysis. Once the problem statement is identified, the next step is to decide on the tool which can be leveraged for doing the analysis. By tools, we mean R, Python, etc. The choice depends upon the Data practitioner’s level of comfort. Once the tool has been zeroed down, the Data practitioners download all the libraries that are required to do the analysis. The next step is to gather all the relevant data from all the internal as well as external sources. It is possible that the data sets could have duplicities and may need to be scrubbed and cleaned for further analysis. Once the data sets are analytics-ready, the next step is to do the exploratory analysis. For this, the data points are first represented on a graph. Any time-series data, by its basic nature, will have spikes and drops in its graph plots. It is essential to employ the exponential smoothing process to cut out the noise. In some instances, employing it just one time may not effective, and it may need to be carried out more than once to smoothen it effectively. Post that, one needs to employ the ARIMA, SARIMA model. This step includes defining the parameters and generating a list of possible combinations. SARIMA can be used to train the system to find out the best possible combination and the model. By leveraging this model, one can find out how effective it is to predict the numbers. If it lies within a designated tolerance limit, it is leveraged for future prediction and forecasting. Examples where Time Series Analytics is Employed When data changes over some time, it can be used to predict the future. Some real-world use cases of this include, Field of economics and share market The government agencies employ social scientists to follow the population’s growth data, school enrollment data, etc. Healthcare organizations need to observe data about seasonal diseases over a period of time to effectively and accurately predict the spread of the diseases and be better prepared to handle those. The field of life sciences is waking up to this phenomenon. Blood pressure, sugar data collected via wearables over a period of time help companies evaluate the efficacy of the drugs and their dosages. Consumers also show seasonality and cyclical nature in their buying patterns. By understanding the time-series aspect of it, organizations can plan better for the future. With their understanding of how much time it takes to manufacture a particular product and how its demand is going to vary over time, they can better plan the production and operation. This also has a severe impact on various aspects, such as inventory planning, distribution, supply chain, etc. have severe implications. Is your enterprise ready to leverage the power of data to predict the future?

The use of the term data science has become very common nowadays, but what does it exactly mean? What is the future of data scientists? What is the scope of work for transitioning to a data scientist? Is everything rumored around the glamour of becoming a data scientist true? Let us get insights into these questions eventually. First, let us see what data science is. As the world entered the era of big data, more and more organizations have started processing and analyzing the data at their disposal to derive meaningful insights. The discovery of hidden patterns from raw data is achieved with a blend of various tools, algorithms, and machine learning principles. This entire process of deriving meaningful insights from raw data is called Data Science. This work is done by data scientists, and as more and more industries are changing to data-driven results, the value of data scientists is also increasing as they are the ones who know how to tease actionable insights out of gigabytes of data. It is becoming more common by the day that there is considerable value in processing and analyzing data – and that is the reason data scientists are in the spotlight. We often hear how science is a cool industry, and how data scientists are like modern-day superheroes, but most of us are still unaware of the value a data scientist holds in an organization. Various organizations have already put data science in action. It is helping them provide personalized customer experience by understanding the audiences at a granular level. It is helping them find when and where their products sell best. It is helping them in product and business model innovations. And it is also helping them in mitigating risks and frauds. As more and more organizations are shifting to data-driven results. The shift in the outlook of organizations has created a huge demand for data scientists, and hence several students and professionals want to shift into a data science career. As with anything new, several myths are bubbling around making a career in data science. Let us see if the transition to becoming a data scientist is smooth or filled with hurdles. Myth 1: You need a Ph. D. degree to become a Data Scientist It is a common belief that it is mandatory to have a Ph.D. to become a data scientist, or a full-time data science degree is a must for making the transition to data scientist. With the amount of interest, data science started developing in the past few years. People assumed that the best way to distinguish themselves as better suited for data science roles is by getting these degrees advertised by institutes. This is a myth created by the educational institutes to run their business. In the vast and complex area like data science, practical experience is the key. There are numerous problems out there that can be picked up and solved by various data science techniques. There is plenty of online material available for study, which can help you transition to becoming a data scientist. Myth 2: Previous experience will automatically translate to data science role This is a myth because there are two aspects to this story. Firstly, if you are changing your role entirely from, say, being a developer or analyst to a data scientist role, then your experience is counted as zero. When given real-world data for that role, you don’t have any experience of how it works and leads to failure. Secondly, if you have some domain knowledge and switch to data science, then you already know about the nuisances of that domain. That will be factored in as an experience and help achieve a better role and salary as a data scientist. Myth 3: Data Science is for Geeks with a degree in Computer Science, Statistics, or Mathematics Most of the folks that you come across as data scientists will either be from Mathematics, statistics, or programming background, but that does not mean that people coming from different backgrounds cannot be a data scientist. The only difference is a person from a background other than the ones above might have to work harder during the initial phase of career and learn everything from scratch. Organizations now are also warming up to the concept of Citizen Data Scientists where domain experts are provided with easy to use tools and are recognized as data scientists delivering business insights. Myth 4: Developing expertise with a specific technology is enough to become a successful data scientist The myth is that being able to write code using NumPy, sci-kit-learn, etc. should be enough to call yourself an expert. That is not true as data science needs a combination of multiple skills, and programming is just one of them. They should have other skills like problem-solving, structured thinking, communicating the right problem, and understanding the right data. Myth 5: Participating in various hackathons and contests (and not working on real-world projects) is enough to be a good data scientist Data science competitions and hackathons are excellent starting points for the career journey. Hackathons and competitions in data science have increased multi-fold in the last few years, and individuals who are aspiring for data science roles are putting these in their resume considering them as good as working in real-life projects.  This concept is a myth, and recruiters have started paying lesser attention to this aspect of the resume. This is because working with real data science projects is way different than working on hackathon projects. For example, in competitions, you will work with spotless data, whereas; in real-world projects, you will have to deal with unclean and unstructured raw data that needs to be processed to make it usable. Anyone can become a data scientist with the right guidance, training, and technology expertise. It is important to not go after the make-belief myths around it. Data Science has a long way to go, and it is good to see that educational institutions around

Almost every organization, government or otherwise, is going through a digital transformation journey. Many organizations are at different points in their maturity models. Digital Transformation enables optimal resource utilization as well as increased customer satisfaction. For instance, sectors like automobile have found out avenues to monetize the data on driving patterns by selling them to insurance companies. Essentially, data is opening up new revenue generation avenues for companies. Gut instinct-based decision making is a thing of the past now, and every major strategic decision is data-driven. In such a scenario, data becomes a priceless organizational asset. 90 percent of the data available worldwide has been generated in the last two years. If the analysts are to be believed, then the revenue of any company can be increased by 66 percent by following best data quality and analysis practices. But before embarking on the journey of being data-driven, one has to understand the key building blocks of a strong foundation. Data – Data has already been categorized as an organizational asset used for making a strategic decision. As per Gartner, by 2025, for 90% of the organizations, data will guide business decisions. Already 69 percent of the organizations are using data from newer sources to understand their market. 64% of organizations have started using predictive and other forms of advanced analytics in their business. 67% are exploring new types of analytics to consume the data they have with them. Analytics – The most common types of analytics include descriptive, prescriptive, and predictive analytics. Descriptive includes day to day reporting or operational reporting. Prescriptive analytics helps in coming up with recommendations. Predictive, as the name suggests, helps in predicting the future so that the organizations can reap the maximum benefits out of it. Technology – Storage, collection, processing of this huge amount of data is being made possible due to the huge strides we have made in technology. There are tools like Python, Spark, Power BI, which enable us to collate, process, store, and visualize data. People – This is one crucial aspect, as people resources and skill form the basis of any significant shift. Becoming data ready requires Data Scientists, Domain experts, and Technology experts. One has to be sure that these relevant skill sets are on board. There is a certain amount of cultural change which has to be brought in and is the result of getting the buy-in of the folks who will be a part of the job day in and day out. Organizations need to understand that this is a team effort, and not one single individual can change the organizational leanings in overnight. Processes – The whole value chain has to become data-centric. Elaborate and a foolproof method of collecting and processing data has to be set up. Data quality is of utmost importance, and the role of CDO has to be created who can ensure that there is a certain methodology in place to ensure sanity in the data process. Strategy and Vision – What does the company want to achieve in the long run? The strategy and vision of an organization could vary across a broad spectrum of activities. It might want to reduce costs and become leaner. Whereas on the other hand, it might want to reach out to newer markets or develop newer products. Recently companies have also started exploring newer avenues of revenue generation. Depending on the needs, the data attributes being captured, and the metrics being measured change. Operating Model – To succeed in the quest of becoming data-driven, one must understand that a robust target operating model should be set up. From the data office who will be interacting with whom and collecting what data and who will be responsible for the data quality – everything becomes extremely important for the smooth steady-state running of the operations A strong data-driven culture, availability of the right data for analysis across the value chain, easy to use tools that can allow quick slicing and dicing of data, and more awareness around data-driven decision-making are some of the key factors for the success of data-driven enterprises. Where are you on your data journey?

The phenomenon known as big data has been growing exponentially. Reports suggest that, by 2020, there will be around 40 trillion gigabytes of data, and the big data analytics market is set to reach $103 billion by 2023! This has been further fueled by the fact that it has become extremely economical to store and analyze petabyte & zettabyte sized data. Once the data is stored and analyzed, organizations need someone to crunch the numbers and provide actionable insights on that. This is where Data Science comes into the picture. With the growing amount of data, the need for qualified data scientists is also growing. No wonder, Data Scientist has been termed as the sexiest job of the 21st century. The demand for the role of Data Scientist is only going to increase from here on, and if various research reports are anything to go by, then we already have a shortage of Data Science talent. The demand for the skill has far surpassed the supply of the same. The growing interest in the field also fuels a lot of misconceptions. While there is already a lot of literature about what Data Science really means, in this blog, we will take a look at what Data Science is not. Data Science is NOT AI/ ML Quite often, the terms data science, artificial intelligence, and machine learning are used interchangeably. However, they are not the same. We do agree on the fact that there is a lot of overlap amongst the three. Data Science is more of a general field where we require a lot of human intelligence to work in tandem with the machines. Use cases to emphasize on this case in point are fraud detection, churn prediction, etc. AI and ML become more niche and are used for repetitive tasks, which, as of now, do not require human intervention. AI use cases like chatbots, and ML use cases like recommendation engines can throw light on the difference. Data Science is NOT a silver bullet Organizations often make a mistake of thinking that Data Science is the panacea to all the problems they are facing. Like what Simon Sinek said, one needs to first understand “why”. Before starting their data science initiatives, organizations need to first define their business case and the problem they plan to solve using data science. This definition then helps in defining the data attributes to be collected and the analysis of the same. Without a clear problem statement, investments in skills, domain knowledge, and top management involvement, there are high chances it will end up being another failed initiative. Data Science is NOT just about Visualization Visualization of data, powered through tools such as Power BI or Tableau, is all good, but it should aid the process of decision-making, and only then it can have a direct bearing on the bottom-line. Data Science, by its very meaning, implies that human intelligence becomes imperative for drawing insights from them. Visualization aids that, but data science is not only about visualization. Visualization is about reporting of the various data points available with the organization. However, it cannot be either predictive or prescriptive without the Data Scientist with domain understanding adding value. Data Science is NOT just R or Python There is a common perception in the market that people with expertise in languages such as Python, R, SPSS, etc. are data scientists. This is very far from the truth. These languages are merely tools for doing data analysis. But one needs to know the problem statement to know which algorithm, model, or which methodology will best suit the purpose. For example, for an OOT provider, Data Scientist is supposed to draw a decision tree to understand what the customers are watching on an OTT channel, create clusters to understand the demographics and do a regression to understand the causal effect relationship. Data Science is NOT only about Data Data ingestion plays a significant role in data science. To get a complete view, organizations need to analyze data available internally, and data also needs to be collected from sources outside the organization. It is possible that the internal data is lying in siloes across various systems such as ERP, CRM, etc. For a complete analysis, all the data needs to be collected and reconciled. But like all the others, this is a crucial subset of Data Science but not data science per se. Based on the end goal and what analysis needs to be conducted, the tools, as well as the data needed for the tools, will change. One needs to acknowledge that Data Management is a huge ask. There are several tools available in the market that can help organizations manage their data. This includes cleansing, scrubbing, de-duplication of the data. Data management is essential for data science and is an integral part of the process, but needless to say, this is not data science in its entirety. Data Scientists are aided by the quality of the data, which is enhanced through robust data management. Data Science is NOT Statistics An ace Data Scientist needs to possess three critical skills – 1) Strong programming knowledge and skills to run the models 2) Understanding of statistics to run the correct statistical tools, and 3) Strong domain knowledge to come up with the correct inference. Many believe that knowledge and understanding of any one of these skills can make you a Data Scientist, which is not valid.   How many such myths did you believe about data science?

Love it or hate it, the fact is that data is gold. As Geoffrey Moore likes to put it, “Without big data, you are blind and deaf and in the middle of a freeway.” And you wouldn’t want to be there, especially when data is fueling businesses in ways more than ever. Qualitative data helps drive businesses in ways more than one. In the hyper-connected world of digital devices and systems, if you are not leveraging data for business, you are lagging the game for no good reason. Also read: Data is NOT Oil – Data Is the New Soil In this article, we will discuss how data-powered business models are carving their way into the future. Data-powered business models: Overview A data-powered business model is the one where businesses utilize data to improve processes, make better business decisions, offer more personalized customer service, design the revenue model based on data, and/or offer products or services completely dependent on data. Data-powered business models are different from the ones that use data in order to accomplish a part of the business process. The data-driven business models make active use of data to make decisions in place of emotions or instincts. Every business leader hunts for data-centric insights to improve the business outcomes, and going the data-powered way seems like a wise choice. Top benefits of having a data-driven business model include: Optimized processes and internal communication Improved workflows for improved efficiency Increased sales and customer retention Enhanced the customer experience with more personalized recommendations Businesses that unlock the true potential of data and place it in the center of the business strategy can be truly termed as a data-powered business. Let us have a look at a top few examples of data-powered business models that are future-leaning and future-ready. Smart Cities Sustainable, environment-friendly, and hyper-connected smart cities are in vogue. Smart cities leverage IoT sensors to collect data from various sources and utilize the data to drive systems within the city. The objectives can be multi-fold, such as improving the energy efficiency and consumption or revamping operations across the city – from traffic management to town planning, waste management, to crime detection. Data can be incremental in planning a smart city for estimating the inflow on public transport, helping to decongest the cities, identifying areas that require more public health penetration with AI-based prediction, and enhancing the overall governance in the city. Connected Cars Connected cars are cars (and other vehicles like buses) that can connect to the other cards or vehicles on the road enabling other cars to share data via IoT devices. More importantly, connected cars usher in driver safety, security, ease of navigation for a simple day-to-day problem, including finding a parking spot. Connected cars generate extensive data about the car performance, speed, and door locking logs, which make it a heavily data-driven and data-dependent model that is as futuristic as it can get. OTT (Over-The-Top Media Services) OTT media services or video streaming is one of the most rapidly expanding entertainment channels at the moment. The OTT services such as Netflix make active use of customer viewing data to predict customer preferences based on their demographics as well as geographies. Making use of this data, the OTT media service providers can improve the customer experience, offering them exactly what they’d like to view instead of exploring a sea of content and being overwhelmed. Netflix for instance, not only collects viewing data but also the ratings, view times, and user ratings to curate a more customized viewing experience as well as onboarding content types that truly sell. Also Read: How Big Data and Analytics Can Transform the World of OTT Subscription-based retail and fashion (eCommerce personalization) Renting out, sharing economy, personalized economy or subscription economy is gaining rapid traction, especially in the developed countries. According to a study,  48% of participants prefer using subscription clothing where experts curate and deliver items based on previous purchases. Clearly, utilizing data for driving personalized recommendations is on the surge in the retail industry. The subscription-based retail businesses work on data-driven models to understand customer preferences and curate unique experiences for them. In fact, data-powered retail has resulted in a replenishment economy where products are intelligently shipped to the customer right before they are replenished, studying vast amounts of data from previous purchase cycles. Dynamic learning and education The educational reward and ranking system have been conventionally limited to the exam performance, assignments, and projects. But over the course of spending years in schools, students exhibit several qualities that are not accurately demonstrated or judged in traditional exam systems. By making use of big data, student achievements (academic or otherwise) can be mapped throughout to create a more holistic and diverse student portfolio. It also helps educators to determine the hurdles in understanding, challenges in learning, and other behavioral traits in students. Big data can be used to reduce student dropouts through prediction analysis. Teachers can study patterns of previous dropouts and provide assistance before it’s too late. Data-driven educational models can be incremental in offering customized programs to improve learning. To summarize… Data-driven businesses and process optimization is a change the world appreciates. But without identifying the right tech, creating a data-driven mindset, overcoming silo thinking, and addressing privacy concerns, the journey can be futile. Approaching with an agile mindset with modern tech and compliance can help data-powered business models to thrive.

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

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

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