Other
Other
Power Bi Developer
Application for – Power Bi Developer Job Description Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools (e.g., Tableau, Power BI). Experience with machine learning pipelines and model deployment. Benefits Competitive salary and performance-based bonuses. Comprehensive health and dental insurance. Opportunities for career growth and development. Work-from-home options and flexible working hours. Application Form Job Application Form First Name* Please enter your First Name. Last Name* Please enter your Last Name. Email* Please enter a valid email address. Gender* Select your GenderMaleFemaleOther Please select your Gender. Address* Please enter your address. Country* India Please select your Country. City* Please enter your City. Country Code* India (91) Please select your Country Code. Phone Number* Please enter your Phone No. Date of Birth* Please enter your DOB. Ready to Relocate?* Choose from the options belowYesNo Please select your response. Attach your CV* Please attach your CV. Submit
Other
Data Enginner
Application for – Data Engineer Job Description Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools (e.g., Tableau, Power BI). Experience with machine learning pipelines and model deployment. Benefits Competitive salary and performance-based bonuses. Comprehensive health and dental insurance. Opportunities for career growth and development. Work-from-home options and flexible working hours. Application Form Job Application Form First Name* Please enter your First Name. Last Name* Please enter your Last Name. Email* Please enter a valid email address. Gender* Select your GenderMaleFemaleOther Please select your Gender. Address* Please enter your address. Country* India Please select your Country. City* Please enter your City. Country Code* India (91) Please select your Country Code. Phone Number* Please enter your Phone No. Date of Birth* Please enter your DOB. Ready to Relocate?* Choose from the options belowYesNo Please select your response. Attach your CV* Please attach your CV. Submit Application for – Data Engineer Job Description Application Form Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools (e.g., Tableau, Power BI). Experience with machine learning pipelines and model deployment. Benefits Competitive salary and performance-based bonuses. Comprehensive health and dental insurance. Opportunities for career growth and development. Work-from-home options and flexible working hours. Job Application Form First Name* Please enter your First Name. Last Name* Please enter your Last Name. Email* Please enter a valid email address. Gender* Select your GenderMaleFemaleOther Please select your Gender. Address* Please enter your address. Country* India Please select your Country. City* Please enter your City. Country Code* India (91) Please select your Country Code. Phone Number* Please enter your Phone No. Date of Birth* Please enter your DOB. Ready to Relocate?* Choose from the options belowYesNo Please select your response. Attach your CV* Please attach your CV. Submit Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools
Other
The Future of Data Science – Trends and Predictions
Today, we live in a world where digital experiences are in the driving seat for nearly every aspect of our lives. From businesses to government services, people are increasingly reliant on digital channels to get what they need and make lives easier. This has resulted in a dramatic increase in the volume of data produced globally. Studies predict that, by 2025, nearly 463 exabytes of data will be generated daily. The biggest takeaway from this data generation scenario is that this very same data is leveraged for accurate decision-making by businesses as well as other entities. While analytics, artificial intelligence, machine learning, etc., are ways that drill deep into piles of data to uncover hidden insights and possibilities, the real mastermind behind the success of such large-scale data initiatives is data science. Data science is responsible for keeping any set of actionable data ready for analysis and processing. The role of data scientists has become a critical part of nearly every major business in all sectors. Together with other technology leaders, data scientists work to build new working models for the large gamut of data collected from across an organization’s operational domain. Data science has been around for quite some time now, and organizations are seeking ways to utilize data science as a key enabler of success and ROI in their business initiatives. So how does the future look for data science? Let us explore some of the top trends and predictions: The AI Takeover Over the last couple of years, and especially since generative AI technology like ChatGPT became a hot topic of interest, there has been widespread fear that machines will take over several jobs. While ethical implications and economic concerns associated with job losses are more likely to prevent or slow down a massive workforce transformation, it is certain that several job roles will undergo a series of transformations to amp up the skill quotient. Data scientists, too, will experience a part of this transitional ordeal as they may have to acquaint themselves with powerful AI-powered tools that can build data models faster and more efficiently than when done manually. Of course, the oversight needed is still a critical area where data scientists can exert their dominance, as most AI algorithms that work on data modeling are still in their learning stage. With industries like weather and banking being reliant on prediction models developed with core data science principles, the window for errors is non-existent. In that light, it might take a while before AI can significantly contribute to the core sophisticated functions. But it sure is invaluable in complementing data scientists’ workflow. Rise of Quantum Data Science In the next decade or so, we will surely witness a massive shift in the way computing power is considered for any kind of analytical initiatives courtesy of quantum computing becoming mainstream. Today, data scientists can build effective data models only through limited sequential matching of scenarios and data patterns. In other words, if a couple of inputs must be run across different scenarios for modeling, it should be done one by one. With quantum computing, however, it becomes possible to run them all parallelly without worrying about the performance of the underlying computing infrastructure. The key takeaway here is that, with near unlimited computing power, data scientists can build larger, more expensive, and more powerful models that can be leveraged to build state-of-the-art digital solutions powered by analytics run through these models. Build More from Models Traditionally, data scientists worked on translating complex business workflows and transactional processes into accurate data models that can be used for automation and analytics-driven decision-making. However, building data models is no longer the main ingredient of successful analytics initiatives. The quest is to operationalize these proven models across the business at the earliest opportunity to prevent any competitive loss in the market. Once these models are implemented, the next attempt will be to scale them. Leveraging of Tools Adding on to the previous point, the secret behind successful data science initiatives is the ability of data scientists to accurately identify which data needs to be where and how it should be processed. Today, all these activities are somewhat automated and handled through no-code and low-code applications. Even businesses with very less technical-focused employees can build extensive data models and scale them to meet the needs of modern problems. In fact, Gartner predicts that, by 2026, about 80% of low-code users will be developers working from outside the formal IT departments. This is a critical development for the data science space, as it’s directly associated democratization of resources and capabilities. Computing on the Cloud For data scientists, the future is highly dependent on how efficient their cloud provider is. From data migration to modern-era no-code and low-code tools, there is a rising dependence on SaaS applications and platforms that can cater to the needs of people hooked to their digital universe. Besides, the evolving capabilities of cloud platforms that can accommodate data engineering, ML engineering, data analytics, business intelligence, AI governance, etc., make way for enterprises to leverage the cloud for their rigorous data science initiatives. Wrapping Up As you can see, the future of data science is bright, as there are several areas where data science can be applied to bring about transformational changes. However, managing the entire transition to cloud and other emerging technologies is a critical task that necessitates maximum care and supervision. This is where Rubiscape can become the key game changer. Get in touch with us to know how we can help supercharge your data science programs.
Other
From Predictive Maintenance to Autonomous Vehicles: Data Science in Automotive Innovation
With applications ranging from driving behavior analysis and driving assistance to safety management and predictive analytics, the potential of data science in the automotive sector goes well beyond what has been achieved with automation alone. Recent analysisshows that the value of the big data market in the automotive industry will reach $10 billion by 2027, up from $4216.8 million in 2025. In fact,McKinsey outlines how automotive companies are shifting away from “engineering-driven to data-driven development.” After all, the application of data and its timely usage paves the way for agile systems engineering, refined product development, revenue optimization, and more. But what does the future hold? What are the trends that’ll spell the value of data science for automotive innovation? Let’s explore. Advanced Driver Assistance Systems (ADAS) ADAS plays a significant role in making driving more secure and comfortable. These intelligent systems leverage data from sensors and cameras to help drivers with knowledge of: The traffic in the area Any alternative routes to pursue to avoid congestion Road blockages due to various reasons (like construction) But they do more than just inform drivers. For example, ACC (Adaptive Cruise Control), a driver assistance system, automatically modifies the car’s speed while using information from radar and cameras to maintain a safe distance from the vehicle in front. Data science helps optimize the ACC algorithms, taking into account variables like vehicle speed, distance, and traffic conditions. Likewise, LDW (Lane Departure Warning) warns drivers when they unintentionally drift out of their lane by using cameras to monitor lane markers. Predictive Maintenance The rise of predictive maintenance across manufacturing facilities can be attributed to the high availability of data on vibrations, pressure, equipment conditions, etc. This data lends itself well to: Big data Machine learning Deep learning techniques These help predict failures before they escalate. Machine learning models trained on large amounts of data can predict failures with high accuracy, eliminating the need for reactive or scheduled maintenance. The only downside is that data volume needs to be significant, which might not always be the case. Automotive manufacturers can also opt for digital twins for more granular diagnosis. AI-Powered Driver Behavior Analysis About 94% of accidents stem from human errors. This can be drastically reduced with automakers using data science and artificial intelligence to analyze driver behavior. Tracking driver actions and facial expressions Gauging focus and attention levels through eye movement, head position, and blink rate Evaluating driving performance by monitoring data like speed, steering patterns, and lane-keeping behavior These intelligent systems can relay precautionary alerts, and in extreme cases, may even take control to maneuver the vehicle to safety. Also Read: The Future of Data Science – Trends and Predictions Safety and Risk Assessment Safety and risk assessment are vital in developing autonomous vehicles, given the high stakes of letting cars make complex decisions without human intervention. Simulation-based testing of various driving conditions Real-world validation under dynamic, unpredictable scenarios Gathering and analyzing vast data to determine system cognitive capabilities These elements help confirm the security and dependability of autonomous systems. Predictive Navigation Traffic congestion for commuters is a constant challenge as urbanization grows. To improve the driving experience, predictive navigation and traffic management are essential. Assessing real-time traffic patterns using data from connected cars, GPS, and sensors Enabling route suggestions that bypass congestion, accidents, or construction Creating predictive parking systems that estimate parking availability using real-time data Through navigation apps, drivers can access this data and find better routes or parking. Additionally, in-car systems can connect to smart sensors or parking meters to streamline parking reservations. Tap Into the Data Economy with Rubiscape Every automotive application discussed above depends on leveraging data at every touchpoint for informed decision-making. But to realize success with these initiatives, companies must move away from fragmented solutions. Adopt a unified, comprehensive data science platform Manage the entire data science life cycle from one place Improve agility and quality across processes Explore Rubiscape’s capabilities here.
Other
Data-Driven Lean Manufacturing: How to Apply Data Science for Continuous Improvement
The convergence of data science and lean manufacturing principles has paved the way for a revolution in how industries optimize their operations and enhance productivity. In fact, the global digital lean manufacturing market has experienced staggering growth in recent years. The market commanded an estimated worth of $23.99 billion in 2025; it then swiftly expanded further to reach$26.86 billion in 2023. There’s no doubt about the manufacturing industry’s commitment to lean principles. However, refining these processes by infusingdata science techniques is an intriguing proposition altogether. What Exactly Is Lean Manufacturing? Lean manufacturing represents a production and management philosophy. This approach has found application across diverse industries, with its primary objective being waste reduction while concurrently enhancing efficiency and customer value. The principles that underpin this system trace back to those initially presented by Toyota during the 1950s–1960s. Thus, Lean is also occasionally referred to as the Toyota Production System (TPS). Key concepts of lean manufacturing include: Value: Value is assessed from the customer’s point of view and is related to how much they are willing to pay for goods and services. Value Stream: A value stream is a product’s whole life cycle, which includes the design of the product, its usage by consumers, and its disposal. Flow: Lean manufacturing is the practice of simplifying processes and procedures in order to decrease waste and, hence, enhance output. Pull: Lean manufacturing is built on a pull system, which means that nothing is purchased or manufactured unless there is a need for it. Perfection: Lean manufacturing stresses the idea of always striving for excellence, which requires identifying and removing the core causes of quality issues via continuous improvement, or “Kaizen.” How Does Data-Driven Decision-Making Enhance Lean Manufacturing? Data-driven decision-making is critical to improving lean manufacturing as it relays important insights and allows for better-informed choices throughout the manufacturing process. This is accomplished in the following manner: Improved Visibility & Real-time Monitoring About 57% of enterprises employ data and analytics to drive strategy and change. Another 60% of companies worldwide use analytics to drive process and cost-efficiency. Manufacturers can leverage real-time insights into their production processes through the use of data-driven tools and technologies. They collect and analyze data from sensors, machines, and other sources to monitor operations at a granular level. This approach provides businesses with real-time visibility that aids in identifying bottlenecks and anomalies. Areas where efficiency can be enhanced are also brought to light. Performance Metrics Manufacturers track a range of metrics: Overall Equipment Effectiveness (OEE), takt time, lead time, cumulative flow, and defect rates — to name just a few. All these metrics play well into ensuring a lean operation. By providing valuable insights into process efficiency and product quality, these metrics act as critical tools for gauging operational effectiveness. Manufacturers, through the analysis of historical performance data and its comparison to current results, can pinpoint areas for improvement. Demand Forecasting & Inventory Management Accurate demand forecasting is a cornerstone of lean manufacturing. According to aMcKinsey report, applying AI-driven forecasting to supply chain management can, in fact, reduce errors by between 20% and 50%. When such forecasts combine with inventory management systems, manufacturers gain the ability to align production directly with real-time customer demands. This helps: Minimize the costs associated with overproduction and excessive inventory. Ensure not only product availability but also responsiveness to altering market demands. How Can Enterprises Drive Data-Driven Lean Manufacturing? Enterprises can successfully carry out data-driven lean manufacturing by embracing a culture of continuous improvement. Here are four crucial components to accomplishing this: Efficient Data Processing By processing and analyzing data from sensors, machines, and other sources, manufacturers can uncover patterns, trends, and anomalies that might go unnoticed through traditional methods. For that, they need to establish a concrete technological framework that helps: Aggregate pertinent data from various data sources, such as data lakes and warehouses Preprocess data so that it can be cleaned and brought into a structured format Transform data into machine-readable form so that it can be sent to the processing unit Processes data using AI/ML algorithms to deliver the suitable outcomes Present the data in a readable format, such as via graphs or tables Data Visualization Continuing from the last point, it bodes well for manufacturers to invest in user-friendly dashboards and data visualization technologies that convert complicated data sets into easily understandable visual representations. Employees at all levels of the company, from shop floor operators to top-level management, may benefit from visualization tools to get insights into production performance, quality measures, and key performance indicators (KPIs). Citizen Data Science Encouraging a “citizen data science” culture within the firm can enable employees to actively participate in data-driven lean manufacturing projects. Citizen data scientists should be able to examine data and develop relevant conclusions with the help of training and tools provided by businesses. This democratization of data analysis can potentially make production nimbler and more responsive. Multi-Persona DSML Platform All the above components are dependent on the data analytics platform that an organization has employed for informed decision-making. This is where the adoption of multi-persona data science and machine learning (DSML) platforms gains importance. These platforms cater to both vertical and horizontal use cases, bring non-technical users to the mix with low-code capabilities, offer a holistic view of operations, and drive robust governance andcollaboration. The Rubiscape Advantage In the quest for data-driven lean manufacturing, multi-persona DSML platforms, likeRubiscape, offer the tools and insights you need to transform your operations, reduce waste, enhance quality, and adapt swiftly to changing market dynamics. From refining data processing and visualization to empowering citizen data scientists, it’s time to explore the future of manufacturing efficiency with cutting-edge data science capabilities!Schedule a demo today to learn more.
Other
Data-Driven Enterprise – What, Why, And How
Being data-driven is a good idea for businesses looking to optimize their assets and growth prospects. In fact, it is fast becoming a widely-accepted system to improve the day-to-day workflow management. Organizations want to be data-driven – they want to be guided by data. While there is a massive amount of data being gathered, simply “having data” does not make one a data-driven organization. It needs to be much more than that. There are several aspects to a data-driven enterprise. Today, let me talk more about those. What Is a Data-Driven Enterprise? Simply put, any business which makes use of analytics to arrive at decisions is a data-driven enterprise. Of course, it’s not as easy as it seems. But basically, they use data for strategic decision-making. Now, what kind of data is it? It is reliable, relevant, accurate, replicable, and has all the other necessities to be called solid, quality data. In one of my earlier posts, I had written about data scientists. They play a huge role for a business to become data-driven. If they are able to provide worthy data, more than half of the task of becoming a data-driven enterprise is already achieved. Why Is It Necessary? Data-driven enterprises can easily tackle a whole lot functions thanks to their analytical decision-making ability. A paper published by MIT’s Sloan School of Management states that data-driven decision-making results in 5-6% higher productivity and output, for a business than its investments in information technology and more like those. Being data-driven, businesses can keep an eye for prospective clients as they have an upper hand at knowing the inside market conditions. This helps set the company apart from its competitors and gives it an image of one which is well informed, competitive, and reliable. Needless to say, it also assists in serving the existing customers since they are better understood and hence, served even before they raise their concerns. That is the power of quality data. It provides a business with such smart details that every department can benefit from it – right from Marketing to Finance to R&D – enabling the business as a whole to make an in-depth analysis of market conditions and eventually even predict market trends. How to Become a Data-Driven Enterprise? Using the right tools is a prerequisite for this objective. However, even before selecting the tools, it is imperative to develop a strategic plan of the objectives the business wishes to achieve. Accordingly, then, the tools need to be decided. For example, Warby Parker, a retailer of prescription glasses and sunglasses, was initially using Excel for computing the key metrics. However, as the company grew, it became virtually impossible to collate the regularly increasing humongous amount of data. Their analysts finally shifted to using MySQL relational database. This helped the company immensely to continue producing an in-depth analysis of the data procured. Second, it is important to adopt data and analytics across all levels in the company by having data-led practices. It actually means sharing vital information with fellow colleagues so that everyone together benefits from the varied bits and pieces received from a chunk of data. Unless and until data flows from various hierarchies in a business, it might not be possible to gain the real positives of becoming a data-driven enterprise; having only a couple of departments using analytics to function will never do any good for achieving the final targets of the business plan. Third, train the staff on using data in their daily work place. Once everyone has access to the data which is relevant to their department, the overall productivity of the business is bound to increase. Take this for an example. Sprig, a food-delivery company in SFO, uses an analytics platform. Now, even their chef has access to this data to study what kind of meals are popular, which ingredients or flavors are more preferred, and then use this information to plan the menus! Imagine the benefits the company reaps thanks to not just by being data-driven but by training its staff to use the data at hand. Before I conclude, I would like to make a very important point here. Being data-driven does not mean only having a set of systems or practices which make use of some xyz data to function. It also involves having a daily work culture which believes in functioning analytically. When everyone on board believes in the results of good, quality data, it will show through all their individual actions. Thus, every employee of the business, right from its head to the accounts person to the marketing trainee needs to get involved in the process and apply the data-driven systems to fine-tune their productivity. Linkedin X-twitter Facebook
Other
How Big Data and Analytics Can Transform the World of OTT
We are truly steeped deep in the age of the customer, where ‘choice’ is the recipe for success. When it comes to entertainment and video, the case is quite the same. The rise of high-speed internet and the proliferation of the smartphone culture has completely changed the way we consume content. Once ‘cable’ dependent, today, we are no longer hostage to a cable or broadcaster. This is all thanks to the rise of ‘Over The Top’ or OTT channels where content is delivered through an internet connection. With services like Netflix and Hulu as a regular part of our vocabulary, the OTT market is all set to explode. Research shows that the OTT market and VoD (video-on-demand) market in the APAC region, for example, is estimated to sit at $42Billion in revenue from 351mn subscribers by 2023. Broad content offerings made the OTT industry achieve its popularity. However, today just broad-based content offerings are no longer enough. Personalization has taken up its place at the heart of streaming services. “To succeed in this brave new video world, you need an alchemy of attractive content, an appealing user experience, the opportunity for personalization, an integration of data and technology, and some early credibility in the existing business ecosystem.” – Howard Homonoff, Digital Media Strategist and Business Transformation Advisor. Today broadcast media companies are going on the OTT mode and are delivering video content to their subscribers via multiple channels. As the OTT market matures, it also gets more competitive. For instance – Customer churn is one of the greatest challenges for OTT businesses Ensuring the highest customer lifetime value out of the customer base is difficult With more providers entering the market, OTT providers have to come up with ways to get their customers to stay with the service after the initial viewing experience and get them to become avid customers The answers lie in big data and analytics. In fact, Dave Hastings, Netflix’s director of product analytics, explicitly stated that “You do not make a $100 million investment these days without an awful lot of analytics”. How big data and analytics can change the world of OTT The key to a great OTT service starts with an understanding of the customer and responding to their needs promptly – whether it is for content, the user experience, or the business model. Since the ‘viewer’ lies in the heart of the business, OTT managers have to look at big data and analytics to enable actionable learning of customer behaviors and manage business rules. Understanding customer churn OTT viewers today are spoilt for choice. The market is getting overcrowded. Along with the number of OTT players, the choice of providers is also increasing for the customer. Customer churn is a real problem to solve to maintain profitability in the OTT universe. Most OTT services struggle with retention once they launch. Customer acquisition is also becoming more expensive and challenging as markets become more populated. However, big data and analytics can level the playing field here by providing detailed churn analytics that answers questions like ‘which customers are most likely to churn next month’? Big data analytics gives OTT providers the capacity to aggregate disparate data sets and develop a 360-degree customer view. OTT providers can use more-accurate churn prediction models and use real-time and historical data, user data and user behavior, and other associated data to identify subscriber clusters with a high risk of churn. They also get detailed insights into the main causes of churn and can proactively take measures to solve this problem. Crossing the content chasm with personalization Personalized, relevant, and contextual content is what OTT viewers demand. OTT has now become mainstream, and the viewers want a lot of content on multiple services. However, with new streaming services that come online almost every other week, there is more content today than ever has been produced in history. The recommendation engines need more customization and personalization powers to deliver the right content to the users. OTT content needs to leverage big data and analytics to get to that ‘Spotify’ model where content can easily be served based on individual preference. By combining large data sets of user data and metadata for analysis, OTT providers can fine-tune their recommendation engine and ensure that the right content reaches the right user. Deep big data analytics also gives OTT providers deeper audience insights. It helps them understand genres of content that are in high demand, what content the audience demands at what time of the day, when do they pause, or what do they skip. Based on this data, OTT providers can make informed decisions on content dissemination. Improve customer experience Understanding territory-specific nuances of user behavior and gaining insights into device demographics and platform infrastructure becomes essential as OTT providers look at wooing international audiences. Additionally, gaining granular insights into real-time across live and on-demand services also becomes essential to improve customer experience and stay on top of the OTT game. Big Data and Analytics play a significant role in providing deep insights into all the influencers of customer experience by looking at all the data intelligence. Analytics helps in getting a complete and multi-dimensional understanding of viewer experience and gives OTT providers information granularity to benchmark things that matter most, identify disruptions that impact engagement, and make smart business decisions without ambiguity influencing it. Using behavior-based audience insights and fan analytics enables OTT providers to profile the viewers accurately. This helps them make more informed business decisions on programming choices, marketing effectiveness, predictable cross-selling, and upselling opportunities, making it more relevant and contextual to the viewer. In Conclusion Big Data and Analytics are transforming the world of OTT by enhancing the user experience through more accurate and personalized recommendations. It allows for advertising to become more targeted based on user preferences. Big data and Analytics also give insights into making more accurate predictions on the next best offers and help
Other
Data Science ROI – How to Measure the (Real) Value of Data Analytics Initiatives
Data analytics is becoming increasingly integral to business success, with many companies deploying analytics in new and innovative ways. After all, data insights provide organizations with the ability to make better strategic decisions, transform products and services, and create a differentiated customer experience. But how do you measure how much value data analytics initiatives are really bringing to the table? Although it may not be an easy answer, this article provides a framework for assessing the ROI of data analytics initiatives. Key Focus Areas for Measuring the Value of Data Analytics Direct data monetization, quicker time to market, etc., are a few important factors to take into account when evaluating ROI for data science projects, but they are not the only ones. Organizations can obtain a comprehensive image of the ROI of data and analytics initiatives by taking into account the following pertinent aspects: Financial Metrics The economic impact of data analytics initiatives can be evaluated using financial indicators that reflect the overall value of insight gained and the impact on the bottom line. For example, you can measure success by calculating the total revenue generated as a result of a data analytics initiative. You can also determine whether cost savings or additional profit was created through increased revenue generation. Some key metrics to consider here are Intrinsic Value of Information (IVI), Business Value of Information (BVI), and Performance Value of Information (PVI). These metrics measure the information value in terms of the impact on the business performance and success. A key focus should also be on cost reduction. Cost reductions can be a result of process simplification, reduced risk, or improved performance. It can also be a result of increased revenue or reduced operating costs. At the end of the day, the ROI of the data analytics projects should reflect the change in cost structure. In other words, it should show if the initiatives have generated savings on the cost of training, services, technology, etc. Impact on Decision-Making To only consider the numerical impact of data analytics initiatives on the bottom line can be misleading. In order to fully understand the value of data analytics, it is important to measure the impact that data-driven insights have on an enterprise’s decision-making process. But how is this “impact” measured and tracked? —For one, the impact can be evaluated based on the accuracy of decisions and forecasts. For example, if a data analytics initiative enables you to make better decisions in real-time about your product pricing, that can translate into improved margins and revenue. —Impact can also be assessed based on how well the customer needs and preferences are looped into the decision process. After all, the overarching goal of data analytics initiatives is to enable organizations to better align their offerings with user requirements. —Finally, enterprises can measure the impact based on how quickly and easily insights from internal and external sources are leveraged to make a better decision. Is it an easy process to identify insights and access data from all sources? Can you easily transform the data into information and then convert the information into intelligence or insight for a particular use case? Overall, by evaluating how well the insights are translated into strategic decisions that improve customer experiences, optimize processes, and stimulate revenue growth, organizations can determine the overall value of the data analytics initiatives. Also Read: A CXO’s Guide to Collaboration Between Citizen Data Scientists and Data Science Teams Usage of Resources This indicator assesses how effectively infrastructure, staff, and computational capacity are allocated for data science projects. For assessing the usage of resources and how that plays into the success of data analytics initiatives, organizations must: Analyze the speed at which data initiatives pay off. They can take into account how long it takes to design, deploy, and begin experiencing advantages. Evaluate the efficiency with which resources are allocated among the many stages of a data project, including data collection, processing, analysis, and implementation. Examine the data projects’ overall adaptability and scalability. It’s essential to check if the resources involved are flexible enough to accommodate changing project requirements. Customer Experience Data-driven improvements that result in positive consumer experiences can promote brand loyalty and business expansion. So, customer experience metrics that assess the influence of data analytics initiatives on customer engagement and satisfaction are immensely useful. Lower churn rates or higher client lifetime value are some examples of metrics that can be worthwhile considering in this regard. For a granular comprehension of the value created, businesses can gauge how data-driven innovations affect customers’ opinions of the brand, products, and services by monitoring measures like Net Promoter Score (NPS) or Customer Satisfaction Score (CSAT). Apart from all these focus areas, businesses can also concentrate on: Assessing the time to market of product or service launch and improvements Evaluating the ROI of specific use cases and user groups Analyzing the value of data-driven initiatives from a regulatory standpoint Tracking changes in customer behavior patterns due to data-driven improvements Monitoring changes in customer responses to data-driven campaigns such as personalized email campaigns or targeted ads In a Nutshell The ultimate objective of assessing the ROI of data science projects is to bring about a significant and measurable impact on the organization’s performance. By evaluating the effect on business operations and calculating the cost savings and revenue generated by data analytics initiatives, businesses can gauge whether their investments increase profitability and efficiency. They can then proceed towards becoming data smart. But what if they can realize success with analytics initiatives right off the bat? That’s where leveraging a unified data platform like Rubiscape becomes critical. Not only can it democratize data access, but it also drives data-backed innovation, increases data literacy across the board, and weaves agility into data science executions. Get in touch with us to learn more.