At Nextogen, we have developed a range of competencies and technological capabilities across its business to develop a set of innovative data analytics solutions. These solutions will assist our client to improve the quality of critical information in their business. Our solutions are either of a “packaged” or “customized” nature, depending on the nature of your requirements. As part of packaged solutions we deliver standard, routine assignments, while with customized solutions you will get complex and specialized assignments. The current business imperative is not just to make decisions more data driven but to make them at a fast clip in response to changing marketplace dynamics. From interactive data processing and machine learning to visualization, the analytics ecosystem is fast evolving along with advances in the software ecosystem. Nextogen has expertise in big data to shape your big technology decisions and build scalable and fault-tolerant big data solutions.
At Nextogen, we have understood the profound industry-need for cohesive analytics platforms, which not only help to assimilate structured and unstructured data, but also takes care of the entire analytics value chain – right from curetting and deciphering massive amounts of granular data to delivering data-driven big decisions. Hence, we have built extremely agile and scalable domain-specific platforms on proven algorithms, software accelerators and reference architectures that addresses the most challenging big data opportunities with high-performance analytics at low latency. These platforms eventually help solve high-impact business problems rapidly. Integrated with very high performing components and service layers, our platforms can adapt itself to changing users, analytics, or business needs. These components and service layers, which were built to fill the technological gaps we identified with our experience, are domain specific; and are designed to bring additional value expected from today’s businesses.
Our Ecosystem has the advanced analytics framework at its heart. It encompasses meticulously placed Data Engineering, Data Science and Decision Science components at the crux. These components, which utilize industry-standard software tools, statistical models, behavioral science, design thinking and decision tools, facilitate businesses to take the most pragmatic approach for data management through till decision-making. Unlike other D2D frameworks that directly collect the Big Data in heaps, Aptus Data Labs instigates by understanding the real business query and assimilates vertical/functional data according to the requirement. The vertical/functional data, which can be structured, un-structured or semi-structured, is sourced from enterprises, businesses, syndicates, machines & sensors, geo-locations, web click streams, server logs or social media. The assimilated data is processed through several integrated layers and modules, encompassing accelerators, reference architectures and algorithms, to arrive at the required insights, as per the business query. Some of the key components of our framework, which help businesses navigate through the data analytics path easily – right from Primary Data to Data Engineering and then Data Science to Decision Science, include:
Applied Data Engineering
Our mission-focused data engineers utilize data engineering concepts to understand a business query requirement, using the raw data. Right from data acquisition, data storage, data processing and data workflow management, our data engineering model leverages upon the power of algorithms, technology and third party data management tools to extract the underlying information from the big data – irrespective of its volume, velocity and variety.
Applied Data Science:
Once the information is extracted from Big Data, the functional data is further processed using Data Science, which is a niche area that extracts nontrivial knowledge from surfeit of functional data, to improve decision making. Built on our customizable accelerators, algorithms and reference architectures, our Data Science components help enterprises make strategic, operational business decisions with right statistical and mathematical techniques to reduce risk, maximize profits, minimize costs, or more efficiently allocate resources. Our computer scientists, operations researchers, mathematicians, statisticians and above all Data Science researchers –who have hands-on expertise in applied mathematical algorithms, econometrics, statistics, pattern recognition, operations research, machine learning and decision science – extrapolate key business values using descriptive, predictive and prescriptive capabilities.
Decision Science:
Since descriptive, diagnostic or predictive analytics are not sufficient to arrive at Big Business decisions, Aptus Data Labs utilizes its key decision-making systems and expertise intervention to deliver streamlined decision models that help optimize business operations and reduce operational costs. Our domain architects, business domain experts and business analysts utilize human-driven decision-making systems, decision support approaches, operational intelligence platforms, intelligent business process management (BPM), business rule processing, management science/operations research and more to transform meaningful insights into Big Business Decisions.
Advanced Analytics through technology:
Covering the complete Advance Analytics value-chain, our Framework is empowered with technological components of Advanced Analytics. Since Advanced Analytics is a niche area of analyzing data using sophisticated quantitative methods to produce insights, our framework helps enterprises to optimize the data supply chain with the right data, at the right time and at the right place, to arrive at Big Business Decisions.​​
​By applying our years of domain experience and industry’s best practices for business process and technology integration, we deliver streamlined decision models that help optimize business operations and reduce operational costs.