Consulting

Make Sense of Your Business Intelligence

Whether you are looking for help with an initial implementation or a more mature deployment, we can help you make sense of your business intelligence. Our team of experts has 15+ years of experience in all facets of data analytics — including data architecture and warehousing — so we can guide you through the process to create an end-to-end solution.

We’ll assess your business requirements by determining how your various groups generate reports and consume information.

We’ll create a project plan and roadmap to direct your initiative.

We have extensive experience working with industry-leading applications, so we can fully manage software migration and upgrades to keep your environment

To derive useful business intelligence from your data, you need to measure planned and unplanned customer actions. We’ll help you identify key metrics that can translate into cost savings and help generate quantifiable returns on your investments.

We’ll help you streamline the analytics process so you can discover efficiencies within your business and find meaningful patterns in your data — and improve business performance and results.

Full stack Business Intelligence

Full stack Business Intelligence

Business Intelligence (BI) comprises the set of strategies, processes, applications, data, technologies and technical architectures which are used by enterprises to support the collection, data analysis, presentation and dissemination of business information.[1] BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunites and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

Data Science

Data Science

Wikipedia defines Data Science as following: Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured,similar to data mining. Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data.[3] It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. ELA AS provides full stack Buinsess Intelligence and Big Data solution across industries and domains where we have more than a decade of experience. Some say we have magical powers too. We’ll let you decide that for yourself!

Data & Mathematical Modelling

Data & Mathematical Modelling

A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in the natural sciences (such as physics, biology, Earth science, meteorology) and engineering disciplines (such as computer science, artificial intelligence), as well as in the social sciences (such as economics, psychology, sociology, political science). Physicists, engineers, statisticians, operations research analysts, and economists use mathematical models most extensively[citation needed]. A model may help to explain a system and to study the effects of different components, and to make predictions about behaviour.

Data Visualization

Data Visualization

Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data, meaning “information that has been abstracted in some schematic form, including attributes or variables for the units of information“. A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.