The power of data related expertises
One of the most important competencies that an organization should have is the data competence. In fact, data expertise is perhaps the most important skill for today’s organizations. Look at Google, when Google starts a bank, bankers get scared, when Google makes cards, card makers have no chance and when Google starts playing the game Go, they even win from the world champion. How can it be that Google is so skilled in many different things?
The answer is simple, Google has many facets of different data related expertise. Data Management, Information Management, Data Engineering, Data Science, Artificial Intelligence, Appplication Integration, Automation and Insights & Visualizations are examples of data related expertise that Google has mastered. Knowledge is power and Google is able to generate knowledge based on data-related expertise. Imagine how your organization will perform if you have people with these skills at your disposal. These competencies ensure that your organization can outsmart competitors. Bravinci’s mission is to increase these competencies among its customers.
Expertises You can count on
Data Engineering
Data Engineering (DE) is about building and managing data pipelines from Systems of Record to other Systems of Record or Systems of Intelligence. Building and managing specific data transformations to fit end state data models that will facilitate reporting, dashboarding and analytics use cases to support decision making processes or automation.
Information Management
Information management (IM) is an integrative discipline for structuring, describing and governing information assets across organizational and technological boundaries to improve efficiency, promote transparency and enable business insight.
Data Management
Data Management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.
Data Science
Data science (DS) is the study of the extraction of knowledge from data. It uses various techniques from many fields, including signal processing, mathematics, probability, machine learning, computer programming, statistics, data engineering, pattern matching, and data visualization, with the goal of extracting useful knowledge from the data. With computer systems able to handle more data, big data is an important aspect of data science.
Automation
Automation, or Labor-saving technology is the technology by which a process or procedure is performed with minimal human assistance. Automation or automatic control is the use of various control systems for operating equipment such as machinery, processes in factories, boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications and vehicles with minimal or reduced human intervention.
Artificial Intelligence
In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. The term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".
As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations.
Insights & Visualizations
Data visualization is the graphic representation of data. It involves producing images that communicate relationships among the represented data to viewers of the images. This communication is achieved through the use of a systematic mapping between graphic marks and data values in the creation of the visualization. This mapping establishes how data values will be represented visually, determining how and to what extent a property of a graphic mark, such as size or color, will change to reflect changes in the value of a datum.
To communicate information clearly and efficiently, data visualization uses statistical graphics, plots, information graphics and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze 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 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.
Data visualization is both an art and a science. It is viewed as a branch of descriptive statistics by some, but also as a grounded theory development tool by others. Increased amounts of data created by Internet activity and an expanding number of sensors in the environment are referred to as "big data" or Internet of things. Processing, analyzing and communicating this data present ethical and analytical challenges for data visualization.
Application integration
Enterprise application integration is an integration framework composed of a collection of technologies and services which form a middleware or "middleware framework" to enable integration of systems and applications across an enterprise.
Many types of business software such as supply chain management applications, ERP systems, CRM applications for managing customers, business intelligence applications, payroll, and human resources systems typically cannot communicate with one another in order to share data or business rules. For this reason, such applications are sometimes referred to as islands of automation or information silos. This lack of communication leads to inefficiencies, wherein identical data are stored in multiple locations, or straightforward processes are unable to be automated.
Enterprise application integration is the process of linking such applications within a single organization together in order to simplify and automate business processes to the greatest extent possible, while at the same time avoiding having to make sweeping changes to the existing applications or data structures. Applications can be linked either at the back-end via APIs or the front-end (GUI).
Propositions you can trust.
Services
Focus on your core business and leave the management and operation of data applications to Bravinci.
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Solutions
Often the wheel does not have to be reinvented. Our data-driven solutions and those of our ecosystem partners can quickly add value for your organization after a rapid implementation.
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