Viera Analytics


Every day, we create several quintillion bytes of data, vast amounts of which lie there idle. New devices and tools, sensors and technology grow these numbers more and more. Of how much of this data do you actually make use? How much knowledge remains inaccessible to you?

Our team brings together deep industry expertise and the necessary technical know-how to boost fast, measurable and fact based decisions to let you monitor every aspect of your business. At Viera Consulting, we not only produce complex business intelligence driven analytics, we also provide the right set of deployment capabilities and help clients to understand and use the analytical findings in order to maximize their return on investments.

How Big Data Platform Works for You

Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer.

The definition of Big Data, given by Gartner is, “Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”.

Viera analytics empowers data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle.

Applications of Big Data:

  • Customer analytics - This is the process of optimizing your website to "rank" higher in search engine results pages, thereby increasing the amount of organic (or free) traffic your website receives. The channels that benefit from SEO include websites, blogs, and infographics.
  • Compliance analytics - This term denotes the creation and promotion of content assets for the purpose of generating brand awareness, traffic growth, lead generation, and customers.
  • Fraud analytics - This practice promotes your brand and your content on social media channels to increase brand awareness, drive traffic, and generate leads for your business.
  • Operational analytics - PPC is a method of driving traffic to your website by paying a publisher every time your ad is clicked. One of the most common types of PPC is Google Ads, which allows you to pay for top slots on Google's search engine results pages at a price "per click" of the links you place.

Experts at Viera Analytics define the examining raw data with the purpose of drawing conclusions about that information.

Data Analytics at Viera involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.

Data Scientists at Viera gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and are able to provide accurate predictions and insights that can be used to power critical business decisions.

Data analysts perform descriptive statistics, visualize data and communicate data points for conclusions. Provide solutions with the understanding of statistics, a very good sense of databases, the ability to create new views, and the perception to visualize the data.

Data Science deals with unstructured and structured data, Data Science is a field that comprises of everything that related to data cleansing, preparation, and analysis.

Viera experts facilitates optimal solutions for client's business requirements Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.

Viera Consulting helps data scientists deals with gathering relevant data from ton of data (including junk) generated by a business. This, then, becomes an extension of data warehousing and mining. Data specialists facilitates data management, analysis, and model building with large data sets and databases.

When we speak about data science the main goals of the process are:

  • Exploration of data analysis
  • Processing, cleansing, verified and integrity of data
  • Data trends identification and make predictions

Data Science Field of application

  • Web research - Search engines use data science algorithms to offer the closest response to queries. And all this in a split second.
  • Digital advertising - Completely all marketing tools are based on data science algorithms. They allow you to increase the coefficient of clickability.
  • Recommender systems - Such systems greatly simplify the search for relevant products and enrich the user experience. Many companies use such platforms to promote their products and services, guided by customer requests. In this case, the recommendations are based on user search histories.

Data analytics Field of application

  • Healthcare - Data management tools and special platforms are used to monitor the health of patients. Such an automation of the treatment process can raise the level of work efficiency.
  • Tourism - Data analytics optimize the buying process. By analyzing data taken from social platforms or search history, tourism agencies can make a profitable offer with a package of services that will meet the needs and interests of the user based on his income level.
  • Gaming - Collecting and optimizing user information can be pretty useful and optimize play experience.
  • Energy management - Control and constant monitoring of the network of devices, dispatching teams and managing in critical situations - data analytics helps with everything.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

A data analyst is usually the person who can do basic descriptive statistics, visualize data and communicate data points for conclusions. They must have a basic understanding of statistics, a very good sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the basic level of data science.

Today's data visualization tools go beyond the standard charts and graphs used in Microsoft Excel spreadsheets, displaying data in more sophisticated ways such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts. The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis. Indicators designed to alert users when data has been updated or predefined conditions occur can also be included.

Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights – look at data differently, more imaginatively. Data visualization will promote that creative data exploration.

Good data visualizations are created when communication, data science, and design collide. Data visualizations done right offer key insights into complicated datasets in ways that are meaningful and intuitive.

Why Does Data Visualization Matter?

  • Better Decision Making - Organizations are using data visualizations, and data tools, to ask better questions and make better decisions. Emerging computer technologies and new user-friendly software programs have made it easy to learn more about your company and make better data-driven business decisions
  • Meaningful Storytelling - Data visualizations and information graphics (infographics) have become an essential tool for today’s mainstream media.

We’ve provided a definition of data visualization, explained what makes this discipline important, mentioned some milestones from its history of evolution, and outlined examples of how data visualization works in practice.

By combining data visualization best practices with modern digital technology, many companies that have to deal with massive amounts of information can quickly analyze it and get data-driven insights in order to streamline various aspects of their operation. All industries, and all companies regardless of size and scope of work can benefit from dataviz and business intelligence it brings to life

Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS). Business Intelligence is sometimes used interchangeably with briefing books, report and query tools and executive information systems.

Business Intelligence systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data. Software elements support reporting, interactive “slice-and-dice” pivot-table analyses, visualization, and statistical data mining. Applications tackle sales, production, financial, and many other sources of business data for purposes that include business performance management. Information is often gathered about other companies in the same industry which is known as benchmarking.

BI has a direct impact on organization's strategic, tactical and operational business decisions. BI supports fact-based decision making using historical data rather than assumptions and gut feeling.

BI tools perform data analysis and create reports, summaries, dashboards, maps, graphs, and charts to provide users with detailed intelligence about the nature of the business.

Good data visualizations are created when communication, data science, and design collide. Data visualizations done right offer key insights into complicated datasets in ways that are meaningful and intuitive.

BI Trends

In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals. Business users are also often included to represent the business side and make sure its needs are met in the BI development process.

To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to business analysts on an incremental and iterative basis. Doing so can enable companies to put BI features into use more quickly and to refine or modify development plans as business needs change or as new requirements emerge and take priority over earlier ones.

Four Types of BI users

  • The Professional Data Analyst - The data analyst is a statistician who always needs to drill deep down into data. BI system helps them to get fresh insights to develop unique business strategies.
  • The IT users - The IT user also plays a dominant role in maintaining the BI infrastructure.
  • The head of the company - CEO or CXO can increase the profit of their business by improving operational efficiency in their business.
  • The Business Users - Business intelligence users can be found from across the organization.


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