Internet of things (IoT) and devices connected to it
Bigdata Analytics
What is Big Data?
Big data represents large, hard-to-manage set of data, which, inundate businesses provides insights, improve decisions and give confidence for making strategies.
Big Data is structured or unstructured data arrays of large volume. They are processed using special automated tools for statistics, analysis, forecasts, and decision making.
Big Data To Predict And Analyze
Uncover hidden patterns and predict events to minimize risks and create opportunities. Let Kleosys help you create and implement predictive analytics solutions that will help you make better decisions in the future. We will help in identifying data, integrate and enrich data, model execution and deployment, trials and Application, monitoring and measurement
Main Sources Of Big Data
Social networks, blogs, and media
Company data: transactions, orders for goods and services, taxi and car-sharing trips, customer profile
Instrument readings: meteorological stations, air, and water composition meters, satellite data
Statistics of cities and states: data on movements, births, and deaths;
Medical data: tests, diseases, diagnostic images
Big Data Services To Make A Big Difference
We help you find meaning in petabytes of data by turning raw numbers into targeted solutions. Collect and analyze important daily data across your business to build reliable architectures that process billions of requests. Our services will help in Data Strategy Consulting, data exploration and discovery, transform raw data, integration and aggregation, artificial intelligence adapted to your needs
Our team is proficient in applying various techniques such as data mining and machine learning. Using a range of software in different fields, we put artificial intelligence at the service of companies worldwide including Data engineering, data management, data analysis, recommender systems, natural language processing, deployment
The 4 Vs Of Big Data!
Volume, velocity, variety, and veracity are the 4 aspects to be taken into account by Big Data
Volume because the number of data to be studied by these new systems is substantial. It is counted in terabytes. For information, the technical-scientific installations would produce the most data in the world. Velocity because to be effective, data processing must respond to a demand for speed. This is when a company sets up a fraud management system or banking system. The variety because this data can come from all types of systems: meteorological, online shopping, and publications on social networks. These are complex data from the Internet, most often in text and image format. Veracity because this data is used by sensitive systems such as banks, governments, or the stock market. In these areas, an error is impossible. Trust is, therefore, a key point in the management of Big Data.
The Promises Of Big Data
In companies, the management of Big Data is a real challenge. When this data is well managed and processed in the right way, it can:
Develop the trust of their customers by focusing their customer service on quality
Get more complete answers because the amount of information is more important. This means more confidence in the data and, therefore, a different approach to solving problems
Optimize the efficiency of operational processes
Renew their Business Model by discovering new sources of revenue
Strategic Goals
Development of methods for the implementation of predictive analytics, development of an automatic search for vulnerabilities in web applications
Development of new technologies for storage and analysis of big data
Development of breakthrough fundamental research
Development of an educational platform and stimulation of the growth of the number of specialists
Creation of infrastructure to form an effective computing base of the widest possible range for existing and new applied problems
There are four main methods of Big Data analysis
Descriptive analytics
It is the most common. It answers the question “What happened?” analyzes real-time and historical data. The main goal is to determine the reasons and patterns of success or failure in a particular area to use this data for the most effective models. For descriptive analytics, basic mathematical functions are used. Predictive analytics- It helps predict the most likely development of events based on the available data. To do this, use ready-made templates based on any objects or phenomena with a similar set of characteristics. With the help of predictive (or predictive, predictive) analytics, you can, for example, calculate a collapse or price change in the stock market. Or assess the potential borrower’s ability to repay a loan. Prescriptive analytics- It is the next level up from predictive. With the help of Big Data and modern technologies, it is possible to identify problem points in a business or any other activity and calculate under what scenario they can be avoided in the future. Diagnostic analytics- It uses data to analyze the causes of what happened. This helps to detect anomalies and random connections between events and activities.
Descriptive analytics
It is the most common. It answers the question “What happened?” analyzes real-time and historical data. The main goal is to determine the reasons and patterns of success or failure in a particular area to use this data for the most effective models. For descriptive analytics, basic mathematical functions are used.
Predictive analytics
It helps predict the most likely development of events based on the available data. To do this, use ready-made templates based on any objects or phenomena with a similar set of characteristics. With the help of predictive (or predictive, predictive) analytics, you can, for example, calculate a collapse or price change in the stock market. Or assess the potential borrower’s ability to repay a loan.
Prescriptive analytics
It is the next level up from predictive. With the help of Big Data and modern technologies, it is possible to identify problem points in a business or any other activity and calculate under what scenario they can be avoided in the future.
Diagnostic analytics
It uses data to analyze the causes of what happened. This helps to detect anomalies and random connections between events and activities.
Versatility of Big Data
Data Is Processed And Analyzed Using Various Tools And Technologies using special software
Data mining
Extracting previously unknown data from arrays using a large set of techniques
AI and neural networks
For building models based on Big Data, including text and image recognition
Analytical data visualization
Animated models or graphs based on big data.
Developers adhere to two criteria for collecting information
Anonymization of data makes personal information of users inaccessible to some extent
The aggregation of data allows us to operate only with average indicators
Supercomputers are used to process large amounts of data online
Their power and computing capabilities are many times greater than conventional ones.
