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Category: Big Data

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Why is Data Visualization important?

One of the processes in data science is data visualization, which asserts that after data has been gathered, reused, and modeled, it must be represented in order to draw conclusions. Data visualization is part of the larger field of data presentation architecture (DPA), which strives to find, locate, modify, format, and transmit data in a clear, concise, and efficient manner.

What is Data Visualization?

Data Visualization

It is the practice of placing information into a visual framework, such as a map or graph, to make data easier for the human brain to understand and draw conclusions from. The main thing of data visualization is to make it easier to see patterns, trends, and outliers in large data sets. Statistics plates, information visualization, and information tiers are all expressions that are occasionally used interchangeably.

The capability to perceive data is pivotal for virtually every career. Preceptors may use it to show test results for scholars, and computer scientists can use it to enhance artificial intelligence( AI), and directors can use it to communicate with stakeholders. It’s pivotal to big data systems as well. Businesses needed a way to fluently acquire an overview of their data as they gathered enormous amounts of data in the early times of the big data trend.

For similar reasons, visualization is essential to advanced analytics. It becomes crucial to see the outputs when a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms in order to track outcomes and make sure that models are operating as planned.

Why is data visualization important?

Using visual data, data visualization offers a rapid and efficient approach to convey information to all audiences. Also, the practice can help businesses in determining the variables that impact consumer behavior, relating areas that bear enhancement or fresh attention, making data more memorable for stakeholders, figuring out the times and locales to vend particular products, and deals.

Data visualization also offers the following advantages:

  • An increased perception of the consecutive steps involved must be figured out to improve the organization.
  • An improved ability to hold an audience’s interest with information they can understand.
  • An easy distribution of information compiled to increase the opportunity to share insights with the team.

Visualization of data is the capacity to take in information quickly, improve insights, and make decisions more quickly.

Data Visualization and Big Data

The increased fashionability of big data and data analysis systems have made visualization more important than ever. Companies are decreasingly using machine literacy to gather massive quantities of data that can be delicate and slow to sort through, comprehend and explain. Data visualization tools offer a means to speed this up and present information to business possessors and stakeholders in ways they can understand.

Data Visualization and Big Data

Big data visualization frequently goes beyond the typical ways used in normal visualization, similar as pie maps, histograms and commercial graphs. It rather uses more complex representations, similar to heat charts and fever maps.

Big data visualization requires important computer systems to collect raw data, process it and turn it into graphical representations that humans can use to draw conclusions.

Big data visualization requires important computer systems to collect raw data, process it and turn it into graphical representations that humans can use to draw conclusions. While big data visualization can be salutary, it can pose several disadvantages to associations.

They are as follows:

To utilize big data visualization tools to their full potential, a visualization specialist has to work on it. This specialist must be suitable to identify the data sets and visualization styles to guarantee associations are optimizing the use of their data.

Big data visualization systems frequently bear involvement from IT, as well as operation, since the visualization of big data requires important computer tackle mechanisms, and effective storehouse systems.

The operability of big data visualization works only as accurate as the information being projected. Thus, it’s essential to have people and processes in place to govern and control the quality of commercial data, metadata and data sources. Big data visualization can only yield perceptivity that is as accurate as the data being displayed.

As a result, it is crucial to have systems in place for managing and regulating the quality of corporate data, metadata, and data sources.

Data visualization examples

The most popular visualization system was turning data into a table, bar graph, or pie map using a Microsoft Excel spreadsheet. Although traditional visualization approaches are still constantly employed, more sophisticated types are now also available, similar as the following

  • Infographics
  • Bullet lists
  • Fever map
  • A heat map
  • Charts of time series

Conclusion

Data visualization tools are increasingly being used as front ends for more complex big data environments as data visualization companies expand the capability of these tools. Data engineers and scientists can use data visualization tools to maintain track of data sources and do simple exploratory analysis on data sets before or after more in-depth advanced analysis.

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Big Data: The Next Big Thing Is Already Here

The last decade was a victim of a big blast in the tech-Industry by the introduction of technologies like Wearable Computers, Ultra-private devices, Devops, Software-defined data centres, Big Data, Smart Mobiles, Cloud Computing, etc. Out of this, Big Data is becoming the next big thing in the IT world.

There is something that is so big that we can’t avoid it, even if we want to. “Big Data” is one of those things.

The Big Data is not just a group of data, but different types of data are handled in new ways. Big Data is nothing but a collection of vast and complex data that it becomes very tedious to capture, store, process, retrieve and analyze it with the help of on-hand database management tools or traditional data processing techniques.

Giant companies like Amazon and Wal-Mart as well as organizations such as the U.S. government and NASA are using Big Data to meet their business. Big Data can also play a role for small or medium-sized companies and organizations that recognize the possibilities to capitalize upon the gains.

Why Big Data?

Big Data is demanded for:

  • Increase of storage capacity
  • Increase of processing power
  • Availability of Data (different data type)

The three V’s in Big Data

The three V’s “volume, velocity and variety” concepts invented by Doug Laney in 2001 to refer to the challenge of data management. Big Data is high-volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.

3 V’s of BIG DATA

1. Volume

Volume refers to the vast amount of data generated every second. (A lot of data, more than can easily be handled by a single database, computer or spreadsheet)

2. Velocity

It refers to the speed at which new data is generated and the speed at which data moves around.

3. Variety

It refers to the different kinds of information in each record, lacking inherent structure or predictable size, rate of arrival, transformation, or analysis when processed.

Additionally, a new V, “Veracity” is added by some organizations to describe it.

4. Veracity

Veracity refers to the reliability and difficulty of the data. The quality of the data being captured can vary greatly. Accuracy of analysis depends on the veracity of the source data.

Big Data Analysis: Some recent technologies

Companies are depending on the following technologies to do Big Data analysis:

  • Speedy and efficient processors.
  • Modern storage and processing technologies, especially for unstructured data
  • Robust server processing capacities
  • Cloud computing
  • Clustering, high connectivity, parallel processing, MPP
  • Apache Hadoop/ Hadoop Big Data

9 ways to build-up Self–Assurance in Big Data

The various process  to build up courage in Big Data

1. Data Exploration

Big Data exploration permits to discover and mine Big Data to find, Visualize and understand all Big Data to improve decision making.

2. Application Consolidation and Retirement

File the old application data and update new application deployment with test data management, integration and data quality.

3. Enhanced 360-degree view of the customer

It allows customer-facing professionals with improved and accurate information to involve customers to develop trusted relationships and improve loyalty. To gain that 360-degree view of the customer, the organization needs to force internal and external sources with structured and unstructured data.

4. Security and Intelligence Extension

The increasing numbers of crimes – cyber-based terrorism and computer interruptions posters a real threat to every individual and organization. To meet the security challenges, businesses need to enhance security platforms with Big Data technologies to process and analyze new data types and sources of under-influenced data.

5. Operations Analysis

It analyzes a variety of machine data for improved business results.

6. Data Warehouse Augmentation

Data Warehouse Modernization (formerly known as Data Warehouse Augmentation) is about building on an existing data warehouse infrastructure, influencing Big Data technologies to ‘augment’ its capabilities. It integrates Big Data and data warehouse capabilities to increase operational efficiency.

7. Improve Application Efficiency

Manage data growth, improve performance, and lower the cost for mission-critical applications.

8. Efficiency Application Development and Testing

It creates and maintains right-sized development, test and training environments.

9. Security and Compliance

It protects data, improves data integrity, and moderate opening risks and lower compliance costs.

If you arrange a system which works through all those stages to arrive at this target, then congratulation!!!

You’re in Big Data.

And hopefully, ready to start reaping the benefits!

– Anupa Thomas