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Data visualization helps handle and analyze complex information using the data visualization tools such as matplotlib, tableau, fusion charts, QlikView, high charts, plotly, chúng tôi etc., as these tools help in getting the graphical representation of the data and information in the form of charts, graph, and maps, using this the data visualization designers can easily create the visual representation of the large dataset which in turn helps in making the practical decision by getting insight from the large dataset.
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Hadoop, Data Science, Statistics & othersWhat is Data Visualization?
Numerous data visualization tools exist, such as Tableau, QlikView, FusionCharts, HighCharts, Datawrapper, Ploty, chúng tôi etc. Though there are humungous data visualization tools used in day-to-day life in Data visualization, one of the most popular plotting tools is matplot. pyplot.
Reasons why Matplotlib from data visualization tools is the most widely used:
Matplotlib is one of the essential plotting libraries in Python.
The developers drew inspiration from the tools available in Matlab when creating the entire plotting module.
Many people from the areas of Mathematics, Physics, Astronomy, and Statistics, as well as many Engineers and Researchers, are accustomed to using Matlab.
Matlab is a popular scientific computing toolbox, especially for scientific computing. When individuals began developing Python-specific plotting libraries for machine learning, data science, and artificial intelligence, they drew inspiration from MATLAB and created a library known as matplotlib.
matplotlib.pyplot: matplotlib. pyplot is used widely in creating figures with an area and plotting the lines, and we can visualize the plots attractively.Examples of Data Visualization Tools
Below are the examples mentioned:import matplotlib.pyplot as plt. plt.plot([2,4, 6, 4])
The above is a list, chúng tôi will plot these list elements of the Y-axis, which is indexed at 0,1,2,3 as their corresponding X-axis.
If we look at the above 2 lines of code, it labels the Y-axis and X-axis, respectively. (i.e, naming both axis.)
The above line of code will give the title to the plot. The title tells us what the plot is all about.
There is one problem with the above plot(screenshot 1); if you have noticed, we don’t see a grid-like structure. A grid helps you to read the values from the plot much easier. Now let’s see how to get the grid.
Code:plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
Look at the above line of code; instead of giving one array, we have two lists which become our X-axis and Y-axis. Here you can notice is if our x-axis value is 2, its corresponding y-axis value is 4, i.e., y-axis values are the squares of x-axis values.
Code:plt.ylabel('squares') plt.xlabel('numbers') plt.grid() # grid on
The moment you give this, it will give a plot with a grid embedded in it.
Now instead of a line plot, We plot a different plot with a different example.
Code:plt.plot([1, 2, 3, 4], [1, 4, 9, 16], ‘ro’)
Every X, Y pair has an associated parameter like the color and the shape, which we can give accordingly using the functionality of the Python keyword pair argument.
In this case, ‘ro’ indicates r – red color and o – circle shaped dots.plt.grid() plt.show()
Let’s say matplot lib works only with the list. Then, we can’t use it widely in the processing of numbers. We can use the NumPy package. Internally, all data is converted to NumPy arrays.
Let’s look slightly at the different plots:
Below are the different plots:
Code:import numpy as np t = np.arange(0., 5., 0.2) Above line creates values from 0 to 5 with an interval of 0.2. plt.plot(t, t**2, 'b--', label='^2')# 'rs', 'g^') plt.plot(t,t**2.2, 'rs', label='^2.2') plt.plot(t, t**2.5, 'g^', label=‘^2.5')
In the above lines of code, ‘b – – ‘ indicates Blue dashes, ‘rs’ indicates Red squares, and ‘g^’ indicates Green triangles.
The above line of code adds a legends-based online label. Legends make the plot extremely readable.
If we want the line width to be more, then a simple parameter called linewidth can do it.
Code:x = [1, 2, 3, 4] y = [1, 4, 9, 16] plt.plot(x, y, linewidth=5.0) plt.show()
The other interesting thing is set properties:
x1 = [1, 2, 3, 4] y1 = [1, 4, 9, 16]
Y1 values are squares of X1 values.
x2 = [1, 2, 3, 4] y2 = [2, 4, 6, 8]
Y2 values are just twice of X2 values.
lines = plt.plot(x1, y1, x2, y2)
Using the above line, we can plot these values in a single line. So what happens here is it will plot X1 vs Y1 and X2 vs Y2, and we are storing these in a variable called lines. Also, we can change the properties of those lines using keyword arguments.
plt.setp(lines, color=’r’, linewidth=2.0)
Here setp is called as set properties, lines corresponding to X1, Y1 respectively, color and linewidth are the arguments. The above line of code utilizes keyword arguments, as shown in screenshot 6.
plt.setp(lines, ‘color’, ‘g’, ‘linewidth’, 2.0)
The above line of code represents the Matlab syntax.
Here lines corresponds to X2, Y2 respectively. We also have two pairs of arguments, ‘colour’,’g’, and ‘linewidth’,’2.0’.
Either way, we can plot the line:
The first way is the native way of how we use Python.
People from a Matlab background preferably use the second way.
In this data visualization tools post, we have discovered the introduction to visualizing the data in Python. To be more specific, we have seen how to chart data with line plots and how to summarise the relationship between variables with scatter plots.Recommended Articles
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Introduction to Data Visualization
The amount of data has changed in the digital age, becoming both a challenge and an opportunity. Data visualization has become an efficient method for communicating insights and making sense of complex information. Making sense of data through visualization facilitates informed decision-making across various sectors by simplifying interpretation and improving understanding.
This article offers ten essential data visualization books to teach you the art and science of converting data into stunning visual stories. These publications cover various topics, ensuring there is something for newcomers and experienced practitioners, ranging from fundamental concepts to cutting-edge methodologies.
So be prepared to enter a world of data stories and new perspectives. Keep on reading.Importance and Benefits of Effective Data Visualization
Facilitates Communication: Data visualization serves as a powerful tool for effective communication.
Increases Data Accessibility: Visualizations can be designed to be user-friendly, allowing even non-technical individuals to interact with data and extract meaningful insights.
Enhances Storytelling: Visualizations can turn data into compelling narratives.Overview of Key Concepts and Principles
Let’s quickly review some fundamental ideas and guidelines that form the foundation of good data visualization before getting into the suggested reading list. These ideas will aid you in comprehending the principles of visual representation and direct your investigation of the suggested readings:Data forms
Determining the right visualization techniques requires understanding the many forms of data (such as category, numerical, and temporal).
Become familiar with well-known software, including Tableau, Power BI, and Python libraries like Matplotlib and ggplot2.Visual Perception
Gain insights into how our brains perceive and interpret visual information, including color, shape, size, and spatial position.Design Principles
Learn about design principles like hierarchy, contrast, balance, and typography, which are crucial in creating compelling visualizations.10 Must-Read Data Visualization Books
Here are our top 10 picks that would give you the best idea about data visualization.Book 1: “The Big Book of Dashboards” by Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave Book Summary
“The Big Book of Dashboards” is a comprehensive manual for designing effective dashboards. It offers information on the finest procedures, methods, and tactics for creating data-communicating dashboards. This book provides professionals looking to improve their dashboard design skills with inspiration and helpful guidance using more than 28 dashboard examples.
Learn the principles of effective dashboard design and layout.
Understand how to choose the right charts and visual elements for specific data.
Discover techniques for integrating interactivity and storytelling into dashboards.Exploration of Data Visualization Techniques and Examples
One of the examples in the big book of dashboards showcases a sales dashboard that effectively presents vital sales metrics such as revenue, units sold, and customer demographics. The authors explain how interactive elements, color coding, and well-designed visual components can enhance the user experience and facilitate quick insights into sales performance.Book 2: “Information Dashboard Design” by Stephen Few Book Summary
The book “Information Dashboard Design” by Stephen Few thoroughly examines efficient data visualization methods for developing educative and aesthetically appealing dashboards. According to the Information Dashboard Design book, the presentation of data-driven insights must be simple, straightforward, and purposeful. It highlights the fundamental concepts and industry-specific best practices of information dashboard design by offering multiple examples of well-designed dashboards from diverse sectors.
Develop a thorough understanding of the guiding concepts and recommended procedures for creating valuable dashboards.
Acquire the ability to select the best visual representations for various data kinds.
Learn how to build dashboards that aid in timely and accurate decision-making.Exploration of Data Visualization Techniques and Examples
A financial dashboard that displays critical financial data for a business, such as revenue, expenses, and profit margins, is the subject of one of the Information Dashboard Design examples. The author emphasizes the significance of employing the right chart kinds, labeling, and color schemes to explain financial performance and aid decision-making.Book 3: “The Wall Street Journal Guide to Information Graphics” by Dona M. Wong Book Summary
Recognize the steps involved in producing successful information graphics.
Acquire the skills necessary to transform complex data sets into appealing visualizations.
Develop an understanding of the value of context, accuracy, and clarity in information graphics.Exploration of Data Visualization Techniques and Examples
One of the examples in the book focuses on a data visualization that represents global population trends over time using an interactive map. The authors explain how thoughtful color choices, interactive features, and clear labeling can transform a complex dataset into a visually appealing and informative representation of population patterns.Book 4: “Storytelling with Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic Book Summary
A helpful book called “Storytelling with Data” emphasizes the significance of storytelling in data visualization. On how to effectively communicate data insights and captivate people through intriguing storytelling, Cole Nussbaumer Knaflic gives insightful suggestions. This book gives readers the know-how to create compelling data tales that appeal to their target market.
Learn the art of storytelling and its application in data visualization.
Understand how to structure a data story to engage and persuade your audience.
Gain insights into visual storytelling techniques for creating memorable and impactful presentations.Exploration of Data Visualization Techniques and Examples
One of the examples in the book focuses on data visualization that tells a compelling story about the impact of a marketing campaign on sales. The author illustrates how using before-and-after visuals, annotations, and contextual information can transform raw data into a persuasive narrative that convinces stakeholders of the campaign’s success.Book 5: “Information Visualization: Perception for Design” by Colin Ware Book Summary
Colin Ware’s “Information Visualization: Perception for Design” explores the cognitive aspects of data visualization and the principles that guide effective design choices. This book bridges the gap between theory and practice, offering a scientific foundation for understanding how visualizations are perceived and processed by the human brain. It provides a comprehensive framework for designing visualizations that optimize perception and cognition.
Exploring the cognitive and perceptual processes involved in data visualization.
Determine how to use visual elements to improve communication and comprehension.
Acquire skills in visualization design that take cognition and perception into account.Exploration of Data Visualization Techniques and Examples
One of the book’s examples focuses on a network visualization that shows the connections among people in a social network. The author examines efficient ways to communicate network structure information using visual signals, including node size, color, and spatial arrangement.Book 6: “Data Visualization: A Practical Introduction” by Kieran Healy Book Summary
The book “Data Visualization: A Practical Introduction” by Kieran Healy provides a clear and thorough overview. The core ideas, methods, and resources needed to produce excellent visualizations are covered in this book. Through real-world examples and hands-on exercises, readers can learn the skills essential to translate data into compelling visual tales.
Build a strong foundation in the concepts and methods of data visualization.
Recognize how crucial context, target audience, and purpose are when building visualizations.
Develop your ability to use several programming languages and visualization tools to produce powerful visualizations.Exploration of Data Visualization Techniques and Examples
One of the examples in the book focuses on a scatter plot that visualizes the relationship between two variables. The author explains how different markers, color schemes, and axes labeling can effectively highlight patterns and correlations in the data.Book 7: “Information Graphics: A Comprehensive Illustrated Reference” by Robert L. Harris Book Summary
Robert L. Harris’s “Information Graphics” is an invaluable reference for anyone interested in the art and science of information graphics. This comprehensive book covers various topics, including the history of information graphics, visualization techniques, and design principles. With over 400 illustrations and examples, this book offers inspiration and guidance for creating visually compelling graphics.
Explore the evolution of information graphics and its various forms.
Learn about different visualization techniques and their applications.
Discover how to communicate complex information through clear and engaging visuals effectively.Exploration of Data Visualization Techniques and Examples
One of the examples in the book showcases an infographic that visually represents the steps involved in a scientific research process. The author discusses using icons, color coding, and sequential layout to guide the reader through the complex process, making it more accessible and engaging.Book 8: “The Truthful Art: Data, Charts, and Maps for Communication” by Alberto Cairo Book Summary
“The Truthful Art” by Alberto Cairo explores the intersection of data visualization, design, and journalism. Cairo emphasizes the importance of truth and accuracy in data representation, providing insights into ethical considerations and the role of visualization in storytelling. This book equips readers with the skills to analyze and create impactful visualizations that inform and engage critically.
Acknowledge your ethical obligations and duties while using data visualization.
Gain skills in finding and conveying the truth in data tales.
Develop an understanding of how it is used in journalism and narrative.Exploration of Data Visualization Techniques and Examples
In this data visualization book’s examples, a line chart shows the increase and fall of global temperatures during the previous century. The author provides examples of how careful design decisions, accurate labeling, and contextual information can aid readers in comprehending the complexity of climate change and its effects.Book 9: “Visual Explanations: Images and Quantities, Evidence and Narrative” by Edward R. Tufte Book Summary
The book “Visual Explanations” by Edward R. Tufte explores the fundamentals of good data visualization, concentrating on the visual presentation of arguments and stories. Tufte looks at various instances to show how visualizations may improve comprehension and develop insight, from scientific diagrams to historical charts. This book questions traditional data visualization methods and encourages readers to think outside the box regarding how they convey information.
Examine novel and unorthodox data visualization techniques.
Realize the value of evidence-based narratives and storytelling in visual communication.
Develop your ability to produce engaging and informative visual explanations for your audience.Exploration of Data Visualization Techniques and Examples
One of the examples in this data visualization book showcases a series of diagrams that visually explain the workings of a complex machinery system. The author discusses using clear labels, simplified illustrations, and step-by-step sequencing to facilitate understanding and provide a comprehensive visual explanation of the machinery’s operation.Book 10: “Interactive Data Visualization for the Web” by Scott Murray Book Summary
Acquire a basic understanding of web-based data visualization techniques.
Get practical experience using well-known visualization libraries like D3.js.Exploration of Data Visualization Techniques and Examples
As you can see, anyone interested in understanding the art and science of data visualization will benefit greatly from reading these ten essential publications on the subject. These books cover a wide range of topics, guaranteeing there is something for everyone, regardless of their degree of skill, from fundamental concepts to cutting-edge tactics. You will learn a lot from reading these books, improve your visualization abilities, and be able to produce powerful visual storytelling.
Here are a few more resources to take into account if you want to continue learning:
Blogs and Websites
Data Visualization Conferences and EventsFrequently Asked Questions
Q1. What are the benefits of data visualization in business?
A. Data visualization in business offers several benefits, including improved decision-making, enhanced understanding of complex information, increased efficiency, and effective communication of insights to stakeholders.
Q2. Which data visualization books are suitable for beginners?
A. “Data Visualization: A Practical Introduction” by Kieran Healy is an excellent book for beginners, as it covers the fundamental concepts and techniques in an accessible manner.
Q3. How can data visualization be used for storytelling?
A. Beautiful data visualization can be used for storytelling by presenting data in a narrative structure, engaging the audience through visual elements, and effectively communicating insights and critical messages.
Q4. Are there any recommended data visualization tools?
Q5. Are there any data visualization books specifically focused on interactive visualizations?
Data quality mangement is a critical issue in today’s data centers. The complexity of the Cloud continues to grow, leading to an increasing need for data quality tools that analyze, manage, and scrub data from numerous sources, including databases, email, social media, logs, and the Internet of Things (IoT).
Comparison Chart of Data Quality Software
Data quality tools clean data by removing formatting errors, typos, and redundancies, while ensuring that organizations apply rules, automate processes, and have logs that provide details about processes. Used effectively, these data quality tools can remove inconsistencies that drive up enterprise expenses and annoy customers and business partners. They also drive productivity gains and increase revenues.
Data quality software helps data managers address four crucial areas of data management: data cleansing, data integration, master data management, and metadata management. These tools go beyond basic human analysis and typically identify errors and anomalies through the use of algorithms and lookup tables.
Also see: Top 15 Data Warehouse Tools
Identifying the right data quality management software is important for data managers who want to assess and improve the overall useability of their databases. Finding a superior data quality tool hinges on many key factors, including how and where an organization stores and uses data, how data flows across networks, and what type of data a team is attempting to tackle.
Although basic data quality tools are available for free through open source frameworks, many of today’s solutions offer sophisticated capabilities that work with multiple platforms and database formats. It is important to understand what a particular data quality tool can do for your enterprise — and whether you may need multiple tools to address more complex scenarios.
Consider these three factors when choosing a data quality management platform to address your business needs:
Incorrect data, duplicate data, missing data, and other data integrity issues can significantly impact — and undermine — the success of a business initiative. A haphazard or scattershot approach to maintaining data integrity may result in wasted time and resources. It can also lead to subpar performance and frustrated employees and customers. To avoid frustrating internal and external responses to data challenges, it’s important to start by conducting an analysis of existing data sources, current tools in use, and problems and issues that occur. This offensive approach delivers insight into gaps and possible fixes.
It’s obvious that not all data quality management tools are created equal. Data cleansing tools offer different strengths and weaknesses: some are designed to enhance specific applications such as Salesforce or SAP, others excel at spotting errors in physical mailing addresses or email, and still others tackle IoT data or pull together disparate data types and formats, so you need to decide which features are most important to your organization. In your decision making process, it’s also important to understand how a data cleansing tool works and what level of automation it offers, as well as specific features that you will need to accomplish key tasks. Finally, it’s crucial to consider factors such as data controls/security and licensing costs.
Value proposition for potential buyers: Cloudingo is a prominent data integrity and data cleansing tool designed for Salesforce. It tackles everything from deduplication and data migration, to spotting human errors and data inconsistencies. The platform handles data imports, delivers a high level of flexibility and control, and includes strong security protections.
The application includes strong security controls that include permission-based logins and simultaneous logins. Cloudingo supports unique and separate user accounts and tools for auditing who has made changes.
Value proposition for potential buyers: The vendor has established itself as a leader in data cleansing through a comprehensive set of tools that clean, match, dedupe, standardize and prepare data. Data Ladder is designed to integrate, link, and prepare data from nearly any source. It uses a visual interface and taps a variety of algorithms to identify phonetic, fuzzy, abbreviated, and domain-specific issues.
The company’s DataMatch Enterprise solution aims to deliver an accuracy rate of 96 percent for between 40K and 8M record samples, based on an independent analysis. It uses multi-threaded, in-memory processing to boost speed and accuracy, and it supports semantic matching for unstructured data.
Data Ladder supports integrations with a vast array of databases, file formats, big data lakes, enterprise applications, and social media. It provides templates and connectors for managing, combining, and cleansing data sources. This includes Microsoft Dynamics, Sage, Excel, Google Apps, Office 365 , SAP, Azure Cosmos database, Amazon Athena, Salesforce, and dozens of others.
The data standardization features draw on more than 300,000 pre-built rules, while also allowing customizations. The system uses proprietary built-in pattern recognition, but it also lets organizations build their own RegEx-based patterns visually.
Value proposition for potential buyers: IBM’s data quality application, available on-premise or in the cloud, offers a broad yet comprehensive approach to data cleansing and data management. The focus is on establishing consistent and accurate views of customers, vendors, locations, and products. InfoSphere QualityStage is designed for big data, business intelligence, data warehousing, application migration, and master data management.
IBM offers a number of key features designed to produce high quality data. A deep data profiling tool delivers analysis to aid in understanding content, quality and structure of tables, files, and other formats. Machine learning can auto-tag data and identify potential issues.
The platform offers more than 200 built-in data quality rules that control the ingestion of bad data. The tool can route problems to the right person so that the underlying data problem can be addressed.
A data classification feature identifies personally identifiable information (PII) that includes taxpayer IDs, credit cards, phone numbers, and other data. This feature helps eliminate duplicate records or orphan data that can wind up in the wrong hands.
The platform supports strong governance and rule-based data handling. It includes strong security features.
Value proposition for potential buyers: Informatica has adopted a framework that handles a wide array of tasks associated with data quality and Master Data Management (MDM). This includes role-based capabilities, exception management, artificial intelligence insights into issues, pre-built rules and accelerators, and a comprehensive set of data quality transformation tools.
Informatica’s Data Quality solution is adept at handling data standardization, validation, enrichment, deduplication, and consolidation. The vendor offers versions designed for cloud data residing in
The vendor also offers a Master Data Management (MDM) application that addresses data integrity through matching and modeling, metadata and governance, and cleansing and enriching. Among other things, Informatica MDM automates data profiling, discovery, cleansing, standardizing, enriching, matching, and merging within a single central repository.
The MDM platform supports nearly all types of structured and unstructured data, including applications, legacy systems, product data, third party data, online data, interaction data, and IoT data.
Value proposition for potential buyers: OpenRefine, formerly known as Google Refine, is a free open source tool for managing, manipulating, and cleansing data, including big data. The application can accommodate up to a few hundred thousand rows of data. It cleans, reformats and transforms diverse and disparate data. OpenRefine is available in several languages, including English, Chinese, Spanish, French, Italian, Japanese, and German.
GoogleRefine cleans and transforms data from a wide variety of sources, including standard applications, the web, and social media data.
The application provides powerful editing tools to remove formatting, filter data, rename data, add elements, and accomplish numerous other tasks. In addition, the application can interactively change large chunks of data in bulk to fit different requirements.
The ability to reconcile and match diverse data sets makes it possible to obtain, adapt, cleanse, and format data for web services, websites, and numerous database formats. In addition, GoogleRefine accommodates numerous extensions and plugins that work with many data sources and data formats.
Value proposition for potential buyers: SAS Data Management is a role-based graphical environment designed to manage data integration and cleansing. It includes powerful tools for data governance and metadata management, ETL and ELT, migration and synchronization capabilities, a data loader for Hadoop, and a metadata bridge for handling big data. Gartner named SAS a “Leader” in its 2023 Magic Quadrant for Data Integration Tools.
SAS Data Management offers a powerful set of wizards that aid in the entire spectrum of data quality management. These include tools for data integration, process design, metadata management, data quality controls, ETL and ELT, data governance, migration and synchronization, and more.
Strong metadata management capabilities aid in maintaining accurate data. The application offers mapping, data lineage tools that validate information, wizard-driven metadata import and export, and column standardization capabilities that aid in data integrity.
Data cleansing takes place in native languages with specific language awareness and location awareness for 38 regions worldwide. The application supports reusable data quality business rules, and it embeds data quality into batch, near-time, and real-time processes.
Value proposition for potential buyers: Precisely’s purchase of Trillium has positioned the company as a leader in the data integrity space. It offers five versions of the plug-and-play application: Trillium Quality for Dynamics, Trillium Quality for Big Data, Trillium DQ, Trillium Global Locator, and Trillium Cloud. All address different tasks within the overall objective of optimizing and integrating accurate data into enterprise systems.
Trillium DQ works across applications to identify and fix data problems. The application, which can be deployed on-premises or in the cloud, supports more than 230 countries, regions and territories. It integrates with numerous architectures, including Hadoop, Spark, SAP, and Microsoft Dynamics.
Trillium DQ can find missing, duplicate, and inaccurate records, but also uncover relationships within households, businesses, and accounts. It includes an ability to add missing postal information as well as latitude and longitude data, as well as other key types of reference data.
Trillium Cloud focuses on data quality for public, private, and hybrid cloud platforms and applications. This includes cleansing, matching, and unifying data across multiple data sources and data domains.
Value proposition for potential buyers: Talend focuses on producing and maintaining clean and reliable data through a sophisticated framework that includes machine learning, pre-built connectors and components, data governance and management, and monitoring tools. The platform addresses data deduplication, validation, and standardization. It supports both on-premises and cloud-based applications while protecting PII and other sensitive data. Gartner rated the firm a “Leader” in its 2023 Magic Quadrant for Data Integration Tools.
The data integrity application uses a graphical interface and drill down capabilities to display details about data integrity. It allows users to evaluate data quality against custom-designed thresholds and measure performance against internal or external metrics and standards.
The application enforces automatic data quality error resolution through enrichment, harmonization, fuzzy matching, and deduplication.
Value proposition for potential buyers: TIBCO Clarity places a heavy emphasis on analyzing and cleansing large volumes of data to produce rich and accurate data sets. The application is available in on-premises and cloud versions. It includes tools for profiling, validating, standardizing, transforming, deduplicating, cleansing, and visualizing for all major data sources and file types.
Clarity offers a powerful deduplication engine that supports pattern-based searches to find duplicate records and data. The search engine is highly customizable; it allows users to deploy match strategies based on a wide array of criteria, including columns, thesaurus tables, and other criteria like multiple languages. It also lets users run deduplication against a dataset or an external master table.
A faceting function allows users to analyze and regroup data according to numerous criteria, including by star, flag, empty rows, and text patterns. This simplifies data cleanup while providing a high level of flexibility.
The application supports strong editing functions that let users manage columns, cells, and tables. It supports splitting and managing cells, blanking and filling cells, and clustering cells.
The address cleansing function works with TIBCO GeoAnalytics as well as Google Maps and ArcGIS.
Value proposition for potential buyers: Validity, the maker of DemandTools, delivers a robust collection of tools designed to manage CRM data within Salesforce. The product accommodates large data sets and identifies and deduplicates data within any database table. It can perform multi-table mass manipulations and standardize Salesforce objects and data. The application is flexible and highly customizable, and it includes powerful automation tools.
The vendor focuses on providing a comprehensive suite of data integrity tools for Salesforce administrators. DemandTools compares a variety of internal and external data sources to deduplicate, merge, and maintain data accuracy.
The Validity JobBuilder tool automates data cleansing and maintenance tasks by merging duplicates, backing up data, and handling updates according to preset rules and conditions.
CloudingoCloudingoSalesforce data Deduplication; data migration management; spots human and other errors/inconsistencies
Data LadderDataMatch Enterprise; ProductMatchDiverse data sets across numerous applications and formatsIncludes more than 300,000 prebuilt rules; templates and connectors for most major applications
IBMInfoSphere QualityStageBig data, business intelligence; data warehousing; application migration and master data managementIncludes more than 200 built-in data quality rules; strong machine learning and governance tools
InformaticaData Quality; Master Data ManagementAccommodates diverse data sets; supports Azure and AWSData standardization, validation, enrichment, deduplication, and consolidation
OpenRefineOpenRefineTransforms, cleanses and formats data for analytics and other purposesPowerful capture and editing functions
Managing data integration and cleansing for diverse data sources and setsStrong metadata management; supports 38 languages
Trillium CloudCleansing, optimizing and integrating data from numerous sourcesDQ supports more than 230 countries, regions and territories; works with major architectures, including Hadoop, Spark, SAP and MS Dynamics
TalendData QualityData integrationDeduplication, validation and standardization using machine learning; templates and reusable elements to aid in data cleansing
TIBCOClarity High volume data analysis and cleansingTools for profiling, validating, standardizing, transforming, deduplicating, cleansing and visualizing for all major data sources and file types
ValidityDemandToolsSalesforce dataHandles multi-table mass manipulations and standardizes Salesforce objects and data through deduplication and other capabilities
Big Data just keeps getting bigger, in a popularity sense. A new IDC report predicts that the Big Data and business analytics market will grow to $203 billion by 2023, double the $112 billion in 2024.
The banking industry is projected to lead the drive and spend the most, which is not surprising, while IT and businesses services will lead most of the tech investing. Overall, IDC finds that the banking, discrete manufacturing, process manufacturing, federal/central government, and professional services will account for about 50% of the overall spending.
Not surprisingly, some of the biggest big data analytics spending — about $60 billion — will go toward reporting and analysis tools. That’s what analytics is all about, after all. Hardware investment will reach nearly $30 billion by 2023.
So as Big Data grows, what will be the major trends? In talking to experts and surveying the research reports, a few patterns emerged.
2) Machine Learning: Big Data solutions will increasingly rely on automated analysis using machine learning techniques like pattern identification and anomaly detection to sort through the vast quantities of data.
3) Predictive analytics: Machine learning is not just for historical analysis, but also can be used to predict future data points. That will start with basic ‘straight-line’ prediction, deducing B from A. But it will eventually grow and become more sophisticated by detecting patterns and anomalies that are about to happen too.
4) Security analytics: To some degree this aready has a significant prescence. Security software, especially intrusion detection, has learned to spot suspicious and anomalous behavior. Big Data, with all of its source inputs, needs to be secured and there will be greater emphasis on securing the data itself. The same processing power and software analytics used to analyze the data will also be used for rapid detection and adaptive responses.
5) The bar is raised: Traditional programmers will have to add gain data science skills to their repertory in order to stay relevant and employable. But just like many programmers are self-taught, there will be a rise in data scientists from nontraditional professional backgrounds, including self-taught data scientists.
6) The old guard fades: A 2024 report from Gartner found Hadoop was fading in popularity in favor of real-time analytics like Apache Spark. Hadoop was, after all, a batch process run overnight. People want answers in real time. So Hadoop, MapReduce, HBase and HDFS are all going to continue to fade in favor of faster technologies.
7) No more hype: Big Data has faded as a buzzword and is now just another technology like RDBMS and CRM. That means the technology has settled into the enterprise as another tool brought to bear. It’s now a maturing product, free of the hype that can be distracting.
8) More Data Scientists: The Data Scientist is probably the most in-demand technologist out there, with people who qualify commanding a significant salary. Nature abhors a vacuum and you will see more people trying to gain Data Scientist skills. Some will go the self-taught route, which is how many programmers acquired their skills in the first place, while others will get training via crowdsourcing.
9) IoT + BD = soulmates: millions of Internet-connected devices, from wearables to factory equipment, will generate massive amounts of data. This will lead to all kinds of feedback, like machine performance, which in turn will lead to optimized performance and earlier warnings before failure, reducing downtime and expenses.
10) The lake gains power: Data lakes, massive repositories of information, have been around for a while but mostly it’s just a store with little idea how to use it. But as organizations demand quicker answers, they will turn to the data lake for those answers.
11) Real time is hot: In a survey of data architects, IT managers, and BI analysts, nearly 70% of the respondents favored Spark over MapReduce. The reason is clear: Spark is in-memory, real time stream processing while MapReduce is batch processing usually done overnight or during off-peak hours. Real-time is in, hours-old data is out.
12) Metadata catalogs: You can gather a lot of data with Hadoop but you can’t always process it, or even find what you need in all that information. Enter Metadata Catalogs, a simple concept where aspects of Big Data analytics, like data quality and security, are stored in a catalog. They catalog files using tags, uncover relationships between data assets, and even provide query suggestions. There are a number of companies offering data cataloging software for Hadoop, plus there is an open source project, Apache Atlas.
13) AI explodes: Artificial intelligence, and its cousin machine learning, will see tremendous growth because there is simply too much data coming in to be analyzed to wait for human eyes. More must be automated for faster responses. This is especially true with the massive amounts of data generated by IoT devices.
14) Dashboard maturity: With Big Data still in its early years, there are a lot of technologies that have yet to mature. You just can’t rush some things. One of them is the right tools to easily translate the data into something useful. Analysts predict that dashboards will finally get some attention from startups like DataHero, Domo, and Looker, among others, that will offer more powerful tools of analysis.
15) Privacy clash: With all the data being gathered, some governments may put the brakes on things for a variety of reasons. There have been numerous government agency hacks and questions about the 2024 Presidential election. This may result on restrictions from the government on how data is gathered and used. Plus, the EU has set some tough new privacy laws regarding how data is used and how models are built, set to take effect in January 2023. The impact is not yet known, but in the future, data might be harder to come by or use.
16) Digital assistants: Digital voice assistants like Amazon Echo and Alexa and Google Home and Chromecast will be the next generation of data gathering, along with Apple Siri and Microsoft Cortana. Don’t think they won’t. These are “always listening” devices used to help people make purchase and other consumption decisions. They will become a data source at least for their providers.
17) In-memory everything: Memory has up to now been relatively cheap, and since 64-bit processors can access up to 16 exabytes of memory, server vendors are cramming as much DRAM into these things as possible. Whether in the cloud or on-premises, memory footprints are exploding, and that’s making way for more real-time analytics like Spark. Working in memory is at three orders of magnitude faster than going to disk and everyone wants more speed.
Analytics Insight has churned out the top Data Science jobs in Gurgaon available in 2023 Data Scientist at Airtel
Airtel is known as one of the largest telecom service providers for customers and businesses in India. It also operates in 18 countries with products such as 2G, 3G and 4G wireless services, high-speed home broadband as well as DTH. The company consists of more than 403 million customers across the world. Responsibilities: The data scientist needs to research, design, implement as well as evaluate novel Computer Vision algorithms, work on large-scale datasets and create scalable systems in versatile application fields. The candidate is required to work closely with the customer expertise team, research scientist teams as well as product engineering teams to drive model implementations along with new algorithms. The candidate also needs to interact with the customer to gain a better understanding of the business problems and help them by implementing machine learning solutions. Qualifications: A candidate is required to have practical experience in Computer Vision and more than three years in building production-scale systems in either Computer Vision, deep learning or machine learning. There should be coding skills in one programming language and a clear understanding of deep learning CV evaluation metrics such as mAP, F_beta and PR curves as well as face detection, facial recognition and OCR. The candidate also needs to have 2-3 years of modelling experience working with Pytorch, MxNet and Tensorflow along with object detection approaches such as Faster RCNN, YOLO and CenterNet.Data Scientist at BluSmart
BluSmart is known as the first and leading all-electric ride-hailing mobility service in India. It has a mission to steer urban India towards a sustainable means of transportation by building a comprehensive electric on-demand mobility platform with smart charging and smart parking. The company will provide efficient, affordable, intelligent as well as reliable mobility. Responsibilities: The candidate is required to do a geospatial and time-based analysis of business vectors like time-travelled, fare, trip start and many more to optimise fleet utilisation and deployment as well as develop strategies to deploy electric vehicles and chargers in Delhi-NCR along with Mumbai by using data from thousands of trip from BluSmart cabs. The data scientist will create a new experimental framework to collect data and build tools to automate data collection by using open-source data analysis and visualisation tools. Qualifications: The candidate is required to have sufficient knowledge of data analytics, machine learning, and programming languages such as R, SQL and Python. The candidate needs to have practical experience with data analytics, machine learning and business intelligence tools such as Tableau with smart mathematical skills.Associate Data Scientist at Pee Safe
Responsibilities: The data scientist should receive actionable insights from data to be used in real-time in all decision-making processes for the company and implement multiple processes across different departments to enhance business metrics. The candidate needs to create new models or improve existing models to be used for the supply chain, demand predictions, logistics and many more. Qualifications: The candidate should have a Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering or any other relevant field. The candidate is required to have at least two to three years of practical experience in quantitative analytics or data modelling. It is essential to have a clear understanding of predictive modelling, machine learning, clustering, classification techniques, algorithms, programming language as well as Big Data frameworks and visualisation tools such as Cassandra, Hadoop, Spark and Tableau. The candidate must have strong problem-solving skills with sufficient knowledge of Excel.Data Scientist at Siemens Limited
Siemens is popularly known as a technology company focused on industry, infrastructure, mobility as well as healthcare. It aims in creating technologies for more resource-efficient factories along with resilient supply chains to transform industries. Responsibilities: The candidate is required to design software solutions supplemented with Artificial Intelligence and machine learning based on the customer requirements within architectural or design guidelines. The candidate also needs to be involved in the coding of features, bug fixing as well as delivering solutions to scripting and quality guidelines. The person is responsible for ensuring integration and submission of solutions into software configuration management system, performing regular technical coordination and timely reporting. Qualifications: The candidate must have a strong knowledge of Data Science, Artificial Intelligence, machine learning, deep learning, exploratory analysis, predictive modelling, prescriptive modelling and Cloud systems with a B.E/B. Tech/ CA/ M. Tech in science background. The candidate should have practical experience in data visualisation tools, statistical computer languages, data architecture and machine learning techniques. It is essential to have a good knowledge of querying SQL, no SQL databases, data mining techniques, AWS services, computing tools as well as end-to-end Data Science pipelines into production.Data Scientist at Mastercard
Mastercard is known as the global technology company in the financial industry, especially payments. It has a mission to connect an inclusive digital economy to benefit everyone by making transactions safe and accessible. It works in more than 210 countries with secure data and networks, innovations and solutions. Qualifications: The candidate should have practical experience in data management, support decks, SQL Server, Microsoft BI Stack, Python, campaign analytics, SSIS, SSAS, SSRS and data visualisation tools. It is essential to have a Bachelor’s or Master’s degree in Computer Science, IT, Engineering, Mathematics, Statistics or any relevant field.
I will show you formatting tricks on how to put think borders on matrix visualizations in Power BI. Evidently, this is a little bit different tutorial compared to what I normally post but it can significantly change the visual of your reports. You can watch the full video of this tutorial at the bottom of this blog.
Let’s take a look at the data.
It is a very simple data model consisting of a calendar table, and we got the calendar for this year.
To illustrate, we have the Weekday column that I’ve formatted to return back to the day of the week, we have the Month column that I’ve formatted to return back to the month, and the Month Sort column which is set to be sorted by Month Sort column.
I created three measures, Metric 1, Metric 2, and Metric 3, and then I just formatted this visualization as a matrix. Afterward, I set the row to be the Weekday, the columns to be the Month, and the three measures as values.
With this in mind, it shows a very simple kind of matrix that is fairly common.
Visually speaking, the issue with this matrix is that it’s a little bit difficult to tell which measure belongs to which month. It’s not that obvious.
If we want to make it a little more obvious, Power BI does have the concept of gridlines.
As a matter of fact, I just turned on the Vertical gridlines, and it’s noticeable that they are very faint by default but at least it gives some distinct way to group these together visually.
To make it more visible, go to Visual then select Grid settings. We can change those gridlines to a darker color.
For this example, let’s set the color to black, and now it’s a little more obvious compared to before.
However, these lines have the same sizes as others and it’s still not really what I would want.
If I was in Excel, I would probably grab these cells and then I would put a thick border around them to make it really obvious that these things belong together.
I tried to find something like this but I can’t set this to the border thickness. We can’t do this per individual column or under the Grid settings. I’ve looked around and I haven’t been able to find anything that allows me to really do what I want to do.
But nothing to worry about, there is a formatting trick that I will show you that allows us to achieve our desired result.
To create the thick borders, let’s select this visual and copy it to a new page.
Next is to select the center alignment for the headers.
To achieve this effect, we start by creating a new measure. It does not matter what you call this measure and what character you use. You can use a space, a period, or whichever you prefer.
For this example, let’s name it Separator 1 and I like to use the pipe character for this.
After creating the measure, let’s drag this to the Values section and add it to the bottom.
Now we can see we have the Separator 1 column with these pipe characters in it.
We can also rename this to a pipe character and now we have this extra column.
The next step is to go to Specific column and select the pipe character. Then, go to Values to change the background color to black.
Finally, we now have a much better-looking visual and I can tell in an instant that these things are grouped together.
Putting simple formatting like thick borders can make your report visually appealing. It looks clean and it’s a natural grouping of values because we know exactly which values belong together in an instant. We also have more visualization options in Power BI that you can explore.
This is a pretty good tutorial that’s why I like to share it with everyone. I hope this will be beneficial for you too. Please don’t forget to subscribe to Enterprise DNA TV Channel for more valuable content like this.
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