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Big Data and Data Visualization

Big Data and Data Visualization Explained

Data Visualization: Turning Complex Data into Clear Insights Data visualization is the practice of converting information into visual formats, such as maps or graphs, to make data more accessible and understandable. The primary purpose of data visualization is to highlight patterns, trends, and outliers within large data sets, allowing users to quickly glean insights. The term is often used interchangeably with information graphics, information visualization, and statistical graphics. The Role of Data Visualization in Data Science Data visualization is a crucial step in the data science process. After data is collected, processed, and modeled, it must be visualized to draw meaningful conclusions. It’s also a key component of data presentation architecture, a discipline focused on efficiently identifying, manipulating, formatting, and delivering data. Importance Across Professions Data visualization is essential across various fields. Teachers use it to display student performance, computer scientists to explore AI advancements, and executives to communicate information to stakeholders. In big data projects, visualization tools are vital for quickly summarizing large datasets, helping businesses make informed decisions. In advanced analytics, visualization is equally important. Data scientists use it to monitor and ensure the accuracy of predictive models and machine learning algorithms. Visual representations of complex algorithms are often easier to interpret than numerical outputs. Historical Context of Data Visualization Data visualization has evolved significantly over the centuries, long before the advent of modern technology. Today, its importance is more pronounced, as it enables quick and effective communication of information in a universally understandable manner. Why Data Visualization Matters Data visualization provides a straightforward way to communicate information, regardless of the viewer’s expertise. This universality makes it easier for employees to make decisions based on visual insights. Visualization offers numerous benefits for businesses, including: Advantages of Data Visualization Key benefits include: Challenges and Disadvantages Despite its advantages, data visualization has some challenges: Data Visualization in the Era of Big Data With the rise of big data, visualization has become more critical. Companies leverage machine learning to analyze vast amounts of data, and visualization tools help present this data in a comprehensible way. Big data visualization often employs advanced techniques, such as heat maps and fever charts, beyond the standard pie charts and graphs. However, challenges remain, including: Examples of Data Visualization Techniques Early computer-based data visualizations often relied on Microsoft Excel to create tables, bar charts, or pie charts. Today, more advanced techniques include: Common Use Cases for Data Visualization Data visualization is widely used across various industries, including: The Science Behind Data Visualization The effectiveness of data visualization is rooted in how humans process information. Daniel Kahneman and Amos Tversky’s research identified two methods of information processing: Visualization Tools and Vendors Data visualization tools are widely used for business intelligence reporting. These tools generate interactive dashboards that track performance across key metrics. Users can manipulate these visualizations to explore data in greater depth, and indicators alert them to data updates or important events. Businesses might use visualization of data software to monitor marketing campaigns or track KPIs. As tools evolve, they increasingly serve as front ends for sophisticated big data environments, assisting data engineers and scientists in exploratory analysis. Popular data visualization tools include Domo, Klipfolio, Looker, Microsoft Power BI, Qlik Sense, Tableau, and Zoho Analytics. While Microsoft Excel remains widely used, newer tools offer more advanced capabilities. Data visualization is a vital subset of the broader field of data analytics, offering powerful tools for understanding and leveraging business data across all sectors. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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What is BI in Salesforce?

Salesforce BI helps to create fast, digestible reports to help you make informed decisions at the right time. Salesforce Einstein is a leading business intelligence software solution that will help streamline your operations. Read on in this insight to learn how Salesforce BI capabilities including Tableau rank in the Gartner Magic Quadrant. Make the right decision every time using analytics that go beyond business intelligence software. See why Gartner named Salesforce (Tableau) a Leader in the Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for the 11th consecutive year. Data and analytics leaders must use analytics and BI platforms to support the needs of IT, analysts, consumers and data scientists. While integration with cloud ecosystems and business applications is a key selection requirement, buyers also need platforms to support openness and interoperability. Analytics and business intelligence (ABI) platforms enable less technical users, including business people, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). For several years, the Magic Quadrant for Analytic and Business Intelligence Platforms has emphasized visual self-service for end users augmented by AI to deliver automated insights. While this remains a significant use case, the ABI platform market will increasingly need to focus on the needs of the analytic content consumer and business decision makers. To achieve this, automated insights must be relevant in context of a user’s goals, actions and workflow. Many platforms are adding capabilities for users to easily compose low-code or no-code automation workflows and applications. This blend of capabilities is helping to expand the vision for analytics beyond simply delivering datasets and presenting dashboards. Today’s ABI platforms can deliver enriched contextualized insights, refocus attention on decision-making processes and ultimately take actions that will deliver business value. In addition to the increasing consumer design focus trend, we see other key market trends, including the need for improved governance of analytic content creation and dissemination, and the demand for a headless, open architecture. For example, a headless ABI platform would decouple the metrics store from the front-end presentation layer, enabling more interoperability with competitive products. ABI platform functionality includes the following 12 critical capabilities, which have been updated to reflect areas of market change, differentiation and customer demand: Gartner added three new critical capabilities as part of our metrics store evaluation criteria this year:  ABI platforms have always been about measurement. For decades, the slicing and dicing of measures by their dimensional attributes was synonymous with the act of performing business intelligence. However, over the last decade, the focus on metrics and measurement was overshadowed by data visualization. As data visualization became the most conspicuous capability, some business executives began to conflate ABI platforms with data visualization — as if ABI platforms are glorified chart wizards. This misconception minimizes much of the work performed and the business value delivered by ABI platforms. Establishing metrics stores as a critical capability to execute makes it clear that defining and communicating performance measures throughout an organization is one of the key purposes of an ABI platform. Analytics collaboration is a combination of many features (such as Slack/Teams integration, action frameworks) that collectively improve an organization’s ability to make decisions with consensus. Data science integration reflects the increasing likelihood that a business analyst may want to use data science to test certain hypotheses, and that data scientists will need to leverage features such as data prep and data visualization. In addition, Gartner is changing “catalogs” to “analytic catalogs” to emphasize a set of requirements that are not being met by ABI platform vendors today. Most large enterprises have thousands of reports built across multiple ABI platforms, but consumers in these organizations have no easy way to access these reports. The name change to analytic catalogs reflects the need for ABI platform vendors to deliver analytic content with the consumer in mind. Three critical capabilities were removed from our evaluation criteria: security, natural language generation (NLG; rolled into data storytelling) and cloud analytics (which will no longer be considered a platform capability, but instead a go-to-market strategy covered in the Magic Quadrant). And one of the security sub-criteria, about the granularity of authorization (e.g., row-based security) has been moved to the enterprise reporting capability. Salesforce (Tableau) Tableau, a Salesforce company, is a Leader in this Magic Quadrant. Its products are mainly focused on visual-based exploration that enables business users to access, prepare, analyze and present findings in their data. CRM Analytics, formerly Tableau CRM, provides augmented analytics capabilities for analysts and citizen data scientists. Tableau has global operations and serves clients of all sizes. In 2022, Tableau reinforced its augmented consumer vision to provide contextualized insights with deeper integration with Salesforce Data Cloud. IT also improved decision intelligence by bringing domain-aware insights into action with Revenue Intelligence and other Salesforce-native apps. The extensible design and x-platform integrations (Salesforce Flow, MuleSoft, UiPath and Looker) further enable composable analytics to bring insights into workflow with agility. Strengths Cautions Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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