Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.
Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a complete picture which, in effect, creates an “intelligence” that cannot be derived from any singular set of data. Amongst myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts.
BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as “BI/DW”or as “BIDW”. A data warehouse contains a copy of analytical data that facilitate decision support.
According to Forrester Research, business intelligence is “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.”Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.). Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack
Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software.
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.
Importance of data visualization
Data visualization has become the de facto standard for modern business intelligence (BI). The success of the leading vendors in the BI space — heavily emphasize visualization. Mostly, all BI software has strong data visualization functionality.
Data visualization tools have been important in democratizing data and analytics and making data-driven insights available to workers throughout an organization. They are typically easier to operate than traditional statistical analysis software or earlier versions of BI software. This has led to a rise in lines of business implementing data visualization tools on their own, without support from IT.
Our Tool helps people transform data into actionable insights. Explore with limitless visual analytics. Build dashboards and perform ad hoc analyses in just a few clicks. Share your work with anyone and make an impact on your business. From global enterprises to early-stage startups and small businesses, people everywhere use DVT (our tool –name yet to be decided) to see and understand their data.
Data is to be visualized and analyzed rather than to read
Data visualization is the graphical representation of information and data. With visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
In the world of big data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions. Our culture is visual, including everything from art and advertisements to TV and movies, and our eyes are drawn to colors and patterns. Our interaction with data should reflect this reality
Make your data better
This visualization can show listing data on a map. Because all of the listings are the same size and color, even with filters, it’s hard to differentiate the value between the listings at a glance. we will make it better for you to analyze and interpret
Explore your data with a purpose
Data visualization is a form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers.
If we can see something, we internalize it quickly. If you’ve ever stared at a massive spreadsheet of data and couldn’t see a trend, you know how much more effective a visualization can be.
There is dependency of stats expert to build the tool, As it should cover all statistical model of representing and analyzing the data. It should be able to suggest the user the best representation of the data and way to analyze it. Simultaneously, there should be an option of switch off the intelligent option as user could be Stats Phd – so allow him to pick and choose.
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Big Data, Any Data
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Combine multiple views of data to get richer insight. Best practices of data visualization are baked right in.
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Tech Specs – can be looked upon (have to make it platform agnostic, open source would be best)
Below is only for example/ suggestion
- Windows 7 or later
- OSX 10.10 or later
- VMWare | Citrix | Hyper-V | Parallels
- Tableau’s products operate in virtualized environments when they are configured with the proper underlying operating system and hardware.
- Our products are Unicode-enabled and compatible with data stored in any language. The interface and documentation are in English, French, German, Spanish, Brazilian Portuguese, Japanese, Korean, and Simplified Chinese
Examples of data visualization
Data visualization tools can be used in a variety of ways. The most common use today is as a BI reporting tool. Users can set up visualization tools to generate automatic dashboards that track company performance across key performance indicators and visually interpret the results.
Many business departments implement data visualization software to track their own initiatives. For example, a marketing team might implement the software to monitor the performance of an email campaign, tracking metrics like open rate, click-through rate and conversion rate.
As data visualization vendors extend the functionality of these tools, they are increasingly being used as front ends for more sophisticated big data environments. In this setting, data visualization software helps data engineers and scientists keep track of data sources and do basic exploratory analysis of data sets prior to or after more detailed advanced analyses.