Business intelligence (BI) refers to a variety of tools and procedures that work together to give organizations a comprehensive, usable view of their pertinent data.
Businesses should be able to see all of their data clearly with the help of BI to make wise decisions.
A business intelligence platform allows people to import, clean, and analyze data from databases, emails, videos, survey results, and other sources.
These data analyses provide real-time business intelligence on mobile, desktop, and other devices.
Decision-makers use this information to advance their firm.
BI platform users can develop scorecards and compare them to key performance indicators (KPIs).
They also customize dashboards and create stunning data visuals.
“Better decisions,” though, might be putting it a bit mildly.
BI platforms aim to enable businesses to compete in today’s data-rich environment by utilising their data as a strategic advantage.
How the Business Intelligence Process Works
Beyond BI software, a business intelligence architecture consists of other components.
Business intelligence data is often stored in a company-wide data warehouse or in smaller data marts.
These data marts connect to an enterprise data warehouse and hold business information subsets for specific departments and units.
Data lakes built on Hadoop clusters or other big data platforms are also being utilised more and more as repositories or landing pads for BI and analytics data, particularly for log files, sensor data, text, and other kinds of unstructured or semistructured data.
BI solutions support strategic and tactical decision-making by using data from source systems.
These systems include both historical data and real-time data as it is generated.
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BI and IT specialists initially dominated BI technology use, running queries, creating dashboards, and reports for business users.
However, as self-service BI and data discovery tools have developed, business analysts, executives, and employees increasingly use BI platforms.
In self-service business intelligence systems, business users can independently query BI data, produce data visualisations, and construct dashboards.
Advanced analytics techniques like data mining, predictive analytics, text mining, statistical analysis, and big data analytics are frequently used in BI applications.
Predictive modelling, which permits what-if examination of many business scenarios, is a typical example.
The majority of the time, however, simple querying and analysis of business data are handled by BI teams.
At the same time, sophisticated analytics projects are carried out by distinct teams of data scientists, statisticians, predictive modellers, and other experienced analytics specialists.
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Analytics vs Business Intelligence – What’s the Difference?
An analytics platform gathers analytical data and displays it visually for users to understand, compare reports, and identify trends.
This section examines web analytics tools that collect data on website traffic and performance, such as page views and conversion rates.
It could be less clear how a business intelligence platform differs from an analytics tool like Google Analytics.
In contrast to analytics tools, which gather and present data for study, business intelligence tools predict trends and outcomes based on a wider variety of business data.
Read: AI-Powered Data Visualization: Transforming Raw Data into Insights
Examples of Business Intelligence Platforms
Microsoft Power BI
Our first business intelligence platform in this post is Microsoft Power BI, and no points are awarded for figuring out what the “BI” stands for.
Microsoft Power BI takes a more comprehensive approach to company performance, whatever that means for your organisation.
This is in contrast to Google Marketing Platform, which concentrates on website and marketing performance.
Power BI gathers data from all your tools and creates reports with all your data.
You can make business decisions using both your data and external data from the Microsoft platform.
Compare financial performance to market trends to determine if revenue changes indicate market movements.
Microsoft’s AI technology detects patterns and trends that your teams might miss or notice later.
You can respond faster to insights, and Power BI’s predictive analytics can foresee performance problems.
Power BI provides essential information to organizations overwhelmed by big data and dissatisfied with their business decisions.
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Qlik, a developer of data intelligence software, offers various data analysis tools.
The company’s two main products are QlikSense, an integrated analytics and business intelligence platform, and QlikView, a standalone analytics tool.
QlikView, the company’s standalone analytics tool, uses Associative Engine technology to give you full control over your data.
Qlik Sense builds on Qlik’s industry experience by integrating AI to offer an augmented intelligence solution.
The platform can send you alerts for new trends, make context-aware suggestions, and use machine learning to make data-driven forecasts.
You can ask the platform questions using common language and receive intelligent answers thanks to conversational analytics.
Tableau
Tableau was one of the first businesses to offer a self-service business intelligence and analytics platform.
However, if you want to make the most of this platform, you must overcome a significant learning curve.
It continues to be one of the most well-known and comprehensive brands in the industry.
Tableau collects data from your marketing and analytics platforms and compiles it into a single database.
Use the platform’s data preparation tools to clean and enrich your data, improving the quality of your analyses.
VizQL, a drag-and-drop builder for producing visualisations on the fly, is one of Tableau’s trademark features.
It’s a terrific feature for team members who struggle to conceptualise data in visual formats.
Tableau also provides fantastic team communication tools and a mechanism for finding content that makes it simple for everyone to access the required data.
IBM Cognos Analytics
The upgrade to Cognos Business Intelligence is IBM Cognos Analytics, which adds new cloud functionality, a web-based user interface, cognitive guidance, and data visualisation tools.
This platform is AI-powered, and Cognos Analytics is a true example of a platform from a true leader in data intelligence.
In addition to external data from third-party sources like market reports and government statistics, you can feed in data from third-party analytics tools like Google Analytics and CRM platforms like ActiveCampaign.
You can design your dashboards and visualisations, employ conversational search, use AI to find hidden trends, work with team members, and exchange data/reports.
IMB provides Cognos Analytics in both a Cloud and an Enterprise Edition.
Smaller organisations can choose from Standard, Plus, or Premium plans, with rates starting at $15 per user per month under the Cloud Edition.
The Enterprise Edition offers a customised solution tailored to your needs. It may be hosted either on-premises or on your own servers in the cloud.
Sisense
Sisense, formerly Periscope Data, is an API-first cloud platform enabling you to create your own analytics apps.
This is a toolbox for developing analytics software, whether it’s for your own company or clients or to offer as a product rather than an analytics platform that is ready to use.
Sisense offers the tools to build custom apps for your customers or an analytics system to suit your needs.
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Read: 8 Beginner-Friendly Data Visualization Tools
Conclusion
The platforms we’ve discussed in this post will assist you if you want to advance your analytics skills.
These are some of the top analytics and business intelligence tools available right now, and while each tool has merits and flaws, they will help you narrow down your list of possibilities.
Please feel free to propose any platforms you think are missing from this article and explain why you believe they should be added.
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