Last Updated on November 20, 2022
Business intelligence (BI) refers to an infrastructure made up of a variety of tools and procedures that work together to give organisations a comprehensive, usable view of their pertinent data. Businesses should be able to see all of their data clearly with the help of BI in order to make wise decisions.
People can import, clean, and analyse data from databases, emails, videos, survey results, and other sources using a business intelligence platform. These data analyses offer real-time business intelligence on mobile, desktop, and other devices so that decision-makers may act on information to advance their firm. Users of BI platforms can develop scorecards and compare them to key performance indicators (KPIs) while also customising dashboards and producing spectacular data visuals.
“Better decisions”, though, might be putting it a bit mildly. The goal of BI platforms is 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 kept in a data warehouse created for the entire company or in smaller data marts, which frequently have connections to an enterprise data warehouse and hold subsets of business information for specific departments and business 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 can support both strategic and tactical decision-making processes by using data from source systems that can include both historical information and real-time data that is collected as it is generated.
To guarantee that BI teams and business users are studying accurate and consistent data, raw data from many source systems typically needs to be merged, aggregated, and cleansed using data integration and data quality management technologies before it is utilised in BI applications.
Initially, BI and IT specialists were the main consumers of BI technologies, running queries and creating dashboards and reports for business users. But as self-service BI and data discovery tools have developed, business analysts, executives, and employees are increasingly using BI platforms themselves. Business users can independently query BI data, produce data visualisations, and construct dashboards in self-service business intelligence systems.
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, while sophisticated analytics projects are carried out by distinct teams of data scientists, statisticians, predictive modellers, and other experienced analytics specialists.
Analytics vs Business Intelligence – What’s the Difference?
An analytics platform is, strictly speaking, a piece of software that gathers analytical data and displays it in visual representations so that human users may understand the data, contrast reports, and identify trends.
The kind of analytics platforms we’re going to be looking at in this article are web analytics tools, which gather various quantities of information on website traffic and performance, such as page views and conversion rates, as well as user location and device information.
How a business intelligence platform differs from an analytics tool like Google Analytics could be less clear. And the straightforward response is that, in contrast to analytics tools, which gather and present data for study, business intelligence tools make predictions about trends and outcomes based on a wider variety of business data.
Examples of Business Intelligence Platforms
Microsoft Power BI
Our first business intelligence platform in this post is Microsoft Power BI, and there are no points 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, in contrast to Google Marketing Platform, which concentrates on website and marketing performance.
In order to create reports with all of your data, Power BI gathers data from all of your data monitoring tools (analytics, sales reports, accounting software, etc.).
You are not just reliant on your own data to make business decisions because the Microsoft platform also incorporates data from outside sources. To determine whether variations in revenue, whether favourable or negative, are indicative of market movements, you could, for instance, compare financial performance to market trends.
Microsoft’s cutting-edge AI technology recognises patterns and trends that your own teams may miss—or at the very least, recognise them earlier. As a result, you can respond to insights more quickly, and Power BI’s predictive analytics can foresee performance problems before they occur.
Power BI can give you the information you need for organisations that are struggling with big data overload and dissatisfied with the existing business decisions they’re making based on all of their data sources.
Qlik
A variety of data analysis tools are available from Qlik, a developer of data intelligence software. The company’s two main products are QlikSense, an integrated analytics and business intelligence platform, and QlikView, a standalone analytics tool.
The company’s cutting-edge standalone analytics tool, QlikView, is based on its ground-breaking Associative Engine technology, which provides you with total control over your data.
The more recent platform, Qlik Sense, builds on Qlik’s many years of analytics industry experience by integrating cutting-edge AI technologies to provide an augmented intelligence solution. The platform has the ability to 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
One of the first businesses to offer a self-service business intelligence and analytics platform was Tableau. There is a significant learning curve to overcome if you want to make the most of this platform because it continues to be one of the most well-known and comprehensive brands in the industry.
Tableau gathers data from your marketing and analytics platforms (such as Salesforce, Google Analytics, etc.) and compiles it into a single database. The built-in data preparation tools on the platform can then be used to clean and enrich your data to raise the calibre 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 data they require.
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. As well as 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 own 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 Edition 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 that is tailored to your needs and may be hosted either on-premises or on your own servers in the cloud.
Sisense
Sisense, formerly known as Periscope Data, is an API-first cloud platform that enables you to create your own analytics apps. This is a toolbox for developing analytics software, whether it’s for your own company, clients, or to offer as a product, rather than an analytics platform that is ready to use.
Sisense offers the tools you need to build custom apps for your customers or an analytics system to suit your own needs. The platform builder reduces the time needed to develop self-service analytics, white-labeled business intelligent apps, interactive dashboards, and other cloud and on-premise solutions.
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 of its own, they will help you narrow down your list of possibilities.
Please feel free to propose any platforms that you think are missing from this article and explain why you believe they should be added.
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