A Sneak Peek Into Business Intelligence and Analytics
With the enormous growth in availability of data coupled with the emergence of automation and advanced technologies, it may seem that there will be reduced need for decision-making and leadership. Contrary to the apparent situation, the opposite is true. Though the progressive trend in technology helps us answer complex questions, effective decision-making and strong leadership that can make intuitive sense of all the answers is in demand. All the data at disposal can help support decisions or give business users insights to make well-informed decisions. Here is where Business Intelligence and Data Analytics play a major role in today’s digital world.
Business Intelligence
The Data Management Review defines Business Intelligence as the "knowledge gained about a business through the use of various hardware/software technologies which enable organizations to turn data into information”.
Business Intelligence or “BI” is one of the hottest buzzwords today and rightly so. Simply put, it is the use of computing technologies to identify, discover and analyze business-related data. These range from costs, revenue, sales, merchandise, incomes, and many more aspects of a business. The core of BI is the concept of finding useful information out of data to make smart decisions.
According to CloudTweaks, in 2015 there were 2.5 quintillion bytes of data produced daily. It’s expected that by 2020, there will be 40 zettabytes of data in the world. For scale, in 2012, the entire Internet only contained ½ of one zettabyte in data. There are potentially a massive number of insights that can be mined from such amounts of data.
Data Analytics
Data Analytics is defined as the technical process of examining data and drawing conclusions about the information they contain by leveraging specialized systems and software technologies. It mainly focuses on online analytical processing (OLAP), reporting and advanced analytics.
Business Intelligence vs Data Analytics
The two terms are used interchangeably nowadays but purists highlight a difference between the two. BI has evolved in the recent past and now represents a group of technologies that help business users and management teams make decisions. On the other hand, data analytics focuses on a broad range of concepts and processes that include data processing, warehousing, information management, performance management and data governance.
Data Visualization : The Bridge
Data visualization in simple terms entails visual representation of data. It is the technology equivalent of visual communication. It mainly leverages statistical analysis, charts, graphs and information graphics to communicate logical quantitative insights. In many ways, it serves as the end product of BI and Data Analytics.
With big data driving all industries towards adopting data-driven decision making, data visualization has become an invaluable part of this journey. It is instrumental in presenting business value metrics and insights to the right people at the right time. With increasing demand, there has been a rise in products that offer business intelligence and data analytics solutions with strong data visualization capabilities.
The Market Players
The most popular name in today’s market when it comes to BI and Data Analytics is Tableau. With over 57,000 customer accounts, it is undoubtedly a leader in its space. It is known for its simplicity, ease-of-use and interactive interface but its most important features are the ability to handle massive, dynamic datasets and the out-of-the-box integrations with some of the most widely used technologies.
Tableau’s biggest competitor with around 40,000 customer accounts is Qlik. It is known for its rich feature-set and highly customizable implementation products- Qlikview and Qliksense. These products require more time than Tableau to provide comprehensive insights and this where Tableau has an edge. Both Tableau and Qlik offer strong business intelligence, analytics, and reporting features.
According to Gartner, Tableau and Qlik are amongst the best business intelligence tools on the market today. The Microsoft BI Suite stands out as a top performer when it comes to providing the most comprehensive and broad BI and analytics solution, but Tableau offers the best value when the focus is data visualization.
The biggest challenger to the leaders in this space in the past year has been MicroStrategy. They have invested heavily in incorporating natural language generation and artificial intelligence into their BI and Analytics solution.
Another mainstay player in this market has been Salesforce. The Salesforce BI solution is very nuanced and offers numerous in-demand capabilities and has been one of the front-runners for the past few years.
The below table gives a tabular comparison of the tools considered on a rating scale of 1 to 10 (10 being the best)
References
http://www.gigabitmagazine.com/big-data/knowing-limitations-big-data
https://www-07.ibm.com/sg/events/blueprint/pdf/day1/Introduction_to_Business_Intelligence.pdf
https://www.datapine.com/blog/business-intelligence-concepts-and-bi-basics/
http://searchdatamanagement.techtarget.com/definition/data-analytics
http://www.dataversity.net/brief-history-business-intelligence/
https://www.businesswire.com/news/home/20180228005456/en/MicroStrategy-Recognized-Sole-Challenger-Gartner%E2%80%99s-2018-Magic
Figure 1 : Gartner’s magic quadrant for business intelligence and analytics tools (2018)
A Comparative Analysis
Based on industry standards, the most important factors or criteria used to gauge the completeness of these tools include the following
Usability
It defines the ease with which the tool can be implemented and used.
It defines the ease with which the tool can be implemented and used.
Scalability
It defines the tools ability to adapt to the demands of computing required.
It defines the tools ability to adapt to the demands of computing required.
Visualization
It defines the variety of visualizations offered by the tool. These include 2D, 3D, n-D, Temporal, Geospatial and Hierarchical.
It defines the variety of visualizations offered by the tool. These include 2D, 3D, n-D, Temporal, Geospatial and Hierarchical.
Integrations
It defines the ability of the tool to integrate with the wide range of applications and technologies that are prevalent in the digital world.
Cost
It defines the costs involved in procurement, implementation, licensing and maintenance of the tool.
It defines the ability of the tool to integrate with the wide range of applications and technologies that are prevalent in the digital world.
Cost
It defines the costs involved in procurement, implementation, licensing and maintenance of the tool.
The below table gives a tabular comparison of the tools considered on a rating scale of 1 to 10 (10 being the best)
References
http://www.gigabitmagazine.com/big-data/knowing-limitations-big-data
https://www-07.ibm.com/sg/events/blueprint/pdf/day1/Introduction_to_Business_Intelligence.pdf
https://www.datapine.com/blog/business-intelligence-concepts-and-bi-basics/
http://searchdatamanagement.techtarget.com/definition/data-analytics
http://www.dataversity.net/brief-history-business-intelligence/
https://www.businesswire.com/news/home/20180228005456/en/MicroStrategy-Recognized-Sole-Challenger-Gartner%E2%80%99s-2018-Magic



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Exploratory data analysisis a process of analyzing a data set to look for patterns, relationships, and anomalies. There are a number of different tools and approaches you can use in order to perform this analysis, each with their strengths and weaknesses. For example, you could use pivot tables to look at the relationships between variables or charts to look at patterns in the data. The key, however, is to decide which approach is best for the data you are working with.
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