Magic Quadrant for Analytics and Business Intelligence Platforms

This document provides an overview of the leaders and visionaries in the analytics and business intelligence platforms market. It discusses Microsoft Power BI, Qlik, Tableau, and TIBCO software, highlighting their strengths and cautions. The document also includes insights from a guest speaker on data analytics and visualization.

Magic quadrant for analytics and
business intelligence platforms
Part A
Introduction
The magic quadrant is divided into four quadrants as used in business intelligence platforms. The
quadrants include; leaders quadrant, challengers quadrant, niche players quadrant and visionaries
quadrant (Howson et al, 2018). The quadrant of interest in this synopsis is leaders’ quadrant and
visionaries’ quadrant. Leaders’ quadrant consists Microsoft, Qlik and Tableau (Sallam et al, 2014).
Some of the software included in the visionaries’ software are Sisense, ThoughtSpot, Salesforce,
SAS, SAP, IBM and TIBCO software (Sun et al, 2015). Among the aforementioned software in
visionaries’ software, TIBCO software will be discussed alongside leaders’ quadrant software.
Leaders’ quadrant software
i. Microsoft
Microsoft power BI offers variety of activities in regards to data. Microsoft power BI gives
preparation of data, offers interactive dashboards, discovery of data and augmented analytics
through single product (Naidoo, 2019). This software is available in SaaS option that runs in the
Azure Cloud. Power BI report server enables users to share reports excluding dashboards but it
lacks the capabilities of the machine learning structured in Power BI SaaS. Power BI Desktop
can be used independently as personal tool for analysis needed when the power users are
handling complex data including the on-premise data sources.
The Microsoft Power BI Pro’s is one of the lowest priced data analytics tool in the market today.
Though in the 2017, Microsoft launched Power BI Premium whose price was scaled in the

market at $4,995 per month (Salvador, 2018). The price was not constant and depended on
scalability and usage requirements. High level of consumer interest and adoption made it appear
in the leaders’ quadrant. Among other things is its ability for clear and visionary products.
Strengths and cautions of Microsoft
Microsoft Power BI is owned by most of the companies because of its current low price. Before
the deployment of Microsoft, the vendor will already have changed modern analytics and the BI
does its shortlisting from greenfield evaluation. Low prices from Microsoft have helped in
bringing down the pricing pressure arising on analytics and BI market with its subscription price.
Also, it is easy to use and visual appealing since it score high in its ease of use winning the hearts
of most of the customers. Other strengths of Microsoft are product vision and customer
experience.
Microsoft Power BI mostly concentrates on the mode 2 analytics while its component on-
premises SQL server reporting services solves the needs of mode 1. It has low breadth of use in
performing complex queries data, preparation and applying predictive models among others. Can
be corrected by integrating all the Microsoft features to eliminate two product deployment.
ii. Qlik
It gains control in discovery of data and agile analytics and BI through its lead product Qlik
Sense. It enables developers to create the customized applications and for the attached use case.
With Qlik, customers are able to build robust, interactive and visual applications (Palanisamy and
Thirunavukarasu, 2017). Engines are opted for by some customers for use as data mart in the
traditional data warehousing. It has feature that support mode 1 BI giving report distribution and
scheduling. The progress of Qlik in augmented analytics and improved market strategy together

with its ease of use has made it listed in the leader quadrant but it has the poorest market
execution compared to other software in the leaders’ quadrant.
Strengths and cautions of Qlik
One of the strengths of Qlik is its robust application in scalable products because it support
complex data models and calculations as well as multiple data sources. Developers are not
limited to menu driven options since the vendors invest in point and click in the data loading
process (Kovačević and Mekterović, 2017). Qlik have struggled to bring about differentiated
marketing thus improving literacy of data as a way of overall analytics in BI program in those
days. Finally on the strengths of Qlik is the product vision where partners were allowed to
develop content for further extension of the platform or even monetize the prebuilt industry
vertical applications and partner network that allow long term relationship with the customers
and understanding their specific requirements.
Caution for use of Qlik was its implementation approach other than self-service data preparation
and analysis. The recent changes have been made to allow for self-service analytics but it has
been partially addressed. Furthermore, Qlik has slow momentum of share growth as well as
migration challenges. It can be improved by increasing its speed of data sharing and
strengthening its analytic workflow to match other competitors in the market.
iii. Tableau
It offers spontaneous interactive visual-based exploration experience that enable the business
users to have access, prepare, analyze and present findings in the data without the need of
technical skills or coding (Santos et al, 2017). People can easily get insight of their data through
using tableau. It has some of the desirable features in modern analytics and BI platforms. Its

efforts in creating product awareness in the world, roadmap such as NLP and the preparation of
augmented and discovery of data etc. has made it appear in the list of leaders’ quadrant.
Strengths and cautions of Tableau
It has standard interactive and visual exploration due to its drag and drop functions such as
forecasting, clustering, assisted formula editing and automated geocoding making it easy for
users to easily explore and manipulate their data (Acharya and Chellappan, 2017). Also, tableau is
focused on customer experience and success contributing to its expansion deployments and
standardization rates.
Its cautions are market mainstreaming and pricing and packaging due to pricing pressure from
low-cost license options that affect the competitive environment. It as well lack complex data
model support which need to be manipulated outside first. It can be improved by creating inbuilt
platform that can handle complex data and data of any format.
Visionaries’ quadrant
i. TIBCO software
It was important in changing the market from traditional to modern analytics and BI reporting. It
offers data preparation in a single design tool resulting to its flexibility. TIBCO is used in wide
range including data science, machine learning etc. this software is placed in Visionaries
quadrant because of clear differentiation for advanced exploration, global presence and
augmented analytics product vision (Birdeau et al, 2012).
Strength and cautions of TIBCO software
The software is well suited for advanced data exploration since it allows the customers to select
the platform for conducting complex analysis. It also allows for the integrated data science and

machine learning to its engine for R analytic language. It is based for customers with advanced
skills such as the data scientists and engineers. This makes it commonly used in engineering
departments and manufacturing domain.
Sales experience and pricing pressure continues to affect the market thus affecting the expansion
and Spotfire’s renewal. The software is less spontaneous in content authoring for casual business
users as compared to other competitive products in the market leading to low customer
experience. This can be improved by increasing the number of content authoring for clarity of its
use.
Part B
Guest speaker was Becky Moore a data visualization scientist at PHP explained that she first
worked in the finance, education and government sectors and in government sectors, where the
key stakeholders are politicians but the system is currently changing to evidence based projects.
She also explained that she worked in the social health sector and emergency services where the
government is asking for private funding from businesses in order to put initiatives in place that
can help improve social outcomes for teenage parents. On data analytics, she mentioned that
some of the aspects are difficult to measure and therefore projects are developed which involve
sitting and understanding what the challenges are and what the projects need to overcome and
measuring success in that respect. Proxies are formulated to help in estimating and measuring
better outcomes. The biggest problem in the project is defining the problem and defining
solutions to the problems. She further explained that people want to see the data in different lines
therefore people apply different abilities of visualization to filter data and draw figures that they
want for them to do the analytics. The tools used were Microsoft power BI and Excel. Also,
SQL, R, Python and Java script for complex visualization were used.

Heavy statistical work is required before getting data which will determine simplicity or
complexity of the data. Challenges faced by data analysts is lack of stakeholder and project
management knowledge thus need assistance. Knowledge on project management would enable
analysts get the outcomes within shortest time possible. Analysts are trying to overcome the
challenges by filling identified loopholes through procedure implementation in projects. In the
government projects, the analysts do not make the final decisions but only offer advice and
implications of making certain decisions.
The problem statement need to be revisited and amended in case other problems arise but the
analyst do not have the data to solve the problem. Data is difficult to come by and in case there is
a particular problem that need to be solved, the analysts consult the stakeholders on the
measurement they would like to measure. The speaker primarily creates interactive dashboards
and constantly works with the stakeholders to know the users’ requirements and what they would
like to see first when they open the dashboard which may be assisted with guided videos. The
management require analytics done for the validation of the hypotheses to ensure that they are in
line with the subject matter in decision making.
Data analytics is used to increase the profits, safety of the workers and improve the efficiency in
reducing the maintenance cost and amount of time of machine operation. Visualization of data is
done at the end of analysis process to ensure that the information is communicated efficiently
and clearly. The objects used in visualization are bar charts, line charts and tables to make
communication understandable. Data analysts are general data handlers, data scientist specializes
in complex algorithms and statistical analysis and data visualization deals with presentation of
data. Companies are looking for the automation of data to make data mining and analytics easy.

In ethical consideration, the privacy of the participants is assured and highly maintained through
enhancing data security.

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