Magic Quadrant for Business Intelligence and Analytics Platforms

The BI and analytics platform market's multiyear shift from IT-led enterprise reporting to business-led self-service analytics has passed the tipping point. Most new buying is of modern, business-user-centric platforms forcing a new market perspective, significantly reordering the vendor landscape.

During the past several years, the balance of power for business intelligence (BI) and analytics platform buying decisions has gradually shifted from IT to the business as the long-standing BI requirement for centrally provisioned, highly governed and scalable system-of-record reporting has been counterbalanced by the need for analytical agility and business user autonomy (see "How to Modernize Your Business Intelligence and Analytics Platform for Agility, Without Chaos" ). The evolution and sophistication of the self-service data preparation and data discovery capabilities available in the market has shifted the focus of buyers in the BI and analytics platform market — toward easy-to-use tools that support a full range of analytic workflow capabilities and do not require significant involvement from IT to predefine data models upfront as a prerequisite to analysis. 
 
This significant shift has accelerated dramatically in recent years, and has finally reached a tipping point that requires a new perspective on the BI and analytics Magic Quadrant and the underlying BI platform definition — to better align with the rapidly evolving buyer and seller dynamics in this complex market. This Magic Quadrant focuses on products that meet the criteria of a modern BI and analytics platform (see "Technology Insight for Modern Business Intelligence and Analytics Platforms" ), which are driving the vast majority of net new purchases in the market today. Products that do not meet the modern criteria required for inclusion in the Magic Quadrant evaluation (because of the upfront requirements for IT to predefine data models, or because they are enterprise-reporting centric) will be covered in our new Market Guide for enterprise reporting-based platforms. 
 
This change in the focus of the BI and analytics Magic Quadrant should not be interpreted by organizations as a recommendation to immediately replace all existing reporting-based system-of-record BI technology with a modern platform featured in the current Magic Quadrant. In many organizations, the existing enterprise reporting systems are integral to day-to-day business processes, and these processes would be exposed to unnecessary risk if disrupted by an attempt to re-create them in a modern platform. However, the problem that most organizations have encountered with lackluster BI adoption relative to the level of investment during the past 20 years stems from the fact that virtually all BI-related work in that time frame has, until recently, been treated as system of record from inception to development to delivery. This traditional approach to BI addresses Mode 1 of the bimodal delivery model, because it supports stability and accuracy, but does not address the need for speed and agility enabled through exploration and rapid prototyping that is essential to Mode 2 (see "How to Achieve Enterprise Agility With a Bimodal Capability" ). 
 
The shift in the BI and analytics market and the corresponding opportunity that it has created for new and innovative approaches to BI has drawn considerable attention from a diverse range of vendors. The list spans from large technology players — both those new to the space as well as longtime players trying to reinvent themselves to regain relevance — to startups backed by enormous amounts of venture capital from private equity firms. A crowded market with many new entrants, rapid evolution and constant innovation creates a difficult environment for vendors to differentiate their offerings from the competition. However, these market conditions also create an opportunity for buyers to be at the leading edge of new technology innovations in BI and analytics and to invest in new products that are better suited for Mode 2 of a bimodal delivery model than their predecessors. 
 
Gartner's position is that organizations should initiate new BI and analytics projects using a modern platform that supports a Mode 2 delivery model, in order to take advantage of market innovation and to foster collaboration between IT and the business through an agile and iterative approach to solution development. The vendors featured in this year's Magic Quadrant (and those highlighted in the Appendix) present modern approaches to promoting production-ready content from Mode 2 to Mode 1, offering far greater agility than traditional top-down, IT-led initiatives — and resulting in governed analytic content that is more widely adopted by business users that are active participants in the development process. As the ability to promote user-generated content to enterprise-ready governed content improves, so it is likely that, over time, many organizations will eventually reduce the size of their enterprise system-of-record reporting platforms in favor of those that offer greater agility and deeper analytical insight. 
 
As described above, this market has experienced a significant multiyear shift that has reached an inflection point — requiring a change in how Gartner defines the 14 capabilities that comprise a modern BI and analytics platform across the four categories — infrastructure, data management, analysis and content creation and share findings — in support of five BI and analytics use cases (see Note 1 for details of how the capability definitions in this year's Magic Quadrant have been modified from last year to reflect our current view of the critical capabilities for BI and analytics platforms). In this increasingly competitive and crowded market, the updated evaluation criteria for this year establish a higher bar against which vendors are measured both for execution and vision. As a result of this change and the resulting effect on the shape and composition of the BI and analytics Magic Quadrant, historical comparison with past years (to assess relative vendor movement) is irrelevant and therefore strongly discouraged. 

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16 analytic disciplines compared to data science

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on?

Here the author of the post compares several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. He also provided typical job titles, types of analyses, and industries traditionally attached to each discipline.

Comparison with other analytic disciplines

Machine learning
Data mining
Predictive modeling
Statistics
Industrial statistics
Mathematical optimization.
Actuarial sciences
HPC
Operations research
Six sigma
Quant
Artificial intelligence.
Computer science
Econometrics
Data engineering
Business intelligence
Data analysis
Business analytics

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Why and How this Indian Realty Portal Exploits Data Science

While banks and ecommerce companies have tried their hands at data science and are benefitting from their initiatives around it, real estate appears to be the next big vertical that intends to leverage data science.

Housing.com, the Mumbai-based startup, is one among the top Indian online businesses that bet on data science and machine learning algorithms as a core priority. The realty portal, which raised $190 mn from Japan’s SoftBank, has come up with many tools such as Traffic Flux, Heat Maps, Listing Decay, and more in their efforts to present information to users in a visually appealing way that is more interactive than traditional plain listings.

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7 innovative Indian tech startups to watch out for

From addressing data center energy issues, empowering kirana stores to transforming Indian agriculture using the cloud, Indian tech startups are amongst a lot of action.

Can an Indian startup provide answers to global data center energy woes?

How an Indian startup is empowering kirana stores by leveraging cloud, analytics and mobile apps

Can a startup transform Indian agriculture by using the cloud?

Cloud helps Classle deliver education to the rest of the pyramid

NanoBi Analytics: A startup building the first analytics app store in India

How a startup is seeking to disrupt the recruitment industry by using the cloud

How an Indian startup is leveraging Big Data analytics to drive genetics research

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Interesting Articles -- 13-05-2013

50 Disruptive Companies of 2013

10-breakthrough-technologies of 2013

Logging Life with a Lapel Camera

The creator of the Wolfram Alpha search engine explains why he thinks your life should be measured, analyzed, and improved

The Dementia Plague: the growing dementia problem could bankrupt the healthcare system

Six Types Of Analyses Every Data Scientist Should Know

Jeffrey Leek, Assistant Professor of Biostatistics at John Hopkins Bloomberg School of Public Health, has identified six(6) archetypical analyses. As presented, they range from the least to most complex, in terms of knowledge, costs, and time. In summary,

Descriptive
Exploratory
Inferential
Predictive
Causal
Mechanistic

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10 Platforms for Obtaining Customer Feedback

To keep an e-commerce store running at peak-performance, merchants need to pay attention not only to analytics, but also to customer feedback. While analytics are a good starting point for figuring out which site elements need to be revamped or tested, customer feedback gets straight to the point by telling merchants exactly what a site visitor liked or disliked about their shopping experience. This type of insight can be used to quickly implement adjustments to a Web store in order to optimize the user experience.

There are many ways that merchants can obtain feedback from their customers, from social media to surveys and forums, which is why Website Magazine has compiled a list of platforms that merchants can leverage to gain insights into their customers’ shopping experiences.

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10 roadblocks to implementing Big Data analytics

Big Data and business analytics are two of the most exciting areas in business and IT these days — but for most enterprises, they are still developmental. Although the opportunities are boundless, the road to an effective Big Data operation is fraught with challenges. Here are some of the obstacles companies are encountering — and some ways to get around them.

1: Budget
2: IT know-how
3: Business know-how
4: Data cleanup
5: The storage bulge
6: New data center workloads
7: Data retention
8: Vendor role clarification
9: Business and IT alignment
10: Developing new talent

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The 7 Social Media Tricks for Your Business

1. How Social Customer Support Brings Social Media beyond Marketing
2. Tap the Social Media Stream for Competitors' Secrets
3. How to Use Social Media to Create Business Value
4. Social CRM for the Enterprise: How Analytics can Move You to Greater Success
5. Enterprise Social Software: What Businesses Need to Do Next
6. Five Reasons Social CRM is the High Ground for Revenue Production
7. 13 Tips for How to Use Pinterest for Business

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Forty (information management technology) Vendors We’re Watching: 2012

For a third year we (http://www.information-management.com) bring you our list of up and coming vendors on our radar that are doing their part to shape the groundswell in information management technology in the 21st Century.

View the complete list