Business Intelligence and Analystics Platforms - Demystified

E

ver walked into a Walmart and wondered why Eggs are placed close to Milk or bread is placed near cereal or for that matter how do they decide what items should be placed on their shelves and where to place them. It is obvious that these decisions are not based merely on gut feeling. They are getting help - these decision are driven by Business Intelligence.

Simply put, it is the ability to make intelligent decisions based on facts. In the Walmart example above, every time you go to a checkout counter, the attendant scans the items, generates a bill and you pay for it. Now imagine this happening 10,000 times per second. Every such transaction is recorded and in essence is a fact. Walmart feeds this data into a magic software and out comes the results. The results are in the form, if a customer buys A and B, then he/she buys C. What then Walmart does is it conveniently places the yogurt right next to the milk and eggs. This is a case of market basket analysis which enables Walmart make intelligent product placement decisions.

So what are the benefits? To put into perspective, in 2009, Amazon’s revenue was $24.5 Billion. A staggering $5 billion came from “recommended” products which was nearly 20% of their total revenue! All of this is made possible using Business Intelligence and Analytics Platforms.

Before we delve deeper into BI platforms, here is the textbook definition of BI:

Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability

Below is a pictorial representation of a BI implementation example:

Business Intelligence and Analytics Platforms

Parameters for comparison

• Intuitive
• This is a measure of how easy is it for a new user to start using the application and generate meaningful results.
• This is  also a measure of the learning curve. Steeper the curve, lower the points awarded
• Cost to implement
• This is an important factor since it could often be the deciding factor in the selection of the BI platform especially for small companies
• Another aspect that this parameter implicitly accounts for is the value for money. Higher points if more features are provided at a lower cost.
• Extra points if there are different versions available to cater to different market segments.
• Dashboards
• This is a platform's ability to enable its users to create rich, visual and interactive dashboard and data visualizations within a short time with minimal technical know-how.
• Extra points for the ability to have dashboards in the cloud and delivery through mobile devices.
• Support
• This is the measure for customer experience during the sales process, after sales support and implementation support.
• This also implicitly measures how often upgrades and updates are issued and how easy or difficult is it to apply the upgrades/updates and how much time it takes (i.e. any downtime that the client might face).
• Security
• In light of the recent security breach and huge financial implications  that it brings with it, Security is as important if not more compared to other parameters.
• Severe point penalties for any vulnerabilities that could compromise data security and integrity.

Comparative Analysis

Below are some of the BI & Analytics offerings from different vendors. For the purpose of this discussion, I have considered offering from the vendors that belong to the Leader’s quadrant in the Gartner’s Magic Quadrant:

Tableau

Strengths - Tableau's biggest strength is that it offers extremely intuitive, interactive data exploration experience. Many competitors have tried to follow in their footsteps. They have carved out a huge market share with their ability to meet dominant and mainstream buying requirements for ease of use, breadth of use and enabling business users to perform more complex types of analysis without extensive skills or IT assistance — and competitive differentiation continue to increase its momentum, even though it operates in an increasingly crowded market in which most other vendors view it as a target.

Areas of improvement - In-spite of its strengths, clients do not use Tableau as their primary BI platform.  With traditional vendors investing heavily in data discovery capabilities, it could threaten Tableau's dominance. Another aspect is poor after sales experience which could keep potential buyers away. Also its enterprise features such as metadata management and BI infrastructure are below average.

Qlik - Qlikview

Strengths - Market leader in data discovery. Selfcontained

BI platform, based on an inmemory associative search engine. But their biggest, most bold move is to address the need for a BI platform standard that can fulfill both business users' requirements for ease of use and IT's requirements for enterprise features relating to re-usability, data governance and control and scalability.

Areas of improvement - Not enterprise ready. Below average meta-data management, BI infrastructure and embeddable analytics. Concerns about its capability for managing security and administering large numbers of named users. 

Microstrategy

Strengths - The key strength of Microstrategy is its ability to store the reports and dashboards on cloud. It also supports BigData and Hadoop. It is a very user friendly tool for non-technical users who can build reports with just drag and drop functionality. Another distinguishing feature is its ability to provide mobile experience and also access the data in offline mode.

Areas of improvement - One of the areas it needs improvement is it allows very rigid data structures so data processing needs to be done before using it for analysis. Also there is no feature of predictive analysis supported by this tool which is disappointing for many users.

SAS

Strengths - SAS's core strength is in its advanced analytical

techniques, such as data mining, predictive modeling, simulation and optimization. Industry and domain specific

advanced analytic offerings. Support for extremely large volumes of data.

Areas of improvement - Higher than normal complexity making it most difficult to use and most difficult to implement. Needs significant improvement in reporting, dashboards,

OLAP, interactive visualization and other traditional BI functionality.

IBM

Strengths - Amazing sales and product strategy coupled with support from IBM Global Services and a global presence. Capability to support larger deployments. Radical new approach to data discovery with the Watson Analytics offering. Simplified licensing model. Innovative features such as natural language query.

Areas of improvement - Significantly high cost of procurement and implementation. Poor sales experience and clients have expressed frustration with IBM's sales and contracting, and high numbers of audits. At 6.2 days, the time taken to generate a report is much higher than the industry average of 4.3 days. The move to "smart discovery", bypassing traditional data discovery progression, may result in technical challenges for customers.

So how do they stack up against each other?

Here is how I rate the different platforms across the parameters discussed above:

Below is a composite graphical representation of the individual factor scores as well as the weighted totals:

My Recommendation

There are many things that many vendors do right and they have their niche. However, if I was to pick one, it would undoubtedly have to be Tableau. And customer feedback echo the same sentiments. Tableau checks all the important boxes such as ease of implementation, super intuitive, particularly with its core differentiator — making a range of types of analysis (from simple to complex) accessible and easy for the ordinary business user, whom Tableau effectively transforms into a "data superhero."



References:
http://www.statisticbrain.com/wal-mart-company-statistics/
http://ecr-all.org/files/I.Liiv_Gaining_Shopper_Insights_Using_Market_Basket_Analysis.pdf
http://en.wikipedia.org/wiki/Business_intelligence
http://www.gartner.com/technology/reprints.do?id=1-1QLGACN&ct=140210&st=sb
http://www.tableau.com/new-features/8.3
http://public.dhe.ibm.com/common/ssi/ecm/yt/en/ytw03250caen/YTW03250CAEN.PDF
http://www-01.ibm.com/software/analytics/cognos/solutions.html
http://www.microstrategy.com/us/analytics
http://www.qlik.com/us/explore/resources
http://www.sas.com/en_us/software/business-intelligence.html