January 1, 2012
The Adjacent Possible in Business Intelligence
It is that time of the year when everyone has the license to write about 2012 trends in their technology areas of focus. In that context, I was inspired by this wonderful book titled 'Where Good Ideas Come From' written by Steven Johnson. There are many fascinating nuggets in this book but I was completely intrigued by a concept called 'The Adjacent Possible' and would like to take about BI Trends for 2012 from that perspective.
First, let me clarify the concept by quoting sentences from the book itself.
"Start Quote" Scientist Stuart Kauffman coined the phrase 'The Adjacent Possible' to indicate a kind of shadow future, hovering on the edges of the present state of things, a map of all the ways in which the present can reinvent itself. Yet it is not an infinite space, or a totally open playing field. What the adjacent possible tells us is that at any moment the world is capable of extraordinary change, but only certain changes can happen. The strange and beautiful truth is that its boundaries grow as you explore those boundaries. Each new combination ushers new combinations into the adjacent possible."End Quote"
Steven Johnson goes on to illustrate many examples and concludes that almost all innovations, from pre-historic life to YouTube, are based on 'The Adjacent Possible'.
Coming to Business Intelligence & Analytics, here is my view of the key trends for 2012, or in other words, 'the adjacent possible' for BI, at this juncture. The Mindmap that describes these key trends can be viewed here - BI Trends for 2012 and they are:
1) Smorgasbord of Analytical workloads
2) Self-service BI
3) Social Media Analytics
4) Mobility in BI
5) Big Data
6) BI on the Cloud
7) Advanced Analytics
8) Data Visualization
Though the list is not something that will surprise BI practitioners, each area offers immense scope for innovation. Exploring the adjacent possible in each of these areas is bound to be very interesting and if you get it right can prove to be quite rewarding too. And in the true spirit of innovation, we should let ideas get connected with one another rather than protecting them.
Wish you all a very happy new year 2012. And thanks for reading.
Posted by Karthikeyan Sankaran at 3:45 AM | Comments (0)
November 26, 2011
Trim Tabs in Business Intelligence
What are Trim Tabs? - Trim tabs are small surfaces connected to the trailing edge of a larger control surface on a boat or aircraft, used to control the trim of the controls, i.e. to counteract hydro- or aero-dynamic forces and stabilize the boat or aircraft in a particular desired altitude without the need for the operator to constantly apply a control force. This is done by adjusting the angle of the tab relative to the larger surface.
As a metaphor, Trim Tabs are used to denote tiny components, which nevertheless have a great impact on things that they are attached to. This blog post is about the relevance of this metaphor to BI in enterprises.
Business Intelligence practitioners acknowledge the fact that BI & Analytics in any organization is a journey, an evolution over a period of time. The canvas for BI is extensive and spans the business technology continuum.
Given that BI can add value in many areas of an organization and there are many solutions possible in each of those areas, it is important to identify the BI 'Trim Tabs', i.e. those small areas that can provide the maximum value for invested money. In one of my earlier blogs, I had talked about the concept of 'analytics anchor points' - business processes that should be considered first for optimization through analytics.
Identifying the analytics anchor points in an organization is a non-trivial exercise. In my humble opinion, such a process should start with a complete understanding of the organization's 'Business Model'. Business Model is a set of assumptions about how an organization will perform by creating value for all the players, on whom it depends, including its customers. Like all good stories, a business model relies on the basics of character, motivation and plot. For a business, the plot revolves around how will it make money. Characters are the different stakeholders (internal & external) in that business and each one of them acting in their own self-interest (motivation) makes the plot plausible and meaningful. All successful businesses have a twist in their plot (think of Google, eBay, Dell etc.) that has made them fantastically successful. You can read more about business models in the book by Joan Magretta & Nan Stone titled "What Management Is".
From BI perspective, once the practitioner understands the business model of the company, many questions gets answered:
1) What drives the company's success and how BI can help?
2) Who are the stakeholders and what information are they looking for?
3) What needs to be optimized and how it can be done?
4) What is the architectural blueprint and how will it evolve?
5) How fast should information get delivered?
6) How much data needs to be collated and how far into the past should one go?
7) What are the regulatory requirements for the company?
And many more. In essence, clarity on one aspect of the problem (business model) will go a long way in selecting the analytics anchor points (BI Trim Tabs) for that particular organization.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 2:15 PM | Comments (0)
September 18, 2011
Wisdom of Crowds in Business Intelligence
This blog is in continuation to my previous post on Collaborative Business Intelligence as Wisdom of Crowds shares a close link with that topic.
The Wisdom of Crowds is a book written by James Surowiecki and the tag line for the title reads as "Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations". In this book, the author argues that in any kind of problem solving / decision making scenarios, collective intelligence of a group of people always produces better decisions than an individual, even the expert, working in isolation. There are 4 factors that characterizes wise crowds - Diversity of Opinion, Independence, Decentralization and more importantly Aggregation.
Aggregating individual opinions and thoughts is a crucial factor in ensuring wisdom of crowds and such aggregation is enabled by software products that fall in the category of 'Collaborative Decision Making (CDM)' platforms and many of them have BI components embedded within them. Here are some of the products - Decision Lens, IBM Cognos, Lyza, Microsoft Sharepoint, Purus DecisionSurface, SAP Streamwork, Cogniti, Panorama Necto. Do check out these products as they offer something unique in the way Information has been delivered to business users.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 2:30 PM | Comments (0)
August 28, 2011
Collaborative Business Intelligence
According to this apocryphal story about the 'Tower of Babel', early humans developed the conceit that, by collaborating to work together, they could build a tower that could take them to heaven. Now, God, angered at this attempt to usurp his power, destroyed the tower, and then to ensure that it would never be rebuilt, he scattered the people by giving them different languages so that they cannot collaborate with each other.
To me, the above story illustrates the power of collaboration and reinforces the fact that 'Wisdom of the Crowds' always produces better decisions than one taken by individuals, even by the so called experts. But then this question arises - "If Business Intelligence is all about decision making, why are BI platforms / tools not conducive for collaborative decision making?"
In my mind, the core features of a Collaborative BI environment are:
1) BI environment should have a 'Facebook' like interface that greatly simplifies interaction between users.
2) Users should be in a position to reuse analytical components developed by others.
3) Users should have the ability to communicate real-time within the context of a particular report / dashboard / scorecard
4) Users should have the ability to subscribe to analysis done by other users
5) Users should have the facility to recommend and promote useful analytical components to others
6) BI platform should have the ability to aggregate the views of multiple users with respect to analytics, so that BI gets "crowd-sourced" within the organization.
I had this 'Aha Moment' in relation to Collaborative BI when I saw a demo of the new BI product developed by Panorama software, called Necto. Panorama, company more famous for their OLAP technology that was sold to Microsoft in 1996, has developed this platform keeping collaboration as the central theme of the product. Do check out the demo of Necto at this link.
Bottomline is that Collaborative BI platforms are here to stay. In my opinion, Collaborative BI along with its Actionable & Embedded BI counterparts form the triumvirate that enables Agility in Business Intelligence for organizations. Paraphrasing the above,
Agility in BI 'Equals' Actionable (A) 'plus' Collaborative (C) 'plus' Embedded (E) BI
I will write about the other two components (Actionable and Embedded BI) of the ACE paradigm in my subsequent posts.
Thanks for reading. Please do let me know your thoughts.
Posted by Karthikeyan Sankaran at 10:45 AM | Comments (0)
July 31, 2011
Business Intelligence and the God complex
This blog is inspired by the recent TED talk by Tim Harford titled 'Trial, error and the God complex'. You can view it here. In this talk, Tim argues that people, the so called experts, in the face of an incredibly complicated world, are nevertheless absolutely convinced that they understand the way the world works. And with that notion of all-knowing expertise (God complex), they propose solutions, which in many cases turn out to be wrong. Tim exhorts business leaders, politicians, etc. to abandon the god complex and turn to the problem-solving technique of 'Trial and Error', i.e. Variation and Selection process to identify the best solutions to complex problems.
The world of Business Intelligence & Analytics is complex too. I do realize that BI is nowhere as profound as some the things that Tim alludes to in his talk but having said that, I think the God complex has relevance to the world of analytics. As a BI practitioner for the last 12 years, I have numerous examples of product vendors, system integrators, consultants, etc. peddle their wares, under the guise of - "I know everything about your problems and this tool / solution will solve it".
Business Intelligence & Analytics, at a fundamental level, is all about optimizing business process in an organization. Given the complexity around business processes (even for a mid-size organization), I for one, feel that solutions have to be found only in that particular business context and not elsewhere. The practitioners have to abandon their BI 'God complex' and seek solutions only after they first understand the specific business situation at hand. Once the problem is understood, a systematic trial-and-error mechanism is to be instituted to select the best BI solution for their needs.
As a BI consultant, I try my best to work around the God complex, by following certain rules:
1) Listen intently to customers to understand their business model, strategy and specific problem / opportunity areas, before anything else.
2) Always develop 'Proof of concept' demos before narrowing down on tools / products / solutions.
3) Follow 'Wisdom of the Crowds' strategy (as opposed to the 'experts' view) by utilizing techniques like the Analytic Hierarchy Process (AHP) to drive consensus.
4) Create business process simulations that provides a dynamic view on the impact of analytics in any organization
5) Adopting Project Management techniques that allow for iterations / course change in the development cycle.
Though the specific techniques might vary, I feel that abandonment of the God complex (and the associated mental model) is the starting point for BI practitioners to seek solutions that are relevant in a particular business context rather than taking a 'templatized' view to solve problems.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 3:15 AM | Comments (0)
June 12, 2011
The Contours of Business Intelligence
Recently, I had given a talk titled 'Next Gen BI', in which I tried a new way of presenting information, using the zooming editor called Prezi. The intent of my talk was to try and draw a boundary around areas in Business Intelligence, as it stands at this juncture. I used 2 artifacts during this discussion which can be accessed at the links below:
1) Prezi based presentation - Link here
2) BI mind-map - Link here
I took each area in the BI stack and tried to explain how traditional BI techniques are getting strengthened in certain areas and also getting morphed in different ways. I have also touched upon the emerging areas, like Mobile BI, Cloud BI etc. which are set for explosive growth in coming years.
Hope you like these artifacts. Please do let me know your feedback.
Posted by Karthikeyan Sankaran at 1:45 PM | Comments (0)
May 22, 2011
Business Datasets – The Analytical DNA of an Organization
From my experience as a BI practitioner, one observation I have is that analytical systems are often built without a clear conceptual framework around the informational capability enabled by such systems. In my humble view, an orientation towards 'thinking by business datasets' and an artifact like the Business Dataset Bus Matrix, illustrated in this blog will help in connecting the dots between business data and the knowledge gleaned out of such data. The focus, as always, is on helping organizations make better business decisions.
What is a Business Dataset?
Business Dataset is a self-contained collection that contains data for a particular business process or for an entity. Examples are - Point of Sales Data, Purchase orders, Customers, Products, Chart of Accounts etc. As you can see from the examples above, business datasets can be transaction oriented (business process) or master data (entities). The business dataset is processed from raw transactional data (can be present in tables of a relational data store) and can also include external data, syndicated data etc.
Why is a Business Dataset important from an analytical standpoint?
A collection of business datasets defines the analytical DNA of an organization. Each dataset represents a particular process or an entity and is typically pre-processed from raw transactional data. The datasets are combined in logically relevant scenarios and each scenario provides insights for a particular aspect of business. Quite obviously, the processing & governance of these datasets is very important and BI systems should be architected taking this into consideration. In my experience, the number of datasets in an organization can range anywhere between 25 (small firms) to 80 (large firms).
How do we organize the Business Datasets in an organization?
I propose the creation of an artifact called the 'Business Dataset Bus Matrix' (BDBM), similar to the Dimensional Bus Matrix. BDBM maps the Functional Area to the required Informational capability which is then mapped to specific business questions. Each row of BDBM contains one business question, while the master and transactional datasets are shown in the columns. A tick-mark is placed at the intersection of rows (business question) and columns (business datasets) wherever appropriate, i.e. a tick-mark signifies that a particular dataset is required to enable the informational capability.
It is important to realize that Business datasets are not intended to replace traditional data warehouses or data marts. They are infact derived from the data warehouses or any other data repositories with the sole purpose of being more amenable for analytics.
Thanks for reading. Please do let me know your thoughts.
Posted by Karthikeyan Sankaran at 3:00 PM | Comments (0)
April 2, 2011
Business Intelligence Explosion - What is the Root Cause?
Answer: Velocity. Let me explain a bit in this blog post.
Across the spectrum of industries there is so much talk about Analytics, Business Intelligence, Information Management, Performance Management, Big Data etc. that it is probably safe to say that we are experiencing an unprecendented demand for BI in its broadest sense. Being a first principles kind of guy, I was trying to figure out what is that one thing that can be considered as the root cause for this BI explosion. I had this eureka moment a couple of days back when I realized that the answer to that question could well be the - "Increase in Velocity of Business Decisioning".
In this context, Velocity should be construed as the tremendous speed with which businesses are trying to make decisions and also ensure that their decisions are in the right direction (i.e. positive impact and reduced risk). Sample this from Vishal Sikka's key-note address on SAP HANA during the recent SAP event at Boston.
1) Global CPG company reduced Real-time Profitability Reporting from 6 minutes to 736 ms
2) Large Manufacturing company reduced Customer Reporting from days to seconds
3) Financial services company reduced the time taken for running the Cross-Sell analytical model calculations from 45 minutes to 5 seconds
4) An Automotive company is able to perform Real-Time Analytics on 233 Million vehicles
And many more. Bottom-line is that 'Organizations have started realizing that speed of decision making is directly proportional to the magnitude of competitive advantage for their firms'. All things remaining constant, faster the decision making cycle greater is the competitive advantage. I remember reading a book called the 'Age of Speed' by Vince Poscente some years back but never realized that BI will be so intimately touched by it.
But for an organization to truly thrive, Speed by itself is just a necessary condition but not sufficient. The sufficiency condition is that the direction of decisions made needs to be constantly evaluated (Think Performance Management) - Does it create positive impact? Is there a cultural fit? Are the decisions in line with the regulatory framework? Do my customers get the intended value? etc.
So 'Speed with Direction' for which physics helps us with a nice descriptor called 'Velocity' is my answer as to the root cause of what is causing this explosion for BI & related services.
As an aside, my mind map on the different dimensions of BI
won the 3rd prize in the recently concluded competition. That mind map is an evolving artifact and so please do take a look once in a while to provide your feedback.
Thanks for reading. Have a great day!
Posted by Karthikeyan Sankaran at 2:45 AM | Comments (1)
January 2, 2011
Business Intelligence Dimensions - A Mind-Map
Wish you all a wonderful new year 2011.
Business Intelligence is evolving at a rapid pace on two fronts:
1) Technology improvements within the domain across the spectrum of Information Management, Reporting & Dashboarding, Analytics and Performance Management.
2) BI is increasing becoming part of a larger collaborative framework that includes other systems (BPM, ECM, SOA etc.)with the ultimate objective of improving business decisions.
I have tried creating a mind-map that illustrates the different BI dimensions. Please do take a look at the following link: BI - The Dimensions
and provide your feedback.
Posted by Karthikeyan Sankaran at 12:30 AM | Comments (6)
July 22, 2010
Big Data - The New Normal in Business Intelligence
Let me reiterate - Big Data is the 'New Normal' in Business Intelligence. Now, What is this Big Data?
Before that let us take a look at this fashionable phrase called the 'New Normal' - Mr Mohammed El-Erian, CIO of Pimco, first introduced the phrase New Normal and it has since been used by many people to signify a significant shift in the way things 'were' as compared to how things 'will be' in the future. Here's an instance of its usage by Microsoft CEO Steve Ballmer in one of his periodic public e-mails.
In the context of BI, Big Data has come to include all the systems & process around data generation, collation, management, control & usage. Data generated by business transaction systems have been increasing rapidly and with the advent of social media (Facebook, Twitter, Blogs etc.), it has exploded exponentially (a little out of control, if I may add!). We are clearing entering the era of petabytes and exabytes as the 'New Normal' for data management systems.
Here goes some of the more famous very large data warehouses:
1) eBay has a 6 1/2 petabyte database running on Greenplum and a 2 1/2 petabyte enterprise data warehouse running on Teradata
2) Facebook has a 2 1/2 petabyte datawarehouse running on Hadoop/Hive
3) Walmart has a 2.5 petabytes warehouse, Bank of America has 1.5 petabytes, Dell with 1 petabyte - All running on Teradata
4) Yahoo, Fox Interactive Media, TEOCO (which runs outsourced DWs' for top US telcos) are all in the hundreds of terabytes range.
Since data management forms the core of analytical systems, it is important for BI practitioners to reset (or should I say, re-engineer) their thought process around managing data. Thinking at the scale of petabytes and beyond does alter certain preconceived notions around BI systems for many of us. For example, larger data sets require that we distribute the data among many units rather than just distributing the workload. Our notion of reliability, recoverability, consistency, scalability etc. can get turned on its head with the requirement to handle data in the petabyte and exabyte range.
Innovations will continue to happen across multiple dimensions to help tame this Big Data. Given below are some dimensions of change I could think of:
1) New data storage & manipulation techniques would continue to unfold - Ex: Hadoop, MapReduce, Columnar databases, MPP architectures etc.
2) Divide and Conquer data - Organizations will develop their business architectures around distributing data across multiple platforms (on-demand and on-premise) to make sense out of them.
3) In-Memory Analytics would help business users in analyzing large datasets rapidly - Faster and More powerful analytics with the proliferation of 64-bit processor families and In-memory based BI tools like BO Explorer, Qlikview, Microsoft PowerPivot etc.
Am sure that there are many more interesting ideas to manage and make sense out of Big Data. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 11:15 AM | Comments (2)
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