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)
June 17, 2010
Non-Linearity - Why should BI Practitioners know about it?
Because Non-linear nature of business is the root cause for the gap between analytics delivered by IT and positive impact of such analytics on business decisions. Let me try to substantiate that statement in this blog post.
Now, what is a non-linear system? - A non-linear system is one where the whole is not equal to sum of its parts. Let us take the example of Friction. Without friction a simple linear equation expresses the amount of energy you need to accelerate, say, a football along the ground (sounds contrived, well, it is FIFA 2010 time - Waka Waka). With friction, the relationship gets complicated, because the amount of energy changes depending on how fast the football is already moving. One cannot assign a constant importance to friction, because its magnitude depends on speed. Speed, in turn, depends on friction.
The whole body of System Dynamics developed by Professor Jay W. Forrester deals with complexity around non-linear systems. The System Dynamics Society says this and I quote - "System dynamics is a methodology for studying and managing complex feedback systems, such as one finds in business and other social systems. While the word system has been applied to all sorts of situations, feedback is the differentiating descriptor here. Feedback refers to the situation of X affecting Y and Y in turn affecting X perhaps through a chain of causes and effects. One cannot study the link between X and Y and, independently, the link between Y and X and predict how the system will behave. Only the study of the whole system as a feedback system will lead to correct results".
As BI practitioners, we are comfortable with questions around Data Management, Reporting, Dashboarding etc. but are stumped when confronted with the question of "How do the Reports, Dashboards or any analytical artifact affect the quality of business decisions"? In my mind, the missing link is the lack of understanding of business as a non-linear system. Let me provide a concrete example here.
In one of my BI consulting engagements for a large voice-based Business Process Outsourcing company, the problem was to predict the number of calls that would be received in a particular day as that number drives a lot of decisioning on the ground, viz. number of associates required, type of skills required, infrastructure, schedule for pickup and drop etc. If this problem is taken only as a predictive analytics problem, ignoring the non-linear nature of the business, the predicted value of the number of calls (using various statistical techniques and BI tools - in this case I used the Microsoft SSAS based Data Mining solution), does not provide a complete solution. When I fed the predicted number of calls to a business process simulation model (I used Powersim in this case) that captured the inter-relationships between various business processes, a much more robust solution was obtained.
The gist of what I am trying to convey is:
1) BI Practitioners would do well to understand what non-linearity is and how businesses processes are inherently non-linear in nature.
2) Goal of analytics is to really improve the quality of business decisioning. BI does not stop with just reports, cubes & dashboards.
3) Analytics in combination with Business Process simulation models (that captures the non-linear nature of business processes) can help organizations increase the quality of business decisioning.
Thanks for reading. Please do provide your feedback.
Posted by Karthikeyan Sankaran at 10:30 AM | Comments (0)
April 27, 2010
ON-DEMAND WITH ON-PREMISE - IT'S GAME ON!
I have been encountering 'this' situation in BI consulting engagements with increasing regularity and thought that it merits a blog post. 'This' situation mentioned above is a scenario where enterprises are utilizing a combination of On-Demand cloud applications (say for CRM functionality) with On-Premise systems (Traditional BIG ERP's like SAP, Oracle Apps etc.) with exchange of data between these systems on a regular basis. What's more! - It's the ubiquitous ETL tools like Informatica, SQL Server Integration Services, etc. that are used to exchange data between the systems.
Here are some instances of such a scenario, that I have seen recently:
1) Large Healthcare information processing company uses SalesForce.com as the cloud based CRM system and needs to interact with On-premise SAP system, using Informatica as the ETL engine.
2) Leading Airline Company uses Oracle CRM On-demand and wants to exchange data with Oracle Apps which is installed in the company's data center. The enterprise standard ETL tool is SQL Server Integration Services and that needs to be leveraged for data integration.
3) A large Telecom company, which uses Jobstreet as an on-demand recruitment engine wants to integrate the recruitment data with Oracle HR Analytics (BI Apps) module installed on-premise, to develop a comprehensive BI platform for HR data.
I am very sure that there are many other scenarios where the integration between on-demand and on-premise software is required, and this trend is bound to accelerate in the future. The good news for BI practitioners is the fact that such integration (for any scenario) can be accomplished with a good understanding of web-services in the context of ETL platforms.
At a high level, the data integration architecture for the On-demand plus On-premise scenario, is as described below:
1) On-demand applications provide webservices for insert, update & delete for each entity
2) Each WSDL file has a set of methods that needs to be understood for its functionality
3) ETL tools have the capability to call webservices within its flow
4) ETL tools increasingly are providing specific integration packs with On-demand solutions that provide for an easier & more comprehensive way of integration. For example, Informatica provides a connector for SalesForce.com that just makes the integration all that more easier.
BI practitioners would do well to understand the business imperatives behind the on-demand with on-premise scenario and think through a solid ETL technology architecture to enable it.
Before signing off, I would like to introduce the EbizQ forum where I have been a forum contributor for the past few months. Given below is a forum question and my reply, that is relevant in the context of this blog post.
Thanks for reading. Please do share your thoughts.
Question:
Is There a Certain Size Business or Certain Vertical Industry for Which SaaS BI Makes Most Sense?
By ebizQ on Mar 1, 2010 at 10:05 AM
- Karthikeyan Sankaran | March 2, 2010 12:16 AM | Reply
I think SaaS BI has a role to play in organizations across industries and of various sizes. Every organization has a set of business functions (Finance, Marketing, Operations, Sales, Strategy etc.) each with its own characteristics that dictate the business decisioning requirements. The applicability of SaaS BI is more a function of these characteristics than the organization themselves. That the characteristics themselves might be dependent on the industry or size is definitely a valid argument but I will leave that for another discussion.
For example, certain business decisions require a tightly coupled information chain (from ERP to DW to Reporting) and this is best served by in-house / on-premise BI platforms. On the other side of the spectrum, certain decisions are to be taken based on a loosely coupled information chain and these decision makers would be well served by on-demand / SaaS platform.
Bottom-line, I expect, every organization, big or small, to have a mix of on-premise and on-demand BI platforms each serving a specific business community and opportunity.
Posted by Karthikeyan Sankaran at 12:00 AM | Comments (0)
March 15, 2010
Innumeracy and Business Intelligence
My inspiration for this post is the wonderful book titled "Innumeracy" authored by John Allen Paulos. In this book, the author hypothesizes that many of us are unable to deal with numbers in the real world and that by understanding the concepts in this book, we can get a clearer, more quantitative way of looking at the world.
Business Intelligence, arguably, is the most quantitative of areas in Information Technology. At a very basic level, BI deals with metrics collected about various business processes. The way the metrics have to be managed and manipulated depends on the mathematical content of these metrics. If that sounds too profound, well, it is intentional and I urge you to read on!
Any Data Warehouse data modeler will appreciate the fact that metrics collected in a fact table have to be understood in the context of the Fact Table grain, viz. A transaction grain fact table has metrics that are to be treated differently than the ones stored at a Periodic snapshot level or as an Accumulating snapshot. Think about Fully Additive, Semi-additive facts and you get the idea.
Similarly, a BI report developer deals with numbers on a daily basis. A good understanding of the numbers (can it be added or averaged or extrapolated) to be shown on a report, is essential to arriving at the right information content and also the correct way to visualize the numbers in question. As a simple example, read Ralph Kimball's classic article on (aren't all his articles classics!) SQL Roadblocks and Pitfalls here and we realize that to decipher an article that exposes the basic limitations of SQL in dealing with moving averages (a very common requirement in BI reporting), we need the ability to think mathematically.
Moving on to the realm of data mining, predictive analytics and its ilk, we as BI practitioners are starting to tread on areas that require a solid quantitative mindset. In one of my earlier blogs titled 'The Esoteric World of Predictive Analytics',I had argued that traditional statistics is not enough to make sense of Predictive Analytics, when it comes to modeling Human Behavioral Systems which is what BI applications are all about. More fundamentally, an understanding of probabilities, central tendencies, cause and correlation, normal distributions, regression models, design of experiments etc. is becoming very important for BI practitioners and with sites like this one - Rice Virtual Lab in Statistics, it is quite possible to get a grasp on the fundamentals in a short time-frame.
Let me close this blog with a paragraph from 'Innumeracy'. John Allen Paulos writes and I quote "In an increasingly complex world full of senseless coincidence, what's required in many situations is not more facts - we're inundated already - but a better command of known facts, and for this a course in probability is invaluable....Probability, like logic, is not just for mathematicians anymore. It permeates our lives".
BI practitioners, whose lofty ideals, relate to helping organizations make sense out of their customers' behavior, would do well to give their "Quantitative Gene" a push or shove in the right direction.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 1:45 AM | Comments (0)
February 8, 2010
'Obvious' Business Intelligence
Recently, I happened to read a couple of books with the "Obvious" word in the title and thought of writing a post around some of those obvious things in BI that we all know but typically forget during the thick of action.
A small note on those 2 "Obvious" books that I read - The first one is a classic called "Obvious Adams: The Story of a Successful Business Man" written by Robert R. Updegraff in 1916 and the second being a more contemporary one by Eliyahu M. Goldratt titled "Isn't it Obvious". Though these books talk about very different business domains (Obvious Adams is on Advertising while Goldratt's book is on Theory of Constraints as applied to the Retail industry), the central theme of these books is the fact that decision makers tend to overlook the basic principles when confronted with problems.
In my humble opinion, a simple, implementable, commonsensical approach based on fundamental principles is the need of the hour in many areas and Business Intelligence is no exception. Based on my experience, given below are some of those basic principles on Business Intelligence that the practitioner would dismiss as being too "obvious" (but hey, isn't that the intent of this post!). Let's roll...
1) Business & Business Stakeholders are the key to successful BI
2) BI should help in making decisions that support the business goals
3) BI systems should provide Hindsight, Insight and Foresight to optimize the business process
4) Quality of BI output and hence the quality of decisioning is directly dependent of the quality of data. Remember "Garbage In, Garbage Out"
5) Ensure that the business units agree on what the business really is. Educate the business about the business, if required.
6) Without strategic focus and executive sponsorship, BI projects are set for failure
7) Organizations are dynamic entities and hence ensure that BI systems can adapt to change
8 ) Enable & Empower the BI users (both operational and strategic)
9) Market the value of DW / BI platforms to the user community
10) Don't boil the ocean - There is no perfect BI system. Try building a successful one instead
11) Always build the BI system with 'detail data'. Summaries & Aggregations can follow the detail
12) Pick the technology based on Business Fitment not on Tool sophistication
13) Prototype and Visualize the end-state before embarking on major BI initiatives
14) Have the right team to sustain and grow the BI infrastructure
15) Be aware of latest developments and trends in BI
And so, here is my quick checklist on the fundamental principles around thinking, building and using BI in organizations. Please do share your thoughts. Thanks for reading!
Posted by Karthikeyan Sankaran at 11:00 PM | Comments (0)
January 6, 2010
Michelangelo and Da Vinci in your BI team
And if you can onboard a Picasso, Salvatore Dali, Vincent Van Gogh or Claude Monet, go for it! Business Intelligence needs Artists more than ever!
My biggest learning from consulting assignments during 2009 is the increasing emphasis on the artistic elements of Business Intelligence. The first question asked by senior executives across organizations was, "So, how will this finally look?" before any of the other elements in the BI landscape had taken shape.
"Form" had taken center-stage or at the very least is sharing the same elevated platform as "Content". For BI practitioners like me, who have been using their left brain more than its opposite number, working through architecture, data elements, models, ETL etc. it is time to take a step back and give the right brain its due. Visual Business Intelligence, which deals with data visualization, is rapidly gaining ground and the BI practitioner would do well to pay attention to it.
Stephen Few, one of the foremost experts in this area, writes this way in the wonderful website of his and I quote:
"We are overwhelmed by information, not because there is too much, but because we don't know how to tame it. Information lies stagnant in rapidly expanding pools as our ability to collect and warehouse it increases, but our ability to make sense of and communicate it remains inert, largely without notice.
Computers speed the process of information handling, but they don't tell us what the information means or how to communicate its meaning to decision makers. These skills are not intuitive; they rely largely on analysis and presentation skills that must be learned".
So true! Yet, I do feel that visualization is not carried out with the same rigor as done for other pieces of the BI landscape, in many organizations. For example, I have seen Data Modelers, ETL architects, Reporting specialists in BI teams but haven't come across a role of "Visualization Specialist" or "BI Usability Architect" in the BI space. And I think that day is not far off!
Given below are some resources that I found extremely interesting from the Visual BI standpoint (am sure that there are many more!)
1) Stephen Few's website
2) Infosthetics
3) Gapminder
4) Dashboard Spy
5) Amazing number of BI gadgets & widgets that can be found across the web (Easiest to try out are the Google gadgets ? Load sample data in Google spreadsheets, insert gadgets like motion charts and get going!)
Going back to the title of this blog post, who knows, dashboards developed by BI visualization experts might be auctioned at Sotheby's for multi-million dollars in the future!
Wish you all a very happy new year 2010.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 7:15 AM | Comments (0)
November 15, 2009
Bounded Rationality, BI and Beyond
Herbert Alexander Simon, an American political scientist, economist, psychologist and professor, coined the term "Bounded Rationality". Bounded Rationality is a concept which relates to the fact that the decision making capability of individuals is limited by the information that they have and the finite amount of time they have to make decisions. This carries a lot of relevance to Business Intelligence managers / decision makers, who have to grapple with a plethora of choices both known & unknown, before zeroing in on the optimal choice of BI solutions for their respective organizations.
To illustrate the fact that the world of BI is moving & innovating at a rapid pace, let us play a small "Do you know this?" game in this blog. Given below are some of the new ideas / developments that I have evaluated in the BI space recently and quite a few of them can be viewed as game-changers in this domain.
As a BI practitioner, "Do You Know" that:
1) Large / Medium sized organizations can completely manage its Planning & Budgeting cycle for as low an investment as $25,000 - Check out Adaptive Planning which is a on-demand 'cloud' solution for Planning & Budgeting.
2) The ability to analyze multi-million rows of data in Excel on your laptop is not far-away - Look-out for PowerPivot 2010 (erstwhile codenamed Project 'Gemini') from Microsoft. Fundamentally, In-Memory Analytics, Column-based storage etc. are having a profound impact on large-volume data analysis.
3) Technology to integrate data sources virtually without the need to have hard-wired ETL is available - Enterprise Information Integration (EII) software like Composite and its variations like Cognos Virtual View Manager are taking its rightful place in the realm of information delivery.
4) Creation of awesome visualization like in Gapminder is possible without writing a single piece of code - Take a look at Google gadgets, Mashup API's at Programmableweb and this is sure to add a lot of power to your BI visualization capabilities.
5) Business Process Simulations are making its way into the BI landscape - Simulations based on System Dynamics and its manifestation in tools like Vensim, Powersim etc. are becoming end-points of the BI value chain.
6) Data in Petabyte range (>1000 terabytes) will become commonplace and will be handled effortlessly by BI tools - Massively Parallel Processing architectures (Greenplum, Project Madison from Microsoft to name a few), DW Appliances (Teradata, Netezza, Oracle Exadata, QimBase etc.) are gearing up to handle these challenges.
7) There are comprehensive BI platforms on the cloud - Products like LiveAccess from Birst can combine data uploaded to the cloud with data in on-demand applications like SalesForce.com and also get into on-premise data to provide comprehensive analytical capabilities, in a matter of minutes.
8) Large Data Warehouses can have their development, test, quality and production environment in ONE SINGLE BOX - Virtualization (both Hardware Partitioning & Hypervisor technology) platforms like Microsoft Hyper V, VMware, Xen, Virtuozzo can enable such platform consolidation in the near future
9) Technology exist to analyze all types of logs (call logs, IVR logs, web logs etc.) to provide profound insights - Products like ClickFox operate in the area of Customer Experience Analytics and provides extensive capabilities to model & analyze semi-structured information as present in log files.
10) Along with providing business foresight thro' Predictive Analytics, BI is also helping organizations analyze data streams in real 'real-time' - Complex Event Processing (CEP) and its manifestation in tools like Streambase, Oracle CEP etc. are making its way into mainstream BI.
So, how much did you score? Am sure that you also would have come across some interesting developments in BI recently. Please do share your thoughts. Thanks for reading.
Let me sign-off with a quote which I think is apt for this post:
"The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man" - George Bernard Shaw, Man and Superman (1903).
Posted by Karthikeyan Sankaran at 5:45 AM | Comments (0)
October 11, 2009
"TO EII OR NOT TO EII" - HAMLET AS BI PRACTITIONER
"To be or not to be, that is the question" the most famous words from Shakespeare's play Hamlet aptly summarizes the conundrum faced by BI decision makers faced with the problem of data consolidation within their organization. Essentially, an organization has to consolidate its data repositories for various reasons, viz. Mergers and Acquisitions, Single View of Customer, Regulatory requirements, Resource optimization etc.
The act of creating consolidated physical repositories is a cumbersome task, especially in large enterprises, that such investments need solid financial models, elaborate planning and even then there is no guarantee of success. For many years, the only viable option for enterprises is to perform "physical consolidation", i.e. create the consolidated logical and physical data models, create hard-wired ETL programs to load data into the consolidated database and use this infrastructure for reporting and analytics. Not anymore! - It is my view that Enterprise Information Integration (EII) provides a viable option for enterprise wide BI consolidation and this post it to initiate practitioners to look at EII as a business solution for problems in the BI domain.
EII falls in the realm of data virtualization and refers to technology behind real-time aggregation of corporate data across multiple, widely disparate data sources. EII delivers comprehensive, reusable "views" that is exposed via SQL and/or web services to whole lot of consuming applications (Reports, Dashboards, etc.). Though the concept of data virtualization is very apt for BI consolidation, there are many other class of problems in which EII technology can play a major role.
At a high level, EII tools work in the following way:
1) The designer uses the data connectivity feature of the tool and can potentially connect to a whole lot of data repositories, viz. databases, XML, excel sheets, etc.
2) Data Modelers then can combine the required data sets and model them in the EII software.
3) EII software then creates a virtual data layer and exposes the metadata in the form of views.
4) The views can be optimized by using the features available within the software.
5) Views are exposed to consuming applications and at run-time, data is fetched from the underlying data repositories
A Simple yet Powerful Value Proposition!
It is important to understand that EII does not replace EAI or ETL. All 3 technologies can co-exist and each on its own provides solutions for different kinds of problems.
EAI stands for Enterprise Application Integration and is used in cases where different applications (Sales & Inventory application for example) want to talk to each other. Target for EAI is an application.
ETL stands for Extract, Transform and Load and is used in cases where data aggregation is required to provide an integrated, consistent definition of data for decision support process. Target for ETL is a database.
EII stands for Enterprise Information Integration and is used as a framework for real-time integration of data from multiple sources both inside & outside an enterprise, empowering business users to "pull" any kind of data from anywhere in the enterprise. Target for EII is the business end user.
A classic reference architecture in which all 3 tools can play a part is when, transactional applications are integrated thro' EAI, data from these applications flow into an Enterprise Data Warehouse (EDW) by leveraging ETL capability and then EII tools help to combine data from OLTP applications, EDW, external data repositories and local excel sheets for business decision making.
In terms of technology, there are specialized EII tools like Composite Software and also many of the standard reporting tools have an EII component embedded in them (Data Federator in Business Objects XI, Virtual View Manager in Cognos 8.x etc.). In my view, EII does have a strong business case and BI practitioners would do well to evaluate this option when faced with data consolidation challenges.
Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 11:30 AM | Comments (0)
September 7, 2009
Analytical Packs For Your BI Environment
In one of my earlier posts,I had written about the availability of packaged BI applications as an alternative to custom built BI solutions. Packaged BI applications from major product vendors, as enticing as it sounds, are not applicable in all kinds of business scenarios. In this blog, let me strike the middle ground and provide the 3rd option - "Analytical Packs". So, in reality, we are looking at 3 options for creating analytical applications:
1) Custom-built BI applications from scratch
2) Implement & Customize Packaged BI Apps
3) Implement "Analytical Packs" - Build connectors and Improve on business functionality based on specific needs.
Analytical Packs are developed for a specific functional purpose. This purpose can be completely domain focused (Insurance Analytics) or can be applicable across multiple industries (Human Resource, CRM analytics etc). The Analytical Packs provide the flexibility of a custom built solution and also the benefit of faster turn-around time as provided by packaged BI apps. Also, the analytical packs can be used by organizations to understand their analytical needs better before embarking on bigger BI initiatives.
The following are the pre-built components of an Analytical pack:
1. List of Subject Areas that make up a functional domain
(Example - HR Analytics will cover the subject areas of Staffing, Retention, Workforce,Organization Effectiveness, Compensation & Benefits, Environment etc.)
2. Set of Business Questions for each subject area
3. Data Model for the functional domain / specific subject areas
4. Semantic Layer for ad-hoc analysis
5. Canned Reports
6. Pre-defined Metrics / KPI's
7. Executive Level Dashboards (based on roles)
8. Predictive Analytics Scenarios and Mining models
9. Connectors to source systems (if feasible)
At Hexaware (the company I work for), we have created close to around 25 analytical packs for multiple industry domains and these are constantly being improved upon. Many more packs around Leasing, Credit-risk, Collections, Accounts Receivables etc. are in progress.
How does an Analytical Pack get built? The steps at a high level are given below:
1) Identify the business process associated with the functional domain
2) Identify the data elements / entities generated by the business processes
3) Identify the analytical scope by listing out the scope of analysis / KPI's etc
4) Organize the data generated by business process in a meaningful way (cut out the operational noise and focus on analysis) - Create the data model
5) Identify meaningful ways to analyze the data - Reports, Graphs, Dashboards etc.
6) Identify scenarios for predictive analytics and build mining models
I will explain specific analytical packs in more detail in subsequent blogs. Thanks for reading. Please do share your thoughts.
Posted by Karthikeyan Sankaran at 2:30 PM | Comments (2)
August 15, 2009
Business Intelligence X-Ray - Calibrating Your BI Infrastructure: Part 2
In my last post, I introduced the concept of a BI X-Ray that helps assess & calibrate the state of the current BI landscape within the organization. First part of the BI X-Ray is on Business Profiling, which was already discussed. The second part involves an evaluation of the BI technology components and at Hexaware (the company I work for), we call it the 10 Point Framework.
10 Point Framework looks at the BI technology infrastructure from ten different dimensions and provides a set of recommendations for each of the areas. The coverage is as tabulated below:
1. Reporting and Delivery
a) Determine user groups and reporting requirements
b) Study the challenges involved in delivery mechanism
c) Identify the security requirements for reports
d) Identify the performance and frequency requirements
2. Data Integration
a) Study the current data integration process
b) Analyze the existing tools used for data extraction
c) Understand the Error handling and auditing process
3. Data Sources
a) Understand various sources of data
b) Analyze the source system data
c) Understand source system dependency, feed layout, frequency and mechanism of data transfer
d) Understand the change data capture process
4. Data Quality
a) Audit current databases
b) Study the accuracy of data
5. Enteprise Data Model
a) Study the existing conceptual, logical and physical data models
b) Study the entity relationships
6. Reference Data Strucuture
a) Understand existing system process and data flow
b) Study the existing data architecture
7. Security Framework
a) Study the existing data security
8. Metadata Availability
a) Study the existing metadata
b) Study the metadata capture process
9. Standards & Process
a) Study existing business process and problems
b) Study the current information flow
c) Analyze existing business requirements
10. Infrastructure
a) Study the current IT Infrastructure
b) Study the existing applications
c) Review the existing ETL tools
d) Review current BI Architecture
The following are key highlights of Ten Point Framework:
1) Covers all aspects of application process, technology, requirements and infrastructure
2) Helps in prioritizing and bucketing the requirements
3) Identifies critical positives and negatives
4) Evaluates against industry best practices
5) Provides a roadmap and recommendation
Hexaware delivers the Ten Point Framework as a consulting engagement spanning 6-8 weeks with distinct Understand, Analyze and Recommend phases
Once the Business Profiling and 10 Point assessment is completed, the organization has a pretty good handle on their current BI environment which is the starting point for newer initiatives.
Thanks for reading. Please do share your thoughts
Posted by Karthikeyan Sankaran at 12:30 PM | Comments (0)
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