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Krish Krishnan

"If we knew what it was we were doing, it would not be called research, would it?" - Albert Einstein.

Hello, and welcome to my blog.

I would like to use this blog to have constructive communication and exchanges of ideas in the business intelligence community on topics from data warehousing to SOA to governance, and all the topics in the umbrella of these subjects.

To maximize this blog's value, it must be an interactive venue. This means your input is vital to the blog's success. All that I ask from this audience is to treat everybody in this blog community and the blog itself with respect.

So let's start blogging and share our ideas, opinions, perspectives and keep the creative juices flowing!

About the author >

Krish Krishnan is a worldwide-recognized expert in the strategy, architecture, and implementation of high-performance data warehousing solutions and big data. He is a visionary data warehouse thought leader and is ranked as one of the top data warehouse consultants in the world. As an independent analyst, Krish regularly speaks at leading industry conferences and user groups. He has written prolifically in trade publications and eBooks, contributing over 150 articles, viewpoints, and case studies on big data, business intelligence, data warehousing, data warehouse appliances, and high-performance architectures. He co-authored Building the Unstructured Data Warehouse with Bill Inmon in 2011, and Morgan Kaufmann will publish his first independent writing project, Data Warehousing in the Age of Big Data, in August 2013.

With over 21 years of professional experience, Krish has solved complex solution architecture problems for global Fortune 1000 clients, and has designed and tuned some of the world’s largest data warehouses and business intelligence platforms. He is currently promoting the next generation of data warehousing, focusing on big data, semantic technologies, crowdsourcing, analytics, and platform engineering.

Krish is the president of Sixth Sense Advisors Inc., a Chicago-based company providing independent analyst, management consulting, strategy and innovation advisory and technology consulting services in big data, data warehousing, and business intelligence. He serves as a technology advisor to several companies, and is actively sought after by investors to assess startup companies in data management and associated emerging technology areas. He publishes with the BeyeNETWORK.com where he leads the Data Warehouse Appliances and Architecture Expert Channel.

Editor's Note: More articles and resources are available in Krish's BeyeNETWORK Expert Channel. Be sure to visit today!

April 2008 Archives

Whenever one looks at Mt.Rushmore or the Eiffel tower, one admires the architecture and the technical finesse behind these constructions. Similarly when we see a fully functional data warehouse with all the bells and whistles on the input and output side, which provides sustained performance, we call it the perfect balance between hardware and software architectures.

When it comes to data warehousing and business intelligence programs, the tools that are needed to implement the pervasive side of the solution is one piece of the giant puzzle. The core of any initiative in this spectrum lies with the architecture.

Why do we emphasize on the architecture? in a nutshell, if the architecture blueprint for the data warehouse is not defined and designed correctly, it starts slowing down the multiple layers of data processing leaving the users frustrated and IT baffled. Incorrectly designed systems often lead to program failures and that often gets blamed on poor requirements definition and so on.

There are several layers of architecture in a data warehouse, you start with data architecture, ETL architecture, database architecture, BI architecture and security architecture. All these layers also are integrated and presented in a solution architecture. Although one can argue that a large part of this is infrastructure, event that team requires and architect to lead the effort.

in order to ensure success of any data warehouse and business intelligence initiative, I strongly recommend that all areas of architecture be considered as a team and be chartered with the responsibility to build out the best suited architecture. As we move towards the cloud computing era, data warehouses will exist in clouds and be accessible across the enterprise. This will demand better architects as just discussed.

Hence in the end of the day it is all about the architectures.

Posted April 21, 2008 9:29 PM
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Microsoft has started its plans to compete in the cloud i.e internet cloud computing. With the announcement of SSDS featuring SQL Server services with storage being offered on pay per use, Microsoft joins amazon and Google in the cloud quest. While there may be differences in all the three offerings from the three vendors, the concept and Web 2.0 behind it as a key driver is powerful. Let us watch the moves as they happen.

Yahoo! hope you are listening to all this chatter.

Posted April 16, 2008 8:57 PM
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I have been looking at this model of building a data warehouse by organizations where a corporate data warehouse is built and all its users are grouped by divisions or departments and charged for using the data warehouse. This is certainly a good model for the data warehouse team, especially if a corporate It team is owning the data warehouse. But there are a few issues that arise from this model

1. A business case - For every change in this environment a business case is needed. Without a strong business case, no changes are feasible in the data warehouse. Simple changes maybe an easy in, but anything new or complex will need investigations and sponsorship.

2. IT driven and business used - The data warehouse largely becomes IT driven. This may not be a good idea, since the business usage will be not embraced by all users. The business may also try to use workarounds to get the data and the information they need

How do you enable a successful chargeback model

1. Organizational Alignment - A key driver, that I have always emphasized on, this is a critical component.

2. Business stakeholders - Involve a business stakeholder in every steering committee and make the business own facets of the problem. This will ensure that even if IT drives the solution, business owns the problem.

3. Roles and Deliverables - A set of clearly defined roles and deliverables will be needed to clarify who owns what components.

4. Defined SLAs - Another key component is the definition of service level agreements on data, availability etc that need to be defined.

For a successful chargeback model to be implemented, a few ideas are outlined here, more analysis and customization of the concepts will be needed to make this a possibility in the real world.

Posted April 15, 2008 11:42 PM
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Let us look at "ever hot" topic of Data Warehouse Architecture. What I define as the Data Warehouse Architecture, is a different perspective. The data warehouse ecosystem consists of layers of infrastructure ie. hardware, network, databases, storage, filesystems, operating systems, business intelligence tools and visualization layers. This is the first half of the spectrum. The second half of this spectrum is the data architecture, data model, data loading, data aggregation, data visualization, master data, metadata which presents a complete inside out perspective of the data warehouse architecture.

Why do we need to look at this entire ecosystem rather than specific aspects. In my humble opinion, the data warehouse architecture is the backbone of your business intelligence solutions. If this backbone is not designed and built with the appropriate layers and the right sizing, there are other issues downstream which result in complex end solutions.

We as an industry have built data warehouses and datamarts to solve the ever growing need for more information in the enterprise. As we continue to mature and grow in providing solutions, let us look at the holistic picture than the silo that needs a solution. As a part of this channel, I will be covering an entire spectrum of the architecture layers across the data warehouse.

Posted April 3, 2008 5:14 PM
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