Streamlining Business Intelligence Projects

Streamlining #BI Projects: How a Techno-Functional Expert Can Help - BI is all about techno-functional feedproxy.google.com/~r/tdwi-news/~…

— Sachin Sinha (@SachinSinha) March 10, 2013

Agile BI: How to Turn Vague Ideas into Tangible Deliverables

Business intelligence has become a catch-all term used within IT to refer to the tools that drive business value and deliver on IT's promise of being innovative and improving an enterprise's top line, rather than IT being simply a cost center.

The majority of BI programs begin with an information or data strategy and a 3-5 year road map for implementing the strategy. The road map, usually built at a high level, categorizes the plan into major groups, such as enterprise data warehouse and business intelligence, data governance and quality, operational reporting, and advanced analytics. When it comes to executing these programs (especially BI), many people struggle with the high-level outline and tight timeline. How do you move towards shaping something concrete? How do you deliver value with some quick wins and maintain the momentum throughout the life of the program?

Read More: Agile BI: How to Turn Vague Ideas into Tangible Deliverables -- TDWI -The Data Warehousing Institute

A Framework for Understanding the Big Data Revolution

We are in the midst of big data revolution that holds significant potential in disrupting most of the industries and sectors. The 3Vs of big data have been touted many times at conferences and in journals, blogs, and periodicals. There are some early adopters and winners mostly in the Internet industry who have been successful in leveraging the big data for tangible top- and bottom-line growth. Apart from these elite few, most organizations are still in their big data infancy. There are multiple challenges that must be addressed before big data analytics goes mainstream.

The big data boom has spawned a variety of technology companies eager to help realize the dream. The existing incumbents have bolstered their product portfolio and revamped their marketing machine around big data. There are also quite a few "disruptors" who have emerged using open source frameworks and/or as spin offs from big Internet companies. The current buzz about big data has led to a plethora of vendors, each one claiming their technology to be the best at lowering the total cost of ownership (TCO).
With all these tools at your disposal, you might think it is easy to get started with your first project. The reality, however, is that most people still find it difficult to start a big data analytics project.

Read More: A Framework for Understanding the Big Data Revolution -- TDWI -The Data Warehousing Institute

BI Teams Need to Take a Page from Apple's Book

Earlier this year, when the iPad2 was released, my wife told me that she was going to upgrade from her first generation iPad. Her actions stood in stark contrast to just a few years ago, in the pre-iPhone world, when I had tried convincing her several times to make the switch to a smartphone. She wouldn't budge. Suddenly, she's become an early adopter, influenced by Apple products. The unexpected transformation of my wife's attitude toward new technology got me thinking.

In the world-before-iPhone, I had always tried to convince her to use a smartphone by touting its superior specs; in this new world, Apple no longer competes on specs and features. Apple's isn't a debate about displays, memory, or wireless options -- it's a debate about the quality of experience.

In this new world, the experience of the product is singularly significant.

Read more: BI Teams Need to Take a Page from Apple's Book -- TDWI -The Data Warehousing Institute

Master Data Management - Some Useful Links

Managing master data, part one

Master-data management – managing key sets of data centrally instead of in application silos – is becoming a element of information management. But where to begin?
By Mike Fleckenstein

Hit the Ground Running with Operational MDM

By focusing on an operational master data management (MDM) approach, one can achieve 80 percent of the value of a traditional MDM implementation with only 20 percent of the effort. The next generation of data integration and master data management tools will accomplish this by providing a virtual MDM registry via inline data services.

MDM is Not Enough - Semantic Enterprise is Needed

This article introduces the concept of semantic enterprise and outlines a connection between semantic enterprise and master data management (MDM) concepts. The article also shows that successful transitioning to semantic enterprise requires significant improvements in enterprise metadata and especially in business metadata management. It explains the importance of supporting an enterprise-level semantic continuum from both business and IT communities by committing to development of enterprise architecture tenets that would bring both communities to a more synergetic environment.
By Semyon Axelrod

Why You Need Master Data Management

The vendor hype machine is up and running, and master data management (MDM) will be in your favorite software salesperson’s messaging and in their quotas by the time you read this. That hype drives business for a while, but soon MDM will have to prove its mettle.
By William McKnight

MDM in the Real World

By now we all know about master data management (MDM) systems and how they can bring about stunning business results. We’ve heard how they generate and maintain an enterprise-wide “system of record” that contains the consistent, reliable information necessary to perform vital business functions across a large organization. And, we’ve heard how implementing a strong MDM strategy can increase revenue and profits, improve customer service, reduce time to market, enhance regulatory compliance and simplify reporting and business intelligence.
By Marty Moseley

Upcoming Webcasts - 2008-03-10

• Back to Business: How Business Modeling Rationalizes Data Warehousing "A new alternative to the data-centric approach is a model-driven approach using a business model-driven architecture. Hear Neil Raden and Cliff Longman explain how this new way of thinking has proven highly effective in delivering useful results to the business in a short period of time."
• Real-Time Information On Demand "The ability to capture and deliver accurate, trusted data to the right people at the right time can be a significant competitive advantage for enterprises looking to streamline their business processes and improve customer service."
• Best Practices for Deploying Collaborative BI "Best practices and key issues surrounding collaborative BI."