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Steve Dine

Recent Posts

An Introduction to Lean BI

Jan 16, 2017 11:10:33 AM   Steve Dine

Topics: Business Intelligence, Program Management, Blog

What is Lean BI and its Purpose?

By: Steve Dine

I have yet to meet a BI team that has too little on its plate. In this article we will look at some of the drivers creating all this work and offer a set of principles that can be used to become more efficient and effective while still focused on delivering value. The main idea for this methodology evolved while leading a BI team for a leading medical manufacturer. We were frustrated by the amount of time spent maintaining our existing infrastructure and the number of features/data elements being added to the data warehouse that weren’t being utilized. During that time, the company was advocating Lean manufacturing principles and practices in our European divisions and there appeared to be some parallels to our situation. Our dabbling into Lean was the original root that grew into this concept.

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Successful Business Intelligence First Requires Defining “Success”

Dec 20, 2016 10:19:31 AM   Steve Dine

Topics: Business Intelligence, Program Management, Blog, ROI

Business Intelligence: Perception and Reality

By: Steve Dine

How many times have you heard a statistic such as "42% of respondents rate their Business Intelligence (BI) program as moderately successful" or "more than 50% of all Business Intelligence projects fail"? When I read these types of statistics I often wonder what's behind these numbers. I'd love to be able to drill down directly to the respondents and ask them how they defined success or failure when answering the question. I recently met with a company that asked if I could come in and help make their Business Intelligence program more successful. Naturally, my first question was to ask them how they define success. Each person in the room defined success a bit differently and meeting turned into a healthy discussion on what constitutes a successful Business Intelligence program.

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Business Intelligence Lifecycle Management

Dec 20, 2016 10:18:55 AM   Steve Dine

Topics: Program Management, Blog, BI Automation

An Introduction to Business Intelligence Lifecycle Management

By Steve Dine, Managing Partner - Datasource Consulting

It’s no secret that Business Intelligence* (BI) projects are both time consuming and resource intensive, often suffer from poor communication between the business and IT, and are usually inflexible to changes once development has started. This is due, in large part, to the method by which BI projects are traditionally implemented. Regardless of the methodology you employ, at the very least, a successful BI iteration requires:

  • Business requirements identification
  • Data analysis
  • Data architecture and modeling
  • Data integration (ETL, ELT, data virtualization, etc)
  • Front-end development
  • Testing and release management.

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Collaborative BI: Is Collaboration the Key to BI in the Future?

Dec 20, 2016 10:15:29 AM   Steve Dine

Topics: Collaboration, Business Intelligence, Blog, Pervasive BI

Collaborative BI Unlocking the Future

By: Steve Dine

I was recently invited to the corporate offices of Lyzasoft for an inside look at their new 2.0 release.  Aside from the impressive advances in charting and visualizations, I was especially blown away by their augmentation of collaboration features to their shared portal, called Lyza Commons.  They provide a web environment that allows authorized users to search, bookmark, combine, create, tag, share, comment, rate, relate, and interact directly with intelligence content in the form of blogs, microblogs, charts, tables, dashboards, and collections.  More simply put, they enable the development and growth of analytic communities.

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Purchasing Business Intelligence Software: Tips From the Trenches

Dec 20, 2016 10:14:06 AM   Steve Dine

Topics: Tool Selection, Business Intelligence, Blog

Business Intelligence Software: How to Avoid Purchasing Shelfware

By: Steve Dine

A colleague of mine recently asked me to chime in on discussion thread about some business intelligence software, and specifically, ETL tools.  Like many discussion board threads, it had drifted significantly from the original topic.  As a general rule, I tend to spend little time on these boards, not because I don't think that they have value, but between Twitter, Blogs and instant messaging, I can't seem to find much time left for additional on-line interaction.  I read through the thread and was immediately struck by the number of comments about the business intelligence software that were phrased as facts but were either based on outdated information, innuendo or rumor. 


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Cloud BI: Something to get Excited About?

Dec 20, 2016 10:11:56 AM   Steve Dine

Topics: Tool Selection, Business Intelligence, Cognos, Blog, Cloud

Cloud BI: Five Areas That Can Be Leveraged

In a few weeks I will be presenting 'BI in the Cloud' for Executive Summit at the TDWI Conference in Las Vegas.  Based on my research during the creation of my presentation, I may be one of only a handful of BI practitioners that are excited about the prospects of leveraging the Cloud for Business Intelligence programs (Cloud BI).  I am not sure whether this is because there are still formidable challenges with leveraging Cloud BI or because I spoke to the wrong people.


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Look Before you Leap into the Data Lake

Jun 22, 2016 9:37:00 AM   Steve Dine

Topics: Big Data, Blog

Look Before You Leap into the Data Lake

If the concept of a data lake is confusing to you, don't worry because you're not alone. A primary reason for this confusion is that the definition of a data lake seems to change depending on which constituency you ask. The big data community will define it as a central location for all your disparate data sources stored in its native format in Hadoop. Even within the big data community, it may be called something different, like enterprise data hub, depending on the vendor you're speaking with. In the Business Intelligence community, a data lake is defined as a staging area, or landing area, for your source system data. They make less of a distinction about where the data is stored.

The two questions I'm asked most often include:

1. If I build a data lake, does it need to be in Hadoop?
2. Is there any value in building a data lake?

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