The data integration process is traditionally thought of in three steps: extract, transform, and load (ETL). Putting aside the often-discussed order of their execution, “extract” is pulling data out of a source system, “transform” means validating the source data and converting it to the desired standard (e.g. yards to meters), and load means storing the data at the destination.
An additional step, data “enrichment”, has recently emerged, offering significant improvement in business value of integrated data. Applying it effectively requires a foundation of sound data management practices. Continue reading
A technique for reporting requirements has emerged as the de facto standard in the business intelligence community. The technique, which emerged in the mid-2000s, is new enough to be as yet unacknowledged by the requirements analysis powers that be. David Loshin describes how it works in this 2007 post:
- Start with a business question about how to monitor a business process using a metric, like “How many widgets have been shipped
by size each week by warehouse?” Continue reading
I recently stumbled upon one of The Martin Agency’s hilarious Geico caveman ads and wondered, rather geekily, why they didn’t do one about data analysis. I think if a caveman suddenly arrived in the 2010s he or she would see parallels between his life and the activities of today’s knowledge worker. When I thought it through, it seemed obvious that knowledge workers need to be more like farmers and less like hunter/gatherers if they want to achieve the full potential of business intelligence.
I hold a strong prejudice that IT paradigms are useful for about 30 years. The PC was dominant from 1980 to 2010, “online” mainframe systems from 1970 to 2000, and so on. If that’s the case then time’s up for Bill Inmon’s data warehousing framework. So far no widely held pattern has emerged to help us envision data management in today’s big data, mobile BI, end-user visualization, predictive analytics world, but at their recent Business Technology conference, Forrester Research took a swing at it by presenting their 2009 “hub and spoke” organizational strategy as a data management vision. Continue reading
Recently I read a thoughtful post
at the PASS Business Analytics Conference site discussing how different the world is now for database professionals. Author Chris Webb focuses on the data science side in this post. His analysis made me think of the challenges and opportunities “big data” serves up to relational database designers.
To me these challenges are fundamental. Big Data and NoSQL bring lots of what we know about data elements, inherent data design, and data management into question. I think considering these elements closely leads to a sensible to-do list for relational database professionals. Continue reading
One common theme in recent tectonic shifts in information technology is data management. Analyzing customer responses may require combing through unstructured emails and tweets. Timely analysis of web interactions may demand a big data solution. Deployment of data visualization tools to users may dictate redesign of warehouses and marts. The data architect is a key player in harnessing and capitalizing on new data technologies. Continue reading
When Tom Petty sang, “Hey baby, there ain’t no easy way out” he wasn’t referring to business intelligence (BI) reporting but he might have been. Current generation reporting engines, AKA data visualization or data discovery tools, market their products with statements like these, emphasizing quick development and ease of use:
- “The democratization of data is here. In minutes, create an interactive viz (sic) and embed it in your website. Anyone can do it— and it’s free.” (Tableau Products Page)
- “Easy yet sophisticated report design empowers your employees to design professional and telling reports in minutes not days” (Windward)
I like these tools, and I do believe that they can provide a leaner, more productive, and more informative approach to BI reporting than some more mature products. However, none is a silver bullet for all data integration and reporting woes. Continue reading
In my experience, some BI projects ultimately finish as a success, but exceed budget and schedule targets and fall short of functional goals along the way. On projects like this, somewhere in the midst of report development, things get sticky and tasks fall behind schedule as the team runs into unexpected complexities. Continue reading
QlikTech’s QlikView reporting and analysis tool is among a new class of Business Intelligence (BI) software tools. As Ben Harden reported in a recent blog post, BI vendors like SAP, Microsoft, and IBM have traditionally sold “to the IT enterprise, but companies like QlikTech and Tableau are targeting the business and bypassing IT. Their tools are quicker to stand up, more intuitive and don’t need the configuration, support, and hardware that the bigger players require.”
A Quick Overview
At first look QlikView is fairly accessible to those experienced with BI tools. A “.qvw” QlikView file contains three classes of user-facing components: a script-based data integration language that runs when the user requests a “reload”, a data modeling component that looks deceptively like a relational data modeling tool, and a familiar array of data visualizations: graphics, charts, lists, etc.