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Topics: Blog, Test Data Management
By: Datasource Consulting
What is Test Data Management anyway? TDM is a set of processes, technologies, and people used to automate the provisioning of non-production data sets to leverage in performing system and application testing in non-production environments.
Topics: Blog, Test Data Management
By: Datasource Consulting
For businesses seeking optimal functionality, Test Data Management (TDM) is a crucial component of the organization’s IT structure and ongoing business strategy. Without TDM, your business is at risk for production environment application failures and subsequent breach fines, which have increased 23 percent over the past two years and can cost up to $500,000 per incident (Source: 2015 Cost of a Data Breach Study: Global Analysis).
At Datasource, we hear from multiple companies that the absence of proper test data is the chief culprit behind lackluster testing plans and problems in production. Applications require different types or volumes of test data, which makes the process even more challenging. Insufficient test data causes nearly one-third of the incidents in QA/non-production environments, and is a major reason why nearly two-thirds of business applications reach production without being properly vetted.
Topics: Business Intelligence, Data Warehousing, Blog, team
By: Datasource Consulting
When considering a Business Intelligence (BI) and Data Warehousing project, it’s extremely important to not overlook the process of gathering, prioritizing, and agreeing on the project’s business requirements. This process of discovery is done after the company’s business objectives are documented, validated and organized by each functional business area.
Topics: Business Intelligence, Program Management, Blog, Project Management
For Business Intelligence projects (BI) to be successful, they must meet user requirements, be designed with strong user involvement, include a manageable scope, provide fast query response times, deliver the required reporting and analytic capabilities and provide value. Also, as nearly all experienced BI practitioners will attest, for business intelligence projects/programs to be successful, they must be built iteratively. In the trenches, that means sourcing, integrating, and developing front-end capabilities that target a subset of the enterprise data in each project. Sometimes the scope of an integration project is limited to a specific organizational process, such as invoicing, and other times by system, such as order-to-cash. Either way, subsequent business intelligence projects add to the scope and volume of data that is integrated and available for analysis. For initial business intelligence projects and subsequent iterations to be successful, the data architecture must be scalable, flexible, understandable and maintainable. However, most organizations put more thought and effort into their selection of BI tools than their data architecture. The end result is that they implement a data architecture that crumbles under the growing requirements of their users and data.
Topics: Business Intelligence, Program Management, Blog, Terms
At Datasource Consulting, it is fairly common that our friend and even some colleagues will ask us about some of the Enterprise Data Management terms we use on a regular basis. It also seems that everyone is aware of how fast the Enterprise Data Management industry is moving and how quickly new terms pop up.
Below, you will find definitions to some of the Enterprise Data Management terms that we come across frequently and have a meaningful tie-in to the Business Intelligence and Data Integration space.
Topics: Collaboration, Business Intelligence, Blog, Pervasive BI
In recent months, I've read numerous blog postings, tweets, articles and interviews from business intelligence pundits that have denounced the viability of BI in the cloud or cloud business intelligence. They point to data volumes, security concerns, lack of control, network reliability and bandwidth as factors that deem the concept unfathomable. While each one of these issues can be a show stopper for a BI project, they are not roadblocks for all business intelligence projects. What's most baffling to me is that I've reached out to many of these critics and I've yet to find one that has any actual experience working in the cloud.
Topics: Tool Selection, Business Intelligence, Blog
By: Steve Dine
I recently led a Enterprise Business Intelligence tool selection project for one of our customers that is classified in the small and mid-sized business (SMB) market. A business with 100 or fewer employees is generally considered small, while one with 100-999 employees is considered to be mid-sized (by this definition, our customer would be mid-sized). Having recently heard a number of the enterprise business intelligence vendors state that they are now targeting this market, I figured that they would be prepared for the differences that these customers require with regard to the sales process. Unfortunately, in most cases I didn't find this to be true.
Topics: Collaboration, Business Intelligence, Blog, Pervasive BI
By: Steve Dine
One of the topics that still seems to garner a great deal of attention is "pervasive BI". Just like Henry Ford's vision of a car in every driveway, the BI vendors envision a license for every employee. Don't get me wrong, I'm at the front of the parade when it comes to increasing the BI footprint in today's organizations. However, I think that we still have a long way to go in large part because it seems that the more things change, the more they stay the same. For example, we learned a long time ago that data without context is just data. Yet, it seems that most of today's BI applications lack all but the most rudimentary metadata capabilities. It is a challenge to import the business and technical metadata, maintain it and expose it to the users. How will BI become pervasive if we can't enable users to become more self-service?
Topics: Tool Selection, Business Intelligence, Blog, Cloud
A few weeks ago I presented a full day course on BI in the cloud at the TDWI conference in Orlando. I was pleasantly surprised by the number of attendees and the overall interest in BI in the cloud from both vendors and BI practitioners. Having spent the better part of the past year working in the Amazon cloud, I took for granted the general confusion that still exists over how the cloud is defined and how it relates to BI. I believe that this is due in large part to the fact that the cloud is still taking shape, especially when it comes to BI. As an industry, we look at the expectation of large data volumes, a seemingly endless desire for faster response times and concerns over data security and wonder how BI infrastructures could possibly exist in the cloud. Given the fact that we are at the early adopter phase of the consumer lifecycle, these concerns are expected. However, these concerns may not be reality for as many BI programs as some might think.
Topics: Tool Selection, Business Intelligence, Cognos, Blog, Cloud
How many times have you heard a statistic such as "42 percent of respondents rate a business intelligence solution as moderately successful" or "more than 50 percent of all business intelligence solutions 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 solution 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 solution.
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