Minimizing Risk with the Best Practices for Data Management
By Philip Russom, TDWI Research Director for Data Management
I recently broadcast a really interesting Webinar with Rob Myers – a technical delivery manager at Informatica – talking about the many critical success factors in projects that migrate or consolidation applications and data. Long story short, we concluded that the many risks and problems associated with migrations and consolidations can be minimized or avoided by following best practices in data management and other IT disciplines. Please allow me to share some of the points Rob and I discussed:
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Posted by Philip Russom, Ph.D. on March 11, 20150 comments
By Fern Halper, TDWI Research Director for Advanced Analytics
What does it take to achieve analytics maturity? Earlier this week, Dave Stodder and I hosted a webcast with a panel of vendor experts from Cloudera, Microstrategy, and Tableau. These three companies are all sponsors of the Analytics Maturity Model, an analytics assessment tool that measures where your organization stands relative to its peers in terms of analytics maturity.
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Posted by Fern Halper, Ph.D. on February 6, 20150 comments
Evolving Best Practices for Data Management
By Philip Russom, TDWI Research Director for Data Management
I recently broadcast a really interesting Webinar with David Lyle, a vice president of product strategy at Informatica Corporation. David and I had a “fireside chat” where we discussed one of the most pressing questions in data management today, namely: How can we prepare great data for great analytics, while still leveraging older best practices in data management? Please allow me to summarize our discussion.
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Posted by Philip Russom, Ph.D. on February 2, 20150 comments
I recently completed TDWI’s latest Best Practices Report: Next Generation Analytics and Platforms for Business Success. Although the phrase "next-generation analytics and platforms" can evoke images of machine learning, big data, Hadoop, and the Internet of things (IoT), most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. For some organizations, next generation can simply mean pushing past reports and dashboards to more advanced forms, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis. The market is on the cusp of moving forward.
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Posted by Fern Halper, Ph.D. on December 18, 20140 comments
Analytics is hot—many organizations realize that it can provide an important competitive advantage. If your company wants to build an “analytics culture” where data analysis plays an essential role, your first step is to determine the maturity of your organization's analytics. To help your organizations measure their progress in their analytics efforts, we recently developed the TDWI Analytics Maturity Model and Assessment, which provides a quick way for you to compare your progress to other companies.
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Posted by Fern Halper, Ph.D. on November 6, 20140 comments
Almost a year has passed since the launch of the TDWI Big Data Maturity Model and assessment tool, which I co-authored with Krish Krishnan. To date, more than 600 respondents have participated in the assessment.
We asked questions in five categories relevant to big data:
- Organization: To what extent does your organizational strategy, culture, leadership, and funding support a successful big data program? What value does your company place in analytics?
- Infrastructure: How advanced and coherent is your architecture in support of a big data initiative? To what extent does your infrastructure support all parts of the company and potential users? How effective is your big data development approach? What technologies are in place to support a big data initiative, and how are they integrated into your existing environment?
- Data Management: How extensive is the variety, volume, and velocity of data used for big data analytics, and how does your company manage its big data in support of analytics? (This includes data quality and processing as well as data integration and storage issues.)
- Analytics: How advanced is your company in its use of big data analytics? (This includes the kinds of analytics utilized, how the analytics are delivered in the organization, and the skills to make analytics happen.)
- Governance: How coherent is your company’s data governance strategy in support of its big data analytics program?
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Posted by Fern Halper, Ph.D. on October 16, 20140 comments