TMA Published Introductory Material
Learn how to properly approach predictive modeling while viewing TMA’s philosophy
and results for data mining through these professional publications.
CIOReview Awards TMA as Top 20 Data Analytics Consulting Company
CIO Review constantly works to identify “the best” in a variety of areas important to tech business. This month they profiled the Top 20 Data Analytics Consulting firms. TMA was proud to take its place next to 19 other top data analytics and data mining consulting companies in the November issue.
Attend TMA’s Highly Informative Data Mining Webinar
Tune into TMA’s highly popular monthly webinar entitled “Data Mining: Failure to Launch – How to Get Predictive Modeling Off the Ground and Into Orbit” hosted by The Modeling Agency. Learn how to get started with predictive analytics and overcome both strategic and tactical limitations that cause data mining projects to fall short of their potential. This production features live polling and a very active Q&A session.
The Modeling Agency’s Data Mining Newsletter
TMA’s quarterly data mining newsletter contains vendor-neutral and non-promotional feature articles, industry announcements, and training schedule updates to guide business professionals in understanding how and when to get started in data mining. Every issue is well organized and concentrated with short abstracts and branches to complete details. Your contact details will never be used for any other purpose, and it’s a snap to unsubscribe. More than 10,000 business professionals are active subscribers. Don’t be left out!
Five Things Predictive Analytics Pros Don’t Do (But Really Need To…)
When projects fail to deliver the expected results, the culprit is often inadequate training and preparation. Program managers and data analysts need to first understand what it is they are getting into. The widespread adoption of predictive analytics has been at the mercy of two opposing forces over the past two decades. Frequent, compelling use cases from the few organizations that have properly implemented predictive analytics projects propelled the discipline into the mainstream. Yet its perceived complexity has slowed adoption. Let’s take a look at five critical things business intelligence (BI) and analytics professionals often overlook, hence depriving their organizations of the substantial benefits of predictive analytics.
BeyeNETWORK Solution Spotlight Interview of TMA
The Modeling Agency’s president, Eric A. King is interviewed by Ron Powell, the editorial director of The BeyeNETWORK. Eric provides an overview of TMA, TMA’s focus on predictive modeling, the strength and uniqueness of TMA’s team, and how TMA guides customers through a complex discovery process to ultimately stand up a self-sustainable internal predictive modeling practice, with existing staff.
Data Management Review’s “How to Buy Data Mining”
Eric King, Founder of The Modeling Agency provides a framework for avoiding costly project pitfalls in predictive analytics. This comprehensive article guides those who are not sure how to approach this seemingly cryptic and intangible practice to take a systematic approach to predictive analytics. Readers will discover that those who start in the typical fashion of most BI projects (with data and software) will fall far short of their objectives!
TDWI’s “Ten Mistakes to Avoid in Predictive Analytics”
The Modeling Agency’s Senior Consultant and Training Director, Thomas A. “Tony” Rathburn describes in this article ten perils to steer clear of when preparing for a data mining and predictive modeling project. The Data Warehousing Institute’s popular “Ten Mistakes to Avoid” series helps practitioners make a more informed entry into various business intelligence practices. This particular issue includes a bonus mistake for TMA referrals.
Making Predictive Analytics “Predictive”
In the increasingly complex and competitive environment of business, it is critical to understand that your data, your software, and your technology will never understand the context in which it operates. It will never understand your decision process. It will never understand how you measure success. The only value of technology is to make your business more effective in achieving your goals. To achieve and maintain competitive advantage, organizations must provide centralized resources to support distributed analytics development efforts. Only the business decision makers can monitor and adapt to a rapidly changing environment, utilize available technology to enhance future performance, and ensure that we do not get bogged down implementing technology that offers only the hype of great features…
TMA’s Data Mining Channel on the BeyeNETWORK
The Modeling Agency’s president, Eric A. King hosts an Expert Resource Channel on Data Mining and Predictive Analytics for the Business Intelligence Network. This channel covers the practical application of strategy, tactics and best practices for predictive modeling. Topics and resources focus on extending the value of business analytics with prospective intelligence derived through knowledge discovery and machine learning technology. Check back frequently for new postings!
101 Ways to Sabotage Your Predictive Analytics Project
The Modeling Agency’s president Eric A. King outlines some of the most common yet elusive pitfalls that most who are new to the practice of data mining and predictive analytics fall into. In this article, Eric describes why new practitioners are not to blame along with the potential rewards awaiting those who take a low-risk / high reward strategic approach to what is essentially a discovery process.
TDWI Radio News Interviews The Modeling Agency
As the data deluge continues and expands, the need for data mining will doubtless increase. Says data mining guru Eric King of The Modeling Agency: “It’s going to cost too much to do nothing.” But what, exactly, constitutes data mining? Is it the same thing as predictive analytics? How and when should these disciplines be used? Tune into this edition of the TDWI Radio News to learn:
- How companies use data mining for competitive advantage
- Common misconceptions about data mining
- Professional tips for beginning a data mining initiative
- When not to use data mining