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Ron Powell

Thanks for visiting my blog. As co-founder of the BeyeNETWORK, acquired by TechTarget in 2010, I am privileged to participate in many industry-related activities as a BeyeNETWORK expert covering business intelligence and analytics. These events provide me with a unique insider perspective into the business intelligence ecosystem, its products and the vendors that provide those products. I'll be sharing that invaluable information with you through this blog, which will also be a reflection my business philosophy. And, just to make this interesting, I'll be sure to include my personal point of view on a wide range of topics and share some of the stories of people I meet in my travels.

About the author >

Ron, an independent analyst and consultant, has an extensive technology background in business intelligence, analytics and data warehousing. In 2005, Ron founded the BeyeNETWORK, which was acquired by Tech Target in 2010. Prior to the founding of the BeyeNETWORK, Ron was cofounder, publisher and editorial director of DM Review (now Information Management). Ron also has a wealth of consulting expertise in business intelligence, business management and marketing. He may be contacted by email at rpowell@powellinteractivemedia.com.

More articles and Ron's blog can be found in his BeyeNETWORK expert channel. Be sure to visit today!

Want to learn more about Jupyter, the open source tool for collaborative data science and AI? If so, I recommend you attend JupyterCon at the New York Hilton Midtown from August 23 to August 25. This is the first Jupyter conference, and it will provide you with real-world examples of how leading data-driven companies are benefiting from this powerful platform. 

Posted August 2, 2017 1:22 PM
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We are seeing a major sea change in how enterprises are harnessing big data through machine intelligence and deep learning. Graphical processing units (GPUs) excel at workloads that require large amounts of mathematical calculations, simulations, streaming data and machine learning. This has resulted in a paradigm shift, enabling a whole new range of applications for autonomous vehicles, Industrial Internet of Things (IIOT) and advanced analytics.

I recently interviewed Amit Vij, CEO of Kinetica, whose company has pioneered a database that uses general purpose graphical processing units that is run SIMD operations in a highly parallel fashion. Its GPU-accelerated analytics database provides real-time insights into large and streaming datasets.

Kinetica was incubated within the U. S. Army Intelligence Command and the NSA.
It started as a geospatial and temporal computational engine and slowly evolved to a highly available and distributed in memory database that is accelerated by GPUs.

Kinetica can scale to meet the needs of very computational intensive applications such as risk mitigation and fraud detection in the financial industry. Their technology can displace current technologies that utilize scale-up hardware as Kinetica leverages scale-out architectures.

According to Amit, a major value proposition is converging machine learning and deep learning into a database running in-database analytics, registering, for example, a Google TensorFlow library to Kinetica, running a machine learning model on a data table that has over one billion rows, and then persisting out a new data table. "We're seeing that as an easy workflow for an organization to use," said Amit.

GPUs are accelerating the use of analytics across a broad range of use cases and having a profound impact across a wide variety of industries. Organizations that are trying to achieve real-time analytics with large and streaming datasets should check out Kinetica.

To read my full interview with Amit Vij, click here.

Posted June 20, 2017 7:49 AM
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At the recent Gartner Data & Analytics Summit in Grapevine, Texas, I had the opportunity to speak with Jim Slagle, vice president of Business Intelligence at Apria Healthcare. A leading provider of home respiratory services and medical equipment including oxygen therapy, inhalation therapies, sleep apnea treatment, enteral nutrition and negative pressure wound therapy, Apria Healthcare has 375 branches throughout the United States and employs more than 8,000 people. 


I learned that over the last 10 years, Apria has had various BI initiatives with mixed results. Most recently, Jim and his team implemented a full cloud-based BI solution with Domo, focusing on sales operations. The intent was not to replace their legacy BI, but to take the next step and facilitate specific sales-oriented use cases.  


At Apria, the platform for sales is designed to provide visibility and insights into sales dimensions and manage base KPIs across the sales team. Domo allows each KPI owner to see his or her KPIs in real-time. This enables them to see where they need to course correct, and identify opportunities to step in and provide direction where needed. This capability means a substantial improvement in operational efficiency as the Apria sales team can now be more agile and adapt on the fly instead of waiting until the end of the month or end of the quarter to look at the sales performance in hindsight.


Jim spoke on the importance of governance. Before they put anything into Domo, they are very meticulous about the accuracy and the sourcing of the data, making sure that the functional people who own the data are part of the process. Before the data even gets to Domo, it is governed, federated, and verified as repeatable and accurate. Jim states that data integrity is non-negotiable because if you're second-guessing the data, it is counterproductive. 


With regard to the ROI of their implementation, Jim points to the fact that with Domo, Apria hit their sales goals for 12 straight months - for the first time in Apria's history. Jim says, "With Domo, we can comparatively know how we're doing at any point of time." 


Jim believes Domo has helped them be successful because it has the all the ideal features of a modern BI solution: Domo is aligned to corporate goals, actionable, simple and intuitive.


Domo gives Apria the ability to actively track KPIs and provides the insight to know where to make adjustments to help meet budget forecasts, and this, Jim says, differentiates Apria from many of its peers in the industry. Additionally, executive-level adoption of BI makes the entire sales budgeting process easier and more effective. In short, Jim feels that after many years of searching for the right BI solution, they have found it with Domo.



Posted June 6, 2017 6:00 AM
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Colony American Finance was able to increase profitability by taking their business intelligence (BI) to the cloud. The importance of empowering business users was critical, and my interview with Matthew March, CIO at Colony American Finance, highlights how efficiently he was able to work with his executive team to create an effective BI platform in just two months.

Over the years I have been discussing several keys to effective BI. These include time to market, integration with operational business processes and timely acquisition of data. In my interview with Matthew, which includes several use cases, he shares how he was able to effectively address all of these key areas by creating a BI infrastructure that encompasses the cloud. He can now provide his business users with safe and secure access to 27 different data sources so they can do their own ad hoc analysis without involving IT.

This is one of the best BI implementations I have seen, and it clearly shows how this company has reached the goal that every BI implementation hopes to achieve.  By eliminating stovepipe BI and enabling business users to effectively do ad hoc analysis themselves, Colony American Finance has realized the promise of BI -- to empower users and do it with minimal IT involvement.


Posted November 14, 2016 8:15 AM
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I recently conducted a podcast with Peter Boncz, Professor at Vrije University in Amsterdam in the Netherlands. Peter is considered to be the father of MonetDB and vector database processing. The podcast focuses on vector processing and what it means for analytics. We also cover why vector processing is different from other database approaches, current benchmarks, application use cases and what customers are doing to benefit from vector processing. Click here to listen to this podcast now or read it here

Posted September 5, 2014 3:00 PM
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