December 4, 2020 9:00-10:30 Why is time series data so important and how to monetize it?
Connect. Share. Learn *****Webinar*****
ATTENTION ALL BI/DW AND ANALYTICS PROFESSIONALS
Dear TDWI St. Louis BI/DW and Analytics Professionals,
We cordially invite you to attend our upcoming TDWI St. Louis meeting on December 4, 2020. Out of an abundance of caution, this meeting will again be held virtually. Expect to see the same high-quality presentations in a vendor-neutral setting, which is the hallmark of TDWI education. Please see our detailed meeting agenda below.
When: |
December, 4 2020 9:00 - 10:30 |
Where:
|
This is a virtual event, access details included in confirmation email. |
Topics: |
Why is time series data so important and how to monetize it? |
DON'T MISS THIS EVENT! Please RSVP below
Click here to register for the next upcoming event
Agenda:
9:00 – 9:10 |
Introductions and Chapter Business |
9:10 – 10:20 |
Why is time series data so important and how to monetize it?
Dr Rado Kotorov| Chairman & CEO| Trendalyze, Inc.
Today the fastest growing data is Granular Time Series Data (GTS), also known as machine data and/or logger data. There is nine times more GTS data than business data. For the past two decades analysts have been using Business Intelligence to squeeze insights from the business data to improve all aspects of the business operations. If data is indeed a resource, imagine the bounty from the growth in GTS data. GTS data is a low granularity data, i.e., it is being collected in minutes, seconds, milliseconds, and nanoseconds intervals, and, thus, it contains a lot of detailed information. At this level of detail, it becomes a diagnostic quality data because it precisely reveals the root causes of all events. For example, if you collect heart rate data on seconds, as most popular watch apps do, you are not likely to see a coming heart attack. But if you collect heart rate data on milliseconds (let us say 180 data points per second), the shape of the heart attack becomes visible to the human eye. Sensor and logger devices collect such “heartbeats” from industrial equipment, vehicle engines, medical devices, high frequency trading systems, and much more. The shapes of these “heartbeats” are the golden nuggets in all GST data. The ability to extract and catalogue meaningful patterns from GTS data is the first step to its monetization. The second step is to leverage the meaningful patterns for decision automation. These two processes: pattern knowledge extraction and decision automation, are not new. High frequency trading operations have developed a specialty for detecting patterns algorithmically and executing trades automatically. What is new is that GTS data has become pervasive in all industries enabling new business practices and models from predictive maintenance in manufacturing to remote patient monitoring in healthcare. In this session we will do a deep dive into what GTS data is, how to analyze it, and how to monetize it. |
10:20- 10:30 |
Wrap up and Give-Aways |
|
|
DON'T MISS THIS EVENT! Please RSVP below
Click here to register for the next upcoming event
Space is limited so sign up early!
For more information about TDWI Membership, contact [email protected].
To contact a Chapter officer, click on name of the officer located on the top right hand column of this page.
Bios of Presenters
Dr Rado Kotorov| Chairman & CEO| Trendalyze, Inc.
Seasoned international technology innovator, digital transformation leader, and software executive. Leverages deep understanding of business intelligence, analytics, machine and deep learning, and AI to solve today’s business challenges and identify untapped revenue opportunities. A proven leader in developing vision, strategies and data-driven business models for the digital economy. A dedicated innovator who has co-created numerous software products and patented technologies. Co-author of the book “Organizational Intelligence: How Smart Companies Use Information to Become More Competitive and Profitable” with Gerry Cohen, CEO of Information Builders Inc. He is currently finishing his second book on “Data-Driven Business Models: How Great Companies Run on Data”.