November 2018: Data Management Maturity

Presented by Melanie Mecca

Director, Data Management Product and Services, CMMI Institute

Data Management Maturity – Why We Need It and How It Can Propel You to DM Leadership

Our industry is continually building capabilities based on its considerable accomplishments over the past decades. Some of the (roughly) sequential milestone markers that most organization share include: data design, data administration, data architecture / warehousing, data quality and governance, MDM, and predictive analytics using both structured and unstructured data.

So why haven’t organizations attained DM perfection? As we know, the data layer in the vast majority of organizations grew project by project, typically to meet specific needs of a line of business. Best practices were not usually shared, useful work products languished in project repositories, etc. – and above all, there was no universal mandate to manage data as a critical corporate asset.

The Data Management Maturity (DMM) Model’s primary goals are to accelerate organization-wide DM programs by: providing a sound reference model to quickly evaluate capabilities, strengths and gaps; accelerating business engagement; launching a collaborative vision / strategy; and identifying key initiatives to extend existing capabilities while building new ones – leading to efficiency, cost savings, creativity, and improved data quality.

In this seminar, we’ll address:

  • Data Management Capabilities and Maturity Evaluation
  • The DMM in action – interactive exercise with the Business Glossary – rate your organization!
  • Case study examples – how organizations have accelerated their progress
  • How to leverage Data Management Maturity to empower your career.
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DAMA Day 2018: Building and Deploying Predictive Models

The DAMA Portland Chapter Board, in conjunction with our sponsors WhereScape, are proud to welcome back one of our most popular speakers, Dean Abbott of Abbott Analytics. For this year’s DAMA Day, Dean will focus on Data Science: Building and Deploying Predictive Models.

Sponsorship has allowed us to lower the price this year! This is an amazing value for your training budget, especially if you are looking for exposure to data science and how to apply predictive modeling to your business use-cases.  Follow @damapdx on Twitter and watch this event page for the latest updates.


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September 2018: Data Virtualization

Presented by Robert Eve & Leo Duncan

TIBCO Software & Nordisk Systems

Modern development cycles are aggressively short.  Agile enterprises can no longer afford to wait weeks for a new object to be delivered to a data store.   Data integration must be immediate in order to maximize that data’s value to the organization.
Extraction, Transformation, & Load (ETL) has been the backbone of data integration for as long as most of us can remember.  We take something from platform X, massage it, and put it on platform Y.  Then the users have to find that data, and manually combine it with data from other sources.  This is a complex, time-consuming, and cumbersome process.

We’ve been doing ETL for so long, that it has literally become synonymous with the term “nightly cycle” at many shops.  Missing that nightly cycle can often result in Day +2 analytics, or even worse.  We’ve become so comfortable with ETL that we’ve forgotten to ask the basic question:  “Is there a better way to integrate data”?  The answer is absolutely “Yes”!

Come join us to see how you can free yourself from the historical constraints of ETL.  Maximize the benefits from your existing technology investments; while also providing a bridge to cloud-based platforms.  Learn how data virtualization can enable you to leverage your data without the need for unnecessary movement.  See for yourself how data virtualization can reduce multiple-week integration projects to just a few hours.

In this session you will learn:

  • What is data virtualization and how does it work
  • Twelve tangible ways data virtualization can help you overcome analytics data challenges
  • Cases studies in, and a proven strategy for, data virtualization implementation success

And as a bonus, session attendees will each receive a free copy of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility.
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July 2018: Real Time Databases

Presented by Amit Vij

Kinetica, President & Co-founder

UPDATE: Click here to download Amit’s presentation

Modern business is fluid.  Critical business events must be responded to when they occur.  Delays mean lost opportunities and sub-optimal outcomes.  Modern analytics must be as agile as the business itself.

The days of looking at your data in the rear-view mirror are coming to an end.  We are at the dawn of a new analytic revolution:  the real-time database.

Millisecond latency is the new analytic performance standard.  A new class of GPU-accelerated databases enable this level of performance.  GPU-accelerated databases are over 100x more performant than even the “fastest” in-memory data stores.

GPU-accelerated databases can provide extreme levels of scalability without negative performance impacts.  They are also easy to implement; thanks to their use of ANSI-standard SQL and hybrid visualization capabilities.  This makes them particularly attractive for some of the most demanding analytic tasks; like scoring of predictive models, and geospatial visualizations.

Join Amit Vij from Kinetica to learn how GPUaccelerated databases can enable real-time businesses.


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