Data Vault 2.0

  • Episode 5: Defining Business Terms and How DV2 Accelerated JP Morgan Chase

    Episode 5: Defining Business Terms and How DV2 Accelerated JP Morgan Chase

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    Join us for Unlocking the Vault with Dan Linstedt It’s time to step back and define KPAs and KPIs in business terms. To do that, we have to ask “why are they important?” We revisit mergers and acquisitions to illustrate their importance, addressing the challenges these business activities introduce and how Data Vault resolves them in

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  • Episode 4:The Need for Sound Methodology

    Episode 4:The Need for Sound Methodology

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    Join Us for Unlocking the Vault with Dan Linstedt Mergers, Acquisitions, and Divestiture of data.  Can the business position its analytics solution to rapidly respond to due diligence demands?  Can it deliver those analytics to the business in a governed manner?  No matter what your business is or where in the business lifecycle you are, you

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  • Episode 3: Auditing and Scaling

    Episode 3: Auditing and Scaling

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    Join Us for Unlocking the Vault with Dan Linstedt This week, Dan dives deep into two of the most common issues taken for granted by a business: The Auditability of data and how to Scale it. To illustrate, Dan takes us through stories of his past, first with Quest missing 15 years of historical data to

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  • Episode 2:  Defining and Solving Business Risks

    Episode 2: Defining and Solving Business Risks

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    Join Us for Unlocking the Vault with Dan Linstedt Audit compliance, global security, distributed privacy policy, and insuring raw data is available for data scientists are just a few of the business risks we face…but are all tackled with the DV2.0 methodology. In this episode, Dan outlines some of the most vexing problems encountered by business

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  • Episode 1:  The Foundation

    Episode 1: The Foundation

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    Join Us for Unlocking the Vault with Dan Linstedt In this first episode, Dan and Cindi walk through the beginnings of Data Vault as developed inside of Lockheed Martin, touch on its applicability for not only large but also any size of business, and how it was intricately designed to bring businesses massive value by mitigating

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  • Building a Snowflake Data Vault

    In this day and age, with the ever-increasing availability and volume of data from many types of sources such as IoT, mobile devices, and weblogs, there is a growing need, and yes, demand, to go from batch load processes to streaming or “real-time” (RT) loading of data. Businesses are changing at an alarming rate and

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  • 7 Reasons For Adopting Data Vault 2.0

    7 Reasons For Adopting Data Vault 2.0

    Data Vault Alliance (DVA) recently hosted a webinar where we discussed 7 Reasons For Adopting Data Vault 2.0. We invited Neil Strange, Founder and CEO of Business Thinking, to be our guest speaker and he took the time to explain why he thinks Data Vault 2.0’s time has come. Data Vault 2.0 adoption is accelerating

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  • Moving Beyond the Data-Driven Enterprise

    Moving Beyond the Data-Driven Enterprise

    By Cynthia Meyersohn, DataRebels LLC Do you really want your business or enterprise to be data–driven?  Before you say, “Of course we do.  Companies that do not embrace a data-driven approach to competition, market strategy, and process improvement will become extinct.”  Understand, this is a trick question. The term data-driven has been around forever it

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  • Does a Pattern-Based DW Architecture Still Hold Value Today? (Part 4 of 4)

    Does a Pattern-Based DW Architecture Still Hold Value Today? (Part 4 of 4)

    By Cynthia Meyersohn To close out this series of articles that have been focused on the data replication introduced by the processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29, 2017, on the AWS Big Data Blog, https://aws.amazon.com/blogs/big-data/build-a-schema-on-read-analytics-pipeline-using-amazon-athena/  (Ratan, 2017), I will talk about an approach that I

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  • Does a Pattern-Based DW Architecture Still Hold Value Today? (Part 3 of 4)

    Does a Pattern-Based DW Architecture Still Hold Value Today? (Part 3 of 4)

    By Cynthia Meyersohn Picking up from Part 2 of this series where we left off having replicated the data a minimum of nine times, we will continue to identify additional data replication stages as we trace through the data processes outlined in “Build a Schema-On-Read Analytics Pipeline Using Amazon Athena”, by Ujjwal Ratan, Sep. 29,

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