AM1: Developing a Modern Enterprise Data Strategy

A modern enterprise data strategy is critical in the digital age, where depth of operational sophistication and management of scale can be existential problems for growing enterprises. Fundamentally, data should serve the strategic imperatives of a business - those key strategic aspirations that define the future vision for an organization. Big data and data science have great potential for accelerating business, but how do you take it beyond aspirations and into a strategy? How do you reconcile the business opportunity with the sea of possible solutions, and make it fit your needs?

In this tutorial, we explain how we work to solve real business challenges with data, and build a platform for the future.

Why Have A Data Strategy?
Connecting Data With Business
Devising A Data Strategy
The Data Value Chain
New Technology Potentials
Project Development Style

We will also review the options for big data architectures, explaining how the various parts of the Hadoop and big data ecosystem fit together in production to create a data platform supporting batch, interactive and realtime analytical workloads.

By tracing the flow of data from source to output, we’ll explore the options and considerations for components, including:

Acquisition: from internal and external data sources
Ingestion: offline and real-time processing
Storage
Providing data services: exposing data to applications
Analytics: batch and interactive
Data management: data security, lineage, metadata and quality

John Akred likes to help organizations become more data driven. Mr. Akred has over 15 years of experience in advanced analytical applications and analytical system architecture. He is a recognized expert in the areas of applied business analytics, machine learning, predictive analytics, and operational data mining. He has deep expertise in the application of various architectural approaches such as: distributed non-relational data stores (NoSQL), stream processing, in-database analytics, event-driven architectures and specialized appliances; to real-time scoring, real-time optimization, and similar applications of analytics at scale. John received a BA in Economics from the University of New Hampshire, and a MS in Computer Science, focused on Distributed Systems from DePaul University.

Scott Kurth is VP, Advisory Services, at Silicon Valley Data Science. Building on 20 years of experience making emerging technologies relevant to enterprises, Scott crafts vision and strategy for organizations. With a background in architecture and engineering, he combines deep technical knowledge with a broad perspective, to focus on business value.