International Data, BI and Analytics Conference, Rome, 22-23 June 2017
Building the Data Driven Smart Enterprise
Barry Devlin will present two sessions in the conference:
- Trends And Directions—All The Way From BI to AI
- Data Warehouse Automation—Time To Stop Hand-Crafting Your Information
As companies invest in digitalization the number of operational applications and processes being made
available through the web, mobile and social computing channels continues to grow.
In many cases, digitalisation has resulted in new structured, semi-structured and unstructured data
being captured in addition to increasing amounts of transaction data. This includes JSON data, sensor
data, text and machine data like web logs recording every click of a mouse or touch of a mobile device
screen. Naturally, when new data is available, business wants to analyse it and so new ‘workload
optimised’ analytical systems have emerged in companies wanting to move beyond traditional data
warehouses. Big data and streaming analytics platforms have been added to data warehouses to create
an extended analytical ecosystem. It is not surprising therefore that with all this data that predictive
and advanced analytics have risen up the priority list as executives realize the strategic importance of
evidence based insights to future business success. Almost everywhere companies are now using or
planning to use analytics to gain a much better understanding of customer behavior and interactions,
to reduce risk and to optimize operations.
In addition, with so much data and analytical opportunity around, business is demanding insights be
produced quickly for competitive gain. They want to modernize data warehouses by introducing agile
data modelling techniques that easily accommodate change. They want to reduce total cost of
ownership by replacing physical data marts with virtual data marts all accessible from self-service BI
tools so they can create insights themselves. They also want to use new modern visualization
techniques like infographics for more effective communication. Furthermore, business is demanding
that we move beyond basic interactive dashboards on historical transaction data to making use of
predictive and advanced analytics on traditional and big data to deliver high value insights.
This content rich conference addresses all these needs by focusing on data warehouse modernization,
governing self-service BI and introducing analytics. It looks at introducing an agility data strategy into
traditional data warehouses by adopting agile Data Vault modeling, data virtualization and data
warehouse automation. It also looks at new data visualization techniques and machine learning
including developing predictive analytics. It discusses advanced analytics such as text and graph
analysis, and how these can be used to drive up sales in digital and traditional channels. Just how
powerful is graph analysis and what would you use it for? Also what happens if you combine advanced
analytics such as machine learning and text analysis or text analysis and graph analysis? How can text
analysis of social media data help improve e-commerce sales?
We also introduce Fast Data – also known as streaming data and discuss you need to do to get ready
for it? We will look at how your architecture needs to change, how to ingest high velocity data at scale
and what’s involved in introducing a streaming analytics platform? We will also look at how selfservice
BI tools integrate with all this and how to integrate it with your traditional data warehouses.
Finally we look at the impact of the EU General Data Protection Regulation (GDPR) and what it means
in terms of governing data in the modern analytical environment. We will answer key questions like
what do we have to do to be compliant with GDPR and how do we get started in implementing data
security and data privacy?
This conference aims to provide an update on all this, how it fits together and how you can use it to
maximize business value. It tries to show the latest advances in technology to help improve your
understanding of when to use what where and for what business purpose. It tries to help get more out
of analytics while introducing governance, flexibility, agility and your existing analytical environment.
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