Conference Outline

The programme starts at 9:30 am and ends at 5:15 pm on both conference days. Registration commences at 8.30 am.

1. The Changing World of Business Intelligence
Rick van der Lans, Managing Director, R20/Consultancy

There was a time when users where happy if they would receive their reports printed on green bar paper by internal mail. Not anymore. The world of BI has dramatically changed for the better. This opening session discusses the business pull and technology push that have been responsible for all these changes. Examples of new users demands are operational intelligence, self-service BI, advanced analytics, and social media analytics. In addition, examples of the technology push are NoSQL data storage technology, sophisticated analytical tools, appliances, data virtualization. All these new developments have raised the bar for data governance and information management.
This session gives a high-level overview of all these new developments and shows how they’re all related together.

 

2. Beyond Social Media – The Real Use for Big Data Analytics
Claudia Imhoff, President, Intelligent Solutions Inc.

Big data has caught the (short) attention span of top level company executives. But do they really understand what big data can do for them? Do they understand how they must transform their organizations to get the full benefits of big data? Do they see understand that it not just acquiring big data that is important; it is what they do with it that is? Finally do they understand the need for and role of the data scientist? These are the topics of Dr. Claudia Imhoff’s session. Specifically attendees will learn about:

• The characteristics and misconceptions of big data
• The need for “advanced analytics”
• The characteristics of analytically-driven enterprises
• The characteristics of, need for and skill sets of data scientists
• How to get started down the path of analytics

3. Taming the Distributed Data Landscape with Enterprise Data Integration
Mike Ferguson, Managing Director of Intelligent Business Strategies Ltd.

Over the last few years the shift to on-line business has skyrocketed as customers and prospects look to the web as their preferred way to do business.  Competition in this fast moving internet era is becoming increasingly fierce with new web-only companies appearing everywhere. In this kind of market, the need to have access to timely, high quality, trusted and integrated information has never been so great. Yet at the same time, complexity in the corporate data landscape is growing with data becoming more and more distributed and the number of new data sources also on the increase. This session looks at this problem and at how companies can move towards enterprise data integration to keep pace with the demand for timely information:

• Business priorities 2014 and beyond
• New information requirements and the data integration dependency
• Information overload in an increasingly complex data landscape
• Data integration initiatives – what have we done to date
• Is it enough? – Observations and problems
• New requirements to systematically deal with information overload
• Unlocking the full potential of your data with enterprise data integration.

4. How Data Science is Changing the Way Companies Do Business
Colin White, President & Founder, BI Research

Data scientists are a new type of analyst – part data engineer, part statistician and part business analyst. And they’re in high demand. Companies are combing through resumes and job websites, interviewing recent university grads, and poaching from their competitors in an effort to bring these new talents into their organizations. Of course, we’ve had statistical analysts in our organizations for years. Unfortunately, while these people are great at analyzing data, they are not always the best at explaining their findings to executives and business workers in understandable terms. Data scientists may be the people who can finally bridge the gap between doing advanced data analysis and using the findings of that analysis to produce business results that align with an organization’s goals. Topics covered in this presentation will include:

• Understanding the role of data science in business analytics
• Building a data science team
• Understanding data science techniques, technologies and tools
• Making data science more approachable
• Using data science to gain competitive advantage

5. Embedding Hadoop and NoSQL in Data Warehouse Environments
Rick van der Lans, Managing Director, R20/Consultancy

The advantages of new data storage technologies such as NoSQL and Hadoop are clear: fast, scalable, and cheap. But most existing BI systems are developed with SQL-based technology. How to embed these newer and promising technologies in current BI systems? This session discusses several ways on how to embed Hadoop and NoSQL in BI systems. Potential application areas are i.e. (1) sandbox for data scientist, (2) storing cold data, (3) ETL preprocessing, (4) storing and analyzing unstructured data, and (5) fast loading and retrieval. The session also focuses on additional technologies for making Hadoop data available to SQL-based BI tools, and specialized ETL and data virtualization tools.

• Advantages of Hadoop and NoSQL for Data Warehouse Environments
• Limitations of Hadoop and NoSQL
• Five application areas for embedding Hadoop and NoSQL
• Hybrid database technology
• How to handle unstructured data?

6. The How and Why of Real-Time Operational Intelligence
Colin White, President & Founder, BI Research

More and more companies are deploying business intelligence for optimizing daily business operations. So far this operational intelligence approach has been accomplished primarily by reducing the latency of data integration and data analysis tasks in a traditional enterprise data warehouse environment. Whereas reducing these latencies allows closer to real-time analysis of business operations, it does not and cannot enable real-time (RT) decisions to be made on real-time data. To date, few organizations have recognized their need for RT operational intelligence. Even where they have, the complexity and costs have often been too high. The introduction of big data technologies, however, changes the situation dramatically. This presentation explores the use cases for RT operational intelligence in the era of big data, examines the technologies that enable RT operational intelligence, and discusses how the traditional enterprise data warehouse environment can be extended to support RT processing. Topics covered in this presentation will include:

• Understanding the business benefits of RT operational intelligence
• Determining technology requirements for RT projects
• Supporting RT operational intelligence and big data
• Extending the data warehouse environment to support RT processing
• Learning from other users: use cases and case studies

7. Is Analyzing Big Data using SQL a Smart Choice?
Mike Ferguson, Managing Director of Intelligent Business Strategies Ltd.

There is no doubt that the early days of Hadoop were seen as purely a programmers realm requiring Data Scientists, business analysts and IT developers to build custom batch analytic applications using Java, Python, C# and other programming languages.  While this serves a highly skilled community, it is nevertheless small compared to the enormous SQL community out there in the world of analytics. Hadoop Hive was the first initiative to interface SQL to Hadoop by converting it to HiveQL which itself was converted to Map Reduce programs. However, in the last 12 months, customer pressure has seen a flurry of announcements introducing SQL interfaces to Hadoop that bypass MapReduce. The key question is how good are these options? What is going on to integrate relational DBMSs and Hadoop? Is SQL a good choice to analyze Big Data? What are the pros and cons of this kind of interface when analyzing un-modeled and multi-structured data in NoSQL platforms. This session looks at the options and discusses the pros and cons of each.  It shows how DBMS and Hadoop vendors are opening up Hadoop to business analysts in the SQL community who want to exploit this platform for freeform exploratory analytics. It also highlights where work still needs to be done.

8. Do-It-Yourself BI – What Works and What Doesn’t
Claudia Imhoff, President, Intelligent Solutions Inc.

You have attended the conferences, read the books and articles, gotten advice from vendors and implementation companies alike, and maybe even created your first BI prototype. You think you are ready to take on the big project. Compare where you are to this presentation’s best practices check list for self-service.
In her years of designing, building, and maintaining BI environments, Dr. Claudia Imhoff has learned what to look for in BI projects that can enhance a team’s chances for success. Her timely and informative presentation will help you determine the likelihood that you will deliver on the promise of the Self-Service BI benefits. Attendees will gain a better understanding of these topics:
What you will learn:

• Introduce briefly the self-service BI architecture
• Business versus IT Alignment – delivering on the strategic goals of the company and how to avoid common BI pitfalls
• Best Practice vs. Cultural Evolution – how to design best practices to maximize the adoption of the BI program and increase collaboration
• Best practices – company compliance and governance, who funds what, etc.

Cases:

1. Bol.com Big Data Platform: BI meets SOA
Edith Kanters, Lead IT Architect, bol.com
Petra Hoefs, BI professional, bol.com

Bernt van Raamsdonk has built a strong track record in the Finance discipline in the FMCG sector. Having worked as controller, CFO and Finance Director with corporations such as Coca Cola, Heineken and Sara Lee he is now working with GrandVision as Regional Finance Director. In this capacity Bernt is responsible for several European countries and for realizing Supply Chain within GrandVision.

Daily schedule: