Are you a manager, controller, or business analyst involved in a data warehouse / big data project, and are you looking for theoretical and, especially, practical frameworks? You might have the following questions:
- You know that “a single version of the truth” is used with a data warehouse, but is it really that simple?
- Do you look forward to the moment when management information no longer has to be “conjured” from a variety of spreadsheets?
- Do you want to have really useful historical information at your disposal for once? How can a data warehouse help you with this, and what do you have to consider?
- How do you deal with the upcoming developments around Big Data?
If you can relate to one or more of these questions, we can heartily recommend participating in our 3-day Data Warehouse & Big Data training course.
Successful establishment & improvement of the data warehouse
All the elements needed for the successful establishment / improvement of the data warehouse & Big Data are addressed in three intensive days. Employees who have followed our three-day training course are perfectly capable of taking the right steps and achieving success with their Data Warehouse project.
Training in practical Data Warehouse tools
Data Warehousing and Data Science in particular are specialized fields. Working on an organization’s intelligence without having the right skills and tools often delivers contrary results. Our practical model, in which all Data Science tools and Data Warehouse methodologies are covered, creates a clear route towards real results.
Discover the Data Science success factors
Our three-day Data Warehouse & Big Data training places both business-related and technical aspects of Data Science on the agenda, such as KPIs, analytics, Big Data, the data warehouse, master data management (MDM), data quality, data modeling and management. But, above all, the success factors of a data warehouse and the pitfalls of Big Data applications come to the fore.
The instructor: Minne van der Sluis
The instructor has more than 25 years experience as a Data Science consultant and is a Certified Business Intelligence Professional (CBIP). This Data Warehouse & Big Data training was designed by him ten years ago and has been continuously refined.
During this 3-day Data Warehouse & Big Data masterclass you will become acquainted with each of the three modules with the term “data warehouse”, the principles of ETL, setting up a data warehouse, Business Intelligence as a higher goal of data warehousing, success factors of data warehouse projects, the developments in the field of Data Science, and Big Data applications fitting within an appropriate architecture.
Day 1: Introduction, goals, alternatives & architecture
Introduction to data warehousing & management information
A data warehouse, which is a central database for management information, supports the principle of one version of the truth through cleaning up and integrating data from various sources (business processes) so that it can be easily linked together.
- How do you transform data into knowledge through information? What transformation battles will this involve? What is the importance of proper information management?
- What goals, such as history build-up and performance improvement, should a data warehouse have? How does the data warehouse contribute to improved data quality, recognition and findability of information? What does a data warehouse architecture roughly look like?
- When are alternatives like appliances & in-memory and data virtualization useful and feasible? What can we learn from compelling case studies, such as Ahold? What are your own experiences and what can be learned from them?
Data warehouse architecture
You need tools to build a data warehouse. These must fit within the overall business architecture. The underlying cohesion is essential for success.
- What is the importance of good data warehouse architecture? How does a data warehouse fit within an enterprise architecture?
- What schools, such as Inmon, Kimball and Lindstedt (Data Vault), are there, and what are the main differences (and similarities)? What are the advantages and disadvantages of the different methods? When do you choose one or a combination of them?
- Which database and modeling tools are there, and how do you make the correct choice?
The Data Warehouse & ETL processes
ETL tools are an indispensable component in the supporting architecture of a data warehouse. They take care of extracting, transforming, and loading the data. They also take care of improvement of the informatization process and data quality.
- What data modeling techniques are applicable? How do you give shape to the various process steps, such as extraction, transformation and loading?
- What ETL tools are available for this? When do you opt for a single supplier or best-of-breed? Discover our 100% independent ETL Tools & Data Integration Survey.
- Which methods for quality improvement, such as at the source or in the DWH, are there? What is data profiling and how can that help?
- What is the difference between ETL and ELT? What is the impact of increasingly decentralized datasets (including Big Data and data lakes) inside and outside of the organization?
Day 2: Analytics, Master Data Management & metadata, management & success
Business Intelligence & Analytics
The goal of the data warehouse is to provide quality data to the end users. They often use Business Intelligence tools to provide insight.
- How does a data warehouse deliver high-quality data to the end users? What Business Intelligence tools are available? Discover our 100% vendor-independent BI Tools Survey.
- What are the latest trends in data warehousing and BI, particularly around Big Data and Data Science? What is the meaning of open source in this arena?
- When is direct access to the data warehouse advisable and useful, and when not? What types of users can be distinguished, with what functional needs? How do you react appropriately to this?
- What role does self-service play in this?
Master Data Management (MDM) & metadata
Having good master and meta data is extremely important in delivering high quality and reliable information. Important data groups such as customers, products, and employees must be well maintained, and insight into the genesis and associated business rules is essential.
- What is actually meant by master and metadata? How does it help with delivering high quality and reliable information (one version of the truth)? Which processes play a role?
- Is master and metadata management purely analytical or can / must it also have an impact on the operational systems? How do MDM and metadata management fit in data warehouse architecture? How do you set up a smart learning loop with an enterprise portal?
Data Warehouse Management & success
Data warehouses require their own technical and functional management processes. Sometimes a Competence Center plays an important role in it. A suitable project approach and a good eye for the success and risk factors are important.
- What are the key success factors for a data warehouse? How important is maintenance and management therein (“build a DWH with support and maintenance in mind”)?
- What technical and functional management processes can be distinguished? What tools are available for this purpose?
- Are special competencies and skills needed for DWH management? How is DWH maintenance different from (or the same as) “traditional” maintenance?
- What roles do the business and IT play? What is the purpose (and lack thereof) of Competence Centers in this?
Day 3: Big Data, typical applications & Data Science
The term Big Data made its debut a few years ago: large amounts of (often unstructured) data, which come at you at a rapidly increasing pace and in varying quality. This abundance of data proposes a number of challenges. What can and should you do with it? How do you derive value and benefit from it?
- What is the impact of the various Vs of Big Data? How do you deal with huge size, great speed (of arrival) and volatility (of existence), large variety (structured and unstructured) and quite varying reliability? What can be learned from the pioneers like Google and Walmart?
- What new storage mechanisms, such as column or NoSQL databases, are relevant in addition to the traditional database techniques? What can (or can’t) you expect from new (open source) tooling like Hadoop and R? How do the current (major) database and BI technology suppliers respond to this?
Typical Big Data applications
Big Data success stories are appearing more and more rapidly. Those stories no longer go unnoticed in the larger media. The 8 o’clock news and the BBC have already reported on the Amsterdam fire department’s use of Big Data to prevent fires. The Amsterdam police earned their place on the Dutch BI Award podium by being able to catch criminals before they can commit a crime.
What can we learn from compelling cases like:
- City of Dublin: detecting traffic congestion
- Social Analytics (KLM)
- Police and Fire Amsterdam (Dutch BI Award)
Which success and failure factors played a role in these success stories? What are your own (possible future) experiences and what can be learned from them?
Data Science is about finding patterns in large data streams and then analyzing and validating them, in order to intervene proactively in what are often operational control processes.
- What does the process of data discovery look like? How important is near-real-time reaction (instantaneous) in the process? What data science tools exist for analyzing these large flows of data?
- What do the other vendors, such as IBM, SAS, SAP, Microsoft, and Oracle, do in this area? Find out in our Big Data & Data Science Tools Survey.
- What quantitative skills (soft & hard skills) are needed in a successful Big Data analytics team? What examples of applications are there, for example in the fields of social and content analytics, predictive maintenance and predictive analytics?
Group discussions take place every training day and participants work on practical assignments. This creates an optimal mix between theory and practice. Upon completion of this unique Data Warehouse & Big Data training, participants receive a certificate from Passionned Academy and a copy of “The intelligent organization” (4th edition).
The masterclass is intended for people who must build and maintain a (big) data warehouse in the future, starting technical and functional project managers, and (future) BI & DWH managers. Before going in-depth with, for example, tools or data models, this three-day masterclass provides a valuable overview of the playing field and provides you with all the ingredients for success.
You can use our registration form to register directly for the next Data Warehouse & Big Data training. If you have questions about this training, please contact us.