Programma-inhoud

Al jaren bestaat de wereld van Business Intelligence (BI) uit het bouwen van rapporten en dashboards. De BI-wereld om ons heen verandert echter snel. (Statistical) Analytics worden meer en meer ingezet, elke student krijgt gedegen R-training en het gebruik van data verplaatst zich van IT naar business. Maar zijn we wel klaar voor deze nieuwe werkwijze? Zijn we in staat om de nieuw verkregen inzichten te delen? En kunnen we echt het onderbuikgevoel van het management veranderen?
Tijdens deze presentatie gaan we in op deze veranderende wereld. We gaan in op hoe we het data-driven storytelling proces kunnen toepassen binnen BI-projecten, welke rollen zijn hiervoor nodig en u krijgt handvatten om nieuw verkregen inzichten te communiceren via storytelling.

• Inzicht in het Data-driven storytelling process
• Visuele data exploratie
• Organisatorische wijzigingen
• Communiceren via Infographics
• Combineren van data, visualisatie en een verhaal.

Have you ever been disappointed with the results of traditional data requirements gathering, especially for BI and data analytics? Ever wished you could ‘cut to the chase’ and somehow model the data directly with the people who know it and want to use it. However, that’s not a realistic alternative, is it? Business people don’t do data modeling! But what if that wasn’t the case?

In this lively session Lawrence Corr shares his favourite collaborative modeling techniques – popularized in books such as ‘Business Model Generation’ and ‘Agile Data Warehouse Design’ – for successfully engaging stakeholders using BEAM (Business Event Analysis and Modeling) and the Business Model Canvas for value-driven BI requirements gathering and star schema design. Learn how visual thinking, narrative, 7Ws and lots of Post-it ™ notes can get your stakeholders thinking dimensionally and capturing their own data requirements with agility.

This session will cover:

• The whys of data modelstorming: Why it’s different from traditional data modeling and why we need it
• Drawing user-focused data models: alternatives to entity relation diagrams for visualising data opportunities
• Using BEAM (Business Event Analysis and Modeling) to discover key data sources and define rich data sets
• How making toast can encourage collaborative modeling within your organisation
• Modelstorming templates which you can download and start using straight away.

Many who work within organizations that are in the early stages of their digital transformation are surprised when an accurate model — built with good intentions and capable of producing measurable benefit to the organization — faces organizational resistance. No veteran modeler is surprised by this because all projects face some organizational resistance to some degree. This predictable and eminently manageable problem simply requires attention during the project’s design phase. Proper design will minimize resistance and most projects will proceed to their natural conclusion – deployed models that provide measurable and purposeful benefit to the organization. Keith will share carefully chosen case studies based upon real world projects that reveal why organizational resistance was a problem and how it was addressed.

• Typical reasons why organizational resistance arises.
• Identifying and prioritizing valid opportunities that align with organizational priorities
• Which teams members should be consulted early in the project design to avoid resistance
• How to estimate ROI during the design phases and achieve ROI in the validation phase
• The importance of a ‘dress rehearsal’ prior to going live.

The close links between data quality and business intelligence & data warehousing (BI/DW) have long been recognised. Their relationship is symbiotic. Robust data quality is a keystone for successful BI/DW; BI/DW can highlight data shortcomings and drive the need for better data quality. A key driver for the invention of data warehouses was that they would improve the integrity of the data they store and process.
Despite this close bond between these data disciplines, their marriage has not always been a successful one. Our industry is littered with failed BI/DW projects, with an inability to tackle and resolve underlying data quality issues often cited as a primary reason for failure. Today many analytics and data science projects are also failing to meet their goals for the same reason.
Why has the history of BI/DW been plagued with an inability to build and sustain the solid data quality foundation it needs? This presentation tackles these issues and suggests how BI/DW and data quality can and must support each other. The Ancient Greeks understood this. We must do the same.
This session will address:

• What is data quality and why is it the core of effective data management?
• What can happen when it goes wrong – business and BI/DW implications
• The synergies between data quality and BI/DW
• Traditional approaches to tackling data quality for DW / BI
• The shortcomings of these approaches in today’s BI/DW world
• New approaches for tackling today’s data quality challenges
• Several use cases of organisations who have successfully tackled data quality & the key lessons learned.

Cloud diensten, in-memory databases en database servers die gebruik maken van massive parallel processing voeren de boventoon in de marketing hype van vandaag. Maar wat is er de laatste tien jaar nu echt veranderd in de databasemarkt? Welke aanbieders zijn in staat om vooruit te blijven lopen op technologisch gebied? Moeten we ons zorgen maken over de invloed van nieuwe hardware zoals GPU en non-volatile memory? En kunnen we wel vertrouwen op programmeurs om keer op keer het wiel opnieuw uit te vinden voor elke database-interactie? Wat zijn de hot technologies in de labs van de database leveranciers?

In deze sessie zal vooral worden gekeken naar:

• Column stores, een de-facto standaard voor Business Intelligence, met haar oorsprong in Nederland
• Van Hadoop tot Apache Spark, wanneer u wel en wanneer u niet de portemonnee moet trekken
• Het slechten van de muren tussen het DBMS en programmeertalen als Java/C/..
• Performance, niet alleen een kwestie van een benchmark score
• Efficiënt omgaan met resources om geld te besparen.

More enterprises are seeking to transform themselves into data-driven, digitally based organisations. Many have recognised that this will not be solely achieved by acquiring new technologies and tools. Instead they are aware that becoming data-driven requires a holistic transformation of existing business models, involving culture change, process redesign and re-engineering, and a step change in data management capabilities.
To deliver this holistic transformation, creating and delivering a coherent and overarching data strategy is essential. Becoming data-driven requires a plan which spells out what an organisation must do to achieve its data transformational goals. A data strategy can be critical in answering questions such as: How ready are we to become data-driven? What data do we need to focus on, now and in the future? What problems and opportunities should we tackle first and why? What part does business intelligence and data warehousing have to play in a data strategy? How do we assess a data strategy’s success?
This session will outline how to produce a data strategy and supporting roadmap, and how to ensure that it becomes a living and agile blueprint for change rather than a statement of aspiration.
This session will cover:

• The relationship between an organisation’s business strategy and data strategy
• What a data strategy is (and is not)
• Building & delivering a data strategy – the key components and steps
• The role of BI/DW in a data strategy – data issues and data needs
• The ‘limit or liberate’ data dilemma and how to resolve it through data governance
• Several use cases of successful data strategies and lessons learned.

Most analytic modelers wait until after they’ve built a model to consider deployment. Doing so practically ensures project failure. Their motivations are typically sincere but misplaced. In many cases, analysts want to first ensure that there is something worth deploying. However, there are very specific design issues that must be resolved before meaningful data exploration, data preparation and modeling can begin. The most obvious of many considerations to address ahead of modeling is whether senior management truly desires a deployed model. Perhaps the perceived purpose of the model is insight and not deployment at all. There is a myth that a model that manages to provide insight will also have the characteristics desirable in a deployed model. It is simply not true. No one benefits from this lack of foresight and communication. This session will convey imperative preparatory considerations to arrive at accountable, deployable and adoptable projects and Keith will share carefully chosen project design case studies and how deployment is a critical design consideration.

• Which modeling approach continues to be the most common and important in machine learning
• The iterative process from exploration to modeling to deployment
• Which team members should be consulted in the earliest stages of predictive analytics project design?
• Misconceptions about predictive analytics, modeling, and deployment
• Costly strategic design errors to avoid in predictive analytics projects
• Common styles of deployment.