When the newly-formed Business Intelligence team at Larimer County set out to tackle warehousing their organization’s financial data, they struggled to complete their work with traditional agile project delivery methodology. Five years later, the team has successfully completed multiple large scale BI projects and has hard-earned lessons to share. Topics covered include how to structure a BI project most efficiently, adapted requirements gathering techniques for analytics projects, creating demand for data and analytics, and other key lessons learned in executing Business Intelligence work.
Learning Objectives:
- Understanding business and technical challenges in executing BI projects
- Insights into adapted methods for effective BI project delivery
- Acquisition of tips and tricks for designing, managing, and executing BI projects
- Understand roles and responsibilities for an effective BI project team
- Insights into maintaining customer-centric project focus and managing customer expectations
Five years ago there were 2.4 billion internet users. Today there are 3.8 billion internet users in 2017. That is 14 users per second.
Those users have one thing in common – data.
BBC reports that there is at least 1,200 petabytes in the major online sources (Amazon, Google, Facebook, and Microsoft) alone. That is BIG data! Big data is a catchphrase that has been tossed around for years. Today data is bigger than ever. Corporations need to know how to manage, analyze, and most importantly how to secure data. Knowledge in Data Management is essential in today’s businesses – big or small.
This session will introduce you to Big Data and Data Management. It will teach you the basics of data governance, security, storage, quality, and analytics. It will prepare you to understand conversations about data and provide a solid foundation to help build more data management skills.
IBM predicts nearly three million data jobs in 2020. Join me to begin preparing for the future of data.
Learning Objectives:
- Describe Data Management and its components
- Identify opportunities to improve data management
- Recognize why data management is essential
With the focus today being Business Capability (action), Data modeling is getting lost. Business needs to define its data, for from the data comes information that can become knowledge that can be used by the business to be successful. But how do we make sense of the different types of data modeling? What are the differences? When are they appropriate? We will look at data modeling techniques from the perspectives of strategy, program, process and tooling and show examples of each technique and how they inter-relate.
Learning Objectives:
- Understand the different type of Data Models
- Understand when to use each type of Data Model
- Introduction to Process-level Data Modeling
In a recent survey conducted by IIBA, we asked our corporate members what the future skill needs were for business Analysts. The answer was that the number one skill area in 3 years was Data Analytics. This indicates that BA’s are challenged to learn more about this field. It does not mean that BA become Data Scientists with deep technical and statistical skills. The IIBA has done additional research supported by many recent articles that talk to the role of the BA in several areas that support Business Data Analytics. One insight that we have seen is that when BA’s are involved in data analytics teams, the outcomes improve and the business is better able to utilize and consume the results.
The BA involvement starts in a familiar way. It starts with understanding the business problem. It looks at what data is needed to understand and gain insights in the problem space. It looks at creating and designing the data experiments and assessing the results. The BA then leverages their skills as the ‘translator’ to make business requirements make sense fo the technical and data scientists. When the results are brought forward, the BA will work to create visualizations to explain the data, but even more importantly they work to do the ‘storytelling’ and what the data says, what are the insights and not just throw out statistical data overload but to craft the story in a way that the business can examine it, and evaluate how it corresponds to their experience and ‘gut’ based sense of the business.
This presentation will highlight these aspects, and show how Business Data Analytics is a logical extension of the practice of business analysis, and as a real improved value proposition to business leaders into how to get better results from their investments in data analytics, and business intelligence.
Cenovus is a Canadian integrated oil and natural gas company headquartered in Calgary. We’re committed to maximizing value by responsibly developing our assets in a safe, innovative and efficient way. Our operations include oil sands projects in northern Alberta, which use specialized methods to drill and pump the oil to the surface, and established natural gas and oil production in Alberta and British Columbia.
In certain areas of Asset Management, Cenovus’s data-driven decision-making philosophy has had analytics play a major role in certain asset management activities. This presentation reviews 2 initiatives: How Text Analytics improved the quality of data used in Asset Failure analysis and how Machine Learning algorithms were used to derive models for Asset Failure prediction. Based on ongoing initiatives, the presentation provides insight into real-world opportunity definitions, ML algorithms, technology, data gathering/cleansing techniques, user-acceptance roadblocks and other learnings from an organizational perspective.
Learning Objectives:
- Understanding ROI for Data Science initiatives
- Understanding Data cleansing techniques
- Overcoming roadblocks to adoption, show business value