Of the companies I’ve studied over the years, I’ve learned that data quality isn’t an outcome or an end-goal; it’s a complex but enriching journey. So far, none would suggest they have reached the end of the data quality process. In fact, many would state they’re beginning or are in the process of an enduring enterprise-wide pilgrimage. At a time when everything is changing, the end state keeps evolving. This being said, in order to become nimble, organizations should mature its data quality practice.

On the other hand, a common scenario that is encountered is when data tells a different story than what the customer holds to be true. Usually, the customer would either blame it on the messiness of the data or the analytics platform itself. After the myriad transformations, matching, schema modifications, unifications and predictive tasks, how can you identify that the data is correct? How can you create a reference data or master data that you can refer to?

Data scientists and engineers today are glamorized, but still spend 80% of their time tiding data. Join me in this course to turn your data quality challenges into an opportunity that makes your AI investment successful. You’ll understand the relevant frameworks, dimensions and metrics of your data quality assessment efforts. In addition, you’ll understand how data virtualization, graph analytics, data governance, data modeling, metadata, business rules, master data, reference data, and data standards fit into the process for ensuring high quality data. You’ll be able to design a data quality assessment, build scorecard visualizations and achieve better matching for for your customer profiles no matter how messy or unstructured you data is.

OUTLINE:

Introducing The Data Quality Challenge:

Assessing how good is the current data quality practice and how to provide scoring and metrics to measure it.

How to ensure continuous monitoring and checking of the data

The methodology needed to set the right data quality assessment strategy for your organization

Understanding the key components of any information management strategy project which might affect data quality (People, Data, Processes, Technology)

Governing data provisioning process using rules-based metadata

Business Need and Data Quality Project Steps:

How do you plan for your data quality project

How do you ensure all stakeholders and data stewards aligned

Design the data discovery, readiness and assessment plan

Gather and analyze information about the current state of data quality and the information lifecycle for root-cause analysis.

Introducing the components of a Data Quality Assessment.

From dimensions to scoring to business rules and scorecard. In this module we’ll go through all the components of a data quality assessment.

Data Quality Assessment Steps:

Technical walk-through of the data quality assessment steps and algorithms needed to get a scoring for the quality of the data. This includes data sampling, profiling, scoring, and after evaluation analysis.

Determine the impact of messy data on your business.

Data Quality Metrics & Dimensions:

In different business environments, the selection of data quality elements will differ. In this module we’ll help you in:

Developing data quality KPIs and formulas based on your industry and business use case. These KPIs can be used as a standard to calculate scores that assess the messiness of datasets.

Evaluate messiness for the data quality dimensions applicable to the issue

Overview of all the dimensions of data quality and how to choose which dimensions will best support business needs and industry.

Best practices on selecting weights for each data quality dimension.

Data Quality Scorecard Development:

Building visualizations of a data quality assessment scorecard that presents metric scores to the data stewards observing the business data sets.

Data Matching & Deduplication Best Practices

Approaches taken to improve the matching process and deduplication of customer profiles to get a 360 view of your customer

Data Governance

How do you relate Data Governance, Stewardship, and Data Quality?

Setting up the different layers of decision making for data governance strategy

How would data governance affect your data quality?

Data Lineage to identify changes and tracks logs in the datasets

Master Data Management, Data Virtualization & Graph Analytics

Helping delegates understand the relevant Master Data Management architecture blueprint as a use case of their business.

Understand the value of graph analytics in customer 360 view and presenting customer case studies.

The role of data virtualization integration techniques in your data quality assessment pipeline.

Communicating actions and recommendations for Champions, data stewards and stakeholders,

Our Course Methodology:

Vendor Agnostic Data quality tools.

Interactive exercises and and a case study will help delegates practice what they learned

Interactive Hands-on using real world data:

Data Quality Score Calculation

Detailed steps and compositions of the layers needed to design a pre-sales data quality assessment.

Target Audience

This course is designed for anyone responsible for or interested in the quality of data in their organization, business processes, software solutions, systems or databases. Also, Software vendors in need for a pre-sales data quality assessment. The roles below would mostly be interested:

• Data Analysts
• Customer Experience Managers
• Customer Loyalty Managers
• Data Quality Managers
• Data Scientists
• Chief Data Officers
• Data Quality Analysts
• Business Analysts
• Data Designers/Modellers
• Data Stewards
• Project Managers
• Interactive Exercises to build detailed steps and compositions of the layers needed to design a pre-sales data quality assessment.

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Trainer Bio:

Ali Rebaie a data anthropologist, industry analyst and global keynote speaker. He is also the President of Rebaie Analytics Group. He founded his company in his early 20s and fastly grown a global Fortune 500 clientele base.

Ali Rebaie is frequently ranked among the most prolific keynote speakers at events around the world. He has been listed consecutively among the top 100 global Big Data & AI influencers since 2013. By now, he delivered keynotes in all world's continents. Ali is one of the initiators of School of Data and trained thousands of data enthusiasts to help them tackle the fear of AI robo-advisors replacing the role of human advisors in the new age.

Ali also appeared in leading global media outlets including WIRED, Reuters, BBC, and Vision Magazine. Ali is also a jury member and reviewer in leading AI competitions and IEEE conferences, and also an Advisory Board member of the internationally renowned Boulder BI Brain Trust (BBBT).

His studies are based on the philosophical foundations of data, with focus on the social underpinnings of technology encounters. As a part of his work at Rebaie Analytics Group, he coined the practice of Data Anthropology™ which aims at bringing the disciplines of data science and anthropology together to study human behavior and culture and tell stories that move us from thoughts, beliefs, and knowledge, to experiencing, empathizing and taking actionable decisions. Ethnographic and phenomenological on-the-ground field methods augment machine intelligence models and data coming from IoT and other sources. This practice will also help us prepare for the age of robots killing jobs and tackle the fear of AI robo-advisors replacing the role of human advisors. You can connect with Ali on social media @AliRebaie or visit his blog at www.alirebaie.com.