Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
3. 2 | Proprietary & Confidential
Data Governance Matters to Data Scientists
Delivers an agile, modern data architecture
Creates data quality and trust
Detects, protects, and governs sensitive data
Speeds up operationalization of analytical models
Enables agile data processes including DataOps
4. 3 | Proprietary & Confidential
Yet There is Work To Do
● Less than half of an organization’s
structured data is actively used in making
decisions
● Less than 1% of its unstructured data is
analyzed or used at all.
● More than 70% of employees have
access to data they should not
● 80% of analysts’ time is spent simply
discovering and preparing data
5. 4 | Proprietary & Confidential
● A survey by Crowdflower found
that 19% of a data scientist time
was spent collecting data sets and
60% of their time was spent
cleaning and organizing data
● Kirk Boone asked whether data
scientists could be better called
data plumbers
Why Making Data Better Matters
6. 5 | Proprietary & Confidential
Suggested Principles for Effective Data Governance
Program
People Centric vs Top, Down Forced
Autonomous--Takes the Labor out of Data Stewardship
Built on Continuous Improvement Principles
Accelerated Business Outcomes from Data