Welcome to Today's Web Seminar

Transcription

1 Welcome to Today's Web Seminar SPONSORED BY HOSTED BY

2 Lenny Liebmann Contributing Editor SourceMedia Lenny Liebmann has spent his career studying how enterprises in key verticals such as healthcare and insurance leverage information technology in the context of their real-world business objectives and constraints. His 40-plus years of engagement with front-line practitioners make him a sought-after speaker and moderator for industry conferences and online events. He has also been widely published as a journalist, analyst, and featured columnist. Lenny wrote his first line of code in 1973, graduated Yale in 1979, and began his career at AT&T Bell Laboratories.

3 Beverly Wright, PhD Chief Analytics Officer, Aspirent Professor, Georgia Institute of Technology Dr. Wright is a leading analytics practitioner and sought-after speaker at industry events. In her current position, Dr. Wright leads the Analytics and Insights practice for Aspirent, a management consulting firm for Fortune 100 clients. She is also a professor at Georgia Institute of Technology s Scheller College of Business, where she also serves on the Board of Directors for the university s Business Analytics Center. Dr. Wright previously held analytics, marketing, and decision-making leadership positions at Cox Communications, SunTrust Bank, Southern Company, and AGL Resources. She has also taught at Georgia State University, East Carolina University, Clayton State University, and Berry College. Dr. Wright earned her PhD in Marketing Science, MS in Analytical Methods, and BS in Decision Sciences from Georgia State University. She is a Certified Analytics Professional (CAP) and received Professional Research Certification from the Marketing Research Association.

4 Peggy Tsai Vice President Analytics & Data Morgan Stanley Wealth Management Peggy Tsai is Vice President in the Analytics & Data department at Morgan Stanley. She is responsible for managing the adoption of Data Governance standards and processes in Wealth Management. She works closely with IT, Business Stewards and Enterprise to identify and resolve data quality issues that impact the business. Peggy has over 15 years of experience in data management, stewardship and governance. Prior to joining Morgan Stanley, Peggy was Data Innovation Lead at AIG where she worked in the enterprise data management practice to support Anti-Money Laundering, Solvency II and GDPR in Latin America and Europe. In addition, she led a winning Innovation Boot Camp team with a proof of concept on Natural Language Processing and Machine Learning aimed at improving the underwriting process. Peggy also worked at S&P Global where she held various positions in enterprise data group and technology in order to drive the value of data between the business and IT. Peggy has a Masters in Information Systems from New York University and a Bachelors of Arts in Economics from Cornell University. She is a member of the Data Governance Professional Organization. In her spare time, she hosts externships for Cornell freshmen students in order to increase the awareness of data and analytics among undergraduates.

5 Machine Learning Data Discovery Process going to the source making data usable developing solid insights Challenges 5

6 what is machine learning? 6 An application of artificial intelligence that automates the process of analyzing data, allowing computers to detect patterns, build and refine models, and make decisions, without being explicitly programmed by humans to do so. The learning term comes into play through iterative model refinement and optimization, converging on higher accuracy. Applications Methods Supervised vs Unsupervised Prediction

7 what is data discovery? 7 The process by which business users make sense of vast amounts of information contained in data warehouses, spreadsheets, flat files, or a mix of them all. Data discovery combines database architect (or other data owner ) knowledge with analysis software and business intuition to turn raw data into a powerful tool for driving business decisions. Data Selection Data Cleaning Data Transformation Data Mining

8 The Process: from data to insights 8 Even with the advancements made in data science and machine learning in recent years, businesses still face lofty hurdles upfront: to analyze the data, you have to first find the data. Source of data Data in its most raw form, often in disparate sources and in different forms Making it usable Interpretation, cleaning, outlier handling, forming metrics Finding insights Data is ready now make sense of it

9 Challenges 9

10 Nature of the data Data Capture Methods Storage Locations the Source who, what and where is the data? 10 Transactions Customer information Business information Product information Sales pursuits External data appends POS systems Surveys (digital or paper) Online vs inperson Booking systems Oldfashioned data entry Purchased Flat files Spreadsheets Relational tables Full Enterprise data warehouse Unstructured vs structured Audio (e.g. customer call recordings)

11 Raw data may come from disparate sources, owned by different data stewards, and require tact and skill to access Source: Location 11

12 Data is often inconsistent even the same variable or metrics may have different measurements Source: Types 12

13 Understanding the data Data validation Cleansing and prep making it usable the data cleaning process 13 Metadata: data about data Data dictionary definitions, data type, and possible values Join keys What does a missing value mean Level of granularity What is a unit Missing value imputation Record validation Verification of uniqueness Joining/merging Deduplication Outlier handling Standardization Incorrect data/invalid characters or out-of-range values Dummy coding Transformation Variable reduction

14 Data is typically dirty even if data is perfect not usually collected and stored for analytics purposes Usage form: Cleaning 14

15 Business Questions Usability Relevance finding insights learning from the data 15 Understanding the reasons behind the analysis How the information will get used Not just the why but the why-why behind the reasons No real value if results are not consumable by the target audience Managing expectations Results that are important to the business Must hold value to the questions asked

16 Finding insights: Uncovering the truths Analytics professionals are truth seekers: extracting meaning from data is the fun part 16

17 summary 17 In today s world, business decisions have to be data-inspired to stay competitive Those decisions are only as good as the data used to make them Garbage in, garbage out Inefficient data pipelines negatively impact IT (more processing, storage, and manpower is required) and the business (slower decision making, incorrect information is shared, costly mistakes) Being data-savvy can translate to business benefits Revenues Reduced operating costs Market advantages Consumer loyalty

18 The Need for Data Governance in Analytics Peggy Tsai Vice President, Morgan Stanley Wealth Management February 27, 2018

19 Data Governance Lifecycle Monitor Scope Dashboard Risk Impact Define Data Stewards Measure glossary Risk Assessment Data Quality Rules

20 Evolution of Data Governance The biggest drivers for data governance is no longer regulatory/compliance Critical Data Elements Governance Councils Data Quality Monitoring Data Sourcing/Lineage BCBS 239 CCAR GDPR MiFid Solvency II

21 Analytics is Driving the Data Evolution Data Steward for Accounts Define Business Terms for Customer Name, Address, Define Data Quality Rules and Thresholds Provide requirements for business usage Define business logic or rules Data Scientist Create models that predict customer behavior/patterns Find, join and create large sets of uncurated data Answer questions that quickly drives the Sales or Marketing campaigns for example

22 Case Study: FreshDirect Online grocer competes on analytics Predicts consumer s shopping patterns to develop custom solutions

23 No organization can succeed in analytics without a strong foundation in data governance

24 Three major disciplines for Data Governance What is different with Analytics Governance?

25 Machine Learning for Data Discovery A framework for the data is still required for structured and unstructured data Data Quality controls still need to exist to ensure completeness and accuracy of the data People still need to ensure the curation of the data is accurate in order to create the training set

26 Key Differentiators in Analytical Governance Visualization Usability Speed Curated Data Crowdsourcing Self-Service

27 Drive value from data quickly and reliably Analytics Data Governance

28 Thank You! Any Questions? SPONSORED BY HOSTED BY