Marketing Analytics, Financial Analysis, And Data Science

Served as a consultant as Wells Fargo needed to migrate a large document storage network from SharePoint
2013 to SharePoint Online (cloud storage) and network storage. Led the initiative of profiling the data from the
old system to determine the migration needs for the Enterprise Data Governance group. Wells Fargo’s Enterprise
Data Governance group needed a database reporting system to organize users/projects/project details for
reports and automated notifications. Designed reporting tools to handle these tasks. Added Agile Sprint
functionality. This resulted in faster completion of critical data governance projects and better prioritization of
work.
• Led SQL coding efforts to profile the universe of files to be addressed. A comprehensive summary of files to
be migrated, archived, and deleted was completed far before deadline.
• Worked with end users and information system group to determine mapping parameters in the new cloud
storage system. An efficient transfer of necessary files resulted.
• Technology: SQ, MS SharePoint, MS Power Apps, MS Power Automate, Power BI, Jira/JQL, MS Access, MS
Excel, Pivot Tables, Data Lake

Wells Fargo, Charlotte needed multiple databases to catalog a large volume of bank policy documents and track
industry concerns that were being covered and which were not. Established requirements gathering for the
databases and then built two databases (front and back ends for each) with Microsoft Access (using SQL and
VBA)
• Built reporting and querying capabilities that can answer a multitude of management questions. The
architecture maximizes response time and has a backend that is scalable to an enterprise grade database
system. Have essentially turned unorganized data into actionable information.
• Technology: MS SharePoint, MS Power Apps, MS Power Automate, Power BI, Jira, MS Access, and MS Excel

Wells Fargo, Charlotte needed data lineage (the history and path of data moving through the organization),
profiling of data fields, and quality control to be performed to ensure new reporting data systems were providing
accurate data for regulatory reporting systems. Participated in meeting each objective; in terms of reporting and
remediation efforts on transformed data fields.
• Queried data across multiple Oracle and Teradata data systems with SQL for data profiling work.
• Found anomalies between systems and worked with upstream parties to understand and eliminate
differences in data with necessary manual modifications.
• Defect resolution reports and plans were created to ensure data quality work was fully carried out.
• Verified Data transformation quality, between systems.
• Technology: Excel, Oracle PL SQL, and Teradata SQL were used for data summarization. Reporting: FR Y9C.

Bank of America, Charlotte client needed data profiling and lineage (the history and path of data moving through
the organization) to be performed to ensure new data systems were providing the same information as aging
data systems for regulatory reporting. Pulled together documentation, service level agreements, and metadata
information to determine what necessary testing had to be done for the data system transition.
• Validated that the documentation was being followed by querying the relevant Oracle and Teradata
databases with SQL and compared results from the different databases and delivered the results to
management in Excel and PowerPoint.
• Also, created a template for data sharing agreements between a reporting system and data providers.
• Technology: Colibra, Excel, Access, SQL Server, Oracle PL SQL, and Teradata. Reporting: FR Y9C and 14Q

The company needed a more effective technique to prioritize retail leads to increase mutual fund sales and
profitability. Gathered requirements, designed databases, transformed data, developed databases, facilitated
testing, documented, and implemented data systems for regular quarterly reporting, and ad hoc reporting.
• The purpose of this was to analyze the effectiveness of multiple sales channels, using new scoring schemes
and previous selection techniques. Average new investor investment amounts increased significantly.
• The reporting led to the expansion of cost effective sales channels and the contraction/elimination of cost
ineffective sales channels, using marketing analytics.
• Created new multivariate scoring schemes for predicting/targeting large opportunity investors using
marketing call center, marketing database, and third party household level data.
• Explanatory Tree maps were created to explain the most important drivers of current and past large new
customer investments.
• Established a methodology and prototype system for monitoring promotion code responses and sales.
• Technology: SPSS and MS Access for data manipulation. SPSS for logistic regression and CHAID tree
modeling. VBA (Access) for enhanced reporting functionality, SPSS and Excel for reporting