Case Study: Delivering Data Analytics to Drive Organizational Change

August 15, 2023

A large client undergoing a reorganization needed visibility on its business practices, products, services, staffing levels, and workloads. To communicate and plan, stakeholders across the organization required data-driven insights about current and future operational needs.

Our Challenge:

The organization’s internal data capture was implemented in varying and often inconsistent ways among the organization’s operating units.
Data definitions reflected complex internal guidance and rules. As a result, data meanings were not readily interpretable or explainable in plain language.
Status quo data insights were being produced in an environment relying heavily on opaque and complex handling methods. This led to time consuming data extraction and results reporting.
Data quality controls were needed to identify problem areas and verify the strengths and weaknesses in the client’s data methods.
Although large amounts of data were generated at operational levels, legacy databases and over-reliance on basic tools (spreadsheets) hampered the targeted insights that decision makers needed most.

Our Solution:

The solution was layered into several steps:

1. Define the business scope

Identify existing operational issues and reorganization questions
Define business processes
Define how the organization leverages IT systems and contribute data to the business

2. Identify and assess available data

Gather and generate data generation across any/all relevant sources
Align data to business processes and requirements

3. Refine the data

Cleanse and validate the large and disparate data sets to a level capable of addressing the critical issues and questions in staffing, funding, and workload

4. Apply a tailored machine-learning approach (k-clustering vector quantization) to identify actionable insights

Posit overarching hypothesis regarding business performance and management
Identify operational issues and verify conclusions leveraging the data

5. Integrate data insights and empirical evidence to update processes and develop a way ahead

Develop data-supported actions to address process deficiencies
Undertake data stewardship to monitor and manage staffing and funding process updates

A visual summary of our solution is provided in Figure 1.

Figure 1. Data Solution Cycle

Outcome:

The innovations captured were made possible by working closely with the organization to gain a deep understanding of their data generation processes and existing gaps. From this understanding, data was uniquely produced as a springboard to efficiently and agilely perform the advanced calculations necessary to analyze workload, workforce, and funding issues. The client’s unique data systems meant the business rules behind the data required a high degree of familiarity that was gained through socialization with the client and their business operations. The depth, breadth, amount and type of data in our stewardship positioned us for impactful data analytic insights which could then be mined with advanced methods and the proper tools for the job. The data groundwork was completed with the first step in a foundation of business-aligned data processes that can continuously be built upon to multiply the value of insights generated.

Multiple deliverables to the client included clean data sets, non-conventional data visuals for problem identification, spreadsheets with formula solutions, and high-quality slide decks.

At the end of our assignment, the client was able to clearly identify key data and underlying process issues and gained a complete picture of where the data was best provided and how data was constructed and being generated. Through discussion and analysis, the client was able to immediately remedy process issues and implement the right internal data extractions to meet demands for presentations, discussions with senior leaders, group meetings, and data reviews with staff.