Data Modeling

Data modeling is the bridge between the many datasets at a company’s disposal. This process is the blueprint for how that data is structured inside a data warehouse or information system. In taking datasets through this process, organizations are primed to uncover more value from what’s been gathered.

The proliferation of platforms used to gather and analyze data has made it increasingly difficult for leaders to understand and communicate a story from all of the rows and columns of information. With so many disparate systems in place for collecting information, data often becomes siloed between departments, with no clear picture of how what’s happening on one side of the business relates to the other. Data modeling examines incoming data sources and analyzes how best to use these sources company-wide. Through that analysis, a blueprint is made to organize information inside a single data lake or warehouse.

Getting all departments to support the entire scope of data science is one of the most complex functions of business leaders today. Businesses need to disseminate data across the organization and train teams on how to use it to become more insights-centric. With the right frameworks and data governance tools in place, a data model can empower an organization to become more data and insights-centric.

The Building Blocks of Leveraging Data

Data modeling consists of three core elements — people, processes and platforms.

  • Who needs access to the data and what do they need?
  • How will data flow between departments?
  • Where is the data currently, and where does it need to go?

Creating a successful data model will be harder to achieve without these three foundational elements. Syncing the many moving parts requires having a strategic framework in place. With that framework, teams are better equipped to determine the right people, the most effective processes, and the right platforms to put in place.

Finding Alignment: The StoryVesting Framework

Data modeling simplifies the process of bringing people, processes, and platforms together to develop exceptional employee and customer experiences. However, building that model requires more than guesswork.

Treating data as a single level or facet of an organization is risky. Instead, teams must continually evaluate and iterate at multiple levels and subsystems, and the data model must follow those consistent iterations. Having a framework like StoryVesting as a resource helps teams keep the guardrails up and builds alignment between what the employees need to make strategic decisions and what customers want.

Designing a Single Source of Truth

There’s no step-by-step roadmap for strategic data modeling. Every situation, company and customer base is unique. When wielded correctly, organizations can drive accurate, predictable, repeatable outcomes that align with the unique company and industry backdrop.

Having a single source of data ensures that every stakeholder has access to the right data at the right time so they can stay focused on the right areas to target growth. This holistic view through data modeling and a single source of truth allow brands to better leverage seemingly abstract numbers and unpack them in a way that yields bigger results.

This is the difference between being data-driven and being insights-centric. With the right data model in place to develop a single source of truth, teams can access consistent, unified analytics to make Customer Experience (CX) oriented strategic decisions.

Data-Driven Vs.

Insights-centric cultures use data to answer the WHY:

  • Why are customers behaving the way they are?
  • Why are we seeing market shifts?
  • Why are our employees leaving?
  • Why are customers flocking to competitors

Data-centric cultures use data to inform and validate; not drive decision making.

  • Data-driven culture goes sideways when:
  • Data is used to inform the wrong thing
  • Data is used as absolute
  • Data is business first

Customer Experience (CX) Terms