Data Identification

Owned by Mike Deskis
May 19, 2023
3 min read

The Product, Customer, and Transaction data, often referred to as the Master Data, are most critical to pricing setting, analytics, and related processes.  Each can have a variety of attributes and relationships that are crucial to pricing processes.  Working together, we help to identify the sources of this data by pinpointing the owners of the data along with associated business processes and systems.  From here, we focus on mapping the data from the source systems to the Pricefx system.  All required data transformations will also be identified as part of this readiness component. 

Criticality of Data

Data is both highly critical and very challenging;

  • Data sets the stage for success of entire pricing project

  • Incomplete or unavailable data is a blocker for almost all configuration

  • Data extraction often requires significant time and effort

  • Projects are often started without necessary due diligence on data topics

There is no shortage of potential obstacles:

  • Data identification and scoping process

  • Disconnects and miscommunication between IT and business team

  • Stakeholder unfamiliarity with critical data

  • Poor data quality or missing data

  • Data is unavailable or difficult to extract

  • Vague or undefined critical calculations

Characteristics of Data Identification

The characteristics of the data identification concept in data readiness methodology are as follows:

  1. Uniqueness: Data identification involves assigning unique identifiers or keys to different data entities or records. This ensures that each piece of data can be uniquely identified and distinguished from others.

  2. Granularity: Data identification considers the level of detail or granularity at which data is identified. It involves determining the appropriate level of detail for data identification based on the needs of the organization and the specific use cases.

  3. Consistency: Data identification ensures consistency in how data is identified across different systems, databases, and processes. It involves establishing standardized naming conventions, formats, and structures for data identification.

  4. Scalability: Data identification considers the scalability of the identification system as the volume of data increases. It involves designing a data identification approach that can handle large amounts of data efficiently without compromising performance.

  5. Integration: Data identification supports data integration by providing a common reference point for linking and connecting related data across different systems and sources. It allows for the seamless integration and correlation of data from multiple sources.

  6. Traceability: Data identification enables traceability by providing a means to track the origin, lineage, and history of data. It allows organizations to understand the source and journey of data, facilitating data governance and compliance.

  7. Flexibility: Data identification should be flexible enough to accommodate changes and updates in data structures, systems, and processes. It should be adaptable to evolving business needs and allow for the incorporation of new data sources or attributes.

By considering these characteristics in the data identification process, organizations can establish a robust and efficient framework for uniquely identifying and managing their data within the data readiness.

Pricing Applications Driven by Accurate Data

Data will form the basis of our business pricing application and it is at the start and the end of analytical processes and its completeness will help assure project success.

Pricefx needs the ability to calculate and measure using quality data to generate accurate results, and this will rely on distinct and consistent factors.

Myth: Customers are Data Mature

Customers may lack an enterprise frame of reference in their approach to data management and their focus may tend to be more siloed on individual applications and not on enterprise integration of data. Additionally, they may not have a formalized process for data management and may not have explicit best practices for managing their data.

Customers may not be fully prepared for their pricing application engagement.

Data Success

Successful implementation of applications should adhere to the single source of truth principle where data is merged into a single repository that has resolved all of the data issues (duplication, cleansing, etc). This master set should be considered the correct version of the data.

Â