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How to create a data-first organization | Baker Tilly

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How to create a data-first organization

Nov. 17, 2023

Data can give your organization a competitive advantage in a rapidly evolving business landscape, allowing you to respond swiftly to market changes, customer demands and emerging technologies.

Modernizing and automating processes through digital transformation can increase efficiency and cost savings while allowing organizations to better understand and engage with their customers by enabling personalized experiences, efficient customer service and the ability to meet evolving customer expectations.

However, advanced data preparation is imperative to achieve a winning digital transformation strategy. Data quality tools are only as good as the information they utilize. Preparing and refining organizational data is critical in enabling your organization to maximize its digital transformation investment.

A data-first strategy

The traditional approach to migrating data to a new system begins with a comprehensive assessment of all existing data sources before the data is mapped to the new system to ensure data is accurately transferred and maintains its integrity during migration. This mechanical approach doesn’t challenge an organization to consider what they want to be able to see and do with their data at the end of the transformation journey.

Instead of waiting to address the challenges in assessing and organizing data within the program or near the end of the program, near cutover, a data-first strategy recommends organizations embrace the extraction of historical and current data on the front end as soon as possible of any digital transformation initiative to enhance its overall success.

By providing visibility into your data on day one, organizations can assess their data quality to understand what is needed to get it into the desired future state that will provide organizational value at the end of the transformation journey.

Baker Tilly’s data quality assessment

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Preliminary assessment: Identifies the key data needed for the new system and is used to perform a current data quality assessment. This provides insight into the current state of your data and identifies any gaps or inconsistencies early in the process.
Stakeholder engagement: Arrange stakeholder workshops to set expectations about the importance of high-quality data and how this can impact business operations and decision-making.
Define quality metrics: Establishes the dimensions for your data quality, including accuracy, completeness, reliability, relevance and timeliness, while outlining key performance indicators to measure the quality of the data against these defined dimensions.
Tools and technology selection: Select the necessary DQA tools and technologies to ensure they’re compatible with the target digital solution while prioritizing tools offering automation features to streamline the DQA process.
Data cleaning and migration: Develop the rules to clean the data from the legacy system before it’s migrated to perform a pilot migration then to help identify any migration process issues or data issues. Continuous data quality monitoring, even post-migration, ensures that the appropriate tools and processes are in place.
Process integration: Integrates the DQA into the extraction, transformation and loading processes, ensuring the data remains high quality as it moves between the systems. Feedback mechanisms are established to quickly flag any data quality issues and correct them.
Training and documentation to staff: Organize training sessions to educate staff on the importance of maintaining data quality and provide thorough documentation explaining the DQA processes including the appropriate tools, metrics and best practices to follow.
Audit and reviews: Schedule periodic audits of your system data to compare results against the established data policy metrics. Audit findings are thoroughly reviewed, and action items are developed to address any shortcomings.
Iterate and refine: Continuously collect feedback from end users about data quality issues they may encounter before performing process refinement on feedback and findings from audits.
Ensure scalability: Ensures the solutions put in place are scalable and can handle increased data volumes and complexities by developing a scalability platform strategy to outline additional platforms that can be leveraged to manage and consume large amounts of data in useable formats.
Governance: Oversees and advises in the establishment of a team responsible for ensuring data quality within your organization. Within this, clear policies and standards for quality data are defined and methods are created to ensure these policies and standards are adhered to across the organization.