It’s no secret data-related project deliverables are critical to digital transformation success. Here’s a way to decide the important ones.
Digital transformation can fundamentally change the way organizations create value. Technology adoption and the growing reliance on digital systems significantly benefit engineers. Data issues are the most common cause of digital transformation disappointment or even failure.
Well-defined digital transformation project deliverables lead to project success. Vaguely defined or missing project deliverables create a risk of missed expectations, delays and cost overruns.
These are the significant project deliverables essential to every digital transformation project plan:
- Project charter
- Data analytics and visualization strategy
- Generative AI strategy
- Data conversion strategy
- Data profiling strategy
- Data integration strategy
- Data conversion testing strategy
- Data quality strategy
- Risk Assessment
- Change management plan
- Data conversion reports
These deliverables merit more attention for digital transformation projects than routine systems projects. We’ll examine the first five deliverables in this article and the remaining deliverables in a follow-on article.
Project charter
Digital transformation projects begin with engineers writing a project charter, which describes the project. The project charter deliverable contributes to digital transformation success by providing the following:
- A tangible document that the project team can use to socialize the project description among stakeholders to build alignment and commitment.
- Clarity on the project goal, usually a future state vision for the business, and related scope.
- A list of data sources that the project will transform and integrate.
- An initial estimate of the required project resources to achieve the goal.
- A summary business case.
- A conceptual project management plan.
- A list of risks and assumptions, with many focused on data issues.
The value of a comprehensive project charter lies in its ability to enable the project team and the stakeholder community to reach a consensus on the key elements of the digital transformation project. Managing digital transformation scope contains ideas engineers can use to write their project charter.
Data analytics and visualization strategy
The data analytics and visualization strategy deliverable is foundational to delivering the expected value from the digital transformation, as it outlines how the organization will measure the project results through data queries and visualization. It defines the following:
- Recommended tools for reporting, data analytics and visualization.
- Plan for the use of a data lakehouse or a data warehouse, if required.
- Planned views for datastores. Views significantly simplify query coding.
- The list of calculated values that will be persisted. These values ensure consistent reporting and speed query performance.
- Support infrastructure for engineers and other end-users who will be developing queries for data analytics and visualization.
Generative AI strategy
Many organizations now expect digital transformation projects to include some use of generative AI. Engineers can respond to this expectation by formulating an AI strategy deliverable for the project. It defines the following:
- Key goals for the use of AI such as improving customer experience or boosting employee productivity.
- Selection criteria for an AI solution. Read Making Smarter AI choices for more details about the selection criteria for an AI solution.
- Recommendation for a Software-as-a-Service (SaaS) or an On-premises AI solution.
- Risk assessment for an AI solution that includes skills and vendor risks. Read How engineers can mitigate AI risks in digital transformation for more details about common AI risks and mitigations.
Data conversion strategy
Not surprisingly, digital transformation projects focus considerable attention on data. These projects are crucially dependent on successfully converting data from multiple sources.
The data conversion strategy deliverable describes the following:
- The list of in-scope internal data sources is likely a combination of structured digital, unstructured digital, and paper.
- The list of in-scope external data sources is almost always structured digital data.
- The data conversion approach that will be used for each data source. The alternatives are automated, mostly automated with some manual augmentation or manual data entry.
- A preliminary indication of how much data transformation will be required for each data source. Data transformation, not to be confused with digital transformation, is a measure of the amount of data restructuring needed between the source and target datastore. The measure affects software development efforts.
- A preliminary indication of how much data quality improvement will be required for each data source.
The contents of the data conversion strategy deliverable provide the organization with a realistic sense of the effort and elapsed time that data conversion will require.
Data profiling strategy
The data profiling strategy outlines how the project will assess the actual level of data quality for specific data elements in each data source. It’s typically impossible and unnecessary to profile all the data elements. The minimum should be the keys and foreign keys.
The results of the data profiling strategy deliverable will inform the following:
- The data conversion approach that will be used for each data source.
- The estimated effort required to bring the data quality in each data source up to the expected data quality level.
- The specific subject matter experts (SMEs) that will be needed to participate in data quality improvement.
The data profiling strategy will help the organization achieve a consensus on the approach, ensuring sufficient data quality across the datastores.
Paying detailed attention to these digital transformation project deliverables will ensure the success of your project.
