One of the most overlooked aspects that can have significant impact on project estimations and timelines is client data readiness. If proper analysis of data availability and cleanliness is not factored into the overall timeline, project estimates will become woefully underestimated.

In this post, Senior Manager Rob Gentry outlines the impacts and considerations of ensuring ample time to complete data readiness activities for software implementation success.

Four Considerations for Data Readiness

How Data Readiness Impacts Timelines and Planning

Project managers (PMs) are often asked to pull together project plans and timelines before knowing much of the project’s detail. To do this, PMs routinely rely upon past experiences with comparable projects of similar scope. Knowledge of the project’s requirements, resources and competing priorities help build a reasonable plan from the bottom up. However, in most cases, the timeline is already predefined, and PMs are asked to make the project fit into that timeline with a given budget.

This is when the planning and estimation process can get tricky.

There are multiple considerations when taking a top-down approach to project estimation. It is not uncommon to start a project and discover the software vendor has done very little to prepare the client’s IT department for the implementation at hand. This oversight is particularly painful when considering the typically massive amounts of data required for a new system.

Four Areas to Consider Regarding Data Readiness

Data Availability

In most software implementations, several data elements are required from multiple legacy systems to support the new software. This includes foundational data, such as hierarchies, as well as transactional data, like sales.

Fully understanding the legacy data requirements to support the new system and knowing where to source that data is critical to building a sound timeline.

Data Transformation

Because most systems vary in methods for storing and exporting data, some sort of transformation is often required to make the data in System A compatible with System B. The level of data manipulation required is not known until detailed system data mapping sessions are complete. Time should be set aside to conduct a proper assessment to account for data transformation efforts, especially if a compatible middleware solution such as Microsoft Azure, Mulesoft, or TIBCO is not already in place.

Data Conversion & Deployment

When it comes time to move from the legacy system, transactions that are already in process or “in-flight” must be converted to align with the new system. The manipulation of existing transaction data through conversion can eat up a significant amount of time in development, testing, and validation activities.

In some cases, it’s possible to cutover to a new system and leave the old system in place, running in parallel through a series of phased rollouts. This approach is often the preferred method of deploying a new solution as it limits risk and exposure. In this approach, the business is running two production systems simultaneously and will need a way to assess the outputs of both systems to ensure accurate reporting.

Data Cleanliness

All data is susceptible to errors, anomalies, or incompleteness for one reason or another. The process of data cleansing works to improve the quality and accuracy of the data to ensure optimal results in future analysis. Before bringing legacy data into a new system, the data should be cleansed to ensure expected results from the new system.

An early assessment of overall data cleanliness must be weighed carefully to ensure time is allocated for any cleansing activities that may be required.

Project estimation without considering client data readiness may create significant risk to timeline, budget, and scope. Consider setting aside time in your preparation phase for due diligence to determine the client’s ability to meet a software vendor’s data requirements. The amount of time required for this type of an assessment will vary depending on several factors including implementation scope as well as the maturity of the client’s organization and IT department. Completing these activities is key to reducing risks with data readiness and ensuring a successful and timely software delivery.

Rob Gentry

Contact Parker Avery
Published On: September 2, 2021Categories: Analytics, Big Data, Project Management, Rob Gentry, System Implementation