New product rollout projects can benefit from a data analysis strategy designed to help identify potential risks and opportunities. By gathering and analyzing the right data, your team will gain valuable insights to guide your decision-making through the entire project lifecycle. Interpreting the data using the right skills and expertise, you can then uncover key information necessary to keep your new product introduction on the path to success.
Collecting the right data
It’s critical that project teams have access to relevant and accurate data as part of their new product introduction, but this is often a primary challenge. Data collection can be a time-consuming process, and it requires careful planning to ensure your group gathers the right types of information at each step. That may mean collecting data on sales figures for current product offerings, costs of new product development, market conditions, and competitor positioning. Depending on the product you plan to launch, additional data points might also be relevant to your needs.
With so much information available through many digital solutions, project teams may opt to ingest all of it simply because it’s there. The result is often a lot of noise without meaningful insights and difficulty surfacing the information that really matters from an overly large sea of data. By understanding your data analysis needs, you can focus your information collection process to include only those datasets that are relevant and known to be accurate.
Integrating the data
Because the types of data that are useful for your product rollout effort will likely come from a variety of sources, you may need to integrate information from many different formats into a single scheme. Some of the incoming data might also contain elements you don’t need or don’t intend to use, and those fields should be identified prior to integration. It’s also possible that you’ll end up with redundant records across the different data streams, which will require careful deduplication before you can combine the data and conduct any overall analysis.
Part of your data hygiene efforts should also include identifying and removing records that may be obsolete, out of date, or irrelevant to the current project. It’s better to eliminate those prior to integration rather than sifting through them later. Consider whether retaining any removed data for future use makes sense or if you must delete it to comply with your organization’s records retention policy or other guidance.
Correctly interpreting the data
Once you’ve gathered, normalized, and integrated the right datasets, it’s time to apply some analytics to make use of the information. It can be tempting to rush into the analysis step because everyone wants to see what the data tells them, but mistakes made at this point could create significant problems later. The results of your data analysis will drive key decisions moving forward, so it’s critical to get this activity right.
While it’s possible for nearly anyone on the project team to look at the data that’s been gathered and draw their own conclusions, much of the data analysis that needs to happen is likely to be more advanced than that. Identifying someone with the specialized skills and knowledge to understand the data—plus incorporate the methodologies behind gathering it into their analysis—and draw correct conclusions is key to success. You may have an individual in-house who can do this, but if not, you’ll be better served by contracting with an outside expert rather than assigning the analysis to someone without the proper background. Misinterpretation of the data can lead to weak or incorrect business decisions and potentially jeopardize your new product launch’s success.
PMAlliance, Inc uses a team of highly experienced and certified professionals to provide project management consulting, project management training and project portfolio management.
