The importance of building a robust data strategy for product life cycle management

By Deepak Sharma, Chief Product Officer, Pepperfry

In a fast-changing and data-driven world, product life cycle management (PLCM) is paramount for any organization, product or feature that wants to survive and thrive. In its simplest form, PLCM is the process of managing a product from its inception to its eventual demise. Seemingly straightforward it’s anything but simple. To ensure that a product is successful throughout its entire life cycle, companies need to have a robust data strategy in place. Without data, it’s impossible to make informed decisions about what changes need to be made to a product to keep it relevant and successful. Data is the great equalizer transcending opinions and ranks. Also fundamentally speaking you can’t fix what you don’t track.

The usage of data is critical towards ensuring you’re solving the right customer problems by bringing the right solutions to market and improving them over time.

Data is a necessary component for improving customer service and processes. It can help in streamlining operations, understand what customers want, and make better decisions about how to serve them.

While there are many dimensions and methods towards collect and activating said collected data one could break them down into near and long-term goals. In the near term, one should use it to track customer behaviour, understand how your products are being used, and identify areas for improvement. With a longer-term lens, data should be leveraged to predict future customer needs and trends.

Having a robust data strategy means getting the right systems and processes in place to collect, store, and analyse data. More importantly, the organization should be inclined towards a data-oriented culture where teams are encouraged to leverage data for driving decision-making at all levels.

Implementation during the build and growth phases

Keeping a longer-term view in mind, there are some key elements while implementing data strategy during the launch and growth phases:

The first is to understand the different types of data used in PLM. There are three main buckets: people, process, and product. Simply put, what you’re offering, who’s the target audience and how they’re using your products/offering (intended vs reality)

Building on the first point, the follow-through is to identify sources from where said data needs to be sourced: This can come from a myriad of sources such as your internal development systems, customer feedback, user analytics, and system logs

Assuming you get the basics in place and an initial product market fit has been achieved, the next step is to use data to make informed decisions about which features need to be added or removed, how to optimize performance, or where to focus your marketing efforts

As your product grows and evolves, so will your data requirements. Therefore, make sure your data strategy is flexible enough to accommodate these changes.

Automation, standardization, and centralization of data should be at the core of your business objectives

Developing a data architecture to drive digitalization

Organizations looking to become data-oriented need a data architecture that can support new processes and data sets. This architecture should be designed to be able to handle the increased volume and complexity of information, and provide the flexibility to accommodate future changes while being agile at its core.

A data architecture should cover:

A centralized repository for product data: A single source of truth for product data will make it easier to manage and update information, ensure consistency across different applications.

A metadata management system: This will help organize and classify the large amount of data generated by digitalization initiatives.

An analytics platform: This will enable organizations to run complex analyses on their product data to identify trends and improve decision-making.

Integration with existing systems: The data architecture should be able to interface with existing enterprise systems, such as ERP and CRM, to avoid duplication of effort

Transforming businesses through a good data strategy framework

Product life cycle management (PLM) is an effective strategy towards creating a robust data strategy framework. Data is a critical part of the PLM process as it helps businesses track progress, identify issues, and make decisions about changes or improvements it can be complex and difficult to manage. Any good strategy encompasses the value of the tool and its utilization. Thus, it’s important to understand the limitations of data to know how and when to leverage it.

Being overly reliant on data can inject increased time into your processes and delay decisions. There’s also the downside that while the past is often a good indicator of future behaviour, it can never fully predict what will happen. Not every aspect has enough data to be analysed, and many intangible, yet important, elements of your business cannot be measured. Being overly reliant on data can inject increased time into your processes and delay decisions. There’s also the downside that while the past is often a good indicator of future behaviour, it can never fully predict what will happen.