Explaining data products lifecycle and their scope in management

Ever since web3 arrived, enterprises have woken up to the fact that the future of their products lies in the ability to deliver on-demand. Likewise, the underlying data & analytics architectures should be able to process data from the capturing to the delivering, with finesse.

As we know, data management platforms are a significant differentiator when accelerating a brand’s core business; it’s time to implement contemporary data cultures. Among many, using data-as-a-product is a transformational strategy.

What is a Data Product?

When re-engineered to serve a specific purpose, a data set delivers like a product, hence known as a data product. Based on the requirement, the data product captures data from multiple relevant source points, processes and filters to make them compliant with policies and regulations. Ultimately, it makes them instantly available to authorized users only.

With data products, consumers are least affected by the underlying complexities of multiple data sources. This makes the data set discoverable and ultimately accessible as a meaningful asset. To further understand the scope of using data as products, let us discuss their lifecycle, which sheds light with more clarity.

The 4 Phases of Data Product Lifecycle

As per Mckinsey Global Institute, data-driven enterprises are 23 times more likely to attract new customers. That’s because these enterprises focus on building products and not mere projects. So using data products as reusable assets for specific business objectives is a game changer in achieving meaningful outcomes.

Like software products, data products follow an iterative lifecycle model. Here are the 4 phases of the data product lifecycle.

Define your data

The starting phase defines the business objectives, governance constraints, inventory requirements, etc. It lays the scope of productizing for the consumption of different services.

Test your data

Like all applications, data product platforms require thorough QA before going live. Since data quality is essential in the production environment, test data management is integral to the data product lifecycle. Here, the data sets are put to the test and qualified for their delivery as expected. Furthermore, once completed, they are cleansed and compliant for on-demand consumption.

Engineer your data

In the next step, the engineering involves source data collecting from the location, integrating and processing as required. In this phase, the data services provide access to the consuming applications while pipelining ensures uninterrupted delivery to analytical data consumers.

Deploy your data product

Finally, the data product is deployed. Here, the product is monitored for performance, usage, and reliability. All support and maintenance to address ad-hoc issues are also done now.

Choosing the right data product platform

While we are at it, choosing a data product platform is essential for an optimized lifecycle process. Off-late, enterprises have steered their investments towards futuristic data fabrics, mesh and other architectures.

For example, K2view has successfully implemented micro-databases to optimize storage and on-demand provisioning. The platform continuously integrates, transforms, and delivers data as a product. Here, every business entity is stored exclusively in its dedicated micro-database. Not to miss, it backs multiple workloads simultaneously and taps into the massive scale and all of it at abbreviated costs. As a result, it achieves unbeatable performance, reliability, scalability and time to value. Likewise, other data fabric solutions, such as Oracle Coherence, IBM Cloud Park, Denodo, Talend etc., lead from the front.

How Does a Lifecycle Approach Helps Data Products?

Despite continuous investment in data management initiatives, enterprises are locking horns with ad-hoc data life cycle management. Primitive approaches are killing businesses, and as per Mckinsey’s survey, lack of a long-term strategy and ignoring advanced techniques are the leading factors. Not only does it cause problems in analytics, but it also keeps them at bay from sustainable growth.
Since data teams continuously experiment with new services, they must ensure timely deployment and monitoring. It’s like trekking the fine line between both ends and the sooner they complete the cycle, the quicker they can churn ROI and deliver value. Henceforth, effective data lifecycle management assures the following benefits:

• Seamless data access: Excessive data can create a mess, and filtering the same would attract additional costs. A high-performing data product lifecycle ensures speedier processing and thus provides on-demand data sets.
• Cost Control: A proper data lifecycle process enables data managers to minimize expenses associated with data storage, identification and collection.

From Data Projects to Data Products

In this post, I discussed using data as a product and how they could rewrite the narrative of data management. Like any other software product, data products mature into valuable assets by the end of their lifecycle. From the above discussion, it is clear that enterprises in this age of Web3 should look beyond and embrace a productisation mindset.