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Data is a precious resource, but not enough companies are skilled at getting the most out of it.
Ideally, analytics and data programs need to provide valuable insights now and preserve data for future use, a thing that in reality can be quite complicated to achieve.
The sheer volume of data we gather increases over time, dramatically outpacing our ability to make sense of it in a timely fashion.
So how should companies handle the ever-evolving, accumulating, and multifaceted data that flows through their organization each day?
It can be argued that companies experience the most success when they treat data not merely as a business asset but similarly to a product they develop for their customers.
When a company builds a commercial offering, it typically tries to build something that can meet the needs of many customer types to drive the highest number of sales.
Variability and customization are key: The data should be tied to a base product that can be applied in different ways to different customers.
The ability to make a feature work for multiple use cases is crucial for a successful product, and this is how data itself should be conceptualized.
Data isn't simply an asset to be categorized but a product to be customized for data consumers within the organization.
Data isn't simply an asset to be categorized but a product to be customized for data consumers within the organization.
With this mindset, companies can expand their ‘data product’ over time — like they would any other product or service, evolving to meet customer needs.
Traditionally, companies look at user feedback, market shifts, and performance reviews on their products to evaluate how to improve them.
The same way, they should proactively evaluate their data and data processes to find creative ways to reuse them and make them applicable for the future.
Treating data like this helps companies experience the dual benefits of generating value with their data in the present, while also ensuring they can derive significant future value from it.
Companies that handle and develop their data like a product experience multiple benefits, from streamlining the time and effort required to implement it, to reducing costs (such as technology, new development, and data maintenance), and mitigating overall data risk and governance requirements.
A data product is simply a robust data set that multiple non-technical stakeholders can access and use to generate data-led predictions that can help address a variety of business opportunities, challenges, and applications.
Unlike data pipelines that can easily turn into a mess that is difficult to manage and reuse, the 'data as a product' approach offers a simple way to access and leverage data in no-code, scalable fashion.
In practice, this methodology allows Go-to-Market domain experts to build micro data warehouses, define a business objective (i.e., KPI) on top of it, and start generating actionable business predictions, which can be pushed into the business tools front line employees use daily.
This innovative approach accelerates GTM efforts and cuts down the time it takes for employees to prioritize their efforts and react to opportunities and risks.
An example of this method could include a comprehensive set from multiple business apps, which contains insights about prospects and customers, including all the details collected by different departments within the organization.
On top of this data set, Marketing, Sales, and Customer Success can define their respective KPIs. Marketing can predict which leads will convert into pipeline. Sales can predict which opportunities will convert into paying customers, and CS can predict which customers will churn.
After each department analyzes its data, predictions and insights can be propagated to the individual employees using Zapier-like automations.
Marketing will see predictions right inside Hubspot, while Sales and Customer Success can see their predictions right inside Saleforce.
The same way a product is designed to make your customer's life easier, a data product is built to make the process of getting answers from data easier and more clear for employees, and not requiring employees to spend significant time or energy searching for the data they need for a particular application.
The same way a product is designed to make your customer's life easier, a data product is built to make the process of getting answers from data easier and more clear for employees, and not requiring employees to spend significant time or energy searching for the data they need for a particular application.
An increasing number of organizations are embracing the data product model, utilizing their most significant resource, and reaping the benefits of this approach.
According to Harvard Business Review, “Companies that treat data like a product can reduce the time it takes to implement and leverage data in new use cases by as much as 90%.”
Data products offer non-technical business leaders simple access to corporate data, superior efficiency for GTM teams, enhanced reusability of data, and flexibility for an unpredictable and evolving market.
Want to learn more about this methodology and how you can implement it in your organization?
Schedule a personalized demo of Forwrd no-code predictive analytics and see how you and your team can fuel revenue growth, in a matter of days, using data you already own.
