For the past few decades, most companies have kept data in an organizational silo. Analytics teams served business units, and even as data became more crucial to decision-making and product roadmaps, the teams in charge of data pipelines were treated more like plumbers and less like partners.
In the current age, things have become different. The most forward-thinking teams are adopting a new paradigm: treating data like a product or DaaP. Fundamentally, Data as a Product is a concept, or methodology, about how data teams can create value in their organizations. Adopting an organizational approach of treating data like a product isn’t just a buzzworthy trend in the data industry. It’s an intentional shift in mindset that leads to meaningful outcomes: increasing data democratization and the ability to self-serve, improving data quality so decisions can be made accurately and confidently, and scaling the overall impact of data throughout the organization.
While approaching data as a product, organizations should think about the entire value chain from ingestion to consumer-facing data deliverables & what are the KPIs against which they want to measure the success of their data products. One must prioritize data quality and reliability throughout the data lifecycle. Data teams are working to find processes and systems that help them advocate for the importance of data on a wider organizational level. There are two broad mandates that data teams tend to get formed with:
- Provide data to the company
- Provide insights to the company
The job of the data team is to provide the data that the company needs, for whatever purpose, be it making decisions, building personalized products, or detecting fraud. This might just sound like data engineering, but it’s not. Many data teams are adopting KPIs related to data quality, such as calculating the cost of data downtime—times when data is partial, erroneous, missing, or otherwise inaccurate—or by measuring the amount of time data team members spend troubleshooting or fixing data quality issues, rather than focusing on innovations or building new data products.
Companies can assess their current state of data quality by mapping their progress against the data reliability maturity curve. Briefly, this model suggests there are four main stages of data reliability:
- Reactive: Teams spend most of their time responding to fire drills and triaging data issues—resulting in a lack of progress on important initiatives, an organizational struggle to use data effectively in their product, machine learning algorithms, or business decision-making.
- Proactive: Teams collaborate actively between engineering, data engineering, data analysts, and data scientists to develop manual checks and custom QA queries to validate their work.
- Automated: At this level, teams prioritize reliable, accurate data through scheduled validation queries that deliver broader coverage of pipelines. Teams use data health dashboards to view issues, troubleshoot, and provide status updates to others in the organization. Examples include tracking and storing metrics about dimensions and measures to observe trends and changes or monitoring and enforcing schema at the ingestion stage.
- Scalable: These teams draw on proven Dev Ops concepts to institute a staging environment, reusable components for validation, and/or hard and soft alerts for data errors. With substantial coverage of mission-critical data, the team can resolve most issues before they impact downstream users. Examples include anomaly detection across all key metrics and tooling that allows every job and table to be monitored and tracked for quality.
We, at Navikenz, have been helping organizations to move from stage 1 to 4 successfully. Also, over the past few years, companies have gotten wise to this, and have started using a different model (in consonance with DaaP) — Data as a Service. Will talk about DaaS in upcoming blogs.
Interested in transforming your organization's approach to data? Contact us at [email protected] to learn how we can help you move from reactive data management to scalable data reliability. Let Navikenz be your partner in creating a culture of data-driven decision-making and innovation in your organization.