On one hand, people fear losing their jobs to AI, and on the other, a survey found that 2 out of every 3 executives are uncomfortable relying on data from advanced analytic systems. Why – because they do not trust the data. Whether it is to manage inventory or make product recommendations, having good quality data is becoming more important every day.
There are 2 ways businesses are approaching this need for higher data quality – data products or data as a product (DaaP). While both concepts involve data, they differ in scope, purpose, and implementation. Let’s find out more.
What is a Data Product?
A data product is an application that makes data fit for consumption to meet a specific objective. They are often used to identify patterns and trends, extract insights, and support data-driven decision-making.
For example, the marketing team may use a data product like a dashboard to evaluate campaign performance and understand its impact. Or the logistics team may use visualization tools as data products to assess inventory levels and optimize delivery schedules.
How do Data Products Influence Data Quality?
Data products process raw data to make it more trustworthy and valuable for stakeholders.
To begin with, data products integrate data from multiple sources to deliver complete records. As part of this process, data products also verify data accuracy and timeliness to ensure data meets your organization’s data quality standards.
Further, they may combine datasets with business logic and your product management practices to bridge the gap between legacy data systems and new infrastructure. This makes data easier to access and use.
Data Product Limitations
While they are beneficial, data products have certain limitations.
- Narrow focus
Data products are designed to address specific business needs. For example, airlines may use a data product that combines historical flight data with GPS coordinates to track flight movement. This data product will not be able to do much else. Hence, the company may need multiple data products. This can result in a fragmented approach to overall data quality management.
- Complexity and Limited Scalability
Data products often have complicated structures that limit their scalability and integration with other existing systems.
- Cultural Resistance
A lack of education on how data products improve data quality and poor internal communication can make employees hesitate to adopt and use data products. Thus, the resource may not be used as it was intended.
What is the Alternative? Data-as-a-Product (DaaP)
While data products focus on using data to meet a certain goal, Data-as-a-Product considers data to be a stand-alone product. Here, the focus lies on collecting and processing data to create value for data users, end consumers, and other stakeholders in the organization. DaaP stresses on ensuring data is easily accessible, reliable, structured, and actionable. You could consider this to be a bundled data set.
For example, DaaP could take the form of a customer insights platform. This would gather data about customer interactions through various touch points and deliver a comprehensive profile indicating their preferences, purchasing patterns, and so on.
How does a DaaP Approach Influence Data Quality?
The DaaP mindset looks at data as an internal asset that can be used in multiple ways. Since it does not serve only one purpose, the perceived value is higher. Hence, businesses are incentivized to maintain high-quality standards.
DaaP also considers data to be a reusable asset. Hence it advocates managing quality throughout the data lifecycle. By gathering data together into a central data warehouse, it breaks through silos and ensures smoother integration. Like a data product, it verifies and validates all data before it is added to the central database. That said, since it has a wider focus, the records created would be more comprehensive.
DaaP Limitations
Some of the limitations of adopting the DaaP approach are:
- High Expense
Adopting a DaaP approach requires considerable financial and human resources. You would need highly trained personnel and sophisticated data analysis engines to dissect data and identify patterns within a sea of data available. This is one of the reasons why smaller companies tend to prefer data products over DaaP.
- Data Privacy Challenges
Collecting large amounts of data and storing it to be reusable raises data privacy concerns. Hence, businesses adopting this practice must pay additional attention to security concerns and compliance with data privacy regulations. This could done by encrypting data, making personal information anonymous, and implementing strict access controls.
Security and privacy concerns also increase the importance of boosting data literacy levels across the organization.
- Cultural Resistance
As with data products, organizations may face resistance to a DaaP approach. This is not only because of data literacy gaps but also because internal departments and domains may compete for data ownership. This can affect the overall data quality and thus make it harder for data managers to prove the value of data.
Choosing Between Data Products and DaaP
Multiple factors must be considered when choosing between a data product and adopting a DaaP methodology.
Firstly, think about your overall data quality concerns. A data product is a good way to address specific data quality concerns. However, if you have broader concerns, a DaaP approach may be more effective.
Next, consider the resources available to you. Organizations with limited budgets tend to favour data products. Since the data is organized to meet a specific goal, data products require fewer resources. On the other hand, the DaaP approach looks at data as a reusable asset and may involve a much higher volume of data. Hence, it is more expensive to maintain. In addition, effective DaaP rides on smooth inter-department data-sharing and cross-functional collaboration.
In Conclusion
Data Products and Data-as-a-Product are both powerful strategies to manage and improve data quality. While there are differences in terms of purpose and scope, both strategies verify and validate data to give users access to reliable, high-quality data. This makes data a valuable, trustworthy asset for the organization. In turn, it gives users the confidence to use data in their day-to-day decision-making.
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