Article

Felipe Henao Brand
Felipe Henao Brand 27 April 2023

“Data as a Product” is the Key to Data Democratization

Unlocking the true potential of data has become a top priority for businesses worldwide. However, despite the abundance of data, many organisations are still struggling to leverage it effectively. In fact, according to the Talend Data Health Barometer, a staggering 97% of businesses face challenges in using data effectively.

The main obstacle preventing organisations from achieving real value from data is not budget or technology. People are the #1 barrier.

As the Talend Data Health Barometer reveals, nearly half of respondents said it's not easy to use data to drive business impact, and 46% don't feel that their data has the speed and flexibility to satisfy the demands of the business. This demonstrates that without the right mindset and skill set, the full potential of data initiatives cannot be unlocked.

The solution to this challenge is to build a data culture within organisations that fosters a common understanding about data and how it's used. This must become a top priority for organisations that want to realise the operational and economic promise of data initiatives.

By creating an environment where data is seen as a product (“data as a product”) and valuable asset and used effectively across all levels of the organisation, companies can finally bring their data initiatives to fruition and drive meaningful business outcomes.

As businesses move forward, it is crucial to focus on the final mile, which involves bringing companies that are lagging behind up to par on their data initiatives, as well as helping those that already prioritise data to get even more value from their investment.

It is imperative to prioritise a data culture to stay ahead of the curve and not be left behind in the data race.

The Data Ownership Imperative

The "data as a product" model flips the traditional data decision-making process on its head. Instead of starting with the data and working up to operational use cases, the approach begins with operational use cases and works down to the data that's needed.

This puts business users in the driver's seat, giving them ownership over the process and enabling them to define the most relevant use cases that are directly tied to their organisation's priorities.

In a survey conducted by Forrester, 47% of respondents said that their organisation is already treating data as a business asset or product, and another 27% plan to do so in the future.

A report by Accenture found that companies that have successfully implemented data as a product initiative have seen a 9% increase in revenue and a 7% increase in profit margins, compared to those that have not.

Let's consider the example of a financial institution that's looking to improve its upsell strategy. In this scenario, business users would determine the data they need to achieve this goal - in this case, data related to ERP and communications preferences.

With the help of IT, a specific data set would be created that allows business users to leverage this information and offer customers new products and services that meet their needs.

This "data as a product" approach can also be used to support broader objectives like risk management or operational excellence, making it a valuable tool for any organisation looking to use data to drive success.

The "data-liberation" approach, which treats data as a product, can be very effective, but it requires a strong sense of data ownership and proper data governance.

In a distributed approach data governance is not centralised but cross-organisational, making every stakeholder responsible for ensuring their usage of data is appropriate and adequate. This can be challenging, as it requires a high level of data literacy and culture.

For example, consider a car dealer finalising the purchase of a brand-new car with a customer. S/he may try to convince the customer to buy more accessories and services to maximise profit.

To do so, the dealer must have a strong knowledge of these additional products, from the way they are manufactured to the benefits for the customer. This highlights the importance of data literacy and culture in achieving the full potential of a "data-liberation" approach.

Treating data as a product requires business users to have a comprehensive understanding of the data they are using. This includes knowledge of its storage location, origin, trustworthiness, and whether there is an opt-in.

Having this level of understanding is crucial to maximising the business uses of data, which in turn supports an organisation's objectives and strategy.

Empowering Business Users

In the world of fashion, trends come and go, constantly renewing themselves. However, in the realm of data management, organisations face an ongoing issue of empowering their business operations. For years, the challenge has been to break down the barriers between IT and business and find better ways to empower the latter.

Simply delivering data to a data lake or warehouse is not enough to enable data usage. The data must be easily accessible and seamlessly integrated into workflows, whether it's through self-service for business users or integration into applications. It's essential that trusted data is available when it's needed.

Traditionally, organisations have implemented a "governance with the no" approach, where business users must go to central IT with requests for data use and wait for approval. This creates a gap between the business and IT in terms of data ownership, which only widens with the proliferation of data.

To make data productisation truly successful, organisations must ensure that their data initiatives are business-driven and outcome-focused, and that data is democratised and accessible throughout the organisation.

This approach involves enabling agile delivery of incremental value through data, establishing a common language between business and IT, achieving efficiencies through reuse of data products, elevating the organisation's trust in data, and future-proofing data architectures with modern approaches such as data mesh, data fabric, or data hub architecture.

To successfully implement a data product strategy, modern data teams should gain stakeholder alignment early and consistently, adopt a product management mindset, prioritise data quality and reliability, invest in self-service tooling, and identify the appropriate team structure for the data organisation.

By following these steps, data teams can achieve their organisation's goals and successfully implement a data product strategy.

However, data professionals face an efficiency gap; they spend too much time getting access to the data they need and putting it into the appropriate business context. The framework of delivering trusted data to business experts at the point of need is critical to liberate data value.

Self-service applications like data preparation tools enable business users to access a data set and then cleanse, standardise, transform, or enrich the data. They can easily share their preparations and datasets or embed data preparations into batch, bulk, and live data integration scenarios.

In order to really democratise data, organisations should start by democratising data quality and give business users access to data quality functions.

For business users to be able to finally act on data before the data populates business dashboards, software providers invest a lot in UX and more user-friendly applications.

Low-code or no-code solutions for non-data specialists can help business users to have a proactive approach to data management, including data quality, and thus support a broader data culture that aligns with an organisation's business objectives.

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