Failure to adopt a data product can significantly undermine a company's data strategy, leading to fragmented data operations and slower decision-making. In practice, this means that without proper implementation of data products, information may be processed inconsistently and its interpretation may vary depending on the department or person. This, in turn, makes decision-making more chaotic and time-consuming because there is no unified, data-driven approach.
Low utilization of data products makes it difficult to fully extract value from data assets. Data that is collected and stored may not be fully utilized, meaning the potential benefits it could bring are lost. This, in turn, limits the company's growth opportunities because data is not effectively used to identify new market opportunities, optimize operational processes, or improve products and services.
Additionally, failure to adopt data products weakens a company's ability to respond more quickly to change. In a dynamic business environment where market conditions can change from day to day, the ability to quickly analyze and interpret data is crucial. Companies that fail to effectively implement and leverage data products may be slowed in their response to changing conditions, giving competitive advantage.
To prevent this from happening, a strategic focus on implementing data products is key. This means that companies must invest in appropriate technological infrastructure, develop employees' competences in data analysis and create organizational culture, which supports and promotes the use of data in decision-making processes. Only then can data products be effectively integrated across organizational functions, promoting consistency, accuracy and reliability in data processing and analysis. Thanks to this, companies can maximize the value of their data resources and gain a competitive advantage in the market.
Here are 6 tips to help data leaders reduce resistance to change and accelerate the adoption of data products across the enterprise.
Involving data stakeholders throughout the design and development process is crucial. This ensures that data products are created with usability in mind and meet user expectations. Involving users at an early stage helps align product functionalities with intended benefits, which increases acceptance and use.
Understanding the user journey and how they interact with your product has a huge impact on design choices. Designing with the user in mind, minimizing the need to change their behavior and the status quo accelerates product adoption. Delivering delightful user experiences is key to rapid adoption of data products.
An iterative approach allows companies to start small with data products that can be refined and scaled as they grow. Early testing and validation with end users help identify potential issues and reduce long-term rework costs. Providing value to stakeholders early on builds trust in the product.
Data products are only as effective as the underlying data. Inaccurate data can lead to unreliable results, which in turn can discourage decision makers from using the product. Therefore, it is crucial to ensure high data quality, which will be the basis for creating valuable data products.
Data products require solid support for data processes, especially in the early stages. Integrating and curating data according to organizational standards may require a dedicated team. For small data teams that support many other teams, this poses a significant challenge. Therefore, it is important to ensure that data teams are adequately resourced and supported.
Data products must be continuously assessed for materiality. Companies often fall into the trap of creating irrelevant products that consume resources without delivering value. Continually assessing impact and improving data products can help deliver incremental returns and build credibility. Regularly reviewing the relevance of data products ensures their usefulness and effectiveness in the long term.
Implementing data products is central to a company's data strategy. Engaging users early, focusing on user experience, iterative development and testing, ensuring high data quality, solid data process support, and continuously assessing product relevance are steps that can significantly accelerate this process. Adopting these guidelines will allow companies to fully leverage the potential of data and accelerate their business growth.
