Imagine your company has a vast amount of data that you’ve processed and analyzed, yielding important insights. Your boss trusts this analysis and makes a decision based on it. But what if the analysis was flawed? What if the data wasn’t reliable? In such a scenario, the decision could harm your company instead of benefiting it.
This underscores the critical importance of data trust. Confidence in your data and its processing is essential to making sound decisions.
Data can be a great tool for survival and top-line for growth, but organizations need a way to safely and securely use their data. Often data becomes locked up in silos, or simply not used to realize its full potential. Without the addition of business data it also lacks context. Furthermore, source systems are typically record based and dont allow you to safely combine data and build contextual data models.
According to a study by HFS Research, 75 percent of business executives do not have a high-level of trust in their data. This lack of trust comes despite 89 percent of executives surveyed saying a high level of data quality was critical for success. This means organizations have a problem operationally, they are limited in their ability to perform certain actions. These limitations are affecting the ability to differentiate, grow and deliver value.
The key reasons cited for data distrust are: accuracy, completeness, consistency, timeliness, uniqueness and validity.
Yet the question remains, how to ensure you can trust your data?
There are different ways to approach the issue of trust. If we can understand the accuracy and veracity of the data, it’s much easier to rely on it for further use and to inform decisions. The key is transparency - documenting and communicating how data is collected, stored, processed, and used. Transparency allows organizations to better understand, get better control and build trust.
Central to transparency is capturing data provenance: the source and journey of data within an organization, offering insights into its quality, security, and validity. By harnessing information about the data in use, enterprises can identify and rectify potential bottlenecks, inconsistencies, or inaccuracies in the data pipeline, therefore enhancing data accuracy and effectiveness. Conversely, without clear data provenance and veracity indicators, trust in data is difficult to establish, impeding and undermining data-driven initiatives.
Trust accelerates action
Data-driven initiatives are driving a new wave of innovation and digital transformation across all industries - especially in light of the current AI boom. In such a competitive global landscape, it can be fatal to pour money into data-related initiatives without return, making it more critical than ever to ensure your data is creating value for you. Data trust is the key component that accelerates action and enables this value generation.
Once data trust has been established, it can not only support a range of data-driven initiatives in your organization, but put your business ahead of the game with reliable data informing critical decisions and actions.
You can increase your competitiveness by significantly improving customer personalization, leveraging new deeper insights to drive better products, optimizing services and predicting future trends to meet customer needs. You can also take advantage of new monetization opportunities, with new data products and smarter selling with cross-sell and upsell within your brand or ecosystem. This is all without even mentioning AI - the impact of trusted data here is huge. Check out this recent blog on that very topic.
Being able to trust your data ultimately can foster higher customer satisfaction, operational efficiency, and revenue growth.
Do you want to be sure you can trust your data? We’d love to chat with you and learn more about how we can help! Get in touch with us today and discover how reliable data can lead to exceptional customer experiences, innovative product and service offerings, flexible applications, and trustworthy AI solutions.
