Despite the rapid generation, gathering, and accumulation of data, it appears most enterprises are not making good use of the information available to them. One study shows that around 68% of enterprise data remains unutilized.

Organizations store massive amounts of information, but they are not leveraging it to stimulate innovation, improve products and services, and enhance marketing and customer relations. If this is going to change, then everyone in an organization will have a role to play, as detailed in the following approaches.

1. Elimination of information silos

Information silos occur when data or knowledge is kept isolated or made exclusively available to certain teams, departments, or systems.

While silos may be purposely created for security or privacy reasons, in most instances, they just emerge because of poor data handling or outdated cultures. Unintentional information silos must be dismantled to make data available to those who can extract value from them.

Organizations should carefully examine their legacy systems for potential silos. It is also advisable to review departmental information strategies and workplace cultures to address siloing and empower everyone to use data for productive ends.

Widening access to data safely can be challenging, so it helps to use solutions like Informatica, a comprehensive AI-powered data governance and access control solution that helps teams to manage data across multiple clouds and hybrid environments. It helps address data isolation and brings together disparate data systems with its data governing, cataloging, and integration capabilities.

2. Promoting a data-driven workplace culture

Implementing a culture of data use in an organization does not happen overnight. Merely directing employees to “use data” does not build a data-driven culture unto itself. It requires a systematic approach and consistency.

One of the key elements in successfully promoting a data-driven culture is leadership. Managers, supervisors, and others in leadership positions should demonstrate and encourage data-driven decision-making. It is important to lead by example, by regularly citing data during meetings and requiring reports to reference relevant metrics and qualitative information whenever applicable.

In addition to leadership commitment, it is important to establish a clear framework and process for data use. Relying on employee initiative is likewise not enough. It is advisable to communicate standards when it comes to data formats, relevance, accuracy, and understandability. Employees should be encouraged to learn to transform raw data into structured and contextualized information in line with the established standards.

Moreover, organizations should make the process of building a data-driven workplace culture a collaborative effort. Allowing employees to have a say on the framework or procedures helps to get them more committed to the goal.

It also helps to establish clear objectives, to easily monitor and quantify the progress toward building a data-driven culture. For example, managers can keep track of the use of relevant data in official documents or communications to check if employees are already building the habit of data use. Managers can also monitor employees’ proficiency in using relevant software tools.

3. Using intuitive AI-enhanced BI tools

With the advent of AI-powered business intelligence software, anyone can perform data analysis or conduct business intelligence without formal training. It’s easier than ever to help resolve problems or arrive at evidence-backed business decisions.

Organizations can invest in intuitive business intelligence software augmented by Natural Language Processing (NLP) and Large Language Models (LLM) that make them significantly easier to use but without being too rudimentary with their outputs.

A good example of an intuitive business intelligence solution is Pyramid Analytics, which brings together the advantages of advanced data analytics and generative artificial intelligence (GenAI) to produce generative business intelligence (GenBI). It allows anyone to interact with data without having in-depth knowledge about data querying and analysis. Users can ask Pyramid to generate insights or produce interactive data presentations through text or voice-based communication.

One challenge with using LLM-enhanced business intelligence solutions, however, is the potential for data leaks. As shown by what happened with ChatGPT, it is possible for the AI systems to reveal information used in training them or details they obtain as they interact with users. As such, it is advisable to carefully examine an LLM-aided BI tool. Preferably, it should not grant LLMs direct access to enterprise data.

Pyramid addresses this issue by adding a layer to the underlying tech, allowing its system to send queries to the LLM of the user’s choice without actually sharing any data with the LLM – all of the analysis and visualization itself happens within the safety of the installed software.

4. Providing data analysis and management courses

There is no excuse for being unable to provide line-of-business team members with training on how to utilize data for their own strategic purposes. There are many data training modules available online. Organizations can partner with online training platforms like Udemy.

Also, you can conduct your own custom training sessions and develop customized data analysis training for different departments to address specific needs. To encourage participation in the training, organizations can inject gamification elements or offer incentives.

Data analysis courses provide everyone in the organization with the foundation they need to have a workable understanding of using reports and dashboards. These courses also empower employees to contribute ideas in building the organization’s data use frameworks. The knowledge they gain from the courses allows them to examine, for example, if the requirements and processes result in greater efficiency or if they’re simply an unnecessary burden.

Before picking a course or developing custom training modules, organizations need to ascertain that the training they provide is in line with their operational and business goals. The training should result in useful skills for “citizen data analysis” and the maximization of data used to promote innovation and improvements in business outcomes.

5. Implementing data quality measures to build trust in data

It is difficult to promote data use if the data available in an organization is unreliable. That’s why it is crucial to establish data quality standards. All the data maintained in an organization should be accurate, complete, consistent, and relevant. The data stored should be properly labelled and structured. Timestamps should also be correctly generated to make it easy to find the most recently uploaded information.

Organizations need a good data management system to ascertain that they only keep and use accurate and relevant data. Unnecessary duplicates must be removed to maximize storage space. Updated data should be properly labeled, and old or obsolete data must be erased unless they are deemed necessary for historical contextualization.

In some cases, it helps to appoint a data steward to resolve data conflicts or perform enrichment to find missing information or fix incomplete datasets.

Organizations can use tools like Datafold to automate data testing and quickly spot data quality issues. The platform can perform sophisticated reconciliations between versions of the same data set. It also executes continuous integration and deployment (CI/CD) testing, enabling users to maximize data visibility and obtain insights into the impact of code changes.

Conclusion

Data should be an asset for businesses. It is unfortunate that not enough organizations take advantage of their data repositories, resulting in unexplored or untapped potential. Teams can use data to improve decision-making, products and services, marketing and sales efforts, customer relations, and many other areas. To get started with productive data use, it helps to demolish information silos, establish a data-driven workplace, use AI-enhanced business intelligence solutions, provide data analysis and management courses, and implement data quality measures to build data trustworthiness.