Each time a decision is made and the consequences are evaluated, information is generated. Customer data is generated through interactions, sales meetings take place and orders are taken in, projects are handed off for delivery, change orders come in, plans are adjusted, and at each step, information is gathered. Some may be written down in meeting notes, and some in the account history and ledgers. Internal initiatives may generate artifacts and files stored on a network. New projects can expand competencies and provide insights on how to improve approaches or experiments better left unrepeated. Data is being acquired and stored in different repositories constantly.
Used properly, this data can lead to new opportunities to serve customers and expand offerings. However, just capturing this data is not sufficient. Information needs to be able to be found, retrieved, and turned into actionable business intelligence to be useful. So, it needs to be transformed from its raw state. This step needs to be approached consciously with information identified, evaluated, and stored so that it doesn’t disappear if the projects and people associated do. It needs to be accessible, relevant, and perhaps most importantly, accurate.
Fortunately, there are several tools that can help. Specialized tools like Customer Relationship Management software for tracking customer interactions and Enterprise Resource Planning suites for tracking business processes. There are also general knowledge management tools to help provide a place and process for storing information captured from initiatives and documented processes.
Collaboration tools can help store the processes, statuses, and products of projects. Document management systems, resource planning, enterprise management, and financial management tools. The list goes on and on. These tools are all helpful in their purpose, with each of them providing specialized pieces of the puzzle that make up a business’s knowledge.
Even with all these tools in place, finding related information across different systems using different nomenclature becomes its own problem. It becomes an issue of knowing that all this information exists and where it is kept, not to mention the effort of pulling all this together to get a complete picture.
Luckily, this is an area where modern data and AI excel. Large Language Models allow a more natural way to search across data sources while being context-aware. Tools like Improving’s Echo can be used to summarize the disparate data without hallucinations of what might be there while providing citations to the source data and critically keeping that data in-house to prevent unintentionally exposing sensitive information. Enabling decisions to be made with confidence that they are being made from a solid foundation and driving real value.
If you are looking for ways to store and access your organization's business intelligence, want to discuss some tool options, or even want to discuss building your own AI solution, please feel free to reach out to Improving or the author of this article.
