Data-Driven Decision Making: How to Establish the Necessary Records Management Architecture
Do your records help to inform data-driven decision making? Here are four steps that you can take to establish a records management architecture that allows for a seamless transfer of information from RIM professional to senior leadership.
In order to be as successful as possible, enterprises must participate in data-driven decision making on a regular basis. Unfortunately, according to the MIT Technology Review, only about 0.5% of digital data is ever analyzed. This trend highlights the fact that decision-makers and stakeholders could make more of an effort to utilize the information at their disposal to inform their practices and policies. Here are four steps that can help you establish the necessary architecture for making data-driven decisions.
1.Define Structured and Unstructured Data
As the Internet of Things continues to grow, enterprises need to define which data is a priority. At this stage, it's important to understand the difference between structured and unstructured data. While structured data can be easily analyzed with algorithms, unstructured data creates a unique challenge — and opportunity — for businesses.
As CIO reports, IDC estimates that "unstructured content already accounts for a staggering 90 percent of all digital data, much of which is locked away across a variety of different data stores, in different locations and in varying formats." Many organizations need to develop a process for prioritizing and unlocking this wealth of information. In order to ensure the best results, the records manager must play an active role in developing this solution. He or she should work closely with different teams and departments to address any challenges caused by physical or digital records.
In order to meet these challenges, records managers must work with CIOs to incorporate the necessary functionality into the enterprise architecture and ensure that all involved parties are updated with the most relevant information. As Federal News Radio stresses, your system should be "designed to facilitate information transfer from a knowledge worker to senior leadership. The system has to support the knowledge worker in finding the necessary data, and provide the tools to present that knowledge to leadership." By creating a solution that integrates both data and records, you can make better data-driven decisions.
As a best practice, you should preserve your records based on priority and usefulness to your organization. Additionally, you should manage these assets in such a way that they are easily searchable. When you have a unified data organization, everyone from knowledge workers to members of your senior leadership team should be able to benefit from the information you have at your disposal.
Once you've identified your structured vs. unstructured data, and developed a strategy for how to use this information, it's time for you to set some priorities. For example, if you need to analyze growth for a particular department, you can use certain metrics to help identify the success of a specific product or service, and indicate areas for improvement. However, it's imperative that your organization doesn't get too bogged down in the weeds of your data. By identifying the metrics that matter most to a particular decision or situation, you can use your data more effectively.
You need to do more than simply collect and organize your data in order for your organization to get all the insights it needs. Data quality and governance are essential to ensuring that your analytics are effective. By implementing text analytics, auto-taxonomy generation, auto categorization, auto-tagging and other formal information-handling techniques, you can make the most out of your information.
In order to use your records to drive data-driven decision making, you must establish an up-to-date records management program that ensures all of the necessary details are communicated to senior leadership. With the right system in place, this communication can be seamless and constant. Once you have the necessary architecture, you can use data-driven decision making to be more productive and profitable in the future.