Data enrichment

Data enrichment can be a useful tool in the effort to add value to and improve the quality of your organization’s data. But just what is data enrichment in the real world? How does it work and what does it look like within an organization’s infrastructure? Experts describe the process of enriching data as the implementation of a set of methods and structures for refining and enhancing information, which may seem like a vague concept. More specifically, the goal of enriching data is to make it a more valuable asset – to get more out of it, to do more with it, to access it more easily and to be more proactive in its use – all without noticeably increasing costs or risks. If this sounds like a worthwhile idea, which it should, the following information will help build upon your understanding of the enrichment process and what it could mean for your organization.

How It Works – A Data Enrichment Overview

Every organization has its own unique goals for adding value to its data, but many of the tools for enriching data are universal in their refinement of content and documents to weed out errors and inconsistencies. This is something any enterprise can appreciate. From ensuring the accuracy of algorithms to adding new data to tables to correcting typographic or spelling errors, these tools are designed to improve quality across all data fronts. Some processes are more complex than others, though, and require more complex tools. Your organization may be interested in refining and simplifying your data, for example. Or you may plan on migrating all of your data to a new content management system after removing inconsistencies and errors to improve quality and simplify the accessibility of all your raw data. Whatever your goals, it’s all a matter of developing a strategic plan and implementing the right tools to take you there.

Key Steps to Enriching Data

A few simple steps can help ensure a successful data enrichment implementation within your organization. First of all, make sure to begin the process with a quality assessment of all the data within your content management system(s). Make sure that you have a team of experts in charge of the assessment process, and that your assessment methodologies cover the essential areas of integrity, accuracy, consistency and completeness. Any data that doesn’t pass this four-part test must either be repaired or gotten rid of altogether. The right strategies for discovering and resolving issues within your data structure will make all the difference in this initial assessment process.

After this important step is complete, then it will be necessary to determine the best process for enriching the unique data within your organization. This process is going to be different for each organization, but in general, it’s safe to say that your team will need to bring new data in to enrich and add value to the current sets of data within your system. Experts recommend beginning the enrichment process by cultivating an individual outline for your organization’s data needs, doing the necessary research to ensure an effective strategy, locating the necessary data from outside sources, extracting the data and checking it for quality before implementation.

Stay One Step Ahead of the Game

Before this data enrichment overview is complete, it’s important to discuss the importance of anticipation in this process. Careful planning and consideration of potential outcomes is essential to the successful enrichment of data. There are so many potential benefits to enriching your data and migrating it to a newer, better, more functional system, but if your team fails to complete this final step, you may be caught off guard by unexpected consequences. So make sure to go through the entire process, and determine what data is being moved, in what order and who will be involved. Consider any unexpected consequences of each planned action. Determine what the best solutions would be if those consequences do in fact arise and who will be in charge of implementing those solutions. Ongoing testing and monitoring will be necessary to maintain the quality of your data and enable members of your team to continue adding value down the road. A high level of anticipation and planning will go a long way in the end.