Written by Nick Driver
Posted on 15/10/2015

Don’t overlook the importance of data enrichment

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Data on customers, suppliers, communities and other sources arrives in various forms. Businesses use this information everyday to make informed decisions across virtually all their functions, so ensuring it is accurate is key. One way of doing this is through data enrichment.

If your business collects and utilises large volumes of data for commercial or other uses, the concept of data enrichment may already be familiar. It is one aspect of the wider data integration task and arguably one of its most important. However, not all firms complete this step or maximise the potential of it. If your firm relies on data to drive business strategy, refine processes or for any other reason, data enrichment should be a frequent task.

Data enrichment explained

In short, data enrichment refers to the task of enhancing, improving and organising data so that you can glean more valuable insights from it. One significant aspect of data enrichment involves fixing the source information you collect to correct errors or add missing details. For example, a database of customer contact information may be missing the final digits of a post code. Data enrichment would add these vital figures and numbers so that you can quickly and accurately contact prospects via direct mail and be almost sure that the letter will be received.

Why is data enrichment needed?

Precisely why data enrichment is required depends on the goals of your business. The main aim may be to simplify the process of analysing information and extracting insights to save time and increase productivity. It is also a valuable undertaking if you are moving data over to a new content management system and want to remove errors first. Whatever your motivation is to enrich data, adding value in some way will underpin it.

Don’t underestimate the size of the task

You would be right to think that firms who deal with vast amounts of data on a daily basis face a big task if they plan to enrich it all. The job is still a significant and testing one even if your own business handles a more manageable amount of information. This is why prior planning is key to success. By building and strictly following a well thought out process you will save time and meet the end goals. Here are building blocks of an effective data enrichment process.

  • Set your goals

Firstly, it is imperative that you set clear goals, as they will mould the entire process. Think about the problem that needs to be solved or what you hope to gain through data enrichment. In many cases there may be multiple outcomes you hope to see. If so, it is even more important that these aims are understood by everyone involved.

  • Data quality assessment

A data quality assessment enables your business to identify issues within the data set and understand where cleansing and enrichment is required. This task should begin by gathering every form of data that relates to the end goals you previously laid out. You need to look for inconsistencies in structure/format, missing data, data in wrong fields and spelling errors. It is also worthwhile to highlight any information that is unnecessary and remove it from your database. This will mean that time isn’t wasted enriching details that aren’t relevant.

  • Locate the required data

Once you know what additional data is needed, it is time to locate the sources that can provide it. There is no shortage of data suppliers whether you are looking for geographic, demographic, psychographic or any other data type. Remember that although some providers will offer information for free, others may charge a fee.

  • Stick to the process

With your goals set, a quality assessment completed and the necessary data sourced, the process can begin. If you are completing the task in-house, it should be delegated to an experienced individual or team who are familiar with the data they will be handling, and who understand the end goals. Follow the pre-agreed process rigidly, and periodically audit the enriched data along the way to make sure there are no issues that will need to be corrected later on.