Data is an incredibly valuable and powerful resource for businesses today. With the right data and analytical tools, businesses can extract the kind of insight that is usually very hard to find. Data analytics software can reveal patterns that would be virtually impossible to discover using just our own intelligence.
However, there are a couple of notable caveats. First of all, your data analytics will only ever be as good or insightful as the raw data that you have to work with. Poor-quality data will lead to unreliable results. In fact, poor data collection can end with you drawing invalid conclusions and not realizing it for quite some time. By the time the errors become apparent, those conclusions might already be wreaking havoc with your internal reasoning and decision-making.
What Is Data Enrichment?
Data enrichment is a process that allows businesses to merge multiple different data sources together. This creates a unified source that is more valuable and useful than its constituents. In many cases, this means combining internal data with information received from external sources. This is a popular way for brands to augment their existing data sets and improve the quality of the conclusions that they can draw.
This enriched data provides the opportunity for a more granular analysis than would otherwise be possible. Whether it is a more in-depth picture of a business’s audience, or better understanding of their competitors’ behavior, the added power of enriched data can make it much more useful than its constituent sources are on their own.
Types of Data Enrichment
No individual dataset can provide a complete and holistic overview of a brand’s customers. To properly understand your customer base, you will need to combine several datasets into a single enriched source. As businesses across the board try to introduce greater levels of personalization, the ability to meaningfully combine different data sources provides them with a powerful tool for enhancing their services.
There are innumerable types of data enrichment, as well as data collection techniques. However, there are two types of data enrichment that are far more prominent than the others. These are the two that you really need to know about.
#2020 sep news #education #data enrichment #data preparation #data science
