How Data Enrichment Is A Force Multiplier In Analytics

data enrichment and analytics
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Based on the definition by Techopedia, data enrichment is the process by which raw data is improved so that it can be better and more easily utilized. While there are a lot of data sources that generate tons and tons of raw data, much of this raw data would be better used if it were first enriched. Data enrichment is the first step in the process by which we gain valuable insights that can benefit a company based on its collected data through analytics or machine learning. Even something as simple as typo-correction can turn raw data into more easily processable data with less data being tossed out as unusable. Data extrapolation is also considered data enrichment, filling in gaps and holes in our data to conform with the mathematical model set out by previous data points. Data enrichment allows for data to be fed into a system in a format that is easily understood by the algorithm to ensure that the outputs we get are consistent with the raw data we put in.

Taking the Next Step

After we’ve enriched our data, where do we go from here? The next rational step in our data processing is augmentation. While collecting the data might be enough for some companies, to get the real benefit out of data enrichment, we need to go beyond this, adding to the data. Using data collection points to collate, arrange, and categorize data makes for a much more robust data enrichment system. This sets the data up for use in analytics and machine learning, where we put our data that we’ve collected and enriched to work for us. Using analytics to generate customer insights or other pertinent information can help us to inform and target our marketing. Forbes states rightly that data is crucial to targeting the right customers with the right experiences.

Machine Learning through Enriched Data

Gathering insights is a long-term effort. Trends don’t usually pinpoint themselves after a single day of data. Usually it takes months, sometimes years, to determine what a trend is and to glean information from that trend. Analytics relies on spotting patterns within the data and figuring out how those patterns apply to the company as a whole. It uses a set of key data points that the company is interested in as a basis for its exploration. While analytics is important and is a huge part of informing marketing tactics in the world today, it falls short in figuring out the big picture. That’s where machine learning comes in. Through specialized algorithms, we can use the enriched data we previously collected and boosted to give us insights into all sorts of customer patterns and trends, not just those that we’ve figured out beforehand. As SAS puts it, machine learning is a type of data analysis that deals with the automation of analytical model-building. The importance of automated model building is that there is no need to limit ourselves to a simple human-processable amount of data. We can literally use all the data we collect, no matter how much data that is. The implications to business are profound, as it means that companies offering eDiscovery services can be informed on a wide range of things that they didn’t even know they were lacking. In essence, machine learning takes data analytics to its logical conclusion by offering true insight into a business through automated processing of enriched data.

Informed Decisions through Analytics

Information is processed data, and information is what the heads of a company need in order to make decisions. With the added power of enriched data boosting the processing of collected data, a company can stand to benefit immensely, giving insights into new and previously uncharted areas. This has implications, not just for customer profiles, but for things like business efficiency and customer impact as well. Machine learning gives a company even more reach and coverage with its collected data and turns that data into a true resource, one that can lead to an increased bottom line for its parent company if utilized effectively.