Data enrichment is a force multiplier in analytics

Introduction

Data enrichment is the first step in gaining important information about a company and plays an important role in the analysis. That's what you need to know.

Based on the definition of Techopedia, data enrichment is the process by which raw data is enhanced so that it can be better and more easily used. Although there are many data sources that generate tons and tons of raw data, many of these raw data would be better exploited if they were first enriched. Data enrichment is the first step in the process by which we obtain valuable information that can benefit a company based on the data collected through analysis or automatic learning. 

Even something as simple as a typo can convert raw data into data that is easier to process, with less data being discarded as unusable. Data extrapolation is also considered data enrichment, filling gaps and gaps in our data to meet the mathematical model established by previous data points. Data enrichment allows data to be entered into a system in a format that the algorithm can easily understand to ensure that the results obtained are consistent with the raw data we enter.

Taking the next step

After enriching our data, where do we go from here? The next rational step in our data processing is the increase. While data collection is sufficient for some companies, to get the real benefit of data enrichment, we need to go further by adding data. Using data collection points to combine, organize, and sort data makes the data enrichment system much more robust. This sets the data for use in machine analysis and learning, where we put the data we collect and enrich to work for us. Using analytics to generate customer information or other pertinent information can help us inform and direct our marketing. Forbes rightly states that data is crucial to serving the right customers with the right experiences.

Automatic learning through rich data

Gathering ideas is a long-term effort. Trends are generally not identified after a single day of data. It usually takes months, sometimes years, to determine what a trend is and get information on that trend. Analytics is based on the detection of patterns within the data and on the discovery of how these standards are applied to the company as a whole. It uses a set of key data points in which the company is interested as a basis for its exploration. 

Although the analysis is important and is an important part of the marketing tactics of information in today's world, it is insufficient to solve the general picture. This is where machine learning comes into play. Through specialized algorithms, we can use the valuable data we previously collected and promoted to give us an idea of all sorts of customer patterns and trends, not just those we discovered earlier. According to SAS, machine learning is a type of data analysis that deals with the automation of the construction of analytical models.

The importance of automated model building is that there is no need to limit ourselves to a simple amount of data that can be processed by humans. We can literally use all the data we collect, no matter how much information it may be. The implications for business are profound because it means that companies that offer electronic discovery services can receive information about a wide range of things they did not even know they did not have. In essence, machine learning takes data analysis to its logical conclusion, offering a real view of a business through the automated processing of rich data.

Decisions reported by Analytics

Information is processed information and information is what the heads of a company need to make decisions. With the added power of rich data that increases the processing of collected data, a company can benefit greatly by providing information on new and unexplored areas previously. This has implications not only for customer profiles but also for efficiency and business impact of the customer. Machine learning gives the company even more scope and coverage with its collected data and turns that data into a real asset, which can lead to increased profitability for your controlling company if it is used effectively.

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