The continuous buzz around data and its benefits to organizational growth takes the focus away from the prime important topic which is the identification of the right and reliable source of data in its original format and structure, which is also called raw data.
Data enrichment is a process or technique used to cleanse, enhance and improvise the raw data so that it becomes more useful and easy to consume in the processes down the data pipeline line.
Challenges of Handling Raw Data
The end-user or customer data irrespective of its source comes in various forms, formats, and structures. For example, the feedback surveys and end-user feedback are collected at the retail locations or just logs or sensor data. It can also be social media feeds or image processing data. When this data is sent further to the data warehouse set up, and it is uploaded to various databases, or this data can be directly loaded to a data lake; obviously this data is not ready to be used now in any analytics tool since this has not been tagged with the context it serves. This becomes a hurdle in data ingestion into ML models or data science statistical analysis.
There are many ways of data enrichment that vary based on data being consumed further. Most of them revolve mainly around preparing the data ready to be consumed. If the right data enrichment technique is applied to the raw data, the utilization and format become much more comfortable to adapt.
Mergers and Data Enrichment
One very frequently used use case for data enrichment is when an organization merges with another. The data both the organizations have been maintaining separately for past years now needs to merge. This seems easy to look at, but the most challenging part is combining two very different data strategies to deliver a harmonized view. Every organization has its data strategy and vision.
The enrichment process begins with basic cleaning techniques such as removing 0 paddings, filing in acceptable missing values, applying spelling correction, converting date formats, etc., and goes as complex as using statistical extrapolation formulas such as fuzzy logic. Categorizing the dataset, segmentation, and adding narrowed-down tags is the next level of the data enrichment process. Categories can be based on Their Data Sources, GeoLocations, or their Demography.
It’s not simple for a company to take critical business decisions like opening a new retail store at a strategic location or shutting down a plant due to redundant DCs nearby or expediting air transport which is more beneficial to the company’s growth and revenue. However, well-maintained and enriched raw data help deliver more significant insights to gain information and stay ahead of the competition.
Benefits of Data Enrichment:
Data enrichments are more than just data cleansing so it has more benefits as well let us check the benefits one by one.
- Gain core value of raw data and generate more opportunities.
- Enhance the Accuracy of the outcome and MAE or MAPE ranks.
- Increase the usability of the raw data and gain ROI on the cost invested in data warehouse maintenance.
- Customer segmentation and personalized reach help gain end-user trust.
- Better customer reach and lead generation.
- Data compliance and organizational growth.
In a way, data enrichment helps businesses in getting data more organized and helps them taking well-informed decisions.
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