Essential Steps to Enhance Data Quality Through Data Enrichment

Data enrichment is a vital process of the data management that aims at increasing benefits of data, making them more pecise and valuable, and enriching the raw data. Data enrichment is the general process of gaining information from an external source, and integrating it with an already existing database, which can be defined as the process of enriching data mass in order to elaborate it, refine it and enhance it.

Therefore, for those organizations who rely tremendously on data related to their planning, competitive strategizing and positioning and decision making, this procedure becomes very important due to its efficiency in ensuring integrity of the data used.

According to Kritikos, the goal of data enrichment is to take steps to enhance data quality and make new data accessible to other applications. Whereas data enrichment is concerned, it may be seen that one does not necessarily require more data. The purpose of enriching data is to:

Develop broader standard frameworks in order to maintain standard data forms.

Produce additional data to boost the value of a dataset which has been collected.

For improving the prospect of specific advertising and marketing plus customer service, for example, the possibilities of providing additional details to currently client databases may encompass the integration of social networking interactivity, purchasing histories, and additional information about the client.

Note that data cleansing is distinct from data enrichment though both activities are commonly used in analytics processing. 

Importance of Data Enrichment

However, the contributions of the enlargement of data at the data-drive commercial competitiveness today should not be overemphasized. Enriching data is not just a strategy it is important that has the possibility to turn into competitive edge. As a result, data becomes an even more effective tool that can help comprehend things, make decisions, and build future strategies.

It has been seen that, the firms who want to sustain their competitiveness in the market should be able to implement change rapidly and adapt to the changes quickly. The main benefit of data enrichment is found here:

Consumers receive information on products and services and firms gain insights into their markets and consumers.

Optimized and enhanced client feel, precise reaching, and efficiency are realized from prognostications.

It is also helpful as it enables programmers and data engineers to think of ingenious yet practical ways of employing data enrichment techniques. The richer data produces better algorithms models, analytics, which lead to platforms and applications that are smarter, more compatible, easier to use, and suited to needs and habits.

Additionally, data enrichment is crucial for compliance and handling of risks involved in data processing. Hence, there is better preparedness in terms of a risk management front, with clean, complete and contextually oriented data, to keep the organizations within the regulatory perimeters.

When it comes to data enrichment, the steps and procedure to be followed are as follows:

From the great importance of data enrichment in increasing the efficiency of data engineer and programmer, and the general improvement of particular industry and specific individual company goals, enhanced procedural knowledge of data enrichment can be highly desired and used.

Steps of Data Enrichment

Step 1: As such, the evaluation of data is a process that has to be carried out in order to determine the validity of the given information.

Before beginning any type of enrichment first, you need to define what types and sources of data are available to your company for the assessment of the data’s condition. If there is no way of how to find where you are missing the data or where you are lacking information in the current datasets, is data enrichment beneficial? Such an action sets the basis for the subsequent phases where the option of calling for more information is made based on the result of an initial assessment.

Step 2: Identify the location of data: The location of data refers to the place where data is collected or generated, this is an important factor when conducting research on different subjects.

The next action in understanding the scope of the data or possible additions of external information is to search for other internal or external data sources which may fit seamlessly into the current information package.

By this, it will be helpful to gather data that could aid your enrichment agenda such as market trends, demographic information, and any other related data sets.

Step 3: This is done by clearing the data, which can be defined as the process of removing any information that is currently written in the simulation.

This means that the steps to enhance data quality added to the model are perfect, and no enrichment has been done incorrectly. This stage consists of harmonising the formats within a given dataset, cleansing the data for exact and duplicate values. Though it is one of the facet of data purification, it consolidates with data ensemble, which is an essential step prior to data enrichment.

Step 4: It is about the Integration of Data

Using external data sources alongside your current dataset is what needs to be achieved in the course of this step. By the methodic inclusion, one can be assured that the additional data is going to strengthen and enrich the primary data set.

Step 5: Steps to enhance data quality assurance and validation refers to a process of regular checks and assessments to ensure that intended steps to enhance data quality standards are attained and maintained by a process or product.

Strict validation procedures must be followed after integration to guarantee:

• Accuracy

• Relevance

• Tight tolerances on the supplied data

• The amount of benefit gained from the enhanced dataset

Step 6: This means that in the process of organizing learning, there should be a continuity of observation and subsequent changes in the context.

Data enrichment, therefore, describes a cyclical process that involves the monitoring of data and enrichment of the same to achieve the intended objectives. This means that markets change, businesses expand or differentiate, and data degrades over time. This implies that for interval enhanced datasets to remain relevant they must be reviewed from time to time.

Here, it remained evident that when firms integrate a rigorous data augmentation policy, the firms will be in a position to enjoy multiple benefits.

Step 7: To bring the business systems more enhanced data about them.

Once you have made your data ready and put into data enrichment practices procedures for refreshing it regularly, you should consider which parts of your organisation should access which datasets. After that, you have to send the enriched data to those systems where the concerned individuals can have it by adopting integration processes.

The benefits allowed in enhanced data

Data enrichment is a process in which unprocessed datasets are subjected to a process of analysis that turns them into more useful insights capable of enhancing creativity and well-organised decision-making.

Among the advantages of data enrichment are:

Enhanced data quality: In this way, data enrichment enhances the data from the starting point by acquiring new data and integrating it with the current data. It makes information more accurate, sets floats errors, and inserts inconsistencies, which paves the way for more reliable data processing and decision-making processes.

Improved consumer insights: This is especially important when aiming to cover your customers comprehensively in your marketing campaigns. Data enrichment, for example, can add more information on client profile data in terms of demographic, psychographic, and behavioral information on clients. As a result, it creates an improved customer satisfaction, strategic marketing, and strong client relationship.

Making well-informed decisions: An organization can add ‘external’ sources pertinent to the given data to give the data even more context. In turn, for data engineers and analysts, this richer context allows for improved discovery of patterns and trends as well as relationships that exist within data, and therefore assist data-driven decision-making in line with organisational objectives.

Simplified risk management: Data enrichment lowers risk because, through its construction of more detailed and diverse perspectives of operations, clients, and market conditions, organizations glean more robust insights with which to evaluate and act upon their situations. By using such a complete strategy, potential threats and vulnerabilities can be identified and real preventive measures can be developed.

Operational efficiency: Companies need more inclusive data to enrich the methods, actions, and processes that can optimize and increase productivity and efficiency. Through data enrichment, the time and effort wasted in typing, rewriting, and making other corrections is cut short. This enables the teams to focus on other important strategic operations.

Regulatory compliance: This means that the data need to be updated frequently to include accurate information to meet the legal requirements in data privacy. Data enrichment helps to minimize legal and financial risks by checking the consistency of datasets and updating outdated information, simultaneously, it contributes to compliance with the norms.

Data enrichment is thus not just a simple addition of content to your datasets but comes with a significant number of advantages. The BTM approach alters the way that enterprise and IT professionals view consumer interactions, data interpretation, and decision making to enhance business outcomes and foster advancement.