One of those things that everyone does but nobody really speaks about is data cleaning. Sure, it isn’t machine learning’s “very exciting” part. And no, tricks and secrets are not hidden to explore. Your project, however, can be made or broken in this step only. This move normally takes professional data analysts  a very large part of their time.

Actually, even simple algorithms can learn incredible insights from the data if you have a properly cleaned data set! In general, data sets contain huge quantities of data that can not be conveniently stored in formats. Therefore, data analyst first need to ensure that data are structured correctly and that it complies with the rules.

Furthermore, it can be difficult to integrate data from multiple sources and another role of data scientists is to make sure that the resulting mixture of data makes sense. The biggest problems are data shortage and formatting incongruities-and data cleaning. Data cleaning is a process that recognizes faulty, incomplete, unreliable or irrelevant data that addresses problems and ensures that such issues are automatically resolved in future.

Naturally, various data types require various cleaning methods. However it can also be a strong starting point for the systemic method outlined here.

Steps of data cleaning

Here are some of the standard steps and strategies for the cleaning of data experienced development teams:

1. Dealing with missing data
2. Standardizing the process
3. Validating data accuracy
4. Removing duplicate data
5. Handling structural errors
6. Getting rid of unwanted observations

Dealing with missing data : 

It is a big mistake to ignore missing values in a data set, since many algorithms don’t just consider them. Some businesses tackle this problem by adding missing values on the basis of certain observations or by replacing observations with missing values.

Standardizing the process : 

It is critical that the entry point is standardized and its importance checked. You will ensure a good entry point and reduce the possibility of duplication by standardizing the data flow. The goal is to turn the information stored in the initial data into a well-defined and coherent way in the data standardization process.

Validating data accuracy : 

For all various types of user input into the application or automated system, data validation is intended to provide some well-defined guarantees for health, accuracy and consistency. Once your entire database is cleaned, we validate the accuracy of your records. We are also looking for and investing in software to clean up the data in real time. In order to better check for accuracy, we have incorporated several AI or machine learning techniques.

Removing duplicate data : 

Identify duplicates which help us save time when analyzing data as soon as the data is cleaned up. This can be avoided if researchers and investors are able to analyze raw data in bulk and to automate the process for you in various tools, as described above.

Handling structural errors : 

These errors occur during the calculation, data transmission and other problems resulting from mismanagement of data. The most common issues here are incorrect punching, typos, and mislabeled grades. Such errors demonstrate the importance of data cleaning very well.

Getting rid of unwanted observations : 

Data science companies also make unintended findings in data sets. Those may be duplicated or unrelated results to the particular question they seek to solve. Checking for irrelevant insights is a perfect technique for simplifying the engineering cycle–the development team would be able to set up models for much easier time. 
When we are finished with the data cleaning process, the next process is data enriching. 
Data enhancement is characterized as a combination of third-party data with existing first-party customer data from an external authoritative source.  

It is used by businesses to strengthen the knowledge they already have and make educated decisions. All consumer information starts in raw form regardless of the source. Once this collected data is transmitted to a central data store, it is also stored in confidential data sets throughout the network. When this happens, you also encounter data deposited in a data lake or a data swamp filled with raw information which is often not valuable outside the restricted environments.

Software mining is more useful for this raw data. Brands get more insight into the lives of their consumers with the introduction of data from a third party. The improved data result is richer and more accurate, allowing marketers to personalize their advertising more quickly, as they know more about consumers. A big part of creating a golden customer record is good data enrichment processes. Each piece of conduct or transaction data required to create a holistic view of the customer, no matter how comprehensive, is included in a single data set. Of this purpose, data optimization is essential for the long-term marketing aim of delivering customer experiences. 

Two Kinds of Data Enrichment

There are as many types of data enrichment as there are sources to acquire data from, but two of the most common are:

1. Demographic Data Enrichment : Enrichment of demographic data includes the creation of and integration into a existing consumer dataset of new demographic data, such as marital status and income. The population forms and the sources are comprehensive. You may receive a dataset containing children, car size, median home value, etc. What counts is the aim of demographic enrichment. For example, you can create a database that contains a person’s credit rating if you want to make credit card offers. This enriched data can be used to enhance the overall targeting of marketing deals, which is critical in an age in which personalized marketing takes hold.
2. Geographic Data Enrichment: Geographic data enhancement involves the addition to the current dataset of customer addresses of postal data or latitude and longitude. There are a range of companies that can buy this data, including ZIP codes, geographical boundaries between cities and towns, mapping insights, etc. In some ways it is useful to bring this kind of insight into your results. Geographically enriched data may be used by retailers to create their next position in the store. If, for instance, 30 miles from a particular range, the retailer needs to catch a plurality of customers, he may use his enriched data to determine this. Marketers can also save on bulk direct marketing using regional enrichment.

The time taken for up-to-date data is a strong reason for process automation. Continuous learning algorithms can significantly streamline the process of data enrichment as they can fit and combine data much faster than a data manager. This leads to a 24-hour, seven-day – a-week data enrichment cycle, leading to data being always the largest it can be. In the end, brands should maintain a high enrichment level and keep the cycle going in real time to improve their customer engagement. 

For a modern business, a effective data enrichment process is important. Maintaining up-to-date information means that the company can target customers more accurately, whether geographically based to create a new store or demographically focused for the enrichment of the next best deals. More targeted targeting leads to better experience, inevitably leading to more knowledge and ensuring that you continue to work with the latest customer data.