Data preparation, also referred to as data wrangling, data munging, or data cleaning, is a critical and crucial step of Data science process. The amount of time required for data preparation for deriving valuable insights directly depends on the quality of data.
Data is often generated with inaccuracies, errors or missing values. Datasets stored in separate databases or files often have different formats that need to be reconciled and corrected. The method of correcting inaccuracies, performing verification, and joining data sets constitutes a big part of the data preparation process. The critical vital function of Data preparation is to ensure the being collated for analysis is consistent and reliable, so the results of business intelligence and analytics applications will be credible and authentic.
Our Data Preparation service includes various activities such as curating, segregating, gathering, structuring, and organizing data so it can be analyzed as part of data visualization, analytics, and machine learning applications and in a form that is easily understandable to any business audience. Our IT and Business Intelligence team pulls together data from different internal systems and external sources and integrates data sets to load into a data warehouse, NoSQL Database, or Big Data repository.
Our Data preparation services follow the below steps:
To put our Data preparation process in simpler words here are the steps we follow
1. Gather data –
We start the data preparation process by finding and collecting the right data, which can come from an existing data catalog or can be added ad-hoc.
2. Discover and assess data –
Next, we discover each dataset and try to get to know the data and understand what has to be done before the data can become useful in a particular context. Discovery can be a challenging task; however, our data preparation platform offers visualization tools that help users profile and browse their data.
3. Cleanse data –
This is the most time-consuming and crucial part of the data preparation process since it deals with removing erroneous data, filling in gaps, and smoothing out noisy data. Important tasks here include:
· Removing extraneous data and outliers.
· Filling in missing values.
· Conforming data to a standardized pattern.
· Masking private or sensitive data entries.
4. Validate Data –
Once the data has been cleansed, we validate it by testing for errors. Often times, an error in the system will become apparent during this step and we resolve it before we move to the next step.
5. Transform and enrich data –
We then transform or update the data to a format or value entries, which would result in a well-prepared, well-researched, and well-defined outcome, and make the data more comprehensible and intelligible to a broader audience. Also, we then enrich the transformed data, which means we add and connect data with other related information to provide deeper insights.
6. Store data –
Once all of the above steps are performed, we prepare the data and store or channel it into a third-party application, such as a business intelligence tool, and clear the way for processing and analysis to be performed.
Use 7AVP’s data preparation service to derive accurate, dependable and consistent data that can aid your business to