Data Preparation: Refine Raw Data Into Value
Find How Refine Raw Data Helps in Data Preparation and Learn The Importance, Present State, Advantage And Challenges Of Data Preparation.
Data preparation is one of the most excellent methods of preparing data for data mining, data discovery, and advanced analytics. Data preparation aims to support business data scientists and analysts by making various kinds of data for their analytical tenacities. The preparation of data can take place either in business departments or centrally by IT.
Data preparation is a sub-domain of data integration that can be implemented with traditional or dedicated tools for data integration like data virtualization, ETL tools or data warehouse automation.
Importance
The increasing digitalization of business processes is making it mandatory for organizations to enable lots of users to gain insights from data (democratization of analytics). Many enterprises today view data preparation as the key to increase their ability to efficiently utilize data in a dispersed way to optimize business processes or to enable new, innovative business models to be on the top.
In the current scenario, achieving resourceful and agile data preparation is of extreme importance. Increasingly unstable and saturated markets create a complex business environment where the ability to differentiate by leveraging the power of analytics is vigorous. Organizations struggle to keep up with the demand for data for analytics to gain insight into changing market scenarios. The pressure is very high on providing analytic data for the in-depth analysis, and addressing these requirements necessitates skilled personnel and a modern approach to data preparation.
Top drivers for Data Preparation
In the present scenario, businesses come across various challenges. The ability to utilize data systems has become a pivotal competitive advantage. Many organizations have recognized this and are striving to solve numerous data usage issues by improving or introducing data preparation. The main drivers behind projects show the hype around data preparation, which undoubtedly exists, is supported by “concrete” requirements.
The advantages of analytics and the need for agility are driving the use of data preparation. So, here are the top three drivers for Data preparation and they are as follows:
1. Higher expectations of a concrete business impact and increased competitiveness through analytics
2. Higher expectations in terms of flexibility, agility, performance in business departments
3. An increasing number of data sources with growing volume, velocity, and variety of data
How to get engaged in Data preparation
There are essentially two ways to engage in data preparation: manually — refer to as “spreadsheet wrangling” — or by using automation tools. Most organizations will choose the latter, but you’ll have to judge the benefits of such products for yourself.
Here’s are the steps of how you can create engagement:
- Discovery: With the help of automation, the foremost step is to discover the data that’s best-suited to the analysis, you need to solve the problems.
2. Cleansing and refining: This is where the data is purged of obvious errors that shouldn’t make it into the final data package.
3. Distillation and Blending: This is the phase where commonly substituted terms and duplicated entries are taken into account, so they don’t cause abnormalities in the final data set. Distillation may involve applying custom data quality rules using automation.
4. Documentation: Documentation is important in case other parties use the same data for new projects in the future. Metadata in the data catalog can include details on relationships between databases, definitions for technical and business terminology, source information, and a list of changes to the data during distillation and when they were implemented.
5. Reformatting and Packaging: This step is important because companies may use any number of tools and procedures for interacting with the data after it’s been discovered. The resulting data package should be ready for importation into other tools for visualization and further manipulation.
Conclusion
The right decisions are the result of the good data in the business. With enhanced technology and practices, enterprises would quickly deal with data preparation challenges. The increasing velocity, variety, and volume of data require enterprises to revise the traditional sharing, reporting and storing of the data. It will make them approachable and smarter, along with that they have a considerable impact on the BI, visual analytics, and data discovery process. Since data is the groundwork of the analytics, the right data will offer crucial information to companies and help them in reacting positively to the market shifts.
Also, see this related article on Data-Visualisation
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