We all keep our houses clean and sanitized, many of us may be keeping a domestic helping hand, especially for it. But, why do we do it? The sole purpose for it is to stay distant from diseases and keep hygiene. Data preparation is considered to be equally important for your organization, as cleaning your household.
Data Preparation Tools are crucial to perform cleansing, organizing, composing, and enhancing data revolving within organizations. It helps in generating cognizance and optimally utilizing data.
Today, every organization knows the value of data. However, not every organization is capable of using it productively. Data preparation is quite an essential task for using the organization’s data productively and precisely for analytics.
Challenges Faced by Organizations During Data Preparation
More than 60% percent of the time given for analytics gets used for preparing data for analytics. Organizations face major hurdles during data preparation. These include;
Numerous Data Sources:
The data-driven organizations collect a mammoth amount of data from varied sources like CRM and social media, sales, marketing, etc. Data collected for preparation from different network hosts and numerous sources poses a bigger challenge for the Data Preparation process. In addition, the data remains completely in a raw form, inculcating diversified data types and structures.
Moreover, the majority of companies that are collecting data from numerous sources are unable to completely prepare and cleanse their data in time. Thus, resulting in delayed data analysis, and sometimes generating inaccurate and insufficient results.
Mislaid and Insufficient Data
Data-driven organizations involve a huge variety of unstructured and unformatted data within their data warehouse. Moreover, the data composed within the organization involves mislaid values within the data and a lot of insufficient data is also included. Thus, always leaving space for margin of errors during analysis.
Only Using Traditional ETL Systems
Traditionally, data preparation or data cleansing has been done with help of data science and IT professionals using data munging methods like ETL(Extract, transform, and Load). ETL system helps these professionals create a conduit for extracting data from various sources, transforming it, and finally putting it into a data repository or data warehouse.
Although, there are many perks for using the traditional ETL systems like creating an ecosystem for data warehouses, regulation and governance of data, data management, securing data integrity, and masking the precious data.
However, the ETL system also includes setbacks like very slow speed and responsiveness, consuming a huge amount of time for data preparation, the data access getting restricted only to data professionals, and pertaining issues within the data flow between IT professionals and end-users of data.
Thus, the ETL system is crucial as a part of the Data Preparation process, but it mustn’t be the only option adopted for data preparation.
Best Policies for Data Preparation
Democratizing Analytics
Democratizing Analytics means empowering the end-users to prepare the required data on their own. Switching the focus from overburdened IT professional to end-user.
Thus, the person using the data can prepare it in the best possible way. However, it can only be achieved through using Self-Service Data Preparation tools for data preparation.
Bridging Self-Service Data Preparation and Traditional ETL Systems
Self-Service data preparation tools are delineated to serve the end-users who demand the data for analysis. These help business analysts to access the data without the use of professional coding language and complex computational syntax to answer their questions and address their doubts.
A robust and effective data preparation process entails a combination of Self-Service data preparation tools and an ETL system. This will help in generating more accurate results and in a very short period.
Using IT Workforce for Data Governance and Management
The end-users must be given only the access to the required dataset, and confidential and important data must be masked for protecting the organization’s integrity. The data warehouses must be managed by IT professionals to ensure data security. It also helps with the data preparation process and to keep the data warehouses up to date.
