Why data preparation should not be overlooked

Why data preparation should not be overlooked

Why data preparation should not be overlooked

Data is the new language today. Data leads to insights, and insights help organizations to make actionable business decisions. However, sourcing the data and preparing it for the analysis is one of the tedious tasks organizations face these days. Analysts devote a lot of time in searching and gathering the right data. According to a research firm, analysts spend around 60 to 80 percent of their time on data preparation instead of analysis. Consequently, an accurate analysis depends on how well the data has been prepared and managed effectively.

Data preparation is an integral step to generate insights. It is one of the most time-consuming and crucial processes in data mining. In simple words, data preparation is the method of collecting, cleaning, processing and consolidating the data for use in analysis. It enriches the data, transforms it and improves the accuracy of the outcome. Some of the key challenges faced by analysts and data scientists in dealing with data preparation include:

Read Also:
Big Data Analytics Use Cases: R/X For Healthcare

Data preparation is mostly done through analytical or traditional extract, transform, and load (ETL) tools. Both of which have their own advantages and limitations. In order to effectively integrate a variety of data sources, organizations should align the data, transform it and promote the development and adoption of data standards. All these things should effectively manage the volume, variety, veracity and velocity of the data.

Data is everywhere. The ability to integrated it and develop insights faster will drive value across the enterprise. Here are the best practices that will speed up the data preparation and integration process:

: The self-service data preparation tools enable automation and help users handle diverse workloads. It crosses out the manual work of searching, cleansing and transforming the data for analysis. Moreover, the self-service data preparation tools reduce the dependence on IT support and decrease the time to prepare data.

 



Data Innovation Summit 2017

30
Mar
2017
Data Innovation Summit 2017

30% off with code 7wData

Read Also:
Expanding the Data Lexicon: Introducing the Edge Analytics Cache
Read Also:
Q&A: Self-Service vs Traditional Business Intelligence

Big Data Innovation Summit London

30
Mar
2017
Big Data Innovation Summit London

$200 off with code DATA200

Read Also:
How Major IoT Software Platforms Stack Up

Enterprise Data World 2017

2
Apr
2017
Enterprise Data World 2017

$200 off with code 7WDATA

Read Also:
Big Data Analytics Use Cases: R/X For Healthcare

Data Visualisation Summit San Francisco

19
Apr
2017
Data Visualisation Summit San Francisco

$200 off with code DATA200

Read Also:
Why Natural Language Processing Will Change Everything

Chief Analytics Officer Europe

25
Apr
2017
Chief Analytics Officer Europe

15% off with code 7WDCAO17

Read Also:
Next Generation Analytics: The Collision of Classic & Big Data Analytics

Leave a Reply

Your email address will not be published. Required fields are marked *