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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.

The importance of data preparation
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:

  •       Multiple data formats
  •       Data inconsistency
  •       Limited/large access to data
  •       Lack of data integration infrastructure

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.

How to rev up data preparation
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:

Self-service data preparation tools: 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 cleansing and manipulation tools: The data cleansing and manipulation tools improve the integrity and quality of data and could be easily connected to multiple sources. The tools save a lot of time, correct and improve the quality of data, and help analysts uncover business insights that are useful for business decision-making.

Advanced analytic techniques: Growing complexity in data necessitates embracing analytical techniques during the ETL process. With these techniques, analysts and data scientists would be able to identify outliers and missing data; know the distribution and variance in data; and use machine learning in reducing, and classifying the data for better analysis.

Sharing metadata: Metadata describes the data and helps in labeling the data variables. A common metadata drives collaboration across the data management and analytical domains. It provides lineage information on the data preparation process and results in the accuracy of business models.  

Increasing data sources: A lot of organizations base their analysis on historical data.  Although this significantly helps in predicting the future, but they miss out a lot of what is currently happening in the real world. Embedded analytics offer real-time information for timely decision-making, and also help in accessing a variety of data sources.

Once the data is ready, analysts and data scientists would be able to build different models and derive useful insights from the data. All these practices will prove helpful to organizations in realizing the true value of data in the form of accurate insights.

Preparation for the future

Good business decisions are the outcome of the good data. With improved practices and technology, organizations would be able to skillfully deal with the data preparation challenges. The increasing volume, variety, and velocity of data require organizations to revise the traditional sharing, storing and reporting of the data. It will make them smarter, approachable and sexier, and will have a considerable impact on the business intelligence, visual analytics, and data discovery process.

Since data is the foundation of the analytics, right data will offer nuggets of information to organizations and help them in reacting positively to the market shifts. As the quote by W. Edwards Deming puts it, “In God we trust, all others bring data”.

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Comment by Amy Flippant on January 31, 2017 at 2:26am

Data prep was a key theme at leats year's Denodo DataFest, take a look at this presentation covering these aspects: Comparing and Contrasting Data Virtualization With Data Prep, Data ...

Comment by Elina Vigand on December 23, 2016 at 3:42am
Besides manual data prep and the use of analytics tools or traditional ETL, lots of data prep activities can be automated, particularly in a more operational setting. Wealthport offers AI-powered data prep for product data in retail, e-commerce and other sectors: https://www.wealthport.com.
Comment by Bill Sengstacken on December 22, 2016 at 11:49am

Data Prep is important, obviously, but we think there is a better way to achieve it.  At Cinchapi, we are leveraging machine learning to make sense of disparate data. Take a look - we'd enjoy the discussion: https://cinchapi.com/

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