Clean data is crucial for insightful data analysis. Data cleansing, data cleaning or data scrubbing is the first step in the overall data preparation process. It is the process of analyzing, identifying and correcting messy, raw data. Data cleaning involves filling in missing values, identifying and fixing errors and determining if all the information is in the right rows and columns. When analyzing organizational data to make strategic decisions you must start with a thorough data cleansing process. Cleaning data is crucial to data analysis. Data cleaning lays the groundwork for efficient, accurate and effective data analysis. Without cleaning data beforehand, the analysis process won’t be clear or as accurate because the information in the dataset will be unorganized and scattered. Good analysis rests on clean data–it’s as simple as that.
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Because good analysis relies on adequate data cleaning, analysts may face challenges with the data cleaning process. All too often organizations lack the attention and resources needed to perform data scrubbing to have an effect on the end result of analysis. Inadequate data cleansing and data preparation frequently allow inaccuracies to slip through the cracks. The lack of data scrubbing leading to inaccuracies is not the fault of the data analyst, but a symptom of a much larger problem of manual and siloed data cleansing and data preparation. Beyond the lackluster and faulty analysis, the larger issue with traditional data cleansing and preparation is the amount of time it takes–Forrester Research reports that up to 80% of an analyst’s time is spent on data cleansing and preparation. With so much time spent scrubbing data, it’s understandable why data cleaning steps are sometimes skipped over. Most organizations need a data cleaning solution that will help with analysis but reduce the time and resources spent on preparation.
Data clean up can be difficult, but the solution doesn’t need to be. Data cleaning tools make the process simpler. We have created a new approach to data preparation that helps organizations get the most value out of their data with proper data scrubbing. With its visual, user-friendly interface, Our data wrangling software allows non-technical users to wrangle data and scrub data of all shapes and sizes for sophisticated analysis. iSales empowers non-technical or business users to do more with their data by guiding them through the process using intelligent suggestions powered by machine learning. What was once the daunting and overwhelming task of data cleansing, is now made simple with iSales. Now data scrubbing won’t consume valuable time, and fewer inaccuracies can slip through the cracks.
Our six-step wrangling process lends itself to a more iterative data cleansing and data wrangling, ultimately leading to a more accurate analysis. The steps involved include:
With iSales, and a six-step approach to data clean up and data preparation, it’s easy to remove or correct records that are inaccurate, missing, or corrupt, whether from a database, a table, or a set of records. This allows organizations to dramatically reduce their time spent on data cleansing, and leads to better, more accurate analysis. iSales helps businesses reduce the amount of resources spent on data scrubbing. With an easier data scrubbing process, businesses are able to analyze more data and get more out of the analysis.
To learn more about Our unique approach to data cleaning, data scrubbing and data wrangling, download our eBook, Six Core Wrangling Activities.
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