In the blogs about Big Data we discussed about the Functional Layers of Big Data and in my last blog I listed Top 11 Cloud Data Storage Tools. The next step after the storage is Data Cleansing Process.
When we talk about Big Data, it is self-explanatory that the data is growing at an alarming rate, whether it is business data or personal data. If we go by the facts then every day 2.5 Quintillion Bytes of Data is created in the world. This data also has the repetitive and erroneous records which we need to remove before mining it for insights in it. Inaccurate Data leads to wrong assumptions and analysis ultimately leading to failure of the project.
Data Cleansing is the name of the process of correcting and eliminating (if required) inaccurate records from a particular database. The purpose of data cleansing is to detect so called Dirty Data to either modify or delete it to ensure that a given set of data is accurate and consistent with other sets in the system.
There are a variety of Data Cleaning tools. A good data cleaning tool helps clean your database of duplicate data, bad entries and incorrect information. These tools could be divided in the below categories depending on the environment in which these are used:
- Offline Data Cleaning Tools
- Cloud Based Data Cleaning tools
- Data Cleaning tools for Salesforce Data.
This blog will acquaint you with some good offline Data Cleaning Tools.
1. Drake –
Drake is simple-to-use, extensible, text-based data workflow tool that organizes command execution around data and its dependencies. Data processing steps are defined along with their inputs and outputs. It automatically resolves dependencies and provides a rich set of options for controlling the workflow. It supports multiple inputs and outputs and has HDFS support built-in.
2. OpenRefine –
OpenRefine, formerly called as Google Refine, is a standalone open source powerful desktop application to work with messy data. It offers the data wrangling feature i.e. data cleanup and data transformation from one format to others. It is similar to spreadsheet application, but behaves more like a database.
It works on data similar to relation database tables, i.e. it operates on rows of data which have cells under the columns. One OpenRefine project is one table. Users can change the display of rows using various filtering criteria. All actions performed on a dataset are stored in a project and can be replayed on another dataset.
3. Trifacta Wrangler –
This tools helps us in the Data Wrangling process. Data wrangling is loosely defined as the process of manually converting or mapping data from one raw form into another format that allows for more convenient consumption of the data with the help of semi-automated tools.
Wrangler dramatically improves how organizations derive value from diverse data. With trifecta wrangler a new approach has been applied to how analysts make data useful by leveraging the latest techniques in data visualization, machine learning, human-computer interaction and data processing. They have a simple aim of spending less time formatting and more time in analyzing the data. It allows interactive transformation of messy, real-world data into the data tables for analysis tools.
4. DataCleaner –
Data cleaner is a data quality analysis application and a solution platform for Data Quality Solutions. Its core is a strong profiling engine, which is extensible and thereby adds data cleansing, transformations, enrichment, DE duplication, matching and merging. Some features of it are as below:
- Find patterns, missing values, character sets and other characteristics of your data values.
- Cleanse your contact details with name and address validations.
- Detect duplicates using fuzzy logic and configurable weights and thresholds. And finally creating a single version of it.
- Build your own cleansing rules and compose them into several use scenarios and target databases.
5. Winpure Clean and Match –
Data Quality control is the most important factor behind the overall success of a project or campaign. It is a data cleansing and matching suite, specially designed to increase the accuracy of business or consumer data. It is an award-winning software suite, ideal for cleaning, correcting and deduplicating mailing lists, databases, spreadsheets and CRMs. It can be used for databases like Access, Dbase, SQL Server, and also Excel tables and Txt files.
6. TIBCO Clarity –
TIBCO Clarity is a data preparation tool that offers you on-demand software services from the web in the form of Software-as-a-Service. It can be used to discover, profile, cleanse, and standardize raw data collated from disparate sources and provide good quality data for accurate analysis and intelligent decision-making. Features of TIBCO Clarity to manage raw data:
- Seamless Integration
- Data Discovery and Profiling
- Address Standardization
- Data Transformation
7. Data Ladder –
Data Ladder Company is a data quality software company, with an objective to help business users get the most out of their data through data matching, profiling, de-duplication, and enrichment tools. The Data Match Enterprise suite is a highly visual desktop data cleansing application specifically designed to resolve customer and contact data quality issues. Data Match Enterprise includes multiple proprietary and standard algorithms for detecting phonetic, fuzzy, miskeyed, and abbreviated variations
Data Deduplication Software offers a complete solution for data quality, cleansing, matching and de-duplication software in one easy to use software suite.
8. Star DQ Pro –
Make sure your data is accurate, genuine and up-to-date. It addresses the key requirements of data quality like accuracy, completeness, consistency, timelines, uniqueness and validity. Features offered by it are
- Cleansing – qualifies type of defects, generate logs of unclean data with comments.
- De-duping – grouping and clustering, identifying misrepresents, ongoing incremental de-duping.
- Monitoring – transaction log, process status alert by mail/SMS, user authentication.
Data cleansing is especially of great importance when a large amount of data is stored. The goal of corrective action on the dirty data then is to make any errors as insignificant as possible. Unless data cleansing is undertaken regularly, mistakes can accumulate and lead to decreasing the efficiency of work. In the next blog on Big data, I would list the cloud based data cleansing tool and tools for Salesforce database.