How Data Preparation Software Can Help Your Business Succeed

More than 75% of data scientists cite preparing data for analysis is the most awful part of their job. Data preparation is the first step towards an attempt to make sense of your data. If done right, it can help your business succeed. If done wrong, it can create more bottlenecks and hindrances in your organization processes.

What is Data Preparation?

Data preparation can be defined as working on raw data to prepare it for fulfilling objectives such as analysis, business intelligence, reporting, data migration and much more. It involves making factual, grammatical and numerical fixes to errors, removing duplicates and creating a standardized structure for data to be used for its intended purpose.

Data processing can be a cumbersome and lengthy task, but it is a crucial step to make sense of raw data. The data must be understood fully in the right context and brought to order before it could help the organization achieve its goals.

Data preparation also entails identifying missing information, removing inaccurate entries, replacing and modifying incomplete and inaccurate data with correct information and consolidating all the gathered data in an orderly fashion under a suitable format. Data preparation software can be a useful tool for helping data science teams with the task of cleaning data efficiently and interactively.

Data preparation software often follows a series of steps that have been chalked previously to make data cleaning jobs systematic and thorough. It begins by extracting raw data, sorting through it with the help of algorithms, extracting data from the records and filling in relevant fields, and saving it up to be used for the intended purposes later. The applications of prepared data are numerous and wide-ranging. It could be used for further munging, visualization to look for patterns and trends and to be used on statistical models to gain valuable insights.

Steps in a data preparation cycle:

The steps of a thorough and well-planned data preparation project comprise of the following:

1. Data collection: Gathering relevant data from various sources and data catalogs is the first step in preparing your data.
2. Data assessment: Getting to know the data is an important step after finding the right data for your business problem. It is crucial that you know what the data consists of before putting it into context to solve an issue. Data visualization is helpful in assessing the shape of your data.

3. Data cleaning: Removing errors and incomplete entries and validating the collected records is data cleaning. It is a time-consuming but extremely important process for making the data worthy of further processing. Data cleansing tools such as Data Ladder make data cleansing tasks a breeze for organizations.

Testing data for errors is also included in this process. Validating data will show any errors in the system which should be resolved before using the data for analysis.

1. Data Transformation: Updating the data to be easily understandable by all audiences is also important. Adding more details to already cleansed data is called ‘enriching’. Data enriching adds context to the collected data and connects it with other entries to provide deeper understanding.
2. Data Storage: Once the data preparation software completes all crucial steps to gather, clean and enrich data, it then stores it efficiently for further processing and analysis.
3. Connecting to other technologies: Each organization employs several different technologies to run their daily operations. A good data processing software is easily adaptable to these technologies and enables the organization to be flexible in its data-related tasks. Connecting to Big Data sources is also an incentive that good data processing software offers to businesses to easily manage data at a large scale.
4. Automating operations: Since data preparation and data cleaning is often an ongoing process in large data-dependent organizations, they are often on the look-out for tools that can automate the process to be smoothly running in the background. Presetting the processes of extracting, cleaning, visualizing, validating and storing the data on regular intervals would be a huge relief for the data science teams in these organizations, and employing a suitable data preparation software which can perform these tasks on its own would equip these teams with a handy tool which can greatly increase their productivity and efficiency.

Why do you need to use data preparation software?

Understanding your data before using it for further analysis in not just good data sense, it is good business sense. Your business problem is highly likely to be solved if you understand the data going into solving that problem. And here is where data preparation software jumps in. It helps you in understanding how the data collected links to your business, and what needs to be added and corrected in it for the data to be of any use.

Organizations consider data cleaning software to be an additional expense without which their business operations can run smoothly. What they fail to understand is that their entire operations stand on the basis of good, clean data, which is only achievable through this handy tool.

According to Steve Lohr, a technology reporter at the New York Times, "Data scientists, according to interviews and expert estimates, spend 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets."

Data preparation software is most useful in saving up precious time of data scientists, which they could spend on any other important task rather than the mundane act of data cleaning.

Data preparation, if done smartly, not only saves time but also lays the foundation for clear analysis and algorithm modeling.

Data extracted from real-world sources is hardly ever clean-it is filled with errors, inconsistencies and missing information, and cannot be relied upon in its raw form for any useful predictions and analysis. Data cleaning tools minimize the number of hours spent by data science teams on mundane tasks and enable them to focus on the real problem: making use of that data to solve a problem.

Farah Kim is an ambitious content specialist, known for her human-centric content approach that bridges the gap between businesses and their audience.