Data that has not been processed for use is referred to as raw data. Raw data is frequently denormalized, out-of-date, and poorly formatted. According to a Forbes survey done in 2018, nearly 2.5 quintillion bytes of data gets produced per day. Raw data may be collected in a variety of formats, be uncoded or unformatted, or some entries may be “suspect” (such as outliers), necessitating verification or citation.
To sustain and be productive in the market, businesses have to clean up their data. With a proper objective, quality data, and utilizing the right Analytics procedures businesses can reach heights. So Business executives must not only be clear on their objectives but also ensure that they maintain quality data.
Unclean data is a concern for businesses of all sizes and in all sectors.
If the data you begin with is faulty, none of the analysis will be worthwhile. All data sets are susceptible to this frequent mishap.
Data cleaning is the process of organizing and fixing erroneous, improperly structured, or otherwise disorganized data.
What is data cleaning?
Data cleaning is the process of first finding inaccurate, undesirable, incomplete, or missing information (often known as “dirty data”), and then altering it (cleaning) to ensure that it satisfies the criteria for valid data, which include correctness, completeness, consistency, and uniformity. This procedure is also known as data cleansing or data scrubbing in addition to being called data cleaning.
While it is possible to complete this manually, it is preferable to utilize specialized data-cleansing procedures first and then have a data scientist conduct a quality control review.
According to McKinsey, organizations may be losing up to 70% of their data-cleansing efforts. Though the process involves significant time and expenses made on it, data cleaning is to be done in an efficient way.
Why is data cleaning essential ?
No business can rely on unstructured data to get useful insights. It is crucial to evaluate their data sets in order to accelerate business growth and make better decisions, but first, it’s crucial to make sure the data sets are clean.
The key advantages of data cleaning are listed below :
Makes data mining simpler
Prior to doing business analysis and gaining insights, Data Cleaning enables firms to identify erroneous data. Data mining can then effectively transform a high volume of unstructured data into structured data in a faster way. Thus the data mining process is made simpler.
Enhances efficiency during decision making
Every organization’s primary goal is to validate data and make well-informed business decisions. The data cleaning procedure will improve the efficiency of data security and storage and many other activity areas in addition to helping to outline real-time decisions.
Boosts Revenue
According to Forbes, “Unclean data impacts a company’s bottom line, losing firms a startling 12 percent of overall revenue”. Higher revenues are a result of lower total costs. Businesses tend to have reduced overall costs in their overall expenses when they mix the appropriate analytics and data cleansing solutions.
Challenges in data cleaning
Dealing with disorganized data
Today’s organizations operate with a lot of data. Typically, this type of data is extremely simple to clean, process, and analyze. However, some of the data is quite disorganized and cannot be used for analysis in its current form. This includes missing data, data that is formatted erratically, and data that is completely irrelevant and not relevant to the analysis.
A data input sheet might, for instance, include dates as raw data in the following formats: “30 March 2005,” “30 march 2005,” “30/3/05,” “30 Mar,” or “today.”
Working with unstructured data presents a challenge because it requires pre-processing before it is ready for analysis. Classic instances of this include audio and video files, documents, and web pages.
Time consuming process
Duplicate data must be removed, missing entries must be added or corrected, values that were entered incorrectly must be fixed, formatting must be consistent, and a host of other activities that take time must be completed.
With many forms of data that can have duplicate entries, GeakMinds solved one such problem for image deduplication. We helped our client in the identification and elimination of duplicates from health facility data. We checked if the two locations are near to each other within a radius of 100m and placed them into similar clusters accordingly.
Backup
Copying data from a main to a secondary place in order to secure it in the event of a catastrophe, an accident, or malicious activity is known as data backup. Modern enterprises rely heavily on data, and losing such data can seriously harm and interfere with daily operations. Because of this, data backups are essential for all businesses, big and small.
Redundant data to be dealt with cannot be completely ignored during the process of data cleaning. In case if there isn’t an efficient backup, then firms can lose the necessary data too. Hence backup is also one of the major challenges during the data cleaning process.
Storage
The Next-Generation Data Storage Market size was estimated at USD 58.40 billion in 2021 and is expected to reach USD 128.94 billion by 2030, increasing at a CAGR of 8.2 percent from 2022 to 2030, according to Verified Market Research in PRNewswire.
After being cleaned, the data must be stored in a secure area. To make sure the correct data goes through the correct process, a log of the entire process must also be preserved. Thus, to retain the cleaned data effectively, highly effective data management and storage techniques become necessary which makes it one of the challenges in data cleaning.
Different types of data format
With data present in many forms, one approach to data cleaning may not suit another type of data. When dealing with images, text-based methodologies of data cleaning cannot be employed. With varying types, human effort gets increased accordingly which poses a major challenge.
Thus this poses an issue of spending considerable time on segregation to structured data even before applying a suitable data cleaning approach.
Conclusion
A firm looking to apply Analytics to their data should definitely have reliable data by their side. Firms can’t gain useful insights from unstructured data and this is where data cleaning thrives in to help them out. According to Forbes, data cleaning and organizing take up to 60% of data scientists’ time.
Though it isn’t an enjoyable part for them, data scientists spend their time on it which emphasizes that for any sector that deals with Analytics, data cleaning is a must-do act. Stay tuned to our blogs to know how data cleaning is done in different industries.