Data cleansing has played a significant role in the history of data management as well as data analytics and it is still developing rapidly. Furthermore, data cleansing in Big Data is considered to be a challenge due to its already high and increasing volume, variety and velocity of data in several applications.
Since real-life data is dirty, it gets costly and, therefore, the significance of data quality management in business is highlighted. Data cleansing or scrubbing or appending is the procedure of correcting or removing inaccurate and corrupt data. This process is crucial and emphasized because wrong data can drive a business to wrong decisions, conclusions, and poor analysis, especially if the huge quantities of big data are into the picture. There are businesses that have lost a huge amount of money due to the big bad data.
It is needless to say that big data is a common feature, both in small as well as big businesses. However, the greater potential of big data is rather elusive. The truth is that data cannot always be used as it is and needs preparation in a way so that it can be used. Also, data cleaning or cleansing manually gets very slow, tedious and difficult.
The problem, however, does not necessarily lie with the tool but with the data as there is a lot of information going around. Though it may not seem like such a bad thing the problem arises when there are no filters for the raw data that the businesses receive. Even if custom solutions are provided, there is very little for them to do as there is little-refined information that is useful present in the noise. This is why data cleansing is important and its importance will become more dominant in the coming years.
Filtering the Data
Data cleansing is the procedure that filters out irrelevant data. Irrelevant data usually includes duplicate records, missing or incorrect information and poorly formatted data sets. A business can expand this option furthermore by eliminating the data records that are not really necessary for certain business processes. While what gets filtered out depends on the discretion of the business, some basic points like outdated data or details that are not verified can be removed.
Though the data cleansing process takes a good chunk of your time as well as resources to complete, it undermines some potential of receiving major insights from the big data.
Need for Clean Data
Inaccurate data analytics result into misguided decision making which can expose the industry to compliance issues since many decisions are subject to requirements in order to make sure that their data is accurate and current.
Though the reduction of the potential for bad data quality can be taken care of by the process management and process architecture, it cannot be completely eliminated. The only B2B solution left is to detect and remove or correct the errors and inconsistencies in a database or data-set and make the bad data usable.
Data Cleansing for Big Data
The always increasing accumulation of semi-structured and unstructured data from a pile of sources makes big data vast and complex by nature. These sources can be anything including mobile devices, sensors application servers, GPS systems, etc. Contrasting source of data is translated into an equally contrasting format. Until a data is not transformed into a unified form, data scientists cannot make sense of the data.
The main problem is that logs, as well as metrics, come in different forms, making both analysis and correlation difficult for each point and almost impossible between the two. The data format of metrics is short and it describes measurements beyond the measurable value including location, type, grouping, and time of measurement. Logs, generated by applications or infrastructure, are used to provide the operational team with very specific details that can help them analyze a particular security or operational event. Therefore, they tend to be longer than the metrics and can come in different shape and forms. The developers sometimes define the format of some logs even though they are standardized.
Data cleansing not only eradicates errors from both the data types but also transforms metrics and log data into a common format, providing teams with shared insights and views across the entire environment of the application. This helps the team to not only speed up their issue remediation with code and update frequency of the production code but also helps them understand the impact that their code has at any production scale and stage.
Parsing and Standardization
There are a number of data quality steps that are to be taken in order to clean big data and remove duplicate data for deriving a single view. For creating a single view of a product or a customer for an instance, we will need to have every detail in a standard format for getting the best match. Dates present in the big data is a headache as there are different formats that are used. After cleansing the data you should be able to match one format from another.
Businesses do not require dirty data for anything for it is known to sabotage the website and product/services of the business. The need for cleansing the data has never been so crucial in a world that is dominated by big data which is of no use at all unless it has been cleaned thoroughly.
When the data is full of inaccuracies, corruptions, mixed formats, and just a mess, then your data lake qualifies as just a mud pit. Big data consists of dirty data, which requires to be cleaned to get good analytics and most probably save a lot of money.