The term Data Cleaning or data-scrubbing ideally deal with the detection or removal of an error in any database that will hamper the overall preciseness of a database or a set of databases. Such cleaning can be achieved by employing the use of data cleansing tools. By applying such tools, the quality of data gets improved for good. Especially when you need to clean a heterogeneous set of data where data gets inducted from various channels, then you need to check them so that any unnecessary error or mismatch does not occur.
Errors to deal with
It also addresses the schema data transformations related to the quantity ascertained. For the companies related to the Information Technology or organizations dealing with the considerable amount of data, data cleaning is the primary component of handling the whole ETL process.
- The errors that the data cleansing tools handles are,
- Spelling mistake.
- Missed information or missing data.
- Invalid and unnecessary duplication of data.
- Extra irrelevant data.
The integration of multiple data sources in data warehouses, global system of website basis abs federated system of data makes it imperative for having the data checked and cleaned accurately. You can now imagine, with so much vast arena and channel, there will be the considerable presence of redundant data. To make provision for consistent and accurate data, there must be a requirement for various representations of data and eliminating the duplicate information in abundance.
The effective warehouse management
The organizations or data warehouses that deal with zillions and zillions of data are the actual support source for cleaned data. They use tools that continuously load and update new entities of databases from the plethora of sources. The amount of data could be of an enormous amount. They efficiently identify and remove corrupted data. Now after transferring the corrupt data, these tools make policies and decisions that would help the organization positively. That is, to make a profitable and unique business decision, it is necessary to avoid incorrect data.
These incorrect or corrupted data could be misleading statistics or missing data or duplicated information that would prove to be drastic. The massive volume of data generated from various sources makes it imperative to go for practical data cleansing. Various data cleansing toolsare right now available, that is used by the Big Data Analyst and Business Analyst to cater to the demands of the companies and data warehouses.
Using a data cleansing toolsmust pertain to satisfy some specific set of necessity. They are:
- The data cleaning must be performed mutually with the data transformations concerning schema related. This schema related data transformations must maintain discursive Metadata. The tools and application used for data cleaning must be able to remove every kind of inconsistencies and discrepancies that have crept in a particular database or multiple databases extracted from various sources. The approach of these programs must support the customization for both the individual detection as well as numerous detections at the same time.
- The mapping of the sources of all the changes and cleanings should be jotted down carefully, and the can be used via query processing and other sources of data.
- In a data warehouse, all the data transformations and the cleaning should be done in step by step manner. These transformation steps should be uniform for multiple sources. So that in later, the innumerable number of data sets can be used efficient and reliable way.
- The cleaner and integrated approach for each step should be kept in mind. Also, there should be provision for backup of the user data in a supposedly cloud-based storage system for future usage.
Application and implementation in the banking sector
The transformation of the functioning both private and publicly recognized banks have been towards the positive amalgamation of both numbers and technologies. Every day, with numerous customer details and the transactions occurring, it has become the primary challenge to all the banking organization for effective data management. These data are essential for both garnering meaningful insight as well as regulate a precise dealing and confidentiality of customer information. Every business and transaction has taken the path of technology which made the functioning of bank extremely data-driven. Bankers now make use of the programs and systems like CRM, ERP, and SCM to manage them. The managing also includes maintaining the standard, privacy, and accuracy of sensitive data.
Owing to such demands, banks are nowadays employing the usage of data cleansing tools and data quality management programs for better functioning. Applying such tools and software the banks effectively makes use of the following functions:
- They use the tools to identify the errors and inaccurate values and elements. Such incorrect entity may include mistyped or misspelling depending on the nature of the data.
- The improvisation of already recorded data by imparting them the required modifications. Such transformations would adhere to the highest standard of technical and business parameter. It may also incorporate elements to interpret data more comfortable and user-friendly. Such things can also be achieved by merging the records and matching the single entity. This way the data ultimately becomes more approachable.
- The employing of filtering the unnecessary, irrelevant and duplicate data. It also identifies any missing entity in a database or a set of database and automatically inputs suitable value.
Now take a look at the steps that have been employed by every successful banking organization to ensure quality data check. They are:
- The headquarter or the regional functioning head body of the organization have hired external IT experts who have considerable knowledge and data audit and Big data. These experts are employed to ascertain the quality of data inflow by conducting numerous data audit.
- Forming a team of Business Analyst, Information Technology consultant and experts from the various profiles that deal with the backend supervision of data. They also ensure the overseeing of data quality from time to time without any lag.
- And lastly, Data Quality Management and Solution is deployed in the mainframe system of the organization with the data warehouse or with any other data management plan.
