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Data Cleansing Services For Improved Decision Making

What is Data Cleansing and its importance in Big Data Management

The term ‘Data Cleansing’ implies the method of preparing the raw data for analysis. This includes the process of modifying or freeing the data from its imperfections. It creates an error-free database which consists of data which can later be used to perform functions relating to business operations management. Data cleansing services are provided by experts who ensure the removal of inconsistent data and values from the company’s system resulting in an enriched database.

Big Data is already considered to be a challenge while acquiring, analysis and management due to its constantly increasing volume and its ever changing pattern. In Big Data Management, data cleansing services is of special significance because of the function of clearing the database and the latent inconsistencies. This is a crucial step as successfully managing Big Data in the contemporary world is essential for companies to perform decision-making functions.

Impact of Data Cleansing in achieving and refining the organizational goals:

The process of data cleansing aids business operations in a company. The functioning of an organization can be gravely hampered by irrelevant and valueless information stored in the system. This inconsistent data can be in the form of duplicate entries, values or records. But, the process does not concern itself with only the removal of irrelevant information. One of the other major purposes fulfilled by data cleansing includes the process of creating an enriched database by increasing the accuracy of the datasets contained in it.

Every company strives towards enhancing their pattern of business and providing customers a pleasurable experience. These and several other goals of companies are directly influenced by the process of data cleansing. As organizations of the modern world function majorly on the basis of data, it is essential for the data to be accessible by both the company and the non-technical or business users in real-time. An enriched database lays the foundation of strong product campaigns and increases the customer acquisition efficiency. This directly results in refined organizational roles that are achieved with ease.

What are the challenges faced in the Data Cleansing process:

The challenges faced by data cleansing services experts are as follows –

  • Data is non-static
  • Soiled data cleansing leads to bad decisions
  • Incorrect data transformation may affect the client relations of companies.
  • A data cleansing framework needs to be deployed in advance.
  • Separate worksheets that are required to be created for data cleansing are time consuming to create.
  • Big data can be difficult to clean through the data cleaning process.
  • The process mistakes in the transformed data have the capacity of ruining the management functions of the company.
  • A duplicate set of raw data has to be created each time before performing data cleansing.

How to ensure safety of data during Data Cleaning:

Usually, all organizations irrespective of their size and business pattern, perform data cleaning in one way or another. Among the benefits of this process, data security is one of the most important advantages of data cleaning. Companies base their business on the data relating to the personal information of customers as well as the changing patterns of need among them.

The safety of this information lies in the hands of the company. For this purpose, the data cleaning process is expected to follow the following steps toward completion:

  • Making a copy of the raw data – The term ‘raw data’ refers to the electronic counterpart of the paper-based information or dataset acquired by the company. Now, for the purpose of undoing any mistakes made during the cleaning process, a duplicate of the dataset comes in handy.
  • Cleansing in a separate worksheet – The process of cleaning must be completed in another worksheet that is different than the original in which the dataset is stored. During this, the ETL process or the Extract, Transform, and Load process is to be followed.
  • Checking with the original dataset – In case some error occurs in the data cleaning process, the original dataset must be compared in order to remove these transformational errors.
  • Using Excel Techniques – Excel is the storehouse of functions that can improve and facilitate the process of data cleaning within an organizational system.

Microsoft Power BI approach towards Data Cleansing:

Power BI is a software service providing business intelligence tools and is utilized for data cleansing services to organizations for performing analytical functions. Clean data is required by companies for the purpose of analyzing the relevant information in real-time which will facilitate the decision-making process.

The method of cleaning the data involves the process of transforming the data in a way that would enhance the marketing activities of the user organization. Power BI is well known for producing interactive displays of data that increase the productivity of the company. But, visuals created with uncleaned data filled with inconsistencies will not produce the same advantages for the company. This means that the cleaner the data, the better the performance of the visuals created from it.

Along with creating visuals from data, Microsoft BI also concentrates on the process of reading the data for the process of data analysis. In order to ready the raw data, Power BI users can use a tool from the service known as Power Query. The tool is decked with features that are dedicated towards cleaning the raw data.

Power Query makes the user capable of simplifying complicated data sets, renaming objects, changing the fields of data sets and modulating their patterns. After cleaning the data, it can be arranged in a pattern through which the company easily tracks the important information.

Data Cleansing Services

Steps for; Data Cleaning in Power BI

The process of Data cleansing in Power Query follows a consolidated step by step method by which the raw set of data acquired by the organization from the multiple sources are cleaned, imported and transformed into data which can be analyzed later. As per the data cleansing services experts following are the steps for data cleansing in Power BI.

The steps of data cleansing in Power BI can be enumerated as follows:-

  1. Step 1- Getting the primary or raw data in the right format

The data can be arranged into the right format through the following pattern:

  • From the DATA tab, the ‘ GET AND TRANSFORM DATA’ has to be chosen. Click on the ‘FROM TABLE/ RANGE BUTTON’
  • ‘CREATE TABLE’ dialog box appears. Click on OK.
  • The POWER QUERY window appears.
  • The data type has to be chosen.
  • Select HOME and then click on ‘CLOSE AND LOAD TO’
  • Click on OK and the data isset into a format.
  1. Step 2 – Creating a sub-category table

After the loading of the Power Query window is loaded a sub-category table is to be created. In order to do so, the multiple unnecessary columns have to be removed from the table previously created. Among the categories of data stored within the table, some of them may be duplicate. These duplicate categories must be removed in order to clean the data.

  1. Step 3 – Performing the final clearance

The HOME tab consists of the ADVANCED EDITOR window which must be clicked and activated.

The data can be set in the right format by writing the above mentioned queries. After these queries are written,the tool performs the data cleansing and transforms the data for analysis.

Increase in Big Data utilization after Data Cleansing:

Big Data forms the basis of organizational data in the contemporary world. The basic features of this kind of data is the vastness of its volume and the consistent change in its patterns. But, due to the wide range of benefits provided by big data, currently it lays the foundation of analytic functions in companies. In order to maintain the functionality of this data, there must be utmost cleansing accuracy in it. This data cleansing services plays high impact on organizations performance who depend on data for predictive and calculated decision making.

Critical data fields might get lost within the irrelevant data due to the soiled data cleansing procedures. On the contrary, traditional data cleansing when performed with big data results in proper utilization of it.

The following are the ways in which big data after cleansing is used –

  • When the data is cleaned properly, the relevant information is displayed in real-time. This results in the improvement of decision making methods in the company.
  • Clean big data can substantially increase the revenue of the user company.
  • The data cleaning method can reduce wastage of relevant data and reduce expenditure.
  • After the cleansing of data, the productivity of the company increases.

Method of Data Cleaning: An EPC Group Approach

The EPC group is dedicated towards providing customized IT solutions and guidance to companies regarding the deployment and usage of Power BI solutions and tools. The main purpose of Power BI is to help the user organization to perform analytic functions. This purpose cannot be fulfilled when the raw data consists of irrelevant values and entities.

So, for removing these needless values from the database and facilitating the functioning of Power BI by an organization, the EPC group provides companies with IT support. These IT support packages include the process of educating the company employees regarding the methods of data cleansing and transformation.


In conclusion, it can be said that data cleansing services are essential in performing analytic functions in an organization. Power BI is equipped to perform raw data cleansing successfully and transforming the data into useful datasets.

Errin OConnor

Errin OConnor

With over 25 years of experience in Information Technology and Management Consulting, Errin O’Connor has led hundreds of large-scale enterprise implementations from Business Intelligence, Power BI, Office 365, SharePoint, Exchange, IT Security, Azure and Hybrid Cloud efforts for over 165 Fortune 500 companies.

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