Rowan Benny


Why is data cleaning crucial? How do you clean the data?

Data cleansing has technically played an important part and vital role in the history of data science and data analytics, so also it continues to evolve at a rapid pace.  But what is data cleansing, and why is it so necessary? If you want to build a good culture around quality data decision-making and data cleaning, also known as data cleansing as well as data scrubbing, is one of the most crucial tasks for your organization to take. We'll look at the necessity of data cleansing in this post, as well as why individuals and corporations should use good data cleansing strategies.

Definition: What is data cleaning?

Cleansing data is a type of data management. Individuals and corporations amass a great deal of personal data over time! The process of ensuring that data is particularly correct and so usable is ideally known as data cleansing. Data cleansing is nothing but an act of going through all of the required data in a database. You can clean data by looking for faults or corruptions, repairing or eliminating them, or manually processing data as needed to avoid repeating the same mistakes. Data cleansing usually entails cleaning up data that has been gathered in one location.

Although software solutions can help with most parts of data cleansing, some tasks must be completed manually. The data cleansing procedure is normally completed all at once, and also it can ideally take quite a long time if the data has been accumulating for years as well.

Why is data cleaning so important and necessary?

Data cleansing that is done on a regular basis and in an organized manner can have a wide range of benefits for an organization. Data cleansing is vital for both enterprises and individuals, despite the fact that it is frequently discussed in the professional sector.

 Avoid making costly mistakes.

Businesses that use the right analytics and cleansing technologies will have a higher chance of spotting new opportunities. When organizations are busy processing errors, correcting erroneous data, or troubleshooting, data cleansing is the greatest answer for avoiding expenditures. For instance, ensuring that deliveries are made to the correct address the first time, avoiding costly redeliveries. Businesses must streamline their operations to the greatest extent possible. Profits are higher when overall costs are lower.

Make particular data to manage multi channels.

Data cleansing paves the way for successful multichannel consumer data management. This outdated data will be cleaned up in favour of new, up-to-date information about your target market. Customer data accuracy, including phone, postal, and email channels, allows your contact plans to be executed successfully across channels. We build systems that automatically incorporate, sort, and parse consumer data in a way that prioritizes the most recent information.

Acquire more customers

Customer behaviours are changing so frequently these days that data might easily become obsolete. Organizations with well-maintained data are in the greatest position to generate prospect lists based on accurate and up-to-date information. When data becomes imprecise, businesses begin to target the incorrect market. As a result, their acquisition and also onboarding activities become more efficient than before.

Ease the decision-making process

One of the most significant benefits is that having access to data allows businesses to make better decisions. Clean data is the best way to assist a transparent decision-making process. Everyone benefits from having accurate information. It's critical to have up-to-date employee data. Accurate data underpins MI and other essential analytics, which give businesses the information they need to make informed decisions.

Increase productivity and efficiency

Productivity suffers as a result of cluttered databases. Data cleansing is also critical since it increases data quality, which leads to higher productivity. Computers take longer to retrieve data. Organizations are left with the highest quality information when inaccurate data is eliminated or updated, which means their staff do not have to waste time wading through irrelevant and incorrect data. When data becomes congested, all of these problems can readily occur.

Data cleansing is important for data quality.

To provide a superior customer experience, acquire a competitive edge, and move your business forward, quality data should be the glue that holds processes together. Because many decisions are subject to standards to ensure that their data is correct and current, inaccurate data analytics can lead to mistaken decision making, which can expose the industry to compliance concerns.

How do you clean the data?

Managing structural errors

Keep track of the patterns that lead to the majority of your errors. When you measure or transfer data and find unusual naming conventions, typos, or wrong capitalization, you have structural issues.

Verify the accuracy of the data.

Validate the accuracy of your data after you've cleaned up your existing database. Maintaining your communication channels will reap far-reaching benefits from reviewing existing data for consistency and accuracy. This ensures that your customers will be able to pay you and that you will be able to meet any legal requirements. Some solutions even employ artificial intelligence (AI) or machine learning to improve accuracy testing.

Look for data that is duplicated.

To save time when examining data, look for duplication. Remove any undesirable observations, such as duplicates or irrelevant observations, from your dataset. Research and invest in alternative data cleaning solutions that can examine raw data in bulk and automate the process for you to avoid repeating data. One of the most important aspects to consider in this procedure is deduplication.

Examine your data.

Use third-party sources to augment your data after it has been standardized, vetted, and cleansed for duplicates. Postcodes that are absent may result in undelivered products, while surnames that are lacking may result in the critical correspondence being misdirected.

Final Lines!

To obtain cleaned data, data cleaning is an integral aspect of the data science process. What is the significance of data cleansing in the corporate world? It all boils down to having accurate information. Consider it your workstation. You'll typically have trouble getting the raw data if you try to bypass the data cleansing stages. It will clog up your database to the point where the data you're pulling is untrustworthy. As a result, the data cleaning procedures and data cleaning methods must be taken into account.


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Buddha Community

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Uriah  Dietrich

Uriah Dietrich


What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data