Robin Moore

Robin Moore


Effective Ways to Ensure Error-Free Data Entry

In today's environment, one of the oldest industry divisions, data entry, has major integrity challenges. Maintaining effective ways to ensure error free data entry and increasing data accuracy is a strong foundation for data and technology-driven organisations in the modern era. Making rapid and correct decisions allows businesses to be first to market and stay ahead of the competition. As a result, one of the primary concerns for modern firms should be speedier access to correct data.

What is a data entry error? 

The units of facts, numbers, or statistics collected as a basis for analysis and decision-making are referred to as data. The act of inputting information into an electronic medium, such as a computer or other electronic device, is known as data input. Data processing errors occur when information is not appropriately set, organized, or encoded in light of these considerations. 

As per a report, poor data quality has an annual financial impact of between USD9.7 million and USD14.2 million on an organization's costs. Aside from the costs associated with inaccuracies, incorrect data can also cause business problems. Even the tiniest of errors, such as a misspelt character, might cause internal or legal strife. 

Let's look at some of the most prevalent forms of data input errors and the functional ways to ensure correct data entry and how they affect organisations in today's environment before we try to remedy them. 

  • Transcription Errors

All typos, duplicate words, and omissions of particular words, names, and numbers while typing are included in this category. 

  • Transposition Errors

These are the most common errors, which occur when you mistakenly exchange letters or numbers to make improper sequences. You may greatly reduce these errors by double-checking the entered data. 

  • Representation Inconsistencies 

Inconsistent date, time, address, and measurement formats and units are among the most likely problems. 

Some of the known methods of fixing data entry errors

Data Entry Rule

The 1-10-100 Rule is one among the error free data entry methods that tries to improve data quality by assigning a monetary value to data prevention, correction, and failure. This approach specifies that at the initial stage, during the batching process, and at subsequent stages, each data record costs USD1, USD10, and USD100 to rectify, correspondingly. 

Data Cleansing 

Data cleansing is another way that can assist firms deal with transaction processing problems. This procedure entails eliminating records that are incomplete, old, duplicated, irrelevant, or badly formatted. 

Effective ways to ensure error free data entry

  • Train employees

Even seasoned agents must train from time to time because it is critical to keep your knowledge and skills up to date. Emphasize the relevance of facts during this time. Open their eyes and minds to the importance of accuracy, as well as the consequences of inaccurate entry in businesses and our daily lives. It may aid in making employees more accountable for their work. Provide them with the knowledge, expertise, and training they require to accomplish their professions effectively.

  • Create a pleasant working atmosphere

Productivity is heavily influenced by the working environment. People would be more productive if they could focus on their duties and obligations. With this in mind, organisations should establish a welcoming environment for employees to be productive, which will provide numerous benefits for the company, including fewer to no data redundancy. 

  • Work is double-checked

When it comes to minimising data input errors, double-checking every data entering activity should be regarded regular operating procedure. It is, without a doubt, an excellent method of preventing human data entry errors. 

Importance of data checking and why it matters

One of the most important aspects of running a corporation is data verification. As a result, it assists firms in tracking their objectives, operating at a higher capacity, and maximising people's time to be productive. Checking and validating data submissions can also help business managers control costs, cope with administrative responsibilities, and focus on essential functions, especially in large companies. 

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Effective Ways to Ensure Error-Free Data Entry
 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


4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company

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