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Why Should Small Ventures Outsource Their Data Entry Services?

Small ventures have to navigate more than usual struggles. The reason is the limited budget and resources. The operational efficiency is something even they cannot compromise with. This is where outsourcing emerges as a silver lining. Back office operations typically involve a lot of data-powered work, which encompasses data entry, management, processing, cleansing, scanning, conversion, analysis, research, etc. Certainly, these tasks are resource-intensive, which is where small companies seem unable to deal with data-based requirements. A report by Grand View Research spotlights the size of the global outsourcing market. It was evaluated at $92.5 billion in 2020 and is likely to consistently scale at a compound annual growth rate (CAGR) of 9.1% between 2021 and 2028. For sure, some more reasons are behind why small companies should hire external support for data input services. Let’s dive in to discover below: Why Outsource Data Entry Services? Certainly, some concrete reasons press...
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Common Data Cleansing Techniques for Accurate Data

Data cleansing or scrubbing is an integral part of data management. Typically, it requires focus on maintaining 99.99% accuracy, consistency, and authenticity of data sources. Businesses, these days, are more inclined to maintain data hygiene because they cannot afford the catastrophic effects of bad decisions. It’s obvious that bad or noisy data leads to bad decisions. So, this data cleanup process is emerging as the backbone of businesses’ forecasting and strategy-making teams. Here, some of the most common data cleansing techniques are shared. Let’s start introducing them. Proven Data Cleansing Techniques for Accurate Data Let’s catch up with some of the proven techniques to maintain hygienic data. 1. Data Validation A study by Experian proves that 83% of organizations trust data quality to be critical for their success. This goal cannot be achieved unless you know how to validate data. It is a significant factor in cleansing data. When the data is stored, certain criteria are made ...
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How to Enrich Lead Data for Marketing Campaigns?

You would be surprised to know that data enrichment solutions market is sizing up at a CAGR of 8.3 % by 2030, according to a report .   The reason is the value of contextual data, which lead to success. If you want to create highly effective and personalized marketing campaigns, you need high-quality contact lists.  Many-a-times, you have email IDs or contact lists, but a few may be incomplete.  What if that contact list has more than half incomplete or wrong details?  Certainly, this can weaken your marketing campaigns. And, you won’t be able to achieve what you think of.  So, the main concern is data enrichment. This blog will help you understand how to enrich leads in your database for marketing campaigns.  Let’s get started with defining what lead data enrichment is.   Lead Data Enrichment Leads refer to the contact list of intended customers (who are interested in investing in the brand).  And lead data enrichment is a defined strategy t...
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What Are the Most Common Data Quality Issues?

  Do you know that IBM’s bad data cost around $3.1 trillion dollars every year? Such a big loss it is! It’s all because of data inaccuracies, which clarify how precious high-quality data is. Therefore, it’s a must to identify, segment, and fix typos, and duplicates, and fill in missing details so that data analysts can draw feasible strategies or business ideas.   Let’s talk about the most common data quality issues that are no less than big challenges. Most Common Data Quality Issues •                      Segmenting Semi-Structured and Unstructured Data Fortunately, we have technologies and data management tools that make it easier to create a centralized database. But, this fortunate value for nothing when data warehouses or servers prove inefficient in effectively dealing with relational datasets. It’s because of different data qualities, which can be good and...
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What is data cleansing and processing?

  Data cleansing is a part of processing wherein it is ensured that the data is correct, consistent and useful. This process involves detecting and filtering errors or corrupt data entries, missing space, incomplete data typos and other related inconsistencies. These are all corrected and then, the data are transformed in a usable form or make it ready for analysis, research or any other business purposes. This is called processing. In the data world, a data cleansing and processing services company covers every step from capturing, web extraction, cleansing to quality testing and tailoring data format as per requirements. Today, these processes are the must-have for big data research, AI and data science.  In short, Data Cleansing Processing can be a combination of, but is not limited to the following sub-processes: •                      Data Migration, which is all about dat...
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How much does a virtual assistant cost UK?

  However, it’s really challenging to project the exact cost for a virtual assistant service in the UK. On the basis of an online research and personal experience, the average starting cost to hire a virtual assistant from the UK is nearly £25 per hour. The professional may vary, so is the cost. As experience and skills develop, you should expect to pay more than this average projected cost. It may rise up to £50 or more for a highly specialised support. The seekers may personally contact or communicate with freelancers on virtual services availing websites over the internet to get the best expert at a reasonable cost.
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Is data science same as data mining?

  No, they differ from each other. Data science is a wide concept, combining machine learning, natural language processing and artificial intelligence, which are used to find a solution that can minimise human efforts. On the flip side, data mining represents only a process or subset of data science, which involves web data extraction, cleansing to transform and loading to produce sources to machines for learning & creating a model that can make them or applications to think & respond as a human does. Some people confuse it with machine learning also. Machine learning is a complementary concept of data mining, which takes modeling to the next step where filtered models are validated to set as a machine learning algorithm.   
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