Artificial intelligence has already proved to be a great disruptor, bringing about radical changes in a range of sectors, from helping with the dispersion of loans in the financial industry to enhancing customer interaction in the BPO industry and improving online business in the retail industry.
Improving efficiency and reducing the time involved in managing data, there is no doubt that we are moving towards an age of automation, and that AI technology will have a significant impact on the way data entry works.
Replacing traditional data entry methods with automated processes decreases the risk of errors and improves speed and consistency. Able to be tailored to specific industry needs and goals, automated data entry also helps to broaden network availability through managing remote data more effectively, and helps businesses to achieve better responsiveness through its capacity to enter data in real time.
Challenges in the Current Data Entry Processes
Data entry is essentially the transfer of data from a physical state to a digital one, and manual data entry can quickly become both tricky and mind-numbing, leading to a few common problems. With technology ever improving and automation on the rise, there is less and less need for manual data entry.
The main challenges in current data entry processes include:
Manual data entry is costly to conduct efficiently, as employees must be properly trained, which takes both time and money.
Increased Error Rate
Unfortunately, when people do not receive sufficient training, or they misunderstand comments or are not able to correctly read forms, there can be an increase in errors, which lower the goodwill of the brand and can have a drastic effect on internal operations and customer satisfaction.
No matter how fast one can process information or type it out, entering information manually takes up a lot of time, and can sometimes be tricky, causing people to lose focus, which takes up additional time. All of this can lead to delays in data availability.
When data entry is conducted by humans, there is always a chance that misunderstandings and misinterpretations could occur.
Increase in Amount of Data Entry
Any sudden increase in the amount of required data entry could also lead to an increase in mistakes. This could be because of an increase in sales or the opening of a new office. This can also put strain on in-house data entry teams.
No Focus on Core Business Tasks
As manual data entry can be so tedious, many companies prefer outsourcing so that their employees have more time to focus on more important business activities.
Quality control is an important aspect of manual data entry and must be handled carefully to ensure completely accurate data.
Relationship between Data Management and AI Applications
AI applications are only as good as accurate as the data used train them, and there has historically been a symbiotic relationship between a desire to build AI applications and the need for best practices for data management. Good data, which is properly conditioned and placed in the right context, can help services to take appropriate actions, while bad data can lead to poor results and steadily diminishing performance.
Seeing the Problem
Many organizations are struggling to maintain good data quality and meet analytics needs, even though they are making use of hybrid, cutting-edge, multi-cloud architectures for their data storage. Thankfully, many are taking steps to address these issues, with more than 90% of respondents saying they will invest more than $1 million in new analytics initiatives.
There is a need to evolve beyond basic data collection and aggregation, and only by parsing key metadata can the enterprise foster the “intelligent data” that is needed to train intelligent algorithms. This is becoming harder to do as data volumes increase, and many data management and analysis solutions are turning to AI and machine learning algorithms - the same that are powering the smart applications that end up consuming the data and metadata.
The entire process needs to be made more intelligent, automating many of the processes which occupy the bulk of data scientists’ time, allowing them to focus on more strategic objectives.
Data from Afar
Intelligent data management systems will need streamlined connectivity with the cloud, because while wide-area networks are becoming flexible and fast, there are still no fine-grain management tools to transfer collate and process data at AI-friendly speeds.
Improving the way the database interprets data can be just as effective, and CEO of database decentralization firm Bluzelle, Pavel Bains, believes that blockchain will contribute to this by creating universal data storage for both structured and unstructured data, allowing data management teams to provide the AI-needed context, while ensuring no critical data is under the control of any one cloud provider.
Data Entry Is Evolving to Support Smart Technologies
Adapting to digital transformation and providing intelligent solutions to businesses in the form of compelling and innovative methods of data entry, smart technologies and accelerating the data collection and entry methods are disrupting the data entry industry.
AI in the Healthcare Industry
Artificial Intelligence Will Redesign Healthcare, a Medical Futurist article indicates at least ten ways in which smart machines will impact healthcare processes, some of which are already in use, such as assisted nursing, medical data upload service, telehealth service and video conferencing.
Machine Learning to Solve Data Management Problems
Every business department has troves of accumulated date that people know nothing about, and by using machine learning and algorithms which address how to handle different types of information, e.g. images, documents, emails, etc., stored on servers, AI and analytics can pre-sort this information for a knowledgeable human to review. Analytics could also produce a set of recommendations as to which data can be purged.
What the Future of Data Entry Industry Would Look Like with AI
- Increased speed of analysis: In the past, information processing and analysis needed to happen manually. With AI technology, these tasks are handled instantly, enabling organizations to solve problems quicker, freeing up data management employees to focus on other critical tasks.
- Real-time, streaming analytics: With further integration of AI into enterprise, organizations will be able to run real–time streaming analytics in order to get up to date information, accurate to the second.
- DevOps workflows will overtake application development: As AI and machine-learning tools learn and evolve, their suggestions and recommendations will become more refined, leading organizations to increasingly implement AI–based DevOps workflows for application development, allowing engineers to constantly integrate and deliver software updates which leverage AI knowledge and learning
- AI will transform all industries: As AI evolves and organizations establish workflows which enable them to maximize value, industries will change drastically. For example, healthcare firms will be able to use AI to deliver more efficient, cost-effective care, with AI-powered remote patient monitoring tools and other innovations.
The need for data entry services have grown significantly in the past few years as its significance as a pillar of data and analytics infrastructure has become clear. Though some might fear that automation and smart technology might take jobs away from those with routine skill sets, artificial intelligence is set to change the face of the data entry industry.
With machine learning, bulk data can be in just a short matter of time, and AI can be taught to recognize patterns which contribute to quality and accuracy. With more than a decade of experience in providing a range of data entry outsourcing services to clients across the globe, Data Entry Outsourced is an experienced data management outsourcing provider that handles data management requests and offers the highest quality.