Role of Data Entry in Artificial Intelligence

Role of Data Entry in Artificial Intelligence and Machine Learning

Published On April 15, 2020 -   by

The global artificial intelligence (AI) market grew 154% in 2019, driven by the need to create competitive differentiation in an increasingly disruptive business landscape.

Retail, transportation, healthcare, finance and technology are 5 industries who are realizing measurable benefits from AI and machine learning (ML) today, with other industries rapidly adopting experimental use cases to benefit from proven value, including cost reduction and increased profits.

From optimizing digital supply chain operations for manufacturing to improving patient diagnoses in healthcare, AI and ML are a key component in leading business strategies.

But without a foundation of diverse, accurate, consistent and valid data representative of relevant populations, AL and ML systems cannot function properly and risk producing unreliable, biased outcomes.

To improve the likelihood for successful results, organizations utilize manual data entry to provide an initial accurate database to build AI and ML systems.

Artificial Intelligence and the Role of Data Entry

Emergence of Artificial Intelligence and the Role of Data Entry

As AI use cases and applications steadily grow, the role of data entry becomes more intertwined with the ability to implement and deploy these intelligent systems and solutions.

Organizations with legacy technologies and those who are heavily dependent on hard copy documentation, such as logistics companies, financial services, healthcare, legal and real estate, are especially dependent on manual data entry to ease the path to intelligent automation.

  • Data Cleansing Data scientists spend about 80% of their time cleansing data, proving the importance of this step in AI algorithm development. As data is often stored in silos or voluminous databases, manual data entry is used to organize, clean and define a chronological order to produce AI driven results that will solve a specific business problem.
  • Classification Contrary to what many people associate with this term, classification in AI is distinct from labeling. Manual data classification is output-focused, and drills down into categories, numbers, actions, action sequences, waveforms and movements. This is crucial to helping build AI use cases, and is a challenging but necessary task before considering inputs.
  • Performance Metrics Before an AI use case goes into initial discovery stages, performance metrics are decided on to ensure if the outcome is worth the effort. Data evaluation, usually performed by qualitative data entry experts, helps system developers and businesses determine if they have enough available, trusted and diverse data, and act as advisors in many capacities for AI projects. 
Importance of Data Entry in Machine Learning

Importance of Data Entry in Machine Learning

ML drives all advanced analytics strategies, where data is collected, processed and analyzed to produce useful insights that further business strategy development. ML thrives on large data sets, and data entry is paramount to streamlining the initial stages of data collection and curation.

  • Data Gathering The accuracy of ML prediction is utterly dependent on the collected data quality and quantity. Manual data entry is extremely useful in this regard, as it can be strategically curated by experienced professionals who understand what’s needed to improve prediction performance.
  • Data Preparation An ML model needs to access data from unique locations. Part of data preparation is loading the data into an appropriate place for the ML training, a step often performed with manual data entry practices. In some instances where the data must be modified, manual data entry normalization and de-duplication also occurs.
  • Labeled Data Manual data entry is used to prepare specific data fields based on the unique ML project requirements, and data is entered into the field following cleansing and enrichment that standardizes the data. The data is then manually tagged, and the ML model can be applied to that data. This is particularly useful in supervised ML, where the algorithm self-learns based on the labeled data.
  • Training Iteration ML models go through a cycle of updating to correct biases or values during training. As training progresses, manual data entry is used to initialize random value sequences, for example, to help the system produce more accurate predictions over time.
Industry Needs that Are Addressed by Data Entry

Unconventional Industry Needs that Are Addressed by Data Entry

While there’s many mature AI and ML applications that are becoming standard for certain industries, like personalization in eCommerce, some unconventional applications are still in the experimental and discovery stages. For these cases, manual data entry can address the often-challenging aspects of building and aggregating high quality data, as well as supporting the diversity of required data types such as image, audio, video or text data.

These unique, unconventional case studies indicate the valuable role of manual data entry in addressing pain points and producing reliable outcomes:

  • Revolutionary Voice Recognition Product: Voice annotation and data entry was integral to the development of a new speech recognition tool that provides more inclusivity and accessibility to various industries. Consistently labeled and validated data across 20,000 audio files helped to establish a trusted sample baseline for the diversity of human speech and formed an accurate database for the system to train on..Read more
  • Dynamic ML Based Learning Devices: Annotated hand gestures were the reliable source input for a top computer vision software company who built a dynamic hand and gesture tracking framework for more authentic human-machine interfaces on smart devices. Gesture characteristics and movements were differentiated, processed, altered and validated for model development.. Read more
  • Professional Sports Analytics: Data tagging was the foundation for a leading web analytics company who utilized rugby statistics to support professional athletic teams and players. Athletic actions were tagged, calculated and compiled for the finished video playbacks to serve as comprehensive training tools..Read more
Pillar of AI and ML Advancement

Partner with the Data Entry Experts That Are Technology Aware

Data Entry Outsourced (DEO) has achieved a number of milestones in delivering a variety of data entry services. For decades DEO has been continuously supporting the needs of our global clientele as the world transitions alongside automated AI and ML technologies.

Our stringent processes, in-demand skills, eye for detail and experience in supporting complex, cross-industry systems enables us to deliver accurate data entry all the time. We continue to evolve, adapt and upgrade our infrastructure, solutions and skills to support businesses with specialized services that are not limited to – CRM data entry, ERP data entry, and ecommerce data entry, etc. For a free consultation and a custom quote, contact us today.

– Data Entry Outsourced

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