Artificial intelligence (AI) is transforming how businesses can collect, store, and analyze large volumes of data for informed decision-making. In the past, data management was a less cumbersome task for organizations and could be handled manually with limited resources.
However, the transition to the cloud and the introduction of new data sources have led to an increase in the volume and diversity of data. Traditional ways of managing data are no longer effective in this new environment.
Businesses have started preferring the use of AI for automating many of the manual tasks involved in data management, such as:
- Data Scraping
- Data Cleaning and Preprocessing
- Data Enrichment
- Data Verification and Validation
However, despite its capability to automate these tasks, AI is not a replacement for human expertise. The human-in-the-loop approach is still necessary to ensure that AI-powered data management processes provide accurate and reliable results. How? Let’s understand.
Advantages of Bringing AI into Data Management
Utilizing AI for data processing and management can help organizations to:
Improved Data Processing Speed & Efficiency
Manually managing large datasets can be time-consuming, resource-intensive, and error-prone for businesses.
AI and ML can automate repetitive tasks of data management that don’t require contextual understanding, such as data capture, cleaning, and verification, to quickly process large amounts of information.
By reducing the time, resources, and effort required to enter and process data, AI can improve process efficiency from 2x to 4x or even more.
Improve Their Data Governance Practices
Organizations can devise governance policies to control data quality and security. Utilizing AI, they can improve the reliability of the data by automating the identification and correction of quality issues (inconsistencies, inaccuracies, missing values) in the datasets.
It can also be used to automate the enforcement of a company’s data governance policies to ensure that the information is processed in accordance with regulations.
Quick Identification of Patterns & Trends in Data
AI can be used to extract insights from the data collected from diverse sources that could be difficult for human analysts to perform.
Such insights can help businesses identify trends, patterns, market opportunities, and potential risks to make more informed business decisions.
For example, AI can be used to analyze large datasets of patient records to identify patterns that may indicate potential risks or benefits of different treatment options.
This can help doctors personalize medical care plans for individual patients, which can lead to better outcomes.
Improve the Master Data Management Process
AI helps businesses to efficiently create and maintain a unified master record (Enterprise Data Catalog) of their data to be used across various enterprise applications, such as ERP & CRM systems.
The information coming from various external and internal data sources is carefully de-duplicated, reconciled, and enriched by AI systems to maintain data accuracy and consistency.
Quickly Detect Anomalies in Large Datasets
Once AI starts identifying patterns and trends in large datasets, it starts understanding the normal representation of the data.
This AI system can be used to identify data points that are significantly different from this normal range of the data present in other data fields. These outliers can be further investigated by AI to decide if they are anomalies or not.
Reduce Operational Cost Involved in the Data Management Process
AI helps organizations scale better by reducing the overhead cost of data processing. Automating the repetitive tasks of data management that require manual efforts, eliminates the need for businesses to invest in a large number of skilled resources and advanced infrastructure.
AI Automation Challenges That Cannot Be Overcome Without Human-in-the-Loop Approach for Efficient Data Management
While AI is a powerful resource that can dramatically improve operational efficiency and the value organizations can derive from their data through automation, there are certain challenges it cannot overcome without human expertise.
Six key areas where AI still requires a human-in-the-loop approach for effective data management are:
Data Quality Assurance
While AI models are capable of handling tasks like data cleaning, verification, enrichment, etc., they can deliver inaccurate results due to low-quality training datasets. To improve their output, human expertise is required.
This can be better understood by the case study of a leading revenue recovery company that utilized AI-based software to identify counterfeit products on the web for various reputed brands. [Source of this case study].
For the efficient working of this tool, they required human experts who could consistently clean data for AI model training, validate the information scraped by the tool, and append the missing data points to perfect the process.
Accurate Data Labeling
To train supervised AI/ML models, accurately labeled datasets are required, which need human expertise. While automated tools can aid in the annotation process, human experts are often needed to identify data points based on the type of annotation required or what the AI model is supposed to do.
Data Security & Privacy
The use of AI models raises concerns about data confidentiality. Despite stringent security measures, these models are still vulnerable to data breaches and cyberattacks.
Therefore, companies need data security experts who can safeguard their data from unauthorized access by following compliance regulations.
Also, while these models can be used to detect unusual data usage and potential threats, only humans can devise strategies, such as blocking the user or system from accessing the data, changing the permissions for the data, or implementing other security measures, to protect the data.
Monitoring AI Outcomes
Despite getting trained on highly representative data, AI models can make mistakes. If these errors are not identified and rectified by humans at the initial stages, these models will continue repeating or amplifying these errors, giving poor outcomes.
Also, the data that AI models are trained on can become outdated over time, as things change. That is why human assistance is required to regularly monitor AI outcomes and improve them for organizational use.
Contextual Understanding & Domain Expertise
When it comes to subjective decision-making, AI still can’t beat humans. Predictive models struggle with contextual ambiguity, where the meaning of a phrase depends on the surrounding context. Humans, on the other hand, excel at understanding such nuances with their domain expertise. This makes them more reliable in complex decision-making.
In several domains like legal, healthcare, and finance, AI needs constant feedback from human subject matter experts to refine its predictions and determine how to take action based on these predictions.
Managing Unstructured Data
Handling unstructured data is challenging for AI systems despite the advancements in technologies, such as ML, NLP, and so on. AI models can work better and more efficiently on structured data since it is easy to understand. However, unstructured data, like images, videos, audio, etc., require pre-processing by humans to be utilized efficiently by AI/ML models.
AI is significantly transforming the way organizations process and manage large amounts of data, enabling them to adapt to dynamic conditions. However, the true potential of AI in revolutionizing data management lies in overcoming automation challenges with human expertise.
Combining a human-in-the-loop approach and AI assistance can make data management more efficient and effective for businesses. Here are a few illustrative examples of this synergy:
- Data de-duplication and enrichment: While AI can identify duplicate records and update missing information, human assistance is required to ensure the updated data is accurate and correct.
- Data analysis & decision making: AI can be used to identify and analyze patterns & trends in the data, but human expertise is required to make informed decisions based on that data.
- Data governance & security: AI can be used to automate the data governance process, but humans need to be involved in the loop to ensure that the data is being used in a compliant and ethical manner.
Businesses can invest in expert resources in-house to analyze and improve AI outcomes for efficient processing of data. If they don’t have enough resources or infrastructure for that, outsourcing data management services to a reliable third-party provider (that utilizes the HITL approach) is a viable option.