Posted on : September 27th 2022
Unstructured data is not organized in a pre-defined structure or data model and doesn't have an identifiable structure. Data exists in two main formats—structured and unstructured—and although structured data is straightforward and can be used and reused in several ways, it’s unstructured data that is abundant and holds critical insights too.
According to International Data Corporation (IDC), by 2025, 80% of all enterprise data will be unstructured. This is going to be a significant challenge for businesses. Data formats matter, as they are crucial in extracting valuable insights required to power business decisions.
If enterprises fail to utilize the nature and volume of data to improve business growth and profitability, then there is a need to evaluate the data strategy. Understanding the difference between structured and unstructured data is key to any enterprise’s data management strategy. Also, dealing with the complexity and immensity of unstructured data requires advanced information security features.
Data labeling involves tagging/ labeling raw information like photographs, videos, textual content, etc. They describe the data's entity type by referring to various attributes of the data point. This allows a machine learning (ML) model to learn to recognize that type of object when operating on unlabeled datasets. Labeling must be precise to the best possible degree so that AI/ML systems can deliver reliable results.
Labeling helps ensure that AI/ML models are trained on a suitable information set. It offers the initial setup for an ML model to provide valuable results. Data annotation is a critical stage of data preprocessing as AI/ML models require significant amounts of data for accuracy. Using cleansed, labeled, and organized data sets is vital to train AI/ML models.
But, labeling is not as straightforward as it sounds. Many enterprises don’t know the amount of unstructured data they are holding. Enough details are unavailable on the types of data stored in unstructured data repositories and access rights. Labeling correctly according to a data classification enhances visibility and supports better analysis of unstructured data.
Exhibit 1: Example of Data Labeling Classification
Data labeling makes objects recognizable and understandable for AI/ML models. It is critical for face recognition, autonomous vehicles, advanced drones, robotics, etc. The labeling schema must be simple and clear, as labeling unstructured data makes searching thousands of digital documents easier, enabling data security analysts to evaluate risk and quality.
Labeling is sometimes also referred to as annotation. There are three types of data annotation.
Source: Analytics India Magazine, Straive.
High-quality labeled data make smooth operations of AI/ML models possible. Thus, a secure and cost-effective data labeling approach is highly sought. Structuring and classifying data so that AL/ML models can distinguish between the human and the background, the road and vehicles, etc., provide critical ground truth data to drive reliable predictions.
The spread of data storage options, exponential jump in unstructured data volumes, and general personnel mobility produce several unstructured data management risks. A governance framework is vital. It should cover several parameters.
Possessing AI/ML-led automated options promotes governance effectiveness at scale. For example, timely threat detection and a breached file’s quarantine become easier. Easier compliance and simplified data security need audit trails. Thus, an enterprise's unstructured data governance policy will help simplify complexities by clearly articulating safety protocols and threat matrices.
Standard operating procedures (SOPs) for responding during breaches will reduce the lack of clarity and human error. Increased awareness levels will minimize panic. SOPs make it easy to identify and audit responsibility ownerships and action trails.
The people connection is essential beyond robust technical specifications and protocols to ensure the success of unstructured data security procedures. Efforts are needed to make employees aware of unstructured data, its importance, and its protection. Training events and networking engagements help develop a people-led information security culture.
A corporate cybersecurity posture based on constant due diligence should reinforce learning, knowledge sharing, and strategic messaging. Taking control of enterprise unstructured data entirely is a challenging task. A zero-trust access governance approach with supported people connections can help locate, assess, and defend data better.
Unstructured data governance presents unique risks to enterprises. Identifying and regulating the flow of unstructured data is a constant endeavor. New ways to share files are emerging. Situations like device mobility produce blind spots in tracking unauthorized access.
Data privacy and security regulations are escalating. Non-compliance can result in severe reputational, legal, and financial risks. Though the piling of unstructured data compounds the problem of analysis and insights generation, its reliable processing holds the key to competitive advantage.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
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