How to deploy AI technologies to create value from your unstructured data and identify product and service improvements
A changing data landscape
The nature of data is evolving, and with it, the need for storage capacity is expanding. IDC predicts that, by 2025, data storage capacity will need to increase by 240% (from 2020).1 However, the kinds of data being stored are also evolving. Alongside structured data (like relational and transactional data), there have been meteoric rises in unstructured and semi-structured data. In fact, analysts estimate that 80% of all global data will be unstructured by next year.1 These are astonishing numbers.
This rapid change in our data landscape from a focus on structured data, often generated by centrally hosted business applications such as ERP and CRM, to one that can accommodate the exponential growth in content and unstructured data has profound implications on how we think about, manage and extract value from our data.
Content vs. unstructured data Though content and unstructured data are closely related — and in fact, content can contribute to unstructured data — they are not one and the same. Content refers to the information available for end-user consumption, i.e. text, images, videos and audio. It can have unstructured elements, but it also includes structured data that provides context to the content, like metadata. In essence, while all content can be considered data (structured or unstructured), not all unstructured data is content; it’s a broader category encompassing other non-content data types.
The power of unstructured data Unstructured data really matters. It can potentially add value at multiple touchpoints across your business by generating new insights into how your customers behave, consume and interact with products and services. However, this type of data often comes from diverse content sources that do not fit into a standard database schema, such as text files, images, videos, social media posts and emails, among other communications. Unlike structured data that can be neatly organized into rows and columns, data from these sources lack pre-defined formats — making it complex, messy and of varying quality — and thus more challenging to draw out correlations and trends.
These defining characteristics and the sheer size of unstructured data mean a different and deliberate strategy is needed to extract meaningful information. Specialized processing and analysis techniques, such as machine learning algorithms, natural language processing (NLP) and data analytics models, are often required.
The rapid growth of unstructured data has been accompanied by rapid growth in AI technologies. This has shifted the delineation of traditional boundaries between data and analytics and created new ways of unlocking value.
A blended approach maximizes data value Unstructured data is now significantly influencing our data architecture, so what can we do to tap into its potential? The challenge is how to leverage its value — in particular, insights around human behavior — and blend it with traditional datasets to unlock wider enterprise value.
Structured and unstructured data are inherently different. Relying on a traditional approach will not work. HCLTech and Microsoft have put together this roadmap to help you develop a modern, AI-ready data strategy that will move you quickly and pragmatically beyond proof of concept to achieve outcomes that drive growth.
Roadmap to manage unstructured data and scale the AI advantage
Combining proven engineering capabilities from HCLTech and scalable AI technologies from Microsoft enables you to confidently leverage business value from your data. Use our roadmap to guide you on this journey.
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Change your mindset
A process of unlearning may be required to monetize unstructured data. As an organization, you will be comfortable with structured data and its format, but adopting a process of converting unstructured data into a structured convention is both cost-prohibitive and inefficient. Avoid thinking about getting all your unstructured data into a single repository — technologies, such as Microsoft Fabric, already exist to exploit the potential of data at scale.
As an integrated data management platform that consolidates disparate data types into a centralized lakehouse architecture, Microsoft Fabric is able to facilitate comprehensive data analytics. By leveraging advanced data integration and processing tools, enterprises can perform in-depth data analysis to drive informed decision-making through sophisticated AI and machine learning algorithms. Incorporating data governance into the management of unstructured data is crucial to ensure adherence to quality standards, remain secure and comply with relevant regulations. Microsoft Fabric supports this robust governance by providing tools and frameworks that help define access controls, categorize data and maintain integrity. This not only protects the data but also enhances its value for business intelligence and decision-making.
Thinking differently will enable you to become a more cohesive data organization.
Use case
Organizations offering fast-paced, consumer-facing services need to mine insights from a wide range of data sources, including social media, text, in-app and emails, to offer their services optimally in near real-time and identify patterns that could influence strategy over time. AI-powered analytics are deployed to unlock the value from unstructured data without formatting or organizing it.
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Lead with a use case or business challenge
Understanding the scope, state and potential of your organization’s entire data landscape is a vast undertaking. Instead, start by defining the business challenge you want to solve and then identify the data required to get you there. Do you want to improve the customer experience? Or identify site improvements based on customer engagement? This efficient and cost-effective approach optimizes resource deployment by ensuring that only relevant proof of concepts that can deliver the desired returns are proven.
Microsoft Fabric enables you to do this by allowing your business to start small and scale your data management capabilities as needed. It offers a pay-as-you-go pricing model, which means companies only pay for the capacity they use — without the need for large upfront investments. This flexible approach enables organizations to scale their data operations up or down based on current demands, ensuring cost efficiency and the ability to adapt to changing business needs — as well as avoiding large-scale CAPEX investments ahead of business value.
Use case
Using video analytics from in-store security cameras to understand movement patterns around retail environments helps determine shelf optimization, aisle spacing and checkout placements to improve the overall consumer experience across a chain of stores. The project's starting point was improving customer engagement in retail environments to drive sales.
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Delineate by cost and future value
The volume of unstructured data is high, so optimizing storage is key. Businesses should deploy a two-fold strategy that assesses which data can deliver current value to the business and which data may have future value. All data that has immediate value in terms of the business objective can be leveraged, while all data that could have future value is stored cost-effectively. This delineation is key to optimizing data storage while retaining its potential business value.
Use case
Starting with the goal of creating a more personalized shopping experience, a chain of retail stores was able to take the findings of a previous project using in-store video analytics around behavior and store layout modification, and overlay this at a future date with customer loyalty program data to unlock a new set of improvement-based experiences.
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Use cross-tabbing to extract maximum value
Bringing together multiple datasets in multiple formats is key to unlocking more value. Unstructured data offers valuable insights into human behavior at individual and macro levels. Combining more unconventional sources of information, such as social media-generated activity, with your existing business data, such as POS and stock monitoring, provides a more holistic view of what’s happening across your organization. It is at the intersection of structured and unstructured data that true business transformation occurs.
Use case
An organization experiences rising sales for the first six months of the year and bases its inventory ordering and projected financial performance figures on its POS systems. The anticipated sales increase fails to materialize, and the company is left with unsold stock and falling sales. It had failed to consider customer reviews which highlighted a product quality issue and an exponential rise in dissatisfaction. Combining wider datasets would have led to better decision-making.
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Maintain quality, governance and security
The principles of managing unstructured data remain the same. Deploy available technologies to overcome data quality issues — think poor audio and background noise — to significantly increase usability. Privacy is key. When combining social media and call center data, filters need to be in place to remove Personally Identifiable Information (PII) and sensitive details. Governance is required to check adherence to regulatory and organizational requirements.
Microsoft Fabric makes data easier to manage and secure. It allows businesses to categorize their information through data tagging. The platform’s security features ensure that data is protected and accessible only to authorized personnel, maintaining confidentiality and compliance with industry standards.
Use case
Reducing wastage is critical as the world’s supply of drinking water decreases. By combining its real-time pipeline flow data with social media and call center activity, a large water company is now able to triangulate a leakage or outage more quickly and resolve it more efficiently. Introducing social media data was key to the speed of response — consumers use this as a first point of call ahead of call centers — but it required strict privacy filters.
Listen to HCLTech and Microsoft in conversation here
Make more accurate business decisions
Access to unstructured data provides valuable behavior-based insights that can be applied to deliver better products and services, both in real-time and the longer term. At HCLTech, we take a pragmatic approach to data modernization. We fuse our proven engineering capability with scalable Microsoft technologies to get your data and analytics infrastructure fully AI-ready.
