Why unstructured data matters for businesses in the age of AI

Aldo van Tonder, Chief Digital Officer at 4Sight.

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Unstructured data is any kind of data that does not have a predefined format or structure, such as text, images, audio, video or social media posts. It is estimated by some that unstructured data accounts for more than 90% of all the data in the world, and it is growing at an exponential rate. However, most businesses are not able to fully utilise this vast and rich source of information, because they lack the tools and skills to analyse it effectively.

This is where AI or artificial intelligence comes in. AI is the ability of machines to perform tasks that normally require human intelligence, such as understanding natural language, recognising faces or generating creative content. AI can help businesses unlock the hidden value of unstructured data by transforming it into actionable insights that can improve decision-making, customer experience, innovation and competitiveness.

One of the leading companies that is leveraging AI to harness the power of unstructured data is Microsoft. Microsoft offers a range of AI solutions and services that can help businesses of any size and industry make sense of their unstructured data and use it to achieve their goals.

Azure Cognitive Services is a collection of cloud-based APIs that can perform various cognitive tasks on unstructured data, such as text analysis, speech recognition, computer vision and natural language processing. Azure Cognitive Services can help businesses to extract meaning, sentiment, intent and emotion from their unstructured data, and use it to enhance their products, services and processes.

Azure OpenAI Service is a cloud-based platform that enables businesses to access and use the powerful OpenAI models, such as GPT-3, which can generate natural language text on any topic, given a prompt. Azure OpenAI Service can help businesses to create engaging and personalised content, such as product descriptions, marketing campaigns or customer reviews, based on their unstructured data.

Microsoft Fabric is a unified data analytics platform. It is an umbrella of Microsoft’s three main data analytics products: Power BI, Azure Data Factory and Azure Synapse. It is responsible for gathering a range of data toolsets under a single umbrella. Think of it as a single solution to crunch numbers and deliver insights and, ultimately, the one source for AI.

Microsoft 365 is a suite of cloud-based productivity and collaboration tools that can help businesses to create, share and manage their unstructured data, such as documents, e-mails, chats and meetings. Microsoft 365 can help businesses to improve their communication, teamwork and workflow, and use their unstructured data to support their decision-making and problem-solving.

By using AI to analyse and understand their unstructured data, businesses can gain a deeper and more holistic view of their customers, markets, competitors and operations, and use it to create value, differentiation and competitive advantage. In the age of AI, unstructured data is not a challenge, but an opportunity.

Some of the best practices for protecting unstructured data are:

• Implementing data classification that can automatically identify and label sensitive unstructured data, such as personal information, intellectual property or trade secrets.
• Applying data encryption, masking or tokenisation techniques to protect unstructured data at rest and in transit, and prevent unauthorised access or leakage.
• Using data loss prevention (DLP) solutions that can monitor and block the movement of unstructured data across networks, devices and applications, and enforce data security policies.
• Adopting a zero-trust approach that assumes no user or device is trustworthy by default and requires verification and authorisation for every data request or action.
• Leveraging a modern data management platform that can handle both structured and unstructured data at scale, and provide fast and easy access to data insights, while ensuring data security and governance.

The difference between structured and unstructured data is that structured data is highly organised and formatted, so that it’s easily searchable in relational databases, while unstructured data has no predefined format or organisation, making it much more difficult to collect, process and analyse. Structured data is quantitative, while unstructured data is qualitative. Structured data is often stored in data warehouses, while unstructured data is stored in data lakes.

Some examples of structured data are:

• Dates, phone numbers, bank account numbers, product SKUs
• Sales transactions, inventory records, flight reservations, customer ratings
• Data that can be easily searched using SQL (structured query language)

More examples of unstructured data are:

• E-mails, songs, videos, photos, reports, presentations
• Social media posts, podcasts, web pages, blogs, reviews
• Policies, documents, delegation of authority, minutes of meetings

Personalised marketing:

Businesses can use structured data from customer databases, such as contact details, demographic data and purchase history, to segment their customers and target them with relevant offers and messages. They can also use unstructured data from social media posts, web pages and reviews to understand their customers’ preferences, sentiments and feedback, and tailor their marketing strategies accordingly.

Product development:

Businesses can use structured data from sales data, inventory data and web analytics to measure the performance, popularity and profitability of their products. They can also use unstructured data from e-mails, surveys and user-generated content to collect customer feedback, suggestions and complaints, and improve their product quality and features.

Competitive intelligence:

Businesses can use structured data from financial records, market reports and industry benchmarks to analyse their own strengths, weaknesses, opportunities and threats. They can also use unstructured data from news articles, blogs and podcasts to monitor their competitors’ activities, strategies and reputation, and gain a competitive edge.

Business-critical decision:

Businesses can use structured data from financial records, operational and business processes and unstructured data from internal governed processes, decision records, conversations and company rules and policies to automate responses and provide answers to key critical business problems and decisions before they need to happen.

Risk management:

Businesses can use structured data from transaction logs, audit trails and compliance reports to detect and prevent fraud, errors and violations. They can also use unstructured data from e-mails, chats and calls to identify and mitigate risks, such as insider threats, cyber attacks and legal disputes.

In the age of AI, the journey to true digital AI transformation will require combining these two critical data elements and a proven and defined methodology. 4Sight can deliver on this methodology and assist with your transformation journey. Contact 4Sight for more on the methodology at sales@4sight.cloud.