The value of unstructured data to CXOs
In this guide, we will cover what every CXO needs to know about unstructured data to take less-risky business decisions on investment in data and artificial intelligence.
Unstructured data includes document, audio recording, video, SMS, image, social media, internet content, etc. Each day, huge volumes generated and captured by businesses and governments to provide a record of their transaction and help inform critical business decisions.
Data drives the world, with far-reaching impacts on us all, from individuals to major corporates. And the facts say that around 80% of the data available is unstructured, meaning it’s not in databases with a format that is easy to collate and analyse at speed. This is why everyone in C-Suite must know what their organisation’s strategy is to use this information.
You can be sure that your competitor is this thinking about it, and that your customer will expect you to use what’s in the unstructured data to provide them with a greater level of tailored serve. In this guide, we will cover what every CXO needs to know about unstructured data to take less-risky business decisions on investment in data and artificial intelligence.
Why should you care? Your colleagues and counterparts are already aware of:
- What is unstructured data?
- Importance of analysing unstructured data
- The role of AI in identifying unstructured data
And soon will you do too. Let’s dive in.
What is unstructured data?
Unstructured data means all information that has not been pre-organised in a logical, retrievable structure – this information can be moved, digested, and analysed by traditional computing processes and software. It includes data in paper and many physical archival systems that governments and businesses rely upon.
So, what kind of information is considered to be unstructured? Quite a lot:
- Audio recordings
- Text & log files
- XML script
- Image or video files
- Social media, news streams, or blog posts
- Indexed and dark web pages
- Call center recordings
- Email, chats, and reviews
- Online comments product reviews
- Chatbot conversations
- Paper and electronic books, documents, or journals
- Micro fiche, CDs and file shares
What these data sources have in common is that they have until now been mostly inaccessible. The fact that they are unstructured (i.e. not in tables), have various formats and language variations, mean that they are expensive to collate and because the amount is so large not humanly possible to take all the relationships in at once. not easy All the above stuff is considered are examples of unstructured information.
Importance of analysing unstructured data
Identify the exact need or the exact problem you can solve for customers.
Finding the information that you trust is like gold for decision making. CXOs need their organisation to mine and extract the right kind of information for business decisions. And, to do this without investing huge amounts of cash and time on fruitless big data sets.
This leads to the next question. Exactly why is unstructured data are useful for an organization?
CXOs need to survive in a highly competitive world
There is no doubt that the current world climate is peaking in competition. Most are struggling in some way to serve their stakeholders and sustain their business through the crisis.
It is essential to gain insight and learn quickly and accurately in such a rapidly changing competitive world. This is the origin of real competitive advantage – no one else is you in your market with access to the information you have in your proprietary unstructured data holdings.
You need to know your data. How else will you identify the exact need or the exact problem you can solve for customers?
Deep understanding of customer behaviour and intent
The benefit of analysing unstructured data is gaining a deeper understanding of customer behaviour. To know what your targeted customers are thinking about your company, product, or services, we need to listen to the range of communications about our products and services and our competitors and broader markets in real-time.
Feel overwhelmed yet? Learning customer behaviour, emotions, preferences, and intentions help your business decisions target product development, marketing, logistics, customer support, and many other strategies.
Respond early to subtle shifts in the market
While unstructured data is not easy to analyse, it does provide a unique perspective on customers’ expectations. For instance, you can build evidence-based scenarios detailing everything from who your customers are to their likes & dislikes, what they think about you, how your products are compared to competitors and more. Those with high market share already are.
By figuring out your competitor’s strategies, what their customers are thinking about them, top industry leader attitudes, robust knowledge derived from unstructured data can be developed and updated at the speed of technology. Not in weeks, in real-time on a screen in your hand, office or at home.
Foster advanced business innovations
It’s wise to assume there’s a gap between customer aspirations and what the current product or services are delivering to them in every business. This mindset helps businesses initiating creative business ideas and innovations.
Analysing unstructured data is an untapped resource. Accordingly, you can newly innovate the current deliverables based on the data insights.
The role of AI in identifying unstructured data
Finding the right AI technologies to automate unstructured data analysis is by far more viable than search or people power.
People can’t read and interpret the sheer bulk of material available in an unbiased way. You would need to invest a lot of cash, energy, and time, you will also need to assure you can fully trust the results. A manual approach is not a practical way out.
Let’s focus on text formats first, as this is likely to be the richest source of insight for most organisations. Artificial Intelligence (AI) can extract relevant information from unstructured sources. However, not all AI is the same. The systems best for analysis of written and spoken language employ advanced semantics and linguistic, cognitive technology – they have achieved what is named in the trade as Natural Language Understanding (NLU).
Many of the other advanced tools are Natural Language Processing (NLP) based on applying statistical methods to test data to understand language patterns. The difference between Natural Language Understanding and Natural Language Processing is important to understand.
Natural Language Processing (NLP) limitations
Human language is ambiguous and complex. Words can have different meanings in context, and sarcasm can give a string of words a completely different meaning. NLP approaches do not understand the words in context. They only analyse patterns of words, not their meaning.
Natural Language Processing (NLP) software is fine. There are a limited number of questions and answered – even if the number of questions and answers are mind-boggling large, the subject is still treated as a closed system. You will just need an extraordinarily large and highly comprehensive data training set. We are talking about many terabytes of trusted data being available to train the AI. An example of NLP software is IBM Watson.
NLP-based AI fail when the information supplied is ambiguous, or if different words or style is used to how it has been trained. Statistical approaches rely on the permutations of language being converted to structured data sets that it may refer to analyse new material.
A group of people speaking or writing on the same matter will rarely use the same words or phrases to convey the same meaning and sentiment. This natural variation and nuance is a problem for NLP.
Natural Language Understanding (NLU) software approaches the problem differently.
From the outset, the learning is focused on how concepts and words are related in common language and which meaning of the word applies. We’ll explain this more in another post. The difference is that NLU mimics the way the human mind reads, interprets, and understands the language. An example of a Natural Language Understanding (NLU) software in use commercially is expert.ai.
As for other unstructured data, spatial pattern recognition recognises and translates descriptions of people, animals, or image file objects. Speech to text can be used to convert audio language into searchable text, and optical character recognition (OCR) converts text on paper, images, and physical media for text-based analytics.
Using Artificial Intelligence (AI), you can accurately figure out customer expectations and threats from your competition, but only if you have the right cognitive technology deployed or have time and cash to burn on a manual approach.
Why waste time?
CXOs ignore the value in the unstructured dataset at their peril and miss a great opportunity to transform your business based on homegrown intelligence. Moreover, the latest AI-driven technologies have made unstructured data easier to access you can use AI for audio to text technology, pattern recognition to star. Make the first move to integrate insight from unstructured information analysis in your decision-making processes now.
It’s the data that can be put into rows and columns and is highly organised. Examples that you may find in your databases include credit card numbers, names, dates, stock information, addresses, financial transactions, and so on.
There are broad categories within AI technologies that can be applied to different scenarios. For unstructured data look at Natural Language Understanding (NLU) and Natural Language Processing (NLP) software.
This data is always in its native format. If it’s in a physical form, it needs to be processed to a machine-readable format to be used in AI analysis.
The name, date, email address of the sender & receiver is in a structured format, but the email body is in an unstructured format. So, in a nutshell, an email can be considered as semi-structured data. The business value is probably in the unstructured data.
PowerPoint, PDF, and simar format are examples of unstructured data. While a spreadsheet has data in rows and columns, it is a standalone file that is not readily integrated with other spreadsheets unless there are consistent definitions and data provenance. From an enterprise perspective, excel spreadsheets are unstructured data for this reason.
IDC Estimated by IDC, that 80% of global data will be in unstructured formats by the end of 2025.