Unstructured Data

There is enough evidence to prove that combining unstructured with structured data helps companies improve operational efficiency and gain higher ROI. But, extracting and using unstructured data requires companies to follow certain prerequisites; it is critical to identify the objective they are working towards, break data silos, and use the right ML and NLP tools to understand and analyse unstructured data.  

In this blog, we deep dive into what comprises unstructured data, why it is important, and how companies can use ML & NLP tools to draw insights from them.  

“This call will be monitored and recorded for training and quality purposes.” Sound familiar? 

Why do you think companies record calls?  

The answer is quite simple. Mid to large-size companies typically have a set of standard protocols to follow during customer service interactions. They record calls to draw insights from them and improve their customer experience based on past insights. In fact, companies don’t just record calls. They also store chat conversations, conversations that have taken place in social media platforms, text messages, email interactions, photos, videos and more. 

Unfortunately, many of these data points remain in silos as unstructured data. Unlike structured data which is readily available and easy to sort through, unstructured data is challenging to find and collate.  

According to Datamation, unstructured data makes up almost 80% of enterprise data. And, this data mountain is growing at 55% to 60% a year. 

But, why do companies have mountains of unstructured data? Usually, this is because;  

  • There is a lack of data sync between departments within a company 
  • The company doesn’t have the technology and tools required to store and analyse data from a centralised location 
  • The company doesn’t have the skilled resources required to understand and draw insights from these data 

Using unstructured data with structured data offers many advantages for companies; 

  • It generates more accurate results on what prospects or customers seek from your company 
  • It results in higher operating profit  
  • It delivers higher organic revenue growth 

In fact, data shows that Fortune 1000 companies that expanded their data accessibility by 10% saw a US $65 million increase in their net income. 

How can companies make the best use of unstructured data? 

Before investing in AI tools that can ease collation and analysis of unstructured data, companies first need to lay down their objectives.  

Stage 1 – Set and Understand Business Objectives 

Determine the objectives behind your marketing strategy – be it to generate X number of leads, increase purchase revenue by X%, improve customer satisfaction score, reduce CAC (customer acquisition cost) or more. Once you know what your goal is, you can determine what data is required to work towards it. 

Stage 2 – Ensure accessibility to data silos 

You’ll need to make sure that all unstructured data is accessible to relevant stakeholders. Typically, in a company, customer data is siloed across departments (IT, marketing, finance, sales) and platforms (social media platforms, emails, chat etc). Ensure that these silos are broken and store all data in a centralised location.  

Stage 3 – Data Compliance  

It’s necessary to have compliance and security measures in place, so that customer data is not misused by anyone within or outside a company. Securing customer data is especially important and can cost companies dearly, especially if it falls under the GDPR compliance regulations.  

Stage 4 – Combine structured and unstructured data 

Combine unstructured data with structured data to analyse and draw holistic insights. Etihad Airways, for example, breaks data silos and uses data lakes and advanced analytics tools to improve its staffing, booking and customer service process. In fact, it even extends this to aircraft repair and maintenance processes.  

One use case is, Etihad works with Dataiku to manage incoming customer queries. Dataiku has built a NLP model which has the ability to classify emails based on words or speech, and route emails to the right customer representative to respond. This reduces response turnaround time, and enhances customer experience and satisfaction. 

Using Advanced AI Tools for Analysing Unstructured Data 

We’ve spoken in our earlier blogs about how the pandemic has accelerated digital transformation, and companies are now grappling with customer journeys that cut across several touchpoints. In such a scenario, it’s all the more important for companies to invest in AI tools to derive meaningful insights on how customers think and respond to a company’s marketing and sales strategies. 

But, has AI come so far as to collate and analyse unstructured data? Not really. It is still in its nascent stages when it comes to processing unstructured data, simply because unstructured data doesn’t fit into specific formats or sequences.  

Having said that, there are ML and NLP (natural language processing) tools that companies can use to derive insights from unstructured data. But, this requires them to invest heavily in time, finance and personnel, and it requires data scientists to train these tools to troubleshoot each set of issues.  

A Merit data collection expert adds, “NLP can process text, images, videos and such, classify data captured, and automate the entire process of culling data from unstructured sources. But, data scientists need to train the NLP tool by showing it how to troubleshoot in case there is a problem, and the AI model will learn from it. Once it has been trained to handle specific issues/ tasks, it can be integrated into workflows.”  

Today, there are several third-party organisations that are using AI and NLP to automate unstructured data, and they are selling this data to other companies. Larger organisations have the resources to invest in ML and NLP tools to automate unstructured data. Ultimately, while there are several advantages to combining structured and unstructured data, the wise thing to do is to extract data based on specific business goals or outcomes. This saves time, cost, and improves operational efficiency. 

A marketing data expert at Merit adds, “From a marketing perspective, it is critical to leverage insights from unstructured data to run better campaigns, deliver a personalised experience to customers and use feedback to build products that customers truly want. Each of the 4Ps of marketing – product, price, place and promotion – can be improved and enhanced when insights from unstructured data sources are added to the mix.”  

Merit Group’s expertise in Marketing Data   

At Merit Group, we partner with some of the world’s leading B2B companies. Our data teams work closely with our clients to build comprehensive B2B marketing contact lists that provide a direct line to their target audience.  

If you’d like to learn more about our service offerings or speak to a marketing data consultant, please contact us here: https://www.meritdata-tech.com/contact-us 

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