Why Enterprise Analytics Must Not Ignore Unstructured Data?
Do you know that 90% of the data generated by any business is unstructured? Or that,
95% of business enterprises do not know how to manage their unstructured data? Adding to that, unstructured data is growing at 55-65% every year.
Most businesses lack the necessary expertise to deal with unstructured data. With Big Data growing exponentially every year, function heads in companies – be it in the marketing or the financial domain – will need to analyse their data to gain a better understanding of their customer and market needs.
According to IBM, 150 trillion gigabytes – or 150 zettabytes – of data will need to be analyzed by the year 2025. A major chunk of this generated data will be unstructured (or semi-structured) in the form of business emails, customer complaints or feedback, social media posts, text and video files, and survey findings.
Why is including unstructured data for effective analysis so important – and what are some of the benefits and challenges of analyzing unstructured data? Keep reading.
Why Unstructured Data is Important for Analytics?
Unstructured data is important for effective analytics – because of its massive volume but also because of the evolving nature of customer communications that are starting to trend towards more natural forms of expression.
While most enterprises have the necessary expertise to deal with structured data using programming languages and database queries, these prove insufficient when acting upon unstructured data.
The bulk of unstructured data is generated by online user activities – and lacks a predefined format or structure that can be stored in database tables. For unstructured data, analytics requires the use of advanced AI algorithms and technology tools to derive any valuable insights from it.
Among its many use cases, unstructured data is vital for customer analytics. This is used to enhance customer relationships based on insights extracted from data sources like call center chats and calls, product reviews or feedback, customer support helpdesk interactions, and social media posts. For example, companies are using AI-based text analysis tools to analyse thousands of customer surveys to understand customer choices.
Another area where analysis of unstructured data is useful is in detecting any compliance-related issues in regulated industries. Valuable insights from unstructured data can help in saving the company’s money, time, and reputation. Typically, non-compliance issues can be detected by analyzing business emails and even instant messenger conversations. The challenge with managing such unstructured data is that they are often decentralized and the responsibility of individual employees – all until
sensitive data gets leaked to the external world.
Benefits of Analyzing Unstructured Data
Unstructured data can be an important key to solving several business problems. Here are some areas where unstructured data can help:
Improve your customer experience
While structured data can provide customer information, it is always the unstructured data that provides important clues about what your customers think of your brand and their intent. For example, how are clients responding to your latest marketing campaign? Or what are the common client problems that your customer support team receives?
Encourages business innovation
Be it any product or service, knowing your customer needs through analysis of unstructured data is a continuous process – and leads to major innovations or breakthroughs. For example, in the case of retail or fashion products, unstructured data can be a gateway to the latest fashion trends in the market –
and what your customers are looking for tomorrow. As a company, you can also develop a deeper understanding of your strengths/ weaknesses as compared to your closest competitors.
Understands customer sentiments
Through positive (or negative) customer sentiments on social media or blogs, your business can determine and respond to what customers feel about your brand. Sentiment analysis is an effective tool for analysing thousands of social media posts through company mentions or keywords.
Upselling products or services
Unstructured data can be the source of positive (and negative) customer responses during sales calls. Effective speech analytics technology can be used to understand the customer’s tone or mood – and use all this data for a successful product upsell.
Challenges of Analysing Unstructured Data
Despite the many benefits of data analysis, most companies manage to only analyse 12% of their generated data. Here are some of the common challenges of analysing unstructured data:
Growing volumes
Each year, unstructured data for any business keeps growing at an exponential rate. It is estimated that 93% of all data by the year 2022 will be unstructured. Despite the availability of large public cloud infrastructure, such data volumes are difficult to store and analyse in an organized manner amenable to drawing out business insights.
Poor data quality
Due to the plethora of data sources, the bulk of unstructured data remain inconsistent and of low quality. This also raises the question of whether they are relevant to business needs and if they need to be analyzed. Add to that, unstructured data sources can be unreliable – due to replication or exaggerated
customer sentiments. Low-quality data analysis can lead to inaccurate insights, thus harming your business in the long run.
Presence of data silos
You cannot fully leverage the potential of unstructured data without breaking down individual data silos. As an alternative, a scalable data hub can help in integrated data storage that is easy to analyse.
Its Hard
Extracting meaningful insights from unstructured data calls for the ability to parse subtle differences. Technology tools need to understand intent, syntax, natural conversation and expression, nuance, and even sarcasm. This is beyond the capability of most easily accessible technology options out there.
The Solution?
Powered by extensive Natural Language Processing (or NLP) capabilities, IBM Watson is the right tool for performing deep text analytics of various sources of unstructured data. Using a combination of AI and NLP technologies, IBM Watson can derive a range of cognitive insights from your business data. Want to unlock the massive potential of your unstructured datasets? Contact us today with your queries.