3 reasons why Big Data Textual Analysis is the next step for businesses to drive their digital transformation
Thu 15 Jun 2017
Proxem, pioneer in the domain of Big Data Text Analytics for businesses, unveiled its new SaaS text mining solution at Big Data World in London. During the event, Proxem spoke to a variety of companies about how Textual Big Data is still an underutilized resource by Data Scientists and Business Directors despite its various applications & advantages.
According to Harvard Business Review, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all…
Yet, unstructured data is a goldmine of pertinent and actionable information. Here are 3 reasons you should consider text mining to achieve your business goals whether you are working in the field of Customer Experience, Data Science, Human Resources or Market Intelligence.
- Text, by nature, is a form of non-structured data. That’s why artificial intelligence is applied to collect, analyze and visualize your textual big data automatically and in real-time.
Information is the oil of the 21st century. It is mostly available as text, because where machines exchange data, humans exchange words. But herein lies the problem … text is essentially an unstructured data source.
In fact, companies are now facing an explosion in the volume of textual data that needs to be classified, analyzed and visualized. However in a Big Data world, this becomes near impossible to achieve without automation – and therefore without artificial intelligence. This applies to all the departments of a company: business, HR, finance, marketing, innovation, legal, etc. By analyzing and understanding all these daily data flows, managers can have a better understanding of their market, customers, employees, production and thus their performance.
Natural Language Processing (NLP, using Artificial Intelligence techniques: Deep Learning and Machine Learning) is used to automate the recognition of concepts (named entities), the automatic classification of a document or themes, the analysis of feeling and emotions and so on. Once these operations are performed, the text is transformed into what is referred to as structured data. Then, data mining allows users to identify correlations, causalities or to see the evolution of various phenomenon over a long time period. These techniques also allow us to “teach” the machine to automatically suggest rules by analyzing the entirety of the input data, therefore minimizing errors and bringing forward new concepts. Before deep learning, it required a great amount of man hours to prepare learning data and also to write code rules for a particular need. Deep Learning aims to reproduce the mechanisms of the human brain (specifically neural networks) to let the machine learn and produce rules automatically.
- Optimizing Textual Big Data is the next big challenge for businesses to drive efficient digital transformation
We all know it; Businesses are currently undergoing a major acceleration in the digital transformation. In this context, we distinguish two unavoidable needs:
- the centralized crawl of Big Data in real time
- the creation of value through their analysis and visualization.
This new “data-centric” vision imposed by Big Data issues puts textual data at the center of decision-making for all the strategic components of the company.
In response, semantic analysis can be used throughout the value chain of the company:
- Customer experience (analysis of customer feedback across all channels),
- Human Resources; Analysis of internal social climate, recruitment, talent management (matching CVs / offers or profile / training, analysis of annual interviews and social barometers, skills mapping, etc.)
- Market intelligence from web documents (mapping of competitors, e-reputation analysis, search for innovation in several languages, detection of weak signals …)
- Control of industrial risks…
The use cases for this type of technology are practically endless due to the wide variety of documents & sources of textual data circulating in Businesses that can be valorized to create actionable insights.
- Think big, think offense data strategy
There are two differences between offense data strategy versus defense data strategy.
Data Defense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. It is about minimizing risk by focusing on compliance with regulations (data privacy, integrity of financial reports, IT limits, etc.). This strategy, used by 40% of big companies, uses analytics to detect compliance risks that limit the agility of companies undergoing digital transformation. Note that this strategy is also built on self-centered skills and tools.
On the other hand, Offensive activities include the need to instantly and continuously prevent fraud, reduce customer dissatisfaction levers, identify weak signals or protect e-reputation etc. By being “data-focused” and working with domain specialists like Proxem, companies can make sure they drive business goals more quickly and efficiently. Offensive activities tend to be most relevant for customer-focused business functions such as sales and marketing and are often more relevant thanks to their real-time analysis.
Of course, every company needs both offense and defense to succeed, but getting the balance right is tricky. One thing is for sure, putting equal emphasis on the two is optimal for some companies. But do not be the one who misses one of the most vital steps of the digital transformation by avoiding the Offensive Strategy…
Over the past 10 years, Proxem’s text mining solutions have contributed to the success of over a 100 Big Data Analytics projects for a variety of clients, including market heavy weights such as Air Liquide, Carrefour, Total and Thales. These companies have been able to better understand the big picture of their market, detect risks and opportunities, and make better commercial decisions thanks to Proxem’s technologies. Using the knowledge acquired through these practical experiences and the expertise of a UX design agency, Proxem has designed a text analytics software suite adapted for business experts that does not require any technical IT skills. Business users can autonomously perform any analysis on their data, regardless of their profile, businesses interests, and without writing a single line of computer code.