We’ve firmly entered into the age of data. Today, we’re all walking, talking, data-generating engines. As standalone analog devices give way to connected digital devices, the latter will generate vast amounts of data that will, in turn, allow us the chance to refine and improve our systems and processes in previously unimagined ways. Big Data and metadata (data about data) will eventually touch nearly every aspect of our lives — with profound consequences. IDC found that by 2025, an average connected person anywhere in the world will interact with connected devices nearly 4,800 times per day — basically one interaction every 18 seconds.

It doesn’t stop there. All of this data needs to be leveraged to both make our lives better and allow for business to be a lot more competitive. This is where smart technologies like AI and even machine learning come into play. Cumulatively known as ‘cognitive systems,’ these solutions can greatly step up the frequency, flexibility, and immediacy of data analysis across a range of industries, circumstances, and applications. In that same report, IDC estimates that the amount of the global datasphere subject to data analysis will grow by a factor of 50 to 5.2ZB in 2025; the amount of analyzed data that is “touched” by cognitive systems will grow by a factor of 100 to 1.4ZB in 2025.

That being said, terms like AI is really an umbrella term which encompasses a range of other technologies. One of those components is natural language processing (NLP). Basically, NLP allows you to create intelligent interactions between machines and humans. To fill the gap between people and machines, NLP leverages code, computational linguistics, and even computer science to help understand and even manipulate human language. So, natural language processing allows us to understand meaning behind various speech and human interaction processes. The resulting capabilities can include sentiment analysis, or the ‘voice’ of the customer, speech inference, relations and patterns between people and entire social groups, and even variations in market trends.

Let’s expand on that point a little bit. That is, how can NLP impact big data? Regardless of the sector, every business today relies on large volumes of text information. For example, a law firm works with large amounts of research, past and ongoing legal transaction documents, notes, email correspondence as well as large volumes of governmental and specialized reference information. A pharmaceutical company will have large volumes of clinical trial information and data, doctor notes, patient information and data, patent and regulatory information as well as the latest research on competitors.

Because these types of data points are largely made up of language, natural language processing for big data presents an opportunity to take advantage of what is contained in especially large and growing stores of content to reveal patterns, connections and trends across disparate sources of data.

So, using data and cognitive systems, you can begin to understand and even visualize market, customer, and even specific service sentiment. This type of solution leverages data, natural language processing (NLP), and a host of other techniques to better understand what makes us, the consumer, happy (or sad). In using NLP, text analysis, computational linguistics, and biometrics we’re able to understand ‘the voice’ of the customer. That’s not all – you can also understand the sentiment around your competition.

So – Why don’t people like a competitor’s products? Or, what can you do to improve your own? Most of all, this data is both in real-time as well as historical. This rich market understanding absolutely helps create competitive advantages. Combine this with data visualization and you create an engine that continuously processes the market to give you a visual around what you can improve and what’s being said about your products and services.

That said, here are some use-cases where you can use big data, NLP, and cognitive systems to better understand the ‘voice’ of your customers:

  • Social media data ingestion to learn about trends, market concepts and purchasing patterns.
  • Integrate with BI tools to visualize business trends and drill down into very specific market segments.
  • Isolate market segments via social and Internet-based research to understand macro and micro trends.
  • Create search capabilities around history, segment, user type, and other multi-dimensional data points to give you the deepest visibility into your market.
  • Leverage cognitive systems to spot trends for you! This part is really cool. You can automate research processes to find data patterns and trends that you might miss entirely.
  • Staying ahead of the market by constantly understanding customer and user market shifts. This allows you to stay ahead of trends and develop strategies around new and upcoming products.

Think of these tools are you own market ‘oracle.’ One that can see deeply into market trends and specifically analyze the data points that your business really needs to thrive. If anything, this is one of the most powerful tools to help you gain a competitive advantage. With an increasingly online customer base, social media, forums, and other online communication platform are fully of great market information. With solutions like NLP and sentiment analysis, organizations can understand what is being said about their brand and products, as well as “how” it’s being talked about—how users feel about a service, product or concept/idea. You’ll learn so much more about the market and about current and potential customers. These solutions will give you visibility into opinions, but also information about customer habits, preferences and needs/wants, as well as demographic information. From there, you can apply these trends, patterns, and analysis to product development, improving business intelligence, creating competitive opportunities, and enhancing market research.

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