On the heels of the Text Analytics Summit, Seth Grimes noted in his article that text-analytics demand is approaching $1 billion. To reach $1 billion in demand is a significant milestone for any category in enterprise software. This type of market size helps validate the impact a given category makes on the success of the enterprise.
Past Use of Text Analytics
Traditionally, the federal government was the biggest user, incorporating text analytics (also known as text data processing) in their fight against terrorism. Many software vendors (content management, e-discovery, and search engines, etc.) have also used text data processing within their offerings to provide overall functionality. So, text analytics has been utilized for several years prior to its current prominence due to the proliferation of the Web and social media and how masses use them.
Text Analytics: Rise to Prominence
The current prominence is due to the number of different organizations using text analytics to gain insight into how the masses use social media and the Web. Customer service and support, brand-reputation management, competitive intelligence, and market research applications are a few examples that highlight the main-stream adoption of text analytics. These applications and others use text analytics for automated, natural-language processing techniques to identify and extract names, facts, relationships, and sentiment, etc. from a range of data sources, including blogs, forums, news, twitter/social updates, and e-mail (going forward I will refer to these as Web/social data).
Many of these packaged applications are an effective, targeted way for front office employees to address specific problems. As information management professionals,
- What does it mean to have these applications within the enterprise?
- What impact does having a vast source of Web/social data have on an enterprise information management (EIM) framework?
Thinking Beyond Established Use Case Patterns for Text Data Processing
No one questions that significant insight lays hidden within Web/social data. Many organizations use it as described above. Others want to dig deeper, and often that requires a well-thought-out EIM framework to handle text data and help derive insights from across the enterprise, including back-office processes.
To understand the use of text analytics (depth/breadth) within your organization and the maturity of your EIM framework to process text data, ask the following questions:
- Can your organization track sentiment around its top-10 customers based on amount of revenue generated over the past four quarters and their top 3 products, including all name variations for their company, products, and subsidiaries?
- Can your organization then effectively use the information above to develop a revenue risk mitigation strategy?
- Can your organization track sentiment around its top-10 suppliers and their products – including all the name variations – and then use that information to mitigate any risks associated with product quality and the impact it might have on your customers?
- Can your company track organizational changes within the top-10 opportunities you have this quarter, including name variations?
- Is your CFO’s office tracking your customers’ and suppliers’ credit downgrades and upgrades to fine tune payment terms, etc.?
These are just a sampling of the questions we can answer by analyzing social/Web data – but it requires the right EIM framework to expand the use of text analytics beyond front office operations, one that is flexible and integrates corporate data and social data queries.
Text Data Pervasiveness in Organizations
Once the framework is in place and IT and end users understand the impact of text analysis on business outcomes, it’s important to examine other text data beyond Web/social that your organization has yet to tap. This could include notes on warranty calls, sales orders, customer calls that reps make, quality inspections, and more. There’s a treasure trove of data that can be tapped using text data processing in your EIM framework.
Text Data Processing: Core to an EIM Framework
The ability to process text data is central to any EIM framework. There’s significant opportunity to gain critical insights from text analytics by methodically planning and incorporating the capability within the EIM landscape.
In my next couple of follow posts, I’ll explore some use cases around text data processing to get your thought processes started around what text analytics can do for your organization.
In the meanwhile, let me know what you’re doing to incorporate text analytics within your EIM architectural framework?