I am kicking off a research stream which will result in the "Text Analytics Roles & Responsibilities" doc. Before I finalize an RFI to our clients to see who/how/when/where they employ for these projects and applications, I'd like to explore what the actual roles and responsibilities are. So far we've come up with the following roles and their respective responsibilities
Business owner. The ultimate recipient of text analytics process results. So far I have
Customer intelligence analyst
Customer service/call center analyst
Competitive intelligence analyst
Product R&D analyst
Linguist/Data Scientist. Builds language and statistical rules for text mining (or modifies these from an off-the-shelf-product). Works with business owners to
Create "golden copies" of documents/content which will be used as base for text analytics
Works with data stewards and business ownes to define corporate taxonomies and lexicon
Data Steward. Owns corporate lexicon and taxonomies
Architect. Owns big data strategy and architecture (include data hubs, data warehouses, BI, etc) where unstructured data is one of the components
Developer/integrator. Develops custom built text analytics apps or embeds text analytics functionality into other applications (ERP, CRM, BI, etc)
Beware of insights! Real danger lurks behind the promise of big data to bring more data to more people faster, better, and cheaper: Insights are only as good as how people interpret the information presented to them. When looking at a stock chart, you can't even answer the simplest question — "Is the latest stock price move good or bad for my portfolio?" — without understanding the context: where you are in your investment journey and whether you're looking to buy or sell. While structured data can provide some context — like checkboxes indicating your income range, investment experience, investment objectives, and risk tolerance levels — unstructured data sources contain several orders of magnitude more context. An email exchange with a financial advisor indicating your experience with a particular investment vehicle, news articles about the market segment heavily represented in your portfolio, and social media posts about companies in which you've invested or plan to invest can all generate much broader and deeper context to better inform your decision to buy or sell.
But defining the context by finding structures, patterns, and meaning in unstructured data is not a simple process. As a result, firms face a gap between data and insights; while they are awash in an abundance of customer and marketing data, they struggle to convert this data into the insights needed to win, serve, and retain customers. In general, Forrester has found that:
The problem is not a lack of data. Most companies have access to plenty of customer feedback surveys, contact center records, mobile tracking data, loyalty program activities, and social media feeds — but, alas, it's not easily available to business leaders to help them make decisions.