In this post I am elaborating the product vision. The Text Analyzer Platform (TAP) enables knowledge workers to gather information rich text and documents, analyse them and share the results of the analytical process. Users will gain helpful support for their knowledge intensive work.
The Product Vision
Text and documents are the form of knowledge transportation which is most frequently used. People write articles, emails, notes, blog posts and other kind of documents to share their knowledge. Other people read those texts to find information. However, extracting knowledge from a document can be hard work. There are plenty cases where text is analysed, annotated, corrected and contained information is utilized. Often information is extracted and transformed into another text. This product should help users to gather documents, extract the information and enhance their knowledge.
I assume there is a need for software, some kind of text analyser platform:
- which allows users to upload files and share it with other people,
- which enables users to annotate text
- which provides the possibility to match text snippets to a topic
- and which supports the users in the analysis process.
The Target Customer
So, who is going to use such a tool? Naturally knowledge workers will profit from this tool, everyone who reads and analyses text in the focus of an interesting topic, e.g. students, pupils, lawyers, programmers, researchers and other kinds of academic workers. When they deal with knowledge intensive topics they search the internet or other sources for information related to their topic.
The Value Proposition
Why should someone use this tool, what is the benefit? The user gains the highest benefit when they start the analysis process and uncovers information they have found in a piece of text. Uploading into a central storage place is a nice thing, but does not bring that much value. What is more valuable is the fact that a piece of text may contain an information which is related to topics a person is interested in. This relationship is implicit. Which is the reason why unstructured information sources like text are so difficult to handle. A knowledge worker will benefit when an implicit relationship becomes explicit. Because every new explicit relationship adds new valuable knowledge.
Currently this is only a very vague idea of a piece of software but I think there is also a whole story to discover. In the next post, I will start the product discovery by applying user story mapping. This will start the Build-Measure-Learn feedback loop.