Of course you know all about web analytics or social media analytics. Earlier I described the three different “…tives” in analytics that are also very important to know, but there is another type of analytics that cannot be overlooked. In Gartner’s Hype Cycle of Emerging Technologies they place Content Analytics at the end of the “Peak of Inflated Expectations” and they expect it to take another 5-10 years before it reaches the “Plateau of Productivity”. But what is Content Analytics, what makes it so special that Gartner includes it and why should you be paying attention to it?
Content analytics can be defined as unlocking business value from unstructured content via semantic technologies to find answers to important questions or discover causes to certain trends. Companies can use content analytics to understand the content that is created, how it is used, the context it is in and the nature of that content. Content analytics is all about unstructured data and it can be used to explain trends in structured data and provide valuable insights to organisations.
Content analytics is especially relevant for organisations where knowledge is at the core of their business and in that case ordinary business intelligence is not sufficient anymore. Knowing who read what content, when, how often it was shared, number of clicks, location of visitors, etc. is not sufficient anymore. You should also care about whether the content is actually useful to your audiences and that it serves the objective it was intended for. Content related trends or insights revealing information such as what content caused a drop in sales can help make better content and thus drive growth or revenue. It is not about having more content; it is about having better content.
Content analytics is an important aspect of big data and should form an integral part of your big data strategy if you are a knowledge-based organisation. It cannot be seen separate but is actually more entwined with different aspects of big data ranging from real-time analytics, predictive analytics and personalization. The objective should be to deliver the relevant content to the right people via the right channels at the right moment to support the right objective. Not an easy task.
There are several benefits from content analytics. It creates machine-readable content from your unstructured data that allows computers and machines to understand the unstructured content and link it to other data for additional insights. In addition, it makes it better retrievable because of semantic metadata that is added to the content. It helps organisations understand what content they have or need and more importantly why that is the case. Finally, it helps to better understand the causes behind the trends and events that can be discovered with ordinary business intelligence. It therefore helps explain why things happen taking into account vast amounts of documents and content in real-time.
Content analytics uses a vast array of different big data technologies. From semantic analytics or ontological analysis to discover correlations and patterns in the content, to natural language processing or sentiment analysis to place the data in the right context to multilingual search or linguistic modelling. In addition, text-analytics or text mining will help to extract meaning that might be buried in the text.
With 80% of the data being unstructured data, content analytics will become more important in the coming years to discover new insights from your enterprise content and deliver smarter customer experiences. Slowly also big data startups are discovering this interesting but difficult field of big data. One of them is Parse.ly, who are a predictive content optimization platform for publishers, helping them make better content decisions. Content analytics will become more important and goes beyond business intelligence. It is a big data tool not to be missed by knowledge-intense organisations when developing a big data strategy.