There is real value to be gained from content analytics – both in business insight and risk mitigation. But enterprises need to have a well-thought-out strategy in place to gain the biggest advantage.
It wasn’t that long ago that content analytics was the new kid on the block that everyone was talking about — now it is an essential tool in the enterprise’s armory. But getting value from data and content is not as straightforward as it sounds, which is where planning comes in.
Enterprises are putting increasing importance on data as we now live in a 24/7 connected world, driven by information. New content is coming into the enterprise at breakneck speed in a host of formats, while they remain overwhelmed by mountains of legacy data. The challenge to businesses is how to disseminate this data and make it work in their favor.
Enterprises are all too aware of the importance of content analytics — 75 percent enterprises in a recent AIIM study, “Content Analytics: Automating Processes and Extracting Knowledge,” were confident that there is real business insight to be gained, further highlighting its position as a technology that adds true value to an organization.
Content analytics — which analyzes and collects insight from inbound and legacy content — is also seen as important in addressing risks associated with incorrectly identified content. Survey respondents said they believed auto-classification of content helps protect against security breaches, sensitive or offensive content, and exposure to compliance regulations. More than half of enterprises (54 percent) said they felt their organization is at major risk from such threats.
Planning is the number one priority
To use any tool successfully requires know-how. Content analytics is no different. Without strategic direction and the right people with the right skills at the helm, enterprises will never reap the maximum benefits from this powerful tool.
Strategy should always be top of the agenda. But despite this, 80 percent of survey respondents said they have yet to allocate a senior role to initiate and coordinate analytics applications. This total lack of designated leadership and also a lack of analytics skills is restricting the potential and holding back the deployment of content analytics tools, according to 63 percent of respondents.
An increased budget for analytics technology will reap small reward if the right skillset is not in place to make the roadmap work. As well as searching externally, many enterprises forget to look at their own in-house talent pool to fill any skills gaps they may have. The content analytics skills shortage in the short term is only going to get wider, so it makes sense for enterprises to set up in-house training and mentoring schemes to bridge the chasm.
So-called “dark data” — the unstructured, untagged and untapped content that is found in data repositories and has not been analyzed or processed — has been an ongoing issue for enterprises. The AIIM research revealed that dark data was a big incentive for deploying content analytics, with other key drivers being process productivity improvements, additional business insight, and adding value to legacy content.
Around 73 percent of respondents said they felt that enhancing the value of legacy content is better than wholesale deletion, while 53 percent went as far as saying that auto-classification using content analytics is the only way to rein in content chaos.
Content analytics is already transforming the way enterprises do business. As data-driven strategies become increasingly important on the business agenda, we will see it become pivotal in competitive differentiation. To ensure that all important edge, enterprises must have a clear strategy on how to use data analytics. They must also know how to deploy the right technology architecture and skills to deliver on that strategy. Any business that ignores these two key areas from the outset will find content analytics an uphill challenge from there on.