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The capacity of computers to recognise meaning in text, sound or images has progressed rapidly, but with the arrival of better hardware and software, we are now, finally, in a position where both the speed and accuracy of that recognition can support a wide range of business applications.
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When we add analysis to recognition, we can match up content with rules and policies, detect unusual behaviour, spot trends and infer sentiments. This makes content analytics a key part of big data-style business intelligence. It also helps with applications, such as auto-classification, content remediation, security correction, adaptive case management and operations monitoring.
That is a very attractive proposition for most companies, because the sheer volume of their in-house legacy content, combined with increasing amounts of new inbound content, means that content analytics has the potential to be the single most valuable tool at an enterprise’s disposal.
However, take-up is still relatively low. AIIM has released a report into enterprise use of content analytics called Content Analytics: Automating processes and extracting knowledge, which might shed some light on what is holding this approach back.
The report shows content analytics is fast becoming a pivotal business tool, with six in 10 enterprises saying it will be essential within five years. Meanwhile, three-quarters believe there is real business insight to be gained from content analytics, further highlighting its position as a technology that adds true value to an organisation.
The problem is that content analytics programmes need strategic direction and people with the right skills to realise the potential. However, 80% of survey respondents are yet to allocate a senior role to initiate and co-ordinate analytics applications. This lack of designated leadership, combined with a shortfall of analytics skills, is restricting content analytics’ potential, according to almost two-thirds (63%) of the research respondents.
The study also highlights the fact that organisations are drowning in content – and that staying on top of high-volume, multi-channel, in-bound content is fast becoming one of their major challenges, especially if they rely on manual processes.
Many organisations are struggling to deal with 'dark data' – the unstructured, untagged and untapped content that is found in data repositories and has not been analysed or processed. This data presents a potential risk, but survey respondents are keen to add business value to it rather than delete it.
Projects to derive business insight from content analytics are proceeding, with 20% of survey respondents already active, and a further 30% saying they have plans in place. The good news is that 68% are reporting ROI within 18 months or less. Improving products or services is the top-rated benefit, followed by knowledge research or core investigations, and then improved compliance.
These business insight or big data projects are still in an early-adopter phase, but a number of other applications based on content analysis techniques are showing strong benefits. The research reveals increasing interest and adoption by information managers in recognition and routing of inbound content, automated classification of records and email, and metadata addition and correction.
Many observers feel content analytics can offer far more than this – with many applications and uses yet to come. By 2020, it could be seen as one of the central tools used by any enterprise around content.
The clear challenge that emerges from our study is that to realise the potential, organisations must have clear priorities, setting the strategic direction, and addressing the lack of in-house skills.
Download the full report: Content Analytics: Automating processes and extracting knowledge.
Doug Miles is chief analyst at AIIM and the writer of a series of AIIM reports on ECM, records management, SharePoint, mobile, cloud and social business.