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4 Information Capture Challenges to Prepare for the Era of AI and Move to the Next Level

Jul 27, 2017 12:13:55 PM by John Mancini

 

In a recent AIIM survey we investigated the question of what information capture “leadership” looks like in user organizations. What does information capture look like in leading organizations that want to position this competency not only as a source of immediate competitive advantage, but also as a long-term competency critical to the coming era of machine learning?

What are the problems that organizations are experiencing with their capture implementations as they consider this evolution? Here 4 key problem areas that surfaced in our survey; we’ll also be discussing this survey and its implications in an AIIM webinar on September 19.

4 Key Information Capture Challenges in Moving to the Next Level

  1. Multiple document types -- Most organizations are struggling with capture complexity that is driven by the sheer volume of document types that must be managed.
  2. Uncertainty about data accuracy -- Data is at the heart of the Digital Revolution. And data quality is at the heart of the challenge facing organizations as they attempt to make their data fit for purpose and fit for use.
  3. Poor usability -- 62 percent of respondents in our survey rate their capture software “very difficult” or “somewhat difficult” to configure.
  4. True information capture is more complicated than simple imaging -- Capture is often assumed to be synonymous with scanning. The reality is that most organizations need to do far more than just process images.

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Topics: information capture, machine learning, intelligent capture, Artificial Intelligence, parascript

The convergence of analytics, cognitive computing and machine learning

Jan 18, 2017 4:38:24 PM by John Mancini

AIIM’s resident Podcaster Extraordinaire, Kevin Craine, recently sat down with Andrea Chiappe, Director of Innovation and Strategy at Systemware to discuss opportunities in the convergence of analytics, cognitive computing and machine learning. The following is a short synopsis of the interview.  You can find the full podcast interview HERE.

Part two of the interview was a discussion with Claudia Kieran, Corporate Senior Accountant at Wildman Business Group, about their efforts at Wildman to become more paper free, and how they did it.  I’ll be following up this post with a second post to tell Claudia’s story.

Kevin:  Why do you feel that the scope and idea of information governance needs to more than just records management?

Andrea:  When I look at the words “records management,” I have to admit that even I think it sounds a little bit boring. It implies a singular objective and a singular solution. I look at information governance as not so singular. Traditional records management is linear in nature – we classify records, we maintain and retain records, and after their retention period is past, we get rid of records.  Information governance is more of an ecosystem. Although records management absolutely is still a legitimate endeavor, I think we need to think in terms of an overall umbrella or ecosystem of governance. 

Kevin:  When you say that content management is critical enabling technology for digital transformation but not in its traditional form, what do you mean by that?

Andrea:  First generation content management systems look at information control as king – “I better hold my information tightly and never let it go.” I see the future as our industry as an absolutely open information ecosystem where yes, compliance and security are key, but the emphasis in on allowing for curated information to get out and be put to use.  I don't think that the traditional way we define content management will be the dominant definition in 2020.

Kevin:  We hear a lot about analytics, cognitive computing, machine learning, and how these technologies can be leveraged to improve things like customer experiences.  What are the things that we should consider now as we map our strategies with respect to analytics and machine learning?

Andrea:  First and foremost, this is not an overnight deal rather it is a journey. Your road map and strategies must align with the objectives and use cases we identify as benefiting from these technologies and know that they will evolve. Benefiting from cognitive technologies require that you measure outcomes and continue to tune and train your systems and users. In fact, ensure that you plan to govern the tuning mechanisms and training sets closely whether human built or system generated. The competitive advantages that businesses stand to gain are undeniable and as these technologies continue to evolve they will take on a life of their own.

Kevin:  You say that we can no longer just put on band-aids onto our infrastructures as we think about moving forward. How can we adjust our focus to include a more transformed governance approach as part of our strategy and not as an afterthought?

Andrea:  It is important to take a step back and consider if the technologies that we leverage in our organizations are providing a foundation and environment to move forward. The current pace of innovation demands that we identify those things that are helping versus hindering our road map initiatives in order to remain competitive. Keep in mind that testing our business plans and technologies against the vision of where we want to be seven to ten years from now should not be a happening it should be an ongoing endeavor.

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Download this free white paper -- Process Improvement and Automation 2016
Research shows many business leaders understand now more than ever before, that information and process form an integrated component of business operations as a whole. This report from AIIM Market Intelligence and underwritten in part by Systemware, takes a look at the current state of BPM.

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You might also be interested in this.  27% of organizations see content analytics (CA) as essential now, with 59% citing they see it as essential within the next 5 years. Beyond “big data” style business intelligence, analytics is driving auto-classification, content remediation, security correction, adaptive case management, and process monitoring and modeling.  Get a copy of the executive summary of AIIM's new market research study -- Using Analytics: Automating Processes and Extracting Knowledge -- to find out more.

DOWNLOAD YOUR EXECUTIVE SUMMARY

 

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Topics: ecm, analytics, machine learning, cognitive computing

Information overload: It affects MACHINES as well as people

Sep 19, 2016 3:01:31 PM by Paul Cleverley

This is a guest post by Paul Cleverley, a geoscientist and practitioner by background and is now an information scientist and researcher in the Department of Information Management with Robert Gordon University in Aberdeen.

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Topics: enterprise content management, business process, enterprise search, machine learning

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