Navigate The Crowded Enterprise File Sync And Share Market By Asking These 3 Questions

Cheryl McKinnon

Forrester defines Enterprise File Sync and Share (EFSS) as the technologies that "allow organizations to share and replicate content across multiple devices, distributing files to employees and/or customers or partners outside the enterprise".

Two Forrester Waves on the EFSS market were published recently, segmenting this crowded market into two categories: cloud solutions and hybrid solutions. Forrester clients can access them here:

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Insight Platforms Have Arrived

Brian  Hopkins

Are you lost in a confusing soup of vendor-speak about what their data analytics stack actually offers? Big data, data platforms, advanced analytics, data lakes, real-time everything, streaming, the IoT, customer analytics, digital intelligence, real-time interaction, customer decision hubs, new-stuff-as-a-service, the list goes on.

Recognize the convergence happening as vendors evolve their technologies from doing just one thing like predictive analytics or search to many things together. For example, data integration, data warehouse, and BI tools are typically sold separately, but breakout vendor Looker combines data integration, model governance, basic BI, and a runtime for data applications all in one software layer that sits on your data lake. As another example, consider predictive analytics vendor Alpine Data Labs or SAS Viya from SAS. These vendors have built out a lot of data management and insight delivery tooling into their platforms because without it users struggle to maximize value. Another trend is big data search vendors like Maana that now also include hooks for predictive model execution as well as more data management functions. Lastly, systems integrators are packaging their IP and offering it as a data management and analytics integrated product — for example, Saama’s Fluid Analytics Engine or Infosys’ Information Platform.

In fact, the list of innovative vendors blending data management, analytics, and insight execution technology is growing by leaps and bounds. To address this trend, I just published a report, Insight Platforms Accelerate Digital Transformation, in which I created a broad definition that labels this trend:

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Pega Buys OpenSpan: Watch Out - RPA Vendor Landscape Is About To Change

Craig Le Clair

Enterprises, in their quest to reduce labor costs, are applying RPA technologies. Yet they do not have a well-defined set of principles and best practices, including how to position RPA with other process tools and initatives. Today it may have become a bit more clear. Pega is the first tech provider, and only BPM market particpant of substance, to purchase an RPA provider (OpenSpan). The combination brings robotics, analytics, and case management together - and that makes sense. Think of Pega's process/rules capibility firing off a set of RPA scripts.

RPA in many respects is an alternative, some would say the polar opposite of Pega's current business model that feasts on the transformitive "big IT spend" for BPM, case management, automation, and customer service projects. RPA does not require invasive integration. It is a quick hit for automation, a “low touch” approach for process improvement for brittle legacy systems. The bottom line. Enterprises that employ labor on a large scale for process work can gain efficiencies by just automating repetitive human tasks for the “as is” process.

OpenSpan is nice pick-up for Pega that will help with back-office BPM work, but more so with contact center environments where the agent requires human and machine multitasking that often spans multiple windows and web applications, few of which are integrated with each other. Cumbersome process flows, rekeying of data, and lack of integration add up to lengthy call times, reduced accuracy, and an overall increase in customer frustration. Pega/OpenSpan will give Jacada and NICE a run for their money, and the future integration with Pega's analytics tracks where the RPA space is heading.

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Deep Learning Will Blow Up Your Data Strategy

Michele Goetz

Day one of the GPU Technology Conference in San Jose and I'm still glowing from watching Steve Wozniak "travel to Mars" through NVIDIA's photo real virtual reality.  Or, holding my stomach as Jen Hsun Huang, CEO of NVIDIA took us soaring over Everest.  Or cringing, as I watch the early attempts at a car teaching itself to drive and being reminded of how my 16 year old daughter is learning to drive (there were a few similarities...). Each emotion illustrates what everyone will experience shortly on NVIDIA's next gen compute platform with announcement for AI, VR, self-driving, SDK and new deep learning appliance.  

This is not your traditional or even big data analytic platform.  It's a complete overhaul of the computing architecture.  It's a complete rethink of data management. It will also change how you think about analytics.  

Stepping back from what may seem like hype and examples steeped in robotics, VR and infrastructure, the truth is, the announcements today show that deep learning in action is at most a year away, and as soon as now.  In addition, the innovation coming out of robotics, VR and infrastructure will allow introduction of new form factors and channels to engage with customers and shape our workforce. In the end, it is a data challenge for the very reason that for every channel we use and add, it always ends up being a data challenge.

The implications for how you manage data are radical. Here is what you need to think about:

  • Deep learning systems are voracious eaters of data. If you think you have volume issues now, it will only get worse. Traditional integration won't cut it.  You need bigger compute on GPUs not CPUs for speed, performance, and efficiency. Don't you want to train your data in 2 hours vs. 2 weeks?
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The Forrester Wave Master Data Management: Which MDM Tool Is Right For You?

Michele Goetz

The Forrester Wave for Master Data Management went live today. The results may surprise you.  

MDM tools today don't look like your father's MDM. No longer an integration hub between applications and DBMSs, today's tools are transitioning or have reinvented MDM to handle the context missing from system traditional implementations. Visualizations, graph repositories, big data and cloud scale, along with application like interfaces for nontechnical users, mean MDM and master data gets personal with stakeholders.  

Semantics and insight are not an outcome of MDM but an integrated part of the engine and hub. Three MDM evolutions stand out:

  • Business-defined views of data: For graph-based vendors such as Reltio and Pitney Bowes, master domains are shaped by business use cases. For example, customer master can be defined beyond the bounds of a household, identity, and account. Customer behavioral characteristics can be the starting points for taxonomies and hierarchies. Integration of master domains is based on physical, logical, linkage, and semantic schemas for a more seamless navigation and querying of master data to align with the explosion of data views created by analytics, applications, and microservices.
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kCura Puts the CAAT Into The Bag . . . Acquires Long-time Partner Content Analyst Company

Cheryl McKinnon

We've seen another acquisition in the shifting eDiscovery market this week as kCura, the developer of Relativity, announced its acquisition of Content Analyst Company, the brains behind the CAAT analytics engine (kCura’s press release is here). The acquisition is not entirely surprising. kCura has been relying on the CAAT engine to power its analytics offering for eight years. According to kCura, use of its Relativity Analytics offering “has grown by nearly 1,500 percent” since 2011, with more than 70% of current kCura’s customers with licenses.

What does this acquisition mean for kCura, its customers, and Content Analyst Company customers?

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The Top 6 Questions To Succeed At Artificial Intelligence

Michele Goetz

You can't turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It's all so very cool!  

But cool doesn't build a business. Results do.

Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:

  • What is artificial intelligence (or insert: machine learning or cognitive computing)?
  • Where can I use artificial intelligence?
  • What tool can I buy?
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Think You Want To Be "Data-Driven"? Insight Is The New Data

Brian  Hopkins

It’s been a while since I’ve blogged; not because I’ve had nothing to say, but rather because I’ve been busy with my colleagues Ted Schadler, James McCormick, and Holger Kisker working on a new line of research. We wanted to examine the fact that business satisfaction with analytics went down 21% between 2014 and 2015, despite big investments in big data. We found that while 74% of firms say they want to be “data-driven,” only 29% say they are good at connecting analytics to action. That is the problem.

Ted Schadler and I published some initial ideas around this idea in Digital Insights Are The New Currency Of Business in 2015. In that report, we started using the phrase digital insight to talk about what firms were really after ― action inspired by new knowledge. We saw that data and analytics were only means to that end. We also found that leading firms were turning data into insight and action by building systems of insight ― the business discipline and technology to harness insights and consistently turn data into action.

Here is a key figure from that report:

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Seven New Buying Patterns Reshape The 2017 Enterprise Collaboration Market

Craig Le Clair

The enterprise collaboration (EC) landscape is rife with innovative products that begin with a narrow feature set (e.g., Box for document collaboration or Slack for group messaging). Viral growth and company value often follow — along with competitors that target the newly identified market. A fragmented and overlapping landscape results as newer entrants pursue broader EC goals. Over the next two years, firms will purchase enterprise collaboration in seven fundamentally different ways. The report below aims to helps companies sift through confusing use cases to best apply EC.

What did we find? Firstly, the torrent of information, lack of critical-mass adoption, and context switching create barriers to effective EC adoption, and secondly, platforms that support lead applications, targeted group messaging, project management tools, external communities, or just finding expertise in an organization are the winning formulae for many firms.

Read the report here: Seven New Buying Patterns Reshape The 2017 Enterprise Collaboration Market.

The Blind Spot For Man-Machine Collaboration

Craig Le Clair

We are kicking off a research series on the future of work for "production services," with a focus on administrative and customer service jobs where a high degree of automation is projected. Basically, cognitive computing may do to white-collar jobs what robotics did to blue-collar jobs. This may lead to radically different work patterns and unintended consequences. Enterprises risk blindly bringing in advanced analytics without a best practice approach that covers change management and identifies gaps in the formerly human-driven process that affect compliance, customer experience, and efficiency. To date, few are doing serious thinking about a force that will lead to a restructuring of work that is more profound and far-reaching than the transition from the agricultural to the industrial age. 

Please take or send this survey to businesses contemplating or using smart machines to augment human-based processes. They will receive a free copy of the report.

Thank you.

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