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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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.

 https://forrester.co1.qualtrics.com/SE/?SID=SV_6RI5qO6FJ2S13z7

 

 

 

The 2016 Enterprise Architecture Awards: Speed And Responsiveness — And EA

Alex Cullen

Businesses of all types are experiencing the ramifications of an accelerating pace of change. Everything, from economies as a whole to the competitive landscape to consumers’ tastes, is more dynamic. As a consequence, business leaders are demanding that their businesses move faster. ‘Moving faster” may encompass continually updated mobile capabilities, digitized products, and more agility in how firms work with customers and suppliers.

This is what enterprise architecture is supposed to help with, right?

The answer is “yes – in theory,” but for reality to match theory requires an EA program attuned to increasing business speed and improving business responsiveness, not cutting costs and constraining change. I’ve talked to a lot of enterprise, business, and information architect leaders, and my take is that while all EA programs should be attuned this way, only a few are today. Forrester and InfoWorld would like to highlight the stories of these EA programs.

I am pleased to invite you to submit your EA program story to the Forrester/InfoWorld 2016 Enterprise Architecture Awards.

What are we looking for this year? Simply said, we’d like to hear how you helped your business move faster and respond better to shifts in its ecosystem. You could have done this a variety of ways:

  • You moved your business onto a new technology platform, specifically to enhance business agility.
  • You led the way to bringing more customer and market insights into strategic processes of your organization.
  • You guided the development and execution of a digital business strategy.
  • You helped your firm’s embrace of Agile methods and scaled these methods to the enterprise.
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Do Not Confuse Data Governance With Data Management

Henry Peyret

Last week, I participated in a roundtable during a conference in Paris organized by the French branch of DAMA, the data management international organization. During the question/answer part of the conference, it became clear that most of the audience was confusing data management with data governance (DG). This is a challenge my Forrester colleague Michele Goetz identified early in the DG tooling space. Because data quality and master data management embed governance features, many view them as data governance tooling. But the reality is that they remain data management tooling — their goal is to improve data quality by executing rules. This tooling confusion is only a consequence of how much the word governance is misused and misunderstood, and that leads to struggling data governance efforts.

So what is “governance”? Governance is the collaboration, organization, and metrics facilitating a decision path between at least two conflicting objectives. Governance is finding the acceptable balance between the interests of two parties. For example, IT governance is needed when you would like to support all possible business projects but you have limited budget, skills, or resources available. Governance is needed when objectives are different for different stakeholders, and the outcome of governance is that they do not get the same priority. If everyone has the same objective, then this is data management.

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Architect Your Predictive Analytics Capability To Unleash The Power Of Digital Business

Charlie Dai

Predictive analytics has become the key to helping businesses — especially those in the highly dynamic Chinese market — create differentiated, individualized customer experiences and make better decisions. Enterprise architecture professionals must take a customer-oriented approach to developing their predictive analytics strategy and architecture.

I’ve recently published two reports focusing on how to architect predictive analytics capability. These reports analyze the trends around predictive analytics adoption in China and discuss four key areas that EA pros must focus on to accelerate digital transformation. They also show EA pros how to unleash the power of digital business by analyzing the predictive analytics practices of visionary Chinese firms. Some of the key takeaways:

  • Predictive analytics must cover the full customer life cycle and leverage business insights. Organizations require predictable insights into customer behaviors and business operations. Youmust implement predictive analytics solutions and deliver value to customers throughout their life cycle to differentiate your customer experience and sustain business growth.You should also realize the importance of business stakeholders and define effective mechanisms for translating their business knowledge into predictive algorithm inputs to optimize predictive models faster and generate deeper customer insights.
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Forrester’s 2016 Predictions: Turn Data Into Insight And Action

Brian  Hopkins

Three of four architects strive to make their firms data driven. But well-meaning technology managers only deal with part of the problem: How to use technology to glean deeper, faster insight from more data -- and more cheaply. But consider that only 29% of architects say their firms are good at connecting analytics results to business outcome. This is a huge gap! And the problem is the ‘data driven’ mentality that never fights it’s way out of technology and to what firms care about - outcomes.

In 2016, customer-obsessed leaders will leapfrog their competition, and we will see a shift as firms seek to grow revenue and transform customer experiences. Insight will become a key competitive weapon, as firms move beyond big data and solve problems with data driven thinking.

Shift #1 - Data and analytics energy will continue drive incremental improvement

In 2016, the energy around data-driven investments will continue to elevate the importance of data and create incremental improvement in business performance. In 2016, Forrester predicts:

  • Chief data officers will gain power, prestige and presence...for now.  But the long term viability of the role is unclear. Certain types of businesses, like digital natives, won’t benefit from appointing a CDO.
  • Machine learning will reduce the insight killer - time. Machine learning will  replace manual data wrangling and data governance dirty work. The freeing up of time will accelerate data strategies.
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IBM And Teradata — A Tale Of Two Vendors’ Struggle With Disruption

Brian  Hopkins

I said that 2015 would be a tough year for enterprise data and analytics vendors in my spring report, "Brief: Turning Big Data Into Business Insights, 2015." I thought two things would happen. First, open source would drag on vendors’ revenues as demand for big expensive products declined. Second, the cloud would create revenue headaches. Turns out, I was right. Teradata's midyear earnings were down 8%, and IBM reported that Q2 revenue was down 12% from a year ago. As further proof, consider the rash of data management vendors running for private equity (e.g. Dell/EMC, Informatica, and TIBCO). It’s been tough times indeed, even though most vendors are keeping their messaging positive to reassure buyers and investors.

Over the past two weeks, I attended Teradata Partners in Anaheim and IBM Insight in Las Vegas — giving me a firsthand look at how two giants of the data and analytics industry are handling disruption. What I saw was a tale of two vendors that couldn’t be any more different:

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Managing The Complexity Of Hybrid Cloud: Learn From Leading Chinese Firms

Charlie Dai

Cloud is becoming the new norm for enterprises. More and more companies across the globe are using a combination of two or more private, hosted, or public cloud services – applying different technology stacks to different business scenarios. Hybrid cloud management is now an important priority that enterprise architecture (EA) professionals should consider to support their organizations on the journey toward becoming a digital business.

I’ve recently published two reports focusing on how to manage the complexity of hybrid cloud. These reports analyze the key dimensions to consider for hybrid cloud management and present four steps to help move your firm further along the path to hybrid maturity. To unleash the power of digital business, analyze the strategic hybrid cloud management practices of visionary Chinese firms on their digital transformation journey. Some of the key takeaways:

  • Align hybrid cloud management capabilities with your level of maturity . Hybrid cloud maturity is a journey of digital transformationcovering four steps: initial acceptance, strategic adoption, hybrid operationalization, and hybrid autonomy; maturity is measured by familiarity with, experience with, and knowledge of how to operate cloud. EA pros should build their management capabilities step by step, aiming to unify and automate cloud managed services by understanding technical dependencies and business priorities.
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