28 Hours as an Industry Analyst at Strata Data

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Last week, I attended Strata Data Conference at the Javitz Center in New York City to catch up with a wide variety of data science and machine learning users, enablers, and thought leaders. In the process, I had the opportunity to listen to some fantastic keynotes and to chat with 30+ companies looking for solutions, 30+ vendors presenting at the show, and attend with a number of luminary industry analysts and thought leaders including Ovum’s Tony Baer, EMA’s John Myers, Aberdeen Group’s Mike Lock, and Hurwitz & Associates’ Judith Hurwitz.

From this whirwind tour of executives, I took a lot of takeaways from the keynotes and vendors that I can share and from end users that I unfortunately have to keep confidential. To give you an idea of what an industry analyst notes, following are a short summary of takeaways I took from the keynotes and from each vendor that I spoke to:

Keynotes: The key themes that really got my attention is the idea that AI requires ethics, brought up by Joanna Bryson, and that all data is biased, which danah boyd discussed. This idea that data and machine learning have their own weaknesses that require human intervention, training, and guidance is incredibly important. Over the past decade, technologists have put their trust in Big Data and the idea that data will provide answers, only to find that a naive and “unbiased” analysis of data has its own biases. Context and human perspective are inherent to translating data into value: this does not change just because our analytic and data training tools are increasingly nuanced and intelligent in nature.

Behind the hype of data science, Big Data, analytic modeling, robotic process automation, DevOps, DataOps, and artifical intelligence is this fundamental need to understand that data, algorithms, and technology all have inherent biases as the following tweet shows:
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Infor and the 80% Solution: Coleman, Birst, GT Nexus, and CloudSuites

Better, Cheaper, Faster is a myth in a cloud-enabled world.
Coleman is Infor's Artificial Intelligence Effort
Coleman is Infor’s Artificial Intelligence Effort

When I represented Amalgam Insights at Inforum, I was wondering if I would be a fish out of water. After all, I am not an ERP analyst. I am not a retail analyst. I am not an HR technology analyst. And those are the first three things I think of when Infor comes to mind. As an analyst who focuses on technology consumption and bridging gaps between the CIO and CFO, I was wondering what would grab my attention other than Infor’s acquisition of Birst.

I was pleasantly surprised by the clarity of Infor’s vision of supporting industry-specific technology consumption. Infor ended up bringing up three key ideas that are core to the future of technology consumption and will end up being strategic considerations for the future of IT.

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Machine Learning and the Rise of the REEP: Role-Based Expert Enhancement Platforms

Scythe - the REEPer
Don’t fear the REEPer

Based on Amalgam Insights’ discussions with over two dozen enterprise application solutions over the past six weeks, we believe that a new generation of applications is starting to emerge. Machine Learning has led to the evolution of a new generation of platforms that are transforming expert productivity by providing insights through self-guided logic that improves over time.

Amalgam Insights calls these solutions Role-Based Expert Enhancement Platforms (REEP), an emerging technology made possible by the increasing use of machine learning and artificial intelligence in the business world. We believe that, in the short-term, the emerging value of machine learning will come not from generalized platforms, but from these role-based solutions that will greatly simplify finance, compliance, and sales enablement tasks. As companies start thinking about the role of machine learning in enhancing their organizations, Amalgam recommends that they consider these four key areas of benefit:

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AI for the Accounting Guy: MindBridge Artificial Intelligence-Auditor

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AritificialFictionBrain from https://zh.wikipedia.org/wiki/User:David290/Artificial_intelligence

Amalgam Insights’ Net-Net Summary of Mindbridge Ai

Based on a briefing with MindBridge Ai on June 19, 2017 and MindBridge Ai’s $4.3 million funding round on the same date, this summary provides guidance to auditors and accountants seeking guidance on applying artificial intelligence to risk, compliance, and audit efforts. Amalgam Insights believes that this emerging approach is an important governance step for financial transactions as digital transactions become increasingly diverse and auditors are responding by either building self-built tools or working with internal data science teams.

Why Mindbridge Ai Matters for Financial Audiences

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Cloud, Watson, & Blockchain: Amalgam Insights’ View of IBM Interconnect

From Pixabay
From Pixabay
From Pixabay

Amalgam Insights (AI) recently attended IBM Interconnect under the Social Influencer program with the goal of understanding how IBM is planning to position itself in context of technology market changes, investor demands to increase revenue, and the challenges of embracing innovation as one of the largest enterprises on the planet.

In observing IBM over the past few years, AI investigators have noted in the past that IBM faces the challenge of needing to create billion-dollar businesses just to maintain existing revenue. It is not enough for IBM to create a single startup such as Pivotal or Airwatch that ends up becoming a market leader in analytic application development or enterprise mobility. To drive 80 billion+ dollars in annual revenue, IBM needs to grow enough businesses to maintain pace while simultaneously divesting cash cows and declining margin businesses that are not strategic to future growth. Over the past couple of years, this has meant selling off assets such as Salary.com and semiconductor chip manufacturing (and possibly its mainframe division) while investing deeply into systems and capabilities that will drive upcoming business capabilities.

At Interconnect, IBM provided its vision for upcoming success focused on three areas: IBM Cloud, Cognitive computing services highlighted by Watson, and the promise of Blockchain.

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Data is the Language of Business: Looker raises an $81M Series D

From Amalgam Insights
From Pixabay
From Pixabay

Accounting has often been called the language of business and it is invaluable in managing the day-to-day financial costs, inputs, outputs, and outcomes associated with business activity. However, as companies start to understand the impact that non-financial drivers ranging from manufacturing outputs to headcount to service transactions to asset utilization rates affect the health of the business, executives have had to broaden the scope of considerations needed to track the health of the company.

As they have done so, businesses have had to shift even their financial departments to focus not just on dollars and cents, but to production units, employees, transactions, uptime, turnover, and loyalty. In doing so, the language of business has started to shift from accounting to a new paradigm of data.

Today, data is the language of business.

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