AI on AI – 8 Predictions for the Data Savvy Pro

When we started Amalgam Insights, we oh-so-cleverly chose the AI initials with the understanding that artificial intelligence (the other AI…), data science, machine learning, programmatic automation, augmented analytics, and neural inputs would lead to the greatest advances in technology. At the same time, we sought to provide practical guidance for companies seeking to bridge the gaps between their current data and analytics environments and the future of AI. With that in mind, here are 8 predictions we’re providing for 2020 for Analytics Centers of Excellence and Chief Data Officers to keep in mind to stay ahead while remaining practical.

1. In 2020, AI becomes a $50 billion market, creating a digital divide between the haves and have nots prepared to algorithmically assess their work in real time. Retail, Financial Services, and Manufacturing will be over half of this market.

2. The data warehouse becomes less important as a single source of truth. Today’s single source replaces data aggregation and duplication with data linkages and late-binding of data sources to bring together the single source of truth on a real-time basis. This doesn’t mean that data warehouses aren’t still useful; it just means that the single source of truth can change on a real-time basis and corporate data structures need to support that reality. And it becomes increasingly important to conduct analytics on data, wherever the data may be, rather than be dependent on the need to replicate and transfer data back to a single warehouse.

3. Asking “What were our 2020 revenues?” will be an available option in every major BI solution by the end of 2020, with the biggest challenge then being how companies will need to upgrade and configure their solutions to support these searches. We have maxed out our ability to spread analytics through IT. To get beyond 25% analytics adoption in 2020, businesses will need to take advantage of natural language queries and searches are becoming a general capability for analytics, either as a native or partner-enabled capability.

4. 2020 will see an increased focus on integrating analytics with automation, process mapping, and direct collaboration. Robotic Process Automation is a sexy technology, but what makes the robots intelligent? Prioritized data, good business rules, and algorithmic feedback for constant improvement. When we talk about “augmented analytics” at Amalgam Insights, we think this means augmenting business processes with analytic and algorithmic logic, not just augmenting data management and analytic tasks.

5. By 2025, analytic model testing and Python will become standard data analyst and business analyst capabilities to handle models rather than specific data. Get started now in learning Python, statistics, an Auto Machine Learning method, and model testing. IT needs to level up from admins to architects. All aspects of IT are becoming more abstracted through cloud computing, process automation, and machine learning. Data and analytics are no exception. Specifically, Data analysts will start conducting the majority of “data science” tasks conducted in the enterprise, either as standalone or machine-guided tasks. If a business is dependent on a “unicorn” or a singular talent to conduct a business process, that process is not scalable and repeatable. As data science and machine learning projects start becoming part of the general IT portfolio, businesses will push down more data management, cleansing, and even modeling and testing tasks to the most dependable talent of the data ecosystem, the data analyst.

6. Amalgam Insights predicts that the biggest difference between high ROI and low ROI analytics in 2020 will come from data polishing, not data hoarding. – The days of data hoarding for value creation are over. True data champions will focus on cleansing, defining, prioritizing, and separating the 1% of data that truly matters from the 99% more suited to mandatory and compliance-based storage.

7. On a related note, Amalgam Insights believes the practice of data deletion will be greatly formalized by Chief Data Protection Officers in 2020. With the emergence of CCPA along with the continuance of GDPR, data ownership is now potentially risky for organizations holding the wrong data.

8. The accounting world will make progress on defining data as a tangible asset. My expectations: changes to the timeframes of depreciation and guidance on how to value specific contextually-specific data such as customer lists and business transactions. Currently, data cannot be formally capitalized, meaning asset. Now that companies are generally starting to realize that data may be their greatest assets outside of their talent, accountants will bring up more concerns for FASB Statements 141 and 142.

Developing a Practical Model for Ethical AI in the Business World: Introduction

As we head into 2020, the concept of “AI (Artificial Intelligence) for Good” is becoming an increasingly common phrase. Individuals and organizations with AI skillsets (including data management, data integration, statistical analysis, machine learning, algorithmic model development, and application deployment skills) have effort into pursuing ethical AI efforts.

Amalgam Insights believes that these efforts have largely been piecemeal and inadequate to meet common-sense definitions for companies to effectively state that they are pursuing, documenting, and practicing true ethical AI because of the breadth and potential repercussions of AI on business outcomes. This is not due to a lack of interest, but based on a couple of key considerations. First, AI is a relatively new capability in the enterprise IT portfolio that often lacks formal practices and guidelines and has been managed as a “skunkworks” or experimental project. Second, businesses have not seen AI as a business practice, but as a purely technical practice and made a number of assumptions in skipping to the technical development that would typically not have been made for more mature technical capabilities and projects.

In the past, Amalgam Insights has provided frameworks to help organizations take the next step to AI through our BI to AI progression.

Figure 1: Amalgam’s Framework from BI to AI

 

 

 

To pursue a more ethical model of AI, Amalgam Insights believes that AI efforts need to be analyzed through three key lenses:

  • Executive Design
  • Technical Development
  • Operational Deployment

Figure 2: Amalgam’s Three Key Areas for Ethical AI

In each of these areas, businesses must ask the right questions and adequately prepare for the deployment of ethical AI. In this framework, AI is not just a set of machine learning algorithms to be utilized, but an enabler to effectively augment problem-solving for appropriate challenges.

Over the next week, Amalgam Insights will explore 12 areas of bias across these three categories with the goal of developing a straightforward framework that companies can use to guide their AI initiatives and take a structured approach to enforcing a consistent set of ethical guidelines to support governance across the executive, technical, and operational aspects of initiating, developing, and deploying AI.

In our next blog, we will explore Executive Design with a focus on the five key questions that an executive must consider as they start considering the use of AI within their enterprise.

Data Science and Machine Learning News Roundup, May 2019

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, Elastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Domino Data Lab Champions Expert Data Scientists While Outpacing Walled-Garden Data Science Platforms

Domino announced key updates to its data science platform at Rev 2, its annual data science leader summit. For data science managers, the new Control Center provides information on what an organization’s data science team members are doing, helping managers address any blocking issues and prioritize projects appropriately. The Experiment Manager’s new Activity Feed supplies data scientists with better organizational and tracking capabilities on their experiments. The Compute Grid and Compute Engine, built on Kubernetes, will make it easier for IT teams to install and administer Domino, even in complex hybrid cloud environments. Finally, the beta Domino Community Forum will allow Domino users to share best practices with each other, as well as submit feature requests and feedback to Domino directly. With governance becoming a top priority across data science practices, Domino’s platform improvements around monitoring and making experiments repeatable will make this important ability easier for its users.

Informatica Unveils AI-Powered Product Innovations and Strengthens Industry Partnerships at Informatica World 2019

At Informatica World, Informatica publicized a number of key partnerships, both new and enhanced. Most of these partnerships involve additional support for cloud services. This includes storage, both data warehouses (Amazon Redshift) and data lakes (Azure, Databricks). Informatica also announced a new Tableau Dashboard Extension that enables Informatica Enterprise Data Catalog from within the Tableau platform. Finally, Informatica and Google Cloud are broadening their existing partnership by making Intelligent Cloud Services available on Google Cloud Platform, and providing increased support for Google BigQuery and Google Cloud Dataproc within Informatica. Amalgam Insights attended Informatica World and provides a deeper assessment of Informatica’s partnerships, as well as CLAIRE-ity on Informatica’s AI initiatives.

Microsoft delivers new advancements in Azure from cloud to edge ahead of Microsoft Build conference

Microsoft announced a number of new Azure Machine Learning and Azure AI capabilities. Azure Machine Learning has been integrated with Azure DevOps to provide “MLOps” capabilities that enable reproducibility, auditability, and automation of the full machine learning lifecycle. This marks a notable increase in making the machine learning model process more governable and compliant with regulatory needs. Azure Machine Learning also has a new visual drag-and-drop interface to facilitate codeless machine learning model creation, making the process of building machine learning models more user-friendly. On the Azure AI side, Azure Cognitive Services launched Personalizer, which provides users with specific recommendations to inform their decision-making process. Personalizer is part of the new “Decisions” category within Azure Cognitive Services; other Decisions services include Content Moderator, an API to assist in moderation and reviewing of text, images, and videos; and Anomaly Detector, an API that ingests time-series data and chooses an appropriate anomaly detection model for that data. Finally, Microsoft added a “cognitive search” capability to Azure Search, which allows customers to apply Cognitive Services algorithms to search results of their structured and unstructured content.

Microsoft and General Assembly launch partnership to close the global AI skills gap

Microsoft also announced a partnership with General Assembly to address the dearth of qualified data workers, with the goal of training 15,000 workers by 2022 for various artificial intelligence and machine learning roles. The two companies will found an AI Standards Board to create standards and credentials for artificial intelligence skills. In addition, Microsoft and General Assembly will develop scalable training solutions for Microsoft customers, and establish an AI Talent network to connect qualified candidates to AI jobs. This continues the trend of major enterprises building internal training programs to bridge the data skills gap.

Data Science and Machine Learning News Roundup, April 2019

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, Elastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Alteryx Acquires ClearStory Data to Accelerate Innovation in Data Science and Analytics

Alteryx acquired ClearStory Data, an analytics solution for complex and unstructured data with a focus on automating Big Data profiling, discovery, and data modeling.  This acquisition reflects Alteryx’s interest in expanding its native capabilities to include more in-house data visualization tools. ClearStory Data provides a visual focus on data prep, blending, and dashboarding with their Interactive Storyboards that partners with Alteryx’s ongoing augmentation of internal visualization capabilities throughout the workflow such as Visualytics.

Dataiku Announces the Release of Dataiku Lite Edition

Dataiku released two new versions of its machine learning platform, Dataiku Free and Dataiku Lite, targeted towards small and medium businesses. Dataiku Free will allow teams of up to three users to work together simultaneously; it is available both on-prem and on AWS and Azure. Dataiku Lite will provide support for Hadoop and job scheduling beyond the capabilities of Dataiku Free. Since Dataiku already partners with over 1000 small and medium businesses, creating versions of its existing platform more financially accessible to such organizations lowers a significant barrier to entry, and grooms smaller companies to grow their nascent data science practices within the Dataiku family.

DataRobot Celebrates One Billion Models Built on Its Cloud Platform

DataRobot announced that as of mid-April, its customers had built one billion models on its automatic machine learning program. Vice President of Product Management Phil Gurbacki noted that DataRobot customers build more than 2.5 million models per day. Given that the majority of models created are never successfully deployed – a common theme cited this month at both Enterprise Data World and at last week’s Open Data Science Conference – it seems likely that DataRobot customers don’t currently have one billion models operationalized. If the percentage of deployed models is significantly higher than the norm, though, this would certainly boost DataRobot in potential customers’ eyes, and serve to further legitimize AutoML software solutions as plausible options.

Microsoft, SAS, TIBCO Continue Investments in AI and Data Skills Training

Microsoft announced a new partnership with OpenClassrooms to train students for the AI job marketplace via online coursework and projects. Given an estimate that projects 30% of AI and data jobs will go unfilled by 2022, OpenClassrooms’ recruiting 1000 promising candidates seems like just the beginning of a much-needed effort to address the skills gap.

SAS provided more details on the AI education initiatives they announced last month. First, they launched SAS Viya for Learners, which will allow academic institutions to access SAS AI and machine learning tools for free. A new SAS machine learning course and two new Coursera courses will provide access to SAS Viya for Learners to those wanting to learn AI skills without being affiliated with a traditional academic institution. SAS also expanded on the new certifications they plan to offer: three SAS specialist certifications in machine learning, natural language and computer vision, and forecasting and optimization. Classroom and online options for pursuing both of these certifications will be available.

Meanwhile, TIBCO continued expanding its partnerships with educational institutions in Asia to broaden analytics knowledge in the region. Most recently, it has augmented its existing partnership with Singapore Polytechnic to train 1000 students in analytics and IoT skillsets by 2020. Other analytics education partnerships TIBCO has announced in the last year include Yuan Ze University in Taiwan, Asia Pacific University of Technology and Innovation in Malaysia, and BINUS University in Indonesia.

The big picture: existing data science degree programs and machine learning and AI bootcamps are not providing a large enough volume of highly-skilled job candidates quickly enough to fill many of these data-centric positions. Expect to hear more about additional educational efforts forthcoming from data science, machine learning, and AI vendors.

How is Salesforce Taking on AI: a look at Einstein at Salesforce World Tour Boston

On April 3rd, Amalgam Insights attended Salesforce World Tour 2019 in Boston. Salesforce users may know this event as an opportunity to meet with their account managers and catch up with new functionalities and partners without having to fly to San Francisco and navigate through the colossus that is Dreamforce.

Salesforce also uses this tour as an opportunity to present analysts with the latest and greatest changes in their offerings. Amalgam Insights was interested both in learning more about Salesforce’s current positioning from a data perspective, including the vendor’s acquisition of Mulesoft as well as its progression in both the Einstein Analytics and Einstein Platform in providing value-added insights and artificial intelligence to Salesforce clients.

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Google Goes Corporate at Google Next

There’s no doubt that Google exists to make money. They make money by getting companies to buy their services. When it comes to selling ads on search engines, Google is number one. When it comes to their cloud business, Google is… well, number three.

I’m guessing that irks them a bit especially since they sit behind a company whose main business is selling whatever stuff people want to sell and a company that made its name in the first wave of PCs. Basically, a department store and a dinosaur are beating them at what should be their game.

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CES 2019 Ramifications for Enterprise IT

Vendors and Organizations Mentioned: IBM, Ose, WindRiver, Velodyne, UV Partners, TDK Corporation, Chirp Microsystems, Qualcomm, Intel, Zigbee Alliance, Thread Group, Impossible Foods

The CES (Consumer Electronics Show) is traditionally known as the center of consumer technology. Run by the CTA (Consumer Technology Association) in Las Vegas, this show brings out enormous volumes of new technology ranging from smart cars to smart homes to smart sports equipment to smart… well, you get the picture. But within all of these announcements, there were also a number of important announcements that affect the enterprise IT world and the definition of IT that will be important for tech professionals to think about in 2019. Amalgam Insights went through hundreds of different technology press releases and announcements to find the most important announcements that will affect your professional career.

Come along with me as we look at Quantum Computing, Gender Equality, Autonomous Vehicles, Disinfected Smartphones, Low Power Virtual Reality, Neural Net Chips, Internet of Things Interoperability, and, yes, the Impossible Burger.

Quantum Computing

On January 8th, 2019, IBM announced IBM Q System One, the “first integrated universal approximate quantum computing system” designed for commercial use. From a practical perspective, this will allow R&D departments to actually have their own quantum computers. Today, the vast majority of quantum computing work is done based on remote access either to quantum computers or quantum computing emulators, which provide limits on the experimenters’ abilities to customize and configure their computing environments.

To create a quantum computing system, IBM had to bring together hardware that provided high-quality and low-error rate qubits, cryogenic equipment to cool the hardware and quantum activity, as well as the electronics, firmware, and traditional computing capabilities needed to support a quantum environment. Of course, IBM is not new to quantum computing and has been a market leader in this emerging category.

Quantum computing fundamentally matters because we are running up against the physical limits of material science that allow microprocessors to get smaller and faster, which we typically sum up as Moore’s Law. In addition, quantum computing potentially allows both for more secure encryption or the ability to quickly decrypt extremely secure technologies, depending on whether one takes a white-hat or black-hat approach. But the ramifications mean that it is important for security organizations to both start understanding quantum computing and to either stay ahead of black-hat quantum computing efforts or provide white-hat security answers to stay ahead.

Gender Equality at CES

At CES, a woman-designed sex toy originally given an innovation award (Warning: may not be Safe For Work) had its award revoked. The Ose vibrator designed by Lora DiCarlo was entered in the robotics and drone category based on its design by a robotics lab at Oregon State University and eight patents pending for a variety of robotic and biomimicry capabilities.

The product was undoubtedly risque. But CES has previously allowed virtual reality pornography to be shown within the show as well as other anatomical simulations designed for sex.

Given CES’ historical standards for other exhibitors to present similar products and objects, the revoking of this award looks biased. This is an important lesson that the answer to providing a gender-equal environment is not necessarily to simply remove all sexual content. The goal is to eliminate harassment and abuse while providing equal opportunity across gender. As long as sex is a part of consumer technology, CES needs to provide equal opportunity for all genders to present.

Autonomous Vehicles

There were a number of announcements associated with Lidar sensors and edge computing innovations. Two that got Amalgam Insights’ attention included:

WindRiver’s integration of its Chassis automotive software with its TitaniumCloud virtualization software. This announcement hints at the need for the car, as computing system, to be integrated with the cloud. This integration will be important as car manufacturers seek to upgrade car capabilities. As we continue to think about the car both as an autonomous data center of its own and set of computing and processing workloads that need to be upgraded on a regular basis, we will need to consider how the operational technologies associated with autonomous vehicles and other “Things” integrate with carrier-grade and public clouds.

Velodyne announced an end-to-end Lidar solution that includes both a hemisphere Lidar sensor called VelaDome as well as its Velia software. This launch reflects the need for hardware components and software to be integrated in the vehicle world, just as it is in the appliances and virtual machines we often use in the world of IT. This is another data point showing how autonomous vehicles are coming closer to our world of IT both in creating integrated solutions and in requiring IT-like support in the future.

Disinfected Smartphones

UV Partners announced a new product called the UV Angel Aura Clean & Charge, which combines both wireless charging with ultraviolet light disinfection. This product matters because, quite frankly, mobile phones tend to be filthy. That’s what happens when people are holding them for hours a day and rarely wash or disinfect the phones. So, this device will be useful for germophobes.

But there is also the practical aspect of being able to clean phone surfaces with this object more easily. This may lead to being able to use the phone to detect biological matter or changes more effectively without additional dirt and biocontaminants. This could make phones or other sensors more accurate in trying to detect trace elements or compounds and increase the functionality of both phones and “Things” as a result.

Low Power Virtual Reality

TDK Corporation announced its work with Qualcomm through the group company of Chirp Microsystems to improve controller tracking for mobile virtual reality and augmented reality headsets (). Most importantly, the tracking system used for these devices is only several miiliwatts, which is a small fraction of the total power within a standard smartphone battery. This compares to several hundred milliwatts for a standard optical tracking system. With this primary technology in development, both AR and VR experiences become more usable simply because they will take significantly less power to support.

This change may not sound exciting, but Amalgam Insights believes that one of the key challenges to the adoption of AR and VR is simply the battery life needed to use these applications for any extended amount of time. This breakthrough could significantly extend the life of AR and VR apps.

Artificial Intelligence

Intel made a number of chip announcements. Amalgam Insights is not a hardware analyst firm, so most of the mobile and laptop-based announcements are beyond our coverage. But the announcement that got our attention was the Intel Nervana Neural Network Processor. This chip, developed with Facebook, is developed to accelerate the detection of inference associated with the algorithmic processing of neural nets and will drive higher performance machine learning and artificial intelligence efforts.

At a time when every chip player is trying to get ahead with GPUs and TPUs, Intel is making its mark by focusing on the detection of iterative inference, which is a necessary part of the “intelligence” of AI. Amalgam Insights looks forward to seeing how the Nervana processor is made available for commercial use and as a cloud-based capability for the enterprise world.

Internet of Things Interoperability

The Zigbee Alliance and Thread Group announced completing the Dotdot 1.0 specification, which will improve interoperability across smart home devices and networks made by different vendors. By providing a standard application layer that works across a wide variety of vendors and works on an IP networking standard, Dotdot brings a level of standardization to application-level configuration, testing, and certification.

This standard is an important step forward for companies working on Smart Home devices or related Smart Office devices and seeking a common way to ensure that new devices will be able to communicate with existing device investments. Amalgam Insights looks forward to seeing how this standard revolutionizes Smart Buildings and the Future of Work.

And, the Impossible Burger

The belle of the ball, so to speak, at CES was the Impossible Burger 2.0, a soy-based protein held together by heme with iron and protein content similar to beef.

So, this is very cool, but why is this relevant to IT? First, this burger reminds us that food is now tech. Think about both how interesting and weird this is. A company has made custom proteins to build a new type of food designed to replace the taste and role of beef. Or at least that’s where they are today.

Meanwhile in the IT world, identity is increasingly based on biometrics: eyes, fingerprints, facial recognition. It is only a matter of time before either protein or DNA profiles are added to this mix. There will undoubtedly be some controversies and hiccups as this happens, but it is almost inevitable given the types of sensors we have and the evolution of DNA technologies like CRISPR that rapidly sequence and cut up DNA.

So, as we get better at replicating the nutrition and texture of meat with plant-based proteins at the same time that our physical bodies are increasingly used to provide access to our accounts… yes, this gets weird. But we’re probably five-to-ten years away from being hacked by some combination of these technbologies as the DNA, protein, and biometric worlds keep coming closer and closer together.

For now, this is just cool to watch. And the Impossible Burger 2.0 sounds like a great vegan alternative to a burger. But putting the pieces together, identity in 2030 is going to be extremely difficult to manage.

Data Science and Machine Learning News, November 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, SnapLogic, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Continue reading “Data Science and Machine Learning News, November 2018”

Artificial Intelligence/Machine Learning (AI/ML) and Learning Systems in the Brain

Simulating Learning Processes in the Brain With AI/ML

Key Stakeholders: Chief Learning Officers, Chief Human Resource Officers, Learning and Development Directors and Managers, Corporate Trainers, Content and Learning Product Managers.

Why It Matters: The skills necessary for success in the corporate world are varied and include hard skills, people skills and situational awareness. While L&D is embracing the use of AI/ML to analyze learners’ data and to personalize learning paths, curate effective content, and attempt to better engage learners, what L&D has failed to embrace is the application of AI/ML to model each of these distinct learning systems, and their interactions.

Top Takeaway: Corporate learning vendors would be well served to develop AI/ML models that capture the processing characteristics of the three learning systems in the brain known to mediate hard skills, soft skills, and situational awareness learning. A comprehensive AI/ML model that captured the processing characteristics of each of these three distinct learning systems could be used to develop and test products and tools that optimize content curation, learning paths, engagement, and delivery processes that will differ substantially across systems and tasks to be learned.

Vendors with the Skillset and Expertise to Build this AI/ML Tool: Cornerstone, CrossKnowledge, IBM, Infor, LTG, Oracle, Saba, Salesforce, SAP, Workday, and likely many others.

Artificial Intelligence/Machine Learning and L&D

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