strategic technology report from simple to smart data ...• the global ai market is expected to...

Research Strategic Technology Report

© 2019 Trace3, Inc. All Rights Reserved

This Trace3 Strategic Technology Report examines the shift in Data Intelligence from Simple to Smart as businesses begin to embrace Artificial Intelligence (AI) and move from a descriptive to a prescriptive focus. This report also explores the drivers and hype behind this change and the downstream technologies that will be impacted by this shift, such as: • The operationalization of AI • Disparate data management • Edge analytics Disclaimer – This document has been prepared solely for Trace3's internal research purposes without any commitment or responsibility on our part. Trace3 accepts no liability for any direct or consequential loss arising from the transmission of this information to third parties. This report is current at the date of writing only and Trace3 will not be responsible for informing of any future changes in circumstances which may affect the accuracy of the information contained in this report. Trace3 does not offer or hold itself out as offering any advice relating to investment, future performance or market acceptance.

Strategic Technology Report From Simple to Smart Data Intelligence

August 1, 2019


© 2019 Trace3, Inc. All Rights Reserved of 17

Table of Contents

Executive Summary ....................................................................................................................................... 3

Report Scope ................................................................................................................................................. 4

Research Methods ........................................................................................................................................ 4

About Trace3 Research ................................................................................................................................. 4

Did you Know? .............................................................................................................................................. 5

Historical Context .......................................................................................................................................... 5

IT Generated BI (1990s – 2000s) ................................................................................................................ 5

Self-Service BI (2010s) ............................................................................................................................... 6

Current Landscape (2015 - 2019) ............................................................................................................... 6

What’s Next? ................................................................................................................................................ 7

Forces Driving Smart AI-Enabled Data Intelligence ..................................................................................... 7 Social Forces .......................................................................................................................................... 7 Technological Forces .............................................................................................................................. 8 Economic Forces .................................................................................................................................... 8 Political Forces ....................................................................................................................................... 8

Baseline Forecast (2021 – 2025) ................................................................................................................. 9 Summary Evidence for Baseline Forecast ............................................................................................... 9 Assumptions ........................................................................................................................................ 10

Short-to-Medium Term Uncertainties for AI Growth ................................................................................ 10 1. AI fails to operationalize .............................................................................................................. 10 2. Disparate dirty data that never gets clean .................................................................................... 12 3. Unintended consequences prevent AI from widespread adoption ................................................ 13

Implications for Data Intelligence ............................................................................................................ 13 1. The importance of analytics at the edge ....................................................................................... 13 2. The hype of AI .............................................................................................................................. 14

Conclusions and Recommendations ............................................................................................................ 15

Appendix .................................................................................................................................................... 16

Featured Use Cases ................................................................................................................................. 16

Relevant Links ......................................................................................................................................... 17

Sources ................................................................................................................................................... 17



Executive Summary Who should read this?

• This report is designed for strategic technology leaders innovating their organizations with Artificial Intelligence (AI) and related emerging technologies. Key Takeaways

• Data Intelligence is experiencing a fundamental shift from a descriptive to a prescriptive focus • The widespread adoption of AI will define and drive the coming era of Data Intelligence • Organizations that fail to embrace AI-enabled Data Intelligence are likely to face disruption • While it is widely accepted AI will continue to grow at a tremendous rate, there are hurdles that could slow the

development and organizational readiness to adopt AI, such as: o Use Case Selection and Operationalizing AI o Disparate Data o Unintended Consequences of AI

• Trace3 foresees the AI-enabled Data Intelligence era impacting enterprise environments in two key ways: o Edge Analytics - Allowing organizations to leverage decentralized data and compute in new and innovative ways,

enabling the realization of cost savings and new competitive advantages. o Hype vs Realism - Separating real AI plays from over-hyped savvy algorithms will be among the greatest

challenges organizations face when selecting the next generation of IT solutions.

Source: Trace3 Research 115,16



Report Scope This Trace3 Strategic Technology Report examines the shift in Data Intelligence from Simple to Smart as businesses begin to embrace Artificial Intelligence (AI) and move from a descriptive to a prescriptive focus. This report also explores the drivers and hype behind this change and the downstream technologies that will be impacted by this shift, such as: • The operationalization of AI • Disparate data management • Edge analytics Although the greater field of AI bleeds into nearly every vertical, this report does not explore the impact of AI on any particular market or sector. This report also does not provide an exhaustive list of the potential applications of AI in various technologies. Rather, this report explores the broad impact of AI, Machine Learning (ML), and Deep Learning (DL) in Enterprise IT and forecasts how the Data Intelligence market may continue to evolve in the coming years. Research Methods This report was compiled and written by the Trace3 Research team. This report’s research area of focus is informed by a variety of factors, including research requests from Trace3 customers and field teams, emerging technology investment trends, and social/media/news momentum. From these factors, relevant areas of the technical landscape were analyzed to determine drivers of change, baseline forecasts, and likely challenges and uncertainties to be experienced. Forecasts and recommendations were developed reflecting the conclusions generated by the analysis. Vendors mentioned in this report are meant to be used for representative purposes only and do not represent an exhaustive list for each use case. About Trace3 Research To solve the IT problems of tomorrow, our research analysts leverage Trace3's unique access across the technology landscape to derive impartial insights. By identifying and analyzing technology and market trends, we enable customers to prepare for and master tomorrow's challenges before they arrive. Trace3 Research leverages our partnerships with numerous established and emerging technology companies, our experienced engineers, a large client ecosystem, and deep relationships with dozens of the top Silicon Valley venture capital firms to spot trends ahead of most industry pundits, allowing you to gain an inside advantage on tomorrow's trends and reduce your technical and business risk.



Did you Know? • The global AI market is expected to reach over $150B by 20251. • Venture Capital funding in AI startups increased 72% YOY in 20182. • The Edge Computing market is expected to reach to $28B by 20253. • 55% of Data Scientists cite quality and quantity of training data as their biggest challenge4. • Firms, including Trace3, are making significant investments in Data Intelligence and Data Science to support long

term AI trends5.

Historical Context While Data Intelligence refers to a space much broader than Business Intelligence (BI), the greater arc of Data Intelligence is closely aligned to that of BI, so this report will use that as a framework for the historical context of Data Intelligence. IT Generated BI (1990s – 2000s) The origins of Data Intelligence reach back to the 1990s when the term Business Intelligence was first introduced in the Business and Technology world. Originally the term was used to describe the process of using a system of facts and data to support business decisions. Businesses began to build purpose-built tools to centrally manage data and generate reports that could be used to guide decisions. As these individual tools became more refined in the 2000s, vendors began to lump together these different purpose-built tools into suites of tools that could provide wider functionality from a single vendor. Throughout this era of Data Intelligence, BI tools were complex to use and any reports and visualizations had to be generated from IT teams. The BI tools of this era were most useful in exploring descriptive information (What had happened) in the business.6

Source: Eckerson Group6



Self-Service BI (2010s) The next significant shift in Data Intelligence came through the revelation of Simple ‘Self Service’. Rather than requesting analysis and waiting for the IT team to turn around a report, new discovery tools enabled members of the business to perform basic reporting and analytics with minimal coding or IT assistance. With these tools, reporting on basic historic aspects of the business such as finances and inventory was made readily available to any audience. In a similar fashion to the previous era, the purpose-built discovery tools began to be rolled into full analytic platforms around 2015. These platforms still largely managed the data in a central repository or data warehouse, which was used as the source of truth for reporting and visualizations. These technologies also allowed for more collaboration and extension, as both business and IT users could interact with different parts of the platform. These platforms enabled businesses to explore new dimensions of their data, such as diagnostic information (Why something happened), and even allowed for early data science exploration into predictive information (What might happen). This era of tools enabled both business and IT users to interact with the data to extract insights from a common platform.7

Source: Infoworld7

Current Landscape (2015 - 2019) There is currently a shift from the era of self-service BI to a new era of Smart, AI-enabled Data Intelligence. Numerous self-service BI platforms, such as Tableau, Looker, and PowerBI amongst many others have already embedded AI generated insights into their standard BI offerings. For more complex analyses involving AI, ML, and DL such as natural language processing (NLP) and computer vision, purpose-built tools surround these platforms to extend the functionality. With the combined set of purpose-built tools and self-service BI platforms, many companies are



beginning to explore prescriptive analytics, which examine how to make an outcome occur given sets of historical data7. The impact of AI on Data Intelligence already influences far beyond the use case of BI and Data Visualization. From GPU accelerated databases to automated data preparation and data flow mapping, AI has seeped into every sector (Architecture, Data Engineering, Advanced Analytics, and Data Governance) of the greater Data Intelligence market. As a result, it is no surprise AI will be at the core of the next era of Data Intelligence. However, the emergence of this next era is heavily dependent on a number of factors, such as the ability to operationalize AI and to find prioritized use cases for AI.

What’s Next? Forces Driving Smart AI-Enabled Data Intelligence There are a number of forces working to shape the coming era of Data Intelligence. A STEP analysis was used to investigate the Social, Technological, Economic, and Political forces driving the change.

Social Forces One of the greater challenges in the enterprise technology community today is the lack of quality talent. Among the top tech jobs in need, cybersecurity professionals, AI/ML engineers, and data scientists take three of the top four spots8. As a result, many companies are considering investment in automation to support their existing talent. For example, security departments are deploying Security Orchestration, Automation, and Response (SOAR) products, such as Exabeam, Siemplify, or Swimlane. These products use AI to automate repetitive and tedious tasks, such as phishing investigation and remediation. Further, many of these high need roles will be filled in the coming years by new entrants to the working space. Given the AI space was able to emerge so significantly amidst a shortage of AI engineers and data scientists, the space will likely continue to grow at an accelerated rate as the talent gap begins to shrink. Privacy is another large implication of AI development. AI and ML models have a tremendous appetite for data, and often these models are built on top of user data. For example, Social Media companies utilize large amounts of user data

Social Forces- Lack of Talent

- Generation Z & Privacy

Technological Forces- Enhanced Hardware

- Open Source Frameworks

Economic Forces- Decade of Economic Health

- Growing 'Smart' Markets

Political Forces- Tariffs and China

- Executive AI Initiative

STEP Analysis



to optimize their content prioritization models. While this usage of individual user data for a generalized model is considered by some to be a violation of their private data, it also provides a more convenient and customized experience for the user. While this tradeoff is generally viewed negatively, millennials display less of a concern than other generations with the usage of their data for the sake of convenience9. As said by Michael M. Roberts, president and CEO of the ICANN when discussing the tradeoff of privacy and convenience, “The record to date is that convenience overwhelms privacy. I suspect that will continue.10” This shift in the value of privacy may open the door to new business models and technologies that can drive the AI market forward. For example, to alleviate the tremendous data demands of AI and ML, enterprises may turn to community-based data consortiums for access to training data. However, the privacy concerns of these consortiums will need to be addressed before they are widely used and accepted. Technological Forces Perhaps the most significant factor driving the emergence of AI in enterprise technology is the advancements of computing hardware. Around since the 1970s, only in the last decade have the algorithms and theory of AI and ML been applied to enterprise environments. ML models have a tremendous appetite for data and training such a model can take days depending on the size of training data set and the complexity of the model. Processing chips such as GPUs, FPGAs, and ASICs allow these models to be trained hundreds of times faster. This purpose-built hardware has been crucial to implementing AI and ML in the enterprise and vendors such as NVIDIA have been at the heart of that movement. Another factor encouraging the development of AI is the abundance of open-source frameworks that have become industry standards for AI and ML (TensorFlow, PyTorch, Keras, etc.). These frameworks have reduced the barriers to entry in AI and enable any developer or data scientist to build and deploy models without any costly software – though it does still require advanced hardware for large scale AI/ML11. Further, the emergence of digital business models, such as software as a service (SaaS) and platform as a service (PaaS) has stimulated AI growth in the enterprise. Nearly all major cloud providers offer AI and ML analytics services that can be easily embedded into an organization’s cloud footprint. Economic Forces The past decade has been one of relative strength in the US, as the GDP growth rate has remained positive for nearly the entire decade12. As a result, many businesses have allocated more resources and investments towards innovation and data science exploration in their organizations. One of the many potential benefits of implementing AI is found in cost savings through automation of repetitive manual processes. With many forecasting a coming US recession in the early 2020s, it is also likely that enterprises will continue to invest in AI as a cost containment strategy. Another large factor driving the adoption of AI is the emergence of ‘Smart’ markets, such as autonomous driving cars, smart retail technology, and wearables. These markets rely heavily on the automation of AI to deliver customized and efficient offerings to their clients. With these markets growing at significant rates13, the development of AI has advanced in turn. Political Forces Starting in January of 2018, the Trump Administration placed an initial round of tariffs on foreign goods, including solar panels and washing machines. This increased trade tensions between the US and China, which in turn further increased the already competitive relationship between the two tech giants. With China already a global leader in the context of government supported AI development, President Trump signed an executive order in February of 2019 to spur the development and regulation of AI. The tense relationship with China is likely to continue to impact the development and emergence of AI for the foreseeable future14.



Baseline Forecast (2021 – 2025) The global AI market is expected to continue to grow at a rapid rate as companies continue to embrace the prescriptive insights and potential cost savings associated with AI. Expected to reach a market value of just under $160B by 2025, AI is driving a compound annual growth rate (CAGR) of 43%. This forecast was determined by taking the arithmetic mean of three forecasts from Allied Market Research1, Statista15, and MarketsAndMarkets16.

Source: Trace3 Research 115,16

Although this growth rate is optimistic, the forces and drivers surrounding the market suggest the outcome is quite plausible given the investments and momentum. The projected AI market growth will be central to the new era of DI, Smart AI-enabled Data Intelligence. Summary Evidence for Baseline Forecast

1. VC Funding in AI startups is at a point of inflection to a period of rapid growth (72% YoY funding growth between 2017 and 2018)2.

2. The AI market is beginning to consolidate through M&A activity evident in the volume and size of the transactions in 2017 and 2018 in the graph below2.



Source: Forbes2

3. AI is widely applicable across verticals for its cost-saving potential (Enterprise IT, Healthcare, Retail, etc.), which makes the market resilient to disruption.

Assumptions 1. The AI market growth rate will remain roughly constant for the forecast period. 2. A new wave of talent will enter the AI workforce to fill the skills gap. 3. AI will be embraced for its cost-savings potential ahead of a potential coming recession.

Given these assumptions, it is reasonable to suggest the AI market will continue to grow rapidly and dominate the enterprise technology conversation. However, there are hurdles that could slow the development and adoption of AI in the short-to-medium term. Short-to-Medium Term Uncertainties for AI Growth

1. AI fails to operationalize One of the most significant barriers to AI adoption is in implementing AI and ML in an enterprise environment. The process of developing, deploying, and monitoring models in production environments is time consuming to properly and effectively deploy. Organizations may fail to find repeatable and efficient ways to deploy AI and ML, and this could slow AI adoption. Further, if businesses fail to identify and select proper business use cases for AI, they will be unable to realize the maximum value and cost savings associated with the technology. Mitigating Factors: To overcome this barrier, enterprises can employ ML Operationalization (MLOps). MLOps tools alleviate the pains of implementing ML models in a production environment. They assist with model definition, deployment, and monitoring. These tools enable organizations to more effectively scale and utilize their ML models while improving reproducibility and visibility. This section of the larger AI market has seen a significant increase in VC investment in the past three years, as shown below.



The figure above includes only funding info for MLOps companies mentioned below as of June 17, 2019

a. Model Definition. Model Definition is the first component of MLOps, as models need to be created before

they can be moved to deployment and monitoring stages. Model definition is typically handled by vendors such as Databricks, DataRobot, Dataiku, and H2O.ai. These platforms enable data scientists to build the models required to predict or analyze the business process.

b. Model Deployment. Model Deployment is needed after ML models have been developed and must be

moved into production environments. A number of vendors offer automated deployment functionality, which makes the process of moving models into production faster and more repeatable. Further, these solutions enable the retraining of models as the results of model monitoring are fed back. Many of the vendors that offer model deployment also offer model monitoring for this reason. Some of those vendors include Seldon.io, Metis Machine, Open Data Group, Data Kitchen, Algorithmia, Datatron, CognitiveScale, ParallelM (acquired by DataRobot), and Dataiku.

c. Model Monitoring. Perhaps one of the most important pieces of MLOps is the ability to monitor the model.

Model monitoring can be performed on multiple levels. The first level is basic model performance to ensure the model is functioning properly. All the vendors mentioned in the Model Deployment section offer this basic monitoring functionality. However, the deeper level of monitoring is measuring the business impact associated with the model workflows. This level allows for exception handling and operational feedback to help determine when model retraining or replacement is needed. Vendors such as ParallelM (acquired by DataRobot), CognitiveScale, and Dataiku are tackling this problem.



d. Business Use Case Selection. While AI and ML can be applied to a wide variety of business problems, it is

crucial to identify use cases where the value of AI can be maximized. AI and ML are phenomenal at discovering patterns from large datasets. This tends AI and ML towards tasks involving regression (ex: budget forecasting) or classification (ex: identifying profitable clients). Tasks that are inherently subjective, or do not have large amounts of data are often poor targets to apply AI. To ensure the maximum value and cost savings cab be gained from AI, it is essential to first discuss the use cases selected for application with a trusted strategic technology partner5.

2. Disparate dirty data that never gets clean

Another significant barrier to the adoption of AI is the quality and disparity of data in enterprise environments. According to a recent report, 55% of Data Scientists cited quality and quantity of training data as their biggest challenge4. Another common measure asserts Data Scientists spend around 80% of their time finding, cleaning, and reorganizing data17. This is a huge opportunity cost for data scientists cleaning and preparing data instead of building and training models. For AI to be leveraged properly, data must be clean and accessible. If companies are unable to solve this problem, they will be unable to broadly apply AI and ML. Mitigating Factors: a. Data Virtualization can help overcome the pains of disparate data. Data Virtualization solutions connect to

an enterprise’s various data sources and create a 'virtualized' data layer that acts as a real-time single access point into your data. Such solutions do not require manual Extract-Transform-Load (ETL) processes to move and connect the data sources as well.

b. Data Governance. Data Virtualization seeks to remove the data silos in an enterprise, which in turn enhances Data Governance in the enterprise. This virtual data layer can then be used to build ML models. Some of the vendors offering this type of solution include Denodo, Dremio, Unifi, Informatica, and TIBCO.

c. Data Preparation. Another way to combat the pains of manual ETL when dealing with disparate dirty data is

to use an automated data preparation platform. These platforms take an AI-based approach to data movement, using machine learning to classify and clean data. This approach automates the majority of the tedious manual ETL process, enabling data analysts and scientists to spend more time exploring data driven insights. Some vendors offering data preparation platforms include Datalogue, Unifi, ClearStory Data (acquired by Alteryx), and Trifacta.



3. Unintended consequences prevent AI from widespread adoption

Although unlikely, it is plausible unintended negative consequences could lead to marginally slower adoption of AI. For example, in 2018 an Uber self-driving car killed an Arizona pedestrian18, resulting in Uber and vendors such as Toyota and Hyundai slowing the development of their autonomous vehicle programs19. Privacy and Legal implications are also potential barriers to the adoption of AI. For example, computer vision (CV), a subset of AI, can be used for facial recognition. This feature was widely accepted when smartphone provider began to embed the technology into their devices. However, there is greater hesitancy around the idea of allowing the government and law enforcement to use the technology for identifying wanted individuals. Such fear and hesitancy have led to increased regulation and legislation around the technology. In May of 2019, San Francisco banned the use of facial recognition software by law enforcement, and the state of California is now considering legislation to the same end20. Another such example is found in the recent move to remove bias in AI algorithms. A new bill, the Algorithmic Accountability Act, was introduced in Congress in April of 2019 to require tech companies to audit their ML algorithms to prevent any bias or discrimination based on gender or ethnicity21. Global AI adoption will also likely be impacted by the ability of governance bodies to appropriately regulate complex technology, including AI. Regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) indicate the privacy and ethics of enterprise data and AI will be under government scrutiny. However, many of the members comprising government and legislation are not experienced technologists, as was displayed when Congress questioned Facebook founder and CEO Mark Zuckerberg following their data breach with consulting firm Cambridge Analytica22. As a result, a key uncertainty to the adoption of AI will be how effectively legislation and governance regulate the technology. While factors such as unintended consequences and increased legislation and regulation may have a negative impact on AI adoption, they are not expected to significantly alter the trajectory of the AI market. Many of these examples are the result of attempting to apply AI where it does not belong yet, such as highly subjective decision-making. These examples will help identify the ideal areas where AI can be applied. As these use cases are better defined, the AI market will likely capitalize and grow.

Implications for Data Intelligence 1. The importance of analytics at the edge

With the shift of Enterprise Data Intelligence from a descriptive to prescriptive state, the next logical step is to accelerate the speed of insights. If businesses can process their data faster, they can make better, data-driven decisions faster as well. Moving data to a central repository is a time-consuming but effective way of gathering data to gain insights from ML. However, many enterprises can't afford to provision the time or network bandwidth required to perform analytics in the data center.



Edge Analytics is an approach to data processing and analytics in which computations are performed at an edge device rather than in a data center. This edge could be a sensor, network switch, or other network device. Data center analytics work well for operations that are not time- sensitive and do not require rapid analysis. However, operations that necessitate instant analytical responses must be processed prior to the data center. Early proponents of Edge Analytics, including Cisco and Intel were able to position their gateways as Edge devices. Historically, gateways performed the function of traffic aggregation and routing. With Edge Analytics, the core gateway functionality has evolved and now do far more than routing data, namely storing data and performing computations. Edge Analytics allows enterprises to pre-process the data where it is created, saving operations from failing due to a shortage of resources, whether that be time, money, or bandwidth. Instead of sending data to the data center and routing back to return with the results, Edge Analytics brings the analysis to the data. Performing analytics against data as it is created provides flexibility to enterprises, allowing them to decide what is and isn't worth sending to a data store for later use. Edge Analytics is gaining traction as IoT devices become more prevalent, due to the large volumes of data generated. Depending on the application, real time analytics are moving from "nice-to-have" to "requirement". In these scenarios, sending data to centralized data stores is impossible. Autonomous vehicles are an obvious example, as they do not have time to wait for the data center to analyze and send back a decision. Their computing must be done at the collection points in the vehicle. Firms like Mythic and FogHorn help in this transition to analytics and AI at the edge, offering embedded intelligence in small footprint edge devices, which enables more robust local analytics. Another such solution is Yellowbrick's Data Warehouse. They provide a full-featured Data Warehouse at the edge. While the approaches vary, numerous vendors bring analytics and AI to the edge. Look for analytics at the edge to be a major characteristic of the next era of Data Intelligence.

2. The hype of AI A note of caution; not everyone who claims to have AI in their product is truly delivering AI. There is a lot of hype and it can be difficult to separate the "Artificial" from the "Intelligent". At a high level, Trace3 believes AI solutions fall into three categories: Simple, Savvy, and Smart23. Simple AI solutions leverage defined algorithms applied to given data. The methods and scenarios are clearly defined and can be addressed by hard-coded behaviors. Examples are endpoint agents, data backup or data replication. Savvy AI makes use of pre-programmed rules, logic, and inferences applied to existing and new knowledge. In a Savvy AI solution, the method is still defined while the scenarios and behaviors are undefined. Examples of Savvy AI include exploit mitigation, tic-tac-toe, and backgammon. Smart AI solutions ingest copious amounts of data to discover patterns and apply them to new situations. For Smart AI, methods and scenarios are undefined, and behaviors are learned utilizing models. Examples of Smart AI include voice recognition, user behavior, and the complex game Go.



As the AI market continues to grow, there likely will be many vendors that try to capitalize on the hype of AI by branding their product as such. While there certainly are plenty of ‘Smart’ vendors out there in the DI market offering true AI, a large number of vendors have only ‘Savvy’ solutions labeled as AI. These tools may promise the ability to perform predictive and prescriptive analytics, but their predictive functionality stops at the limits of their algorithms, significantly capping the dynamic cost savings that can come with ‘Smart’ AI. For further insight into this topic, take a look at Trace3 Chief Innovation Officer’s presentation from the 2019 Trace3 Evolve conference (link).

Conclusions and Recommendations We are amidst a shift from an era of Data Intelligence defined by Self-Service BI and descriptive/diagnostic analytics to one defined by AI enabled intelligence and predictive/prescriptive analytics. AI has seeped into virtually every area of enterprise IT and across every vertical. Trace3 believes the AI market is primed for continued rapid growth. However, as discussed, there are uncertainties to the development and adoption of AI that may tamp down growth in the short-term. Regardless, those uncertainties are material for any organization considering the deployment of Smart Data Intelligence solutions. Looking forward to the coming era of Smart Data Intelligence, the Trace3 Research Team offers some recommendations to prepare for the new era.

1. Develop a framework for prioritizing and selecting AI use cases that will maximize value and cost savings. 2. Plan to operationalize AI and employ MLOps to ensure ML models are scalable, reproducible, and visible in an

enterprise environment. 3. Establish a solution for cleansing and consolidating disparate and dirty data sources. Consider analytics solutions

that connect directly to disparate data sources rather than relying on manual movement and transformation of the data.

4. Take every opportunity to perform your computation and analytics close to the data, especially for use cases requiring rapid analytics, such as Internet of Things (IoT).

5. When evaluating Artificial Intelligence features within a solution, identify genuinely smart solutions as opposed to simple or savvy solutions.

6. Consider how AI could impact the various groups of your enterprise IT department (Data Intelligence, Cloud, Security, ITOps, IoT, etc.).

7. Create an enterprise evaluation framework that looks across people, process, technology and financial domains for AI.



Appendix Featured Use Cases

1. Business Intelligence (BI) - Business intelligence combines a broad set of data analysis applications, including ad hoc analysis and querying, enterprise reporting, online analytical processing (OLAP), mobile BI, real-time BI, operational BI, cloud and software as a service BI, open source BI, collaborative BI, and location intelligence. BI platforms provide a window into the data for building reports and extracting insights.

2. Data Preparation – Data Preparation is the process of collecting, cleaning, and consolidating data into one file or data table for use in analysis. Data Preparation is used to handle "dirty" and disparate data sources to get clean data into a central repository.

3. Data Science Platform – Data science combines statistics, data analysis, and machine learning to understand and analyze actual phenomena with data. Algorithms and processes are utilized to extract knowledge and insights from data in both structured and unstructured formats. Data science is often used to uncover business insights that do not initially meet the eye when looking at your data.

4. Data Virtualization – Data Virtualization provides a single 'virtualized' layer between applications and data sources. It integrates data from disparate sources to provide a single view into data without replication or movement of the data. This virtualized layer can be used for data visibility, reporting, and processing all with the data remaining at rest.

5. Data Visualization – Data visualization is the presentation of data in a pictorial or graphical format. For centuries, people have depended on visual representations such as charts and maps to understand information more easily and quickly. Data Visualization capabilities are most frequently paired with BI or Data Science platforms for data exploration, communication, and presentation.

6. Edge Computing & Analytics – Edge Analytics is an approach to data collection and analysis in which data is processed and analyzed at an edge device rather than in a data center. This edge device could be a sensor, connected device, or other platform where data is initially generated and collected.

7. Machine Learning (ML) – Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine Learning utilizes pattern recognition and inference to extract insights without explicit direction.

8. ML Operationalization – MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML and/or deep learning lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements.



Relevant Links 1. Trace3 Evolve Conference – A two-day leadership & technology conference focusing on forward-thinking and

cutting-edge IT solutions. 2. Highlights from Evolve 2019 – A two-day leadership & technology conference focusing on forward-thinking and

cutting-edge IT solutions. 3. Emerging Tech Trends in AI – Presentation from Trace3 CIO, Mark Campbell, outlining the leading edge, the

cutting edge, and the bleeding edge in AI. 4. How Do I AI? – A community site driving to better understand AI and how to apply it in an enterprise. Note: Site

is currently under construction. 5. Mining Your Path Through Dark Data – Over half of our data today is not being used. Can emerging tech help

bridge the gap? 6. Trace3 Research – To solve the problems of tomorrow, our researchers leverage Trace3’s unique access across

the technology landscape to derive impartial insights. 7. Trace3 Data Intelligence – The Trace3 Data Intelligence team provides the experience and expertise to guide

your business to effectively and securely harnessing the power of your data.

Sources

1 Artificial Intelligence (AI) Market Report (2018-2025) from Allied Market Research 2 Baptiste Su, Jean (2019) “Venture Capital Funding for Artificial Intelligence Startups Hit Record High In 2018” 3 Edge Computing Market Report (2019) from Grand View Research 4 The Figure Eight 2018 Data Scientist Report 5 Ring, Katy (2017) “Trace3 solidifies its place as a big-data intelligence service provider” from 451 Research 6 AI: The New BI (2018) Eckerson Group 7 Hartanto, Jerry (2019) “AI, ML, and DL: Everything you need to know” from InfoWorld 8 DeNisco Rayome, Alison (2018) “The 10 most in-demand tech jobs of 2019” 9 King, Jennifer (2018) “Trust Social Networks with Your Data? Nah. Use Social Anyway? Yep.” From eMarketer 10 Anderson, Janna and Luchsinger, Alex (2018) “Artificial Intelligence and the Future of Humans” from the Pew Research Center 11 AI Trends To Watch in 2019 from CBInsights 12 US Economy: Statistics at a Glance (2019) from Financial Times 13 Autonomous Vehicle Market Industry Forecast (2019-2026) from Allied Market Research 14 Metz, Cade (2019) “Trump Signs Executive Order Promoting Artificial Intelligence” from The New York Times 15 Artificial Intelligence (AI) Software Market Worldwide (2018-2025) from Statista 16 Artifical Intelligence Market Report (2018 – 2025) From MarketsAndMarkets 17 Ruiz, Armand (2017) “The 80/20 Data Science Dilemma” from InfoWorld 18 Wakabayashi, Daisuke (2018) “Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam” from The New York Times 19 “Uber Self-Driving Death Could Have Long-Term Impact on Autonomous Cars” (2018) From Cars.com 20 Daniels, Jeff (2019) “California Senate considers ban on facial recognition software for police body cams” from CNBC 21 Hao, Karen (2019) “Congress wants to protect you from biased algorithms, deepfakes, and other bad AI” from MIT Technology Review 22 Wichter, Zach (2018) “2 Days, 10 Hours, 600 Questions: What Happened When Mark Zuckerberg Went to Washington” from The New York Times 23 Campbell, Mark (2018) “AI: Separating Artificial from Intelligent” from ChiefExecutive

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