Category Archives: Strategy

Analytics Insights and Careers at the Speed of Data

How to make smarter data-driven decisions at scale: http://bit.ly/3rS3ZQW

The determination of winners and losers in the data analytics space is a much more dynamic proposition than it ever has been. One CIO said it this way, “If CIOs invested in machine learning three years ago, they would have wasted their money. But if they wait another three years, they will never catch up.”  Well, that statement was made five years ago! A lot has changed in those five years, and so has the data landscape.

The dynamic changes of the business requirements and value propositions around data analytics have been increasingly intense in depth (in the number of applications in each business unit) and in breadth (in the enterprise-wide scope of applications in all business units in all sectors). But more significant has been the acceleration in the number of dynamic, real-time data sources and corresponding dynamic, real-time analytics applications.

We no longer should worry about “managing data at the speed of business,” but worry more about “managing business at the speed of data.”

One of the primary drivers for the phenomenal growth in dynamic real-time data analytics today and in the coming decade is the Internet of Things (IoT) and its sibling the Industrial IoT (IIoT). With its vast assortment of sensors and streams of data that yield digital insights in situ in almost any situation, the IoT / IIoT market has a projected market valuation of $1.5 trillion by 2030. The accompanying technology Edge Computing, through which those streaming digital insights are extracted and then served to end-users, has a projected valuation of $800 billion by 2028.

With dynamic real-time insights, this “Internet of Everything” can then become the “Internet of Intelligent Things”, or as I like to say, “The Internet used to be a thing. Now things are the Internet.” The vast scope of this digital transformation in dynamic business insights discovery from entities, events, and behaviors is on a scale that is almost incomprehensible. Traditional business analytics approaches (on laptops, in the cloud, or with static datasets) will not keep up with this growing tidal wave of dynamic data.

Another dimension to this story, of course, is the Future of Work discussion, including creation of new job titles and roles, and the demise of older job titles and roles. One group has declared, “IoT companies will dominate the 2020s: Prepare your resume!” This article quotes an older market projection (from 2019), which estimated “the global industrial IoT market could reach $14.2 trillion by 2030.”

In dynamic data-driven applications, automation of the essential processes (in this case, data triage, insights discovery, and analytics delivery) can give a power boost to ride that tidal wave of fast-moving data streams. One can prepare for and improve skill readiness for these new business and career opportunities in several ways:

  • Focus on the automation of business processes: e.g., artificial intelligence, robotics, robotic process automation, intelligent process automation, chatbots.
  • Focus on the technologies and engineering components: e.g., sensors, monitoring, cloud-to-edge, microservices, serverless, insights-as-a-service APIs, IFTTT (IF-This-Then-That) architectures.
  • Focus on the data science: e.g., machine learning, statistics, computer vision, natural language understanding, coding, forecasting, predictive analytics, prescriptive analytics, anomaly detection, emergent behavior discovery, model explainability, trust, ethics, model monitoring (for data drift and concept drift) in dynamic environments (MLOps, ModelOps, AIOps).
  • Focus on specific data types: e.g., time series, video, audio, images, streaming text (such as social media or online chat channels), network logs, supply chain tracking (e.g., RFID), inventory monitoring (SKU / UPC tracking).
  • Focus on the strategies that aim these tools, talents, and technologies at reaching business mission and goals: e.g., data strategy, analytics strategy, observability strategy (i.e., why and where are we deploying the data-streaming sensors, and what outcomes should they achieve?).

Insights discovery from ubiquitous data collection (via the tens of billions of connected devices that will be measuring, monitoring, and tracking nearly everything internally in our business environment and contextually in the broader market and global community) is ultimately about value creation and business outcomes. Embedding real-time dynamic analytics at the edge, at the point of data collection, or at the moment of need will dynamically (and positively) change the slope of your business or career trajectory. Dynamic sense-making, insights discovery, next-best-action response, and value creation is essential when data is being acquired at an enormous rate. Only then can one hope to realize the promised trillion-dollar market value of the Internet of Everything.

For more advice, check out this upcoming webinar panel discussion, sponsored by AtScale, with data and analytics leaders from Wayfair, Cardinal Health, bol.com, and Slickdeals: “How to make smarter data-driven decisions at scale.” Each panelist will share an overview of their data & analytics journey, and how they are building a self-service, data-driven culture that scales. Join us on Wednesday, March 31, 2021 (11:00am PT | 2:00pm ET). Save your spot here: http://bit.ly/3rS3ZQW. I hope that you find this event useful. And I hope to see you there!

Please follow me on LinkedIn and follow me on Twitter at @KirkDBorne.

RPA and IPA – Their Similarities are Different, but Their Rapid Growth Trajectories are the Same

When I was growing up, friends at school would occasionally ask me if my older brother and I were twins. We were not, though we looked twin-like! As I grew tired of answering that question, one day I decided to give a more thoughtful answer to the question (beyond a simple “No”). I replied: “Yes, we are twins. We were born 20 months apart!” My response caused the questioner to pause and think about what I said, and perhaps reframe their own thinking.

This story reminds me that two things may appear very much the same, but they may be found to be not so similar after deeper inspection and reflection. RPA and IPA are like that. Their similarities are different!

What are RPA and IPA? RPA is Robotic Process Automation, and IPA is Intelligent Process Automation. Sound similar? Yes, in fact, their differences are similar!

In the rest of this article, we will refer to IPA as intelligent automation (IA), which is simply short-hand for intelligent process automation.

Process automation is relatively clear – it refers to an automatic implementation of a process, specifically a business process in our case. One can automate a very complicated and time-consuming process, even for a one-time bespoke application – the ROI must be worth it, to justify doing this only once. Robotic Process Automation is for “more than once” automation. RPA then refers specifically to the robotic repetition of a business process. Repetition implies that the same steps are repeated many times, for example claims processing or business form completion or invoice processing or invoice submission or more data-specific activities, such as data extraction from documents (such as PDFs), data entry, data validation, and report preparation.

The benefits of RPA accrue from its robotic repetition of a well-defined process (specifically some sort of digital process with defined instructions, inputs, rules, and outputs), which is to be repeated without error, thus removing potential missteps in the process, or accidental omission of steps, or employee fatigue. The process must be simple, stable, repetitive, and routine, and it must be carried out the same way hundreds or thousands (or more) times. This robotic repetition of the process assures that the steps are replicated identically, correctly, and rapidly from one application to the next, thus yielding higher business productivity. Consequently, as organizations everywhere are undergoing significant digital transformation, we have been witnessing increases both in the use of RPA in organizations and in the number of RPA products in the market.

So, what about Intelligent Automation? IA refers to the addition of “intelligence” to the RPA – transforming it into “smart RPA” or even “cognitive RPA”. This is accomplished by adding more data-driven, machine learning, and AI (artificial intelligence) components to the process discovery, process mining, and process learning stages. IA incorporates feedback, learning, improvement, and optimization in the automation loop. The market and business appetite for IA is growing rapidly, particularly as more organizations are seeking to add AI to their enterprise functions and to step up the value derived from their process automation activities.

The pivot from RPA to IA right now has spurred Automation Anywhere, Inc. (AAI) to conduct a survey of businesses and organizations in numerous sectors and industries, to assess the current and future (Now and Next) state of RPA in the enterprise. AAI’s recently published “Now and Next State of RPA” report presents detailed results of that survey.

The AAI report covers these industries: energy/utilities, financial/insurance, government, healthcare, industrial/manufacturing, life sciences, retail/consumer, services/consulting, technology, telecom, and transportation/airlines.

The first part of the report addresses the “Now” – the present-day impact of RPA and IA – how organizations are deploying process automation, what their priorities are, how much are they investing in it, and what are the benefits being achieved. The “Next” part of the report probes organizations’ forward-looking strategies and goals over the next one to two years. Of course, both the “now” and “next” sections are affected and informed by the COVID-19 pandemic.

Some top trends from the report include:

  • Cloud is becoming the platform of choice for RPA deployments. Cloud is top of mind for leaders everywhere, and that includes cloud migration and cloud security.
  • Organizations are thinking big about RPA, with millions of bots already implemented by AAI customers. Some AAI customers have individually deployed tens of thousands of bots.
  • The pandemic has rapidly increased demand for RPA, especially in customer-facing functions, such as call centers.
  • Most organizations (63%) are deploying or scaling their efforts, while 27% are still evaluating RPA solutions, and 10% have no plans at this time.
  • Spending on RPA is increasing, with an estimated doubling of the number of bots deployed in the next 12 months in most organizations who are already actively using RPA bots.
  • Productivity improvement is the single most important driver for adopting RPA, IA, and AI.
  • The average ROI from RPA/IA deployments is 250%.
  • Interest in AI is high and growing, specifically in the areas of smart analytics, customer-centricity, chatbots, and predictive modeling. Many organizations are seeing a strong alignment of their AI and RPA (hence, IA) projects.
  • The top barriers to adoption are (in order of significance): lack of expertise, insufficient budget, lack of resources, lack of use cases, limited ROI, organizational resistance, scalability concerns, and security concerns.
  • Top use cases in the back office include finance (general ledger data reconciliation), accounts payable (invoice processing and payment), and HR (new employee onboarding).
  • Top use cases in the front office include customer records management (including account updates), customer service (request handling and call center support), and sales order processing.
  • RPA education and training are on the rise (specifically online training). This reflects the corresponding increase in interest among many organizations in data literacy, analytics skills, AI literacy, and AI skills training. Education initiatives address the change management, employee reskilling, and worker upskilling that organizations are now prioritizing.
  • Organizations report an increased excitement for RPA/IA, an awareness of significant new opportunities for adoption, and a desire for more mature RPA – that is, intelligent automation!

There are many more details, including specific insights for different sectors, in the 21-page “Now and Next State of RPA” report from Automation Anywhere, Inc. You can download the full report here: https://bit.ly/3p0IZpJ. Download it for free and learn more how the robotic twinning and repetition of business processes is the most intelligent step in accelerating digital transformation and boosting business productivity.

Productivity is the top driver of RPA and AI.
Open-ended survey questions gave insights into organizations’ planned AI projects.
Training initiatives are a high priority for increasing ROI from RPA and AI projects.

How We Teach The Leaders of Tomorrow To Be Curious, Ask Questions and Not Be Afraid To Fail Fast To Learn Fast

I recently enjoyed recording a podcast with Joe DosSantos (Chief Data Officer at Qlik). This was one in a series of #DataBrilliant podcasts by Qlik, which you can also access here (Apple Podcasts) and here (Spotify). I summarize below some of the topics that Joe and I discussed in the podcast. Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more.

The Age of Hype Cycles

The data age has been marked by numerous “hype cycles.” First, we heard how Big Data, Data Science, Machine Learning (ML) and Advanced Analytics would have the honor to be the technologies that would cure cancer, end world hunger and solve the world’s biggest challenges. Then came third-generation Artificial Intelligence (AI), Blockchain and soon Quantum Computing, with each one seeking that honor.

From all this hope and hype, one constant has always been there: a focus on value creation from data. As a scientist, I have always recommended a scientific approach: State your problem first, be curious (ask questions), collect facts to address those questions (acquire data), investigate, analyze, ask more questions, include a sensible serving of skepticism, and (above all else) aim to fail fast in order to learn fast. As I discussed with Joe DosSantos when I spoke with him for the latest episode of Data Brilliant, you don’t need to be a data scientist to follow these principles. These apply to everyone, in all organizations and walks of life, in every sector.

One characteristic of science that is especially true in data science and implicit in ML is the concept of continuous learning and refining our understanding. We build models to test our understanding, but these models are not “one and done.” They are part of a cycle of learning. In ML, the learning cycle is sometimes called backpropagation, where the errors (inaccurate predictions) of our models are fed back into adjusting the model’s input parameters in a way that aims to improve the output accuracy. A more colloquial expression for this is: good judgment comes from experience, and experience comes from bad judgment.

Data Literacy For All

I know that for some, the term data and some of the other terminology I’ve mentioned already can be scary. But they shouldn’t be. We are all surrounded by – and creating – masses of data every single day. As Joe and I talked about, one of the first hurdles in data literacy is getting people to recognize that everything is data. What you see with your eyes? That’s data. What you hear with your ears? Data. The words that come out of your mouth that other people hear? That’s all data. Images, text, documents, audio, video and all the apps on your phone, all the things you search for on the internet? Yet again, that’s data.

Every single day, everyone around the world is using data and the principles I mention above, many without realizing it. So, now we need to bring this value to our businesses.

How To Build A Successful Enterprise Data Strategy

In my chat with Joe, we talked about many data concepts in the context of enterprise digital transformation. As always, but especially during the race toward digital transformation that has been accelerated by the 2020 pandemic, a successful enterprise data strategy that leads to business value creation can benefit from first addressing these six key questions:

(1) What mission objective and outcomes are you aiming to achieve?

(2) What is the business problem, expressed in data terminology? Specifically, is it a detection problem (fraud or emergent behavior), a discovery problem (new customers or new opportunities), a prediction problem (what will happen) or an optimization problem (how to improve outcomes)?

(3) Do you have the talent (key people representing diverse perspectives), tools (data technologies) and techniques (AI and ML knowledge) to make it happen?

(4) What data do you have to fuel the algorithms, the training and the modeling processes?

(5) Is your organizational culture ready for this (for data-informed decisions; an experimentation mindset; continuous learning; fail fast to learn fast; with principled AI and data governance)?

(6) What operational technology environment do you have to deploy the implementation (cloud or on-premise platform)?

Data Leadership

As Joe and I discussed, your ultimate business goal is to build a data-fueled enterprise that delivers business value from data. Therefore, ask questions, be purposeful (goal-oriented and mission-focused), be reasonable in your expectations and remain reasonably skeptical – because as famous statistician, George Box, once said “all models are wrong, but some are useful.”

Now, listen to the full podcast here.

Key Strategies and Senior Executives’ Perspectives on AI Adoption in 2020

Artificial intelligence (AI) has become one of the most significant emerging technologies of the past few years. Some market estimates anticipate that AI will contribute 16 trillion dollars to the global GDP (gross domestic product) by 2030. While there has been accelerating interest in implementing AI as a technology, there has been concurrent growth in interest in implementing successful AI strategies. Some key elements of such strategies that have emerged include explainable AI, trusted AI, AI ethics, operationalizing AI, scaling sustainable AI operations, workforce development (training), and how to speed up all of this development.

The 2020 year of the pandemic has forced organizations to speed up their digital transformation and advanced technology adoption plans, essentially compressing several years of anticipated developments into several months. These accelerated developments cover a wide scope, including: technology-enabled remote work solutions, technology-enhanced health and safety programs, AI-powered implementations of “all of the above”, and sharpened focus and attention on their workforce: future of work in the age of AI, AI-assisted human work and process enhancements, and training initiatives (including data literacy and AI).

In the recent 2020 RELX Emerging Tech Study, results were presented from a survey of over 1000 U.S. senior executives across eight industries: agriculture, banking, exhibitions, government, healthcare, insurance, legal, and science/medical. The survey, carried out by RELX (a global provider of information-based analytics and decision tools for professional and business customers), focused on the state of interest, investment, and implementations in AI tech during the pandemic period. But it was not just a snapshot on the state of AI in 2020. The survey also had forward-looking questions, as well as historical comparisons and trends from results of similar “state of AI in the enterprise” surveys in 2018 and 2019.

Some of the most remarkable findings in the 3-year trending data include these changes from 2018 to 2020:

1) The percentage of senior executives who stated that AI technologies are being utilized in their business dramatically increased from 48% to 81%.

2) The percentage who are concerned about other countries being more advanced than the U.S. in AI technology and implementation increased from 70% to 82%.

3) The percentage who believe that government programs should assist in AI workforce development increased from 45% to 59%.

4) There was a slight increase (though still a minority) in those who believe that the government should leave the promotion of AI technologies to the private sector, growing from 30% to 36%.

5) There is a continued (and growing) strong belief that U.S. companies should invest in the future AI workforce through educational initiatives such as university partnerships, increasing from 92% to 95%.

6) The percentage who are offering training opportunities in AI technologies to their workforce increased significantly from 46% to 75%.

Some of the interesting findings specifically in the 2020 survey results include:

1) Across all sectors, a strong majority (86%) of survey respondents believe that ethical considerations are a strategic priority in the design and implementation of their AI systems, ranging from 77% in government and 80% in the legal sectors, up to 93% in banking and 94% in the insurance sectors, with healthcare and science/medical near the middle of that range.

2) The majority (68%) of respondents stated that they increased their investment in AI technologies, 48% of which invested in new AI technologies. The sectors with the greatest increases in investment were insurance, banking, and agriculture, followed closely by healthcare and science/medical.

3) 82% stated that AI technologies are most likely to be used to increase efficiencies and worker productivities.

4) Only 26% of respondents reported that AI technologies are being used to replace human labor.

Regarding the last point, this minority view regarding AI’s potential negative impact on employment is consistent with a 2019 worldwide survey of 19,000 employers, which found that 87% plan to increase or maintain the size of their staff as a result of automation and AI, and that just 9% of companies across the globe and 4% in the U.S. anticipate cutting jobs. Another report stated, “Rather, than being replaced, humans will be redeployed into higher-order jobs requiring more cognitive skills.”

The broader takeaways and insights drawn from the 2020 RELX Emerging Tech survey are these:

(a) COVID-19 drove increased AI tech investment and adoption.

(b) The use of AI has increased across all sectors that were polled.

(c) Ethical AI is viewed as both a priority and a competitive advantage.

(d) International competition remains a concern for U.S. organizations.

(e) AI workforce training and development is a major component of AI strategy, though AI implementations consistently outpace training initiatives.

Vijay Raghavan, Executive Vice President and Chief Technology Officer for the Risk & Business Analytics division of RELX, has summarized the survey results very well in the following statements:

  • “Businesses’ response to COVID-19 has confirmed the view of US business leaders that artificial intelligence has the power to create smarter, more agile and profitable businesses.”
  • “Businesses face more complex challenges every day and AI technologies have become a mission-critical resource in adapting to, if not overcoming, these types of unforeseen obstacles and staying resilient.”
  • “Companies that do not dedicate the necessary resources to training existing employees on new AI technologies risk leaving growth opportunities on the table and using biased or otherwise flawed systems to make and enforce major decisions.”

Therefore, when we consider AI strategy, a global perspective is no less important than local organization-specific mission objectives. Understanding your competition, the marketplace, and both the current expectations and future needs of your stakeholders (customers, employees, citizens, and shareholders) is vital. Furthermore, non-technology considerations should be incorporated alongside technology implementation and operationalization requirements. Performance metrics and goals associated with AI governance, ethics, talent, and training must be on the same balance sheet as AI tools, techniques, and technologies. How to bring all of these pieces together in a successful AI strategy has become clearer with the results and insights revealed in the 2020 RELX Emerging Tech survey.

You can find and read the full RELX survey report here: http://bit.ly/relx-emerging-tech-kb

Key Finding in Emerging Tech Survey
Key Findings in RELX 2020 Emerging Tech Survey