Machine Learning (ML) and AI powering “Systems that Learn at scale” are at the bleeding edge of data science, deep learning and predictive search today.
Everyone is jumping on this AI enabled engagement (“ambient experience and convenience”) trend in retail, banking and even healthcare.
Salesforce CEO Marc Benioff said at a recent conference: “This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.”
Facebook is a case study of where AI/ML are being used to transform user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.
Chief Data Officers, Chief Analytics Officers, Chief Data Science Officers and Chief Digital Officers are showing up everywhere. The job is to leverage the latest in predictive analytics, data science, machine learning, and multi-tenant cloud architecture to bring innovation to traditional processes.
This is a pivotal moment in data driven business models but there is no getting around the inherent difficulties associated with either altering organizational behavior, data ownership politics or managing wholescale transformation of the data infrastructure. And while the challenges are real, many firms are getting closer to achieving a data science and data management environment.
What are data and analytics officers overseeing… A variety of foundational & plumbing strategies:
- Data-as-a-Service: Data Provisioning, Management, Lineage, Quality
- Reporting-as-a-Service: Dashboards, KPIs, Drilldowns/Aggregates…. Descriptive
- Analytics-as-a-Service: Predictive Modeling and BI… Prescriptive analytics
- Information-as-a-Service: Threshold based Alerts, Exceptions, Mobile Prompts
- Insights-as-a-Service: ML/AI based “Systems that Learn”…automated learning – ambient intelligence, Next best Offer/Action
At core of all these, Data Management and Data Science tools are core technical and business capabilities. Some firms are more mature and further along than others.
Why Mature Data Management as a Function
Organizations live or die by the quality of their data.
Data is an underlying factor of input into business operations and essential in order to facilitate process automation, digitize operations, support financial engineering and enhance customer facing analytical capabilities.
An effective data management program requires a planned strategic effort
- Integrate multi-discipline efforts
- Inculcate a shared vision and understanding
- Data is a ‘thing’ – vital infrastructure element foundation of the n-tier architecture
- Not a project, more than a program…it’s part of the core foundation
There is no question about it – the foundational levels of people, process, governance and technology required to establish data management on a sustainable basis are coming together under the CDO umbrella.
What does a Chief Data Officer (CDO) do?
“Looking to the future, the next big step will be for the very concept of the “device” to fade away. Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world.”
— Sundar Pichai, CEO Google
- A global oil and gas company has trained software robots to help provide a prompt and more efficient way of answering invoicing queries from its suppliers.
- A large US-based media services organization taught software robots how to support first line agents in order to raise the bar for customer service.
Software agents or Robotic process automation (RPA) is becoming a mainstream topic at leading corporations. I have seen a massive uptick in corporate strategy work in this area as C-Suite execs look at new ways to do more with less.
Software robots ∼ Apple Siri, Microsoft Cortana, IBM Watson, Google DeepMind, drones and driverless cars ∼ are now mainstream. What most people are not aware of is the rapidly advancing area of enterprise robots to create a “virtual FTE workforce” and transform business processes by enabling automation of manual, rules based, back office administrative processes.
This emerging process re-engineering area is called Robotic process automation (RPA).
Machine Learning (ML) and graph processing are becoming foundations for the next wave of advanced analytics use cases. Speech recognition, image processing, language translation have gone from a demo tech to everyday use in part because of machine learning. Machine learning models, e.g., in driverless cars, teaches itself how to discover relevant things like a stop sign with snow partially obscuring the sign.
The market opportunity of artificial intelligence has been expanding rapidly, with analyst firm IDC predicting that the worldwide content analytics, discovery and cognitive systems software market will grow from US$4.5 billion in 2014 to US$9.2 billion in 2019, with others citing these systems as catalyst to have a US$5 trillion – US$7 trillion potential economic impact by 2025.
RPA – What?
According to Blue Prism, “Robotic automation refers to a style of automation where a machine, or computer, mimics a human’s action in completing rules based tasks.”
RPA is essentially the novel application of analytics, machine learning and rules based software to capture and interpret existing data input streams for processing a transaction, manipulating data, triggering responses and communicating with other enterprise applications (ERP, HRMS, SCM, SFA, CRM etc.).
RPA is not a question of “if” anymore but a question of “when.” This is truly the next frontier of business process automation, enterprise cognitive computing, predictive analytics and machine learning. To make a prediction, you need an equation and parameters that might be involved.
Industrial robots are remaking factory and warehouse automation by creating higher production rates and improved quality. RPA, simple robots and complex learning robots, are revolutionizing the way we think about and administer business processes (e.g. customer service), workflow processes (e.g., order to cash), IT support processes (e.g., auditing and monitoring), and back-office work (e.g., data entry).
I strongly believe that as cognitive computing slowly but surely takes off, RPA is going to impact process outsourcers (e.g., call center agents) and labor intensive white collar jobs (e.g., compliance monitoring) in a big way over the next decade. Any company that uses labor on a large scale for general knowledge process work, where workers are performing high-volume, highly transactional process functions, will save money and time with robotic process automation software.
Business Impact of RPA – Where?
Everyone knows that the retail industry is being transformed by digital, analytics and big data. Winning requires continual data-driven experimentation and transformation.
Shortened time from idea-to-app is a constant challenge.
Evidence of this “digital disruption” by category are mounting every day. Wal-Mart closes 269 stores as it retools portfolio to compete with online natives like Amazon.com. Macy’s said that it will shutter over 36 stores as store traffic declines faster than expected, and Finish Line said that it would close 150 stores by 2020. Gap, J.Crew, American Apparel, Sears and Kmart are all facing similar headwinds.
Starbucks CEO Howard Schultz laid out his thoughts on the future prospects for retail business, “three years ago we began to envision that there would be a seismic change in consumer behavior, and that seismic change was due in large part to e-commerce, omni-channel and smartphone shopping.”
It’s fascinating to watch retailers trying to shift tech/platform strategies to deal with digital disintermediation, showrooming, asset-light models, physical-to-digital channel integration, mobile shoppers, same-day delivery/fulfillment, programmatic targeting, online native models and now the new buzz.. virtual and augmented reality.
Several retailers have invested in Big Data and Hadoop platforms to mine massive volumes of structured transactional, operational data and unstructured data—web logs, clickstream data, geo-location data, social interactions and sensor data.
While most retailers understand the mega-shift and seems to know what to do….they are unable to execute consistently or effectively. A talent gap in many cases. A platform gap in others. Others are hindered by legacy IT systems or don’t have the necessary technology capabilities in place.
I think the digital induced pain is going to get worse in 2016 and 2017. Consumers will continue to diversify their retail activity across channels in search of the best value, forcing retailers to spread out their digital investments. This puts additional stress on execution and leadership.
New data driven FinTech business models built on Hadoop, Spark and Machine Learning are rapidly emerging and disrupting wealth management. Here is my recent posting from disruptivedigital.wordpress.com about one such use case… Robo-Advisors.
We are in the early stages of a generational shift in wealth management, especially “plain vanilla” investing for the mass affluent and millennial segment. Until recently, you had only two options when investing:
- Do-it-yourself (DIY)
- Hire a registered investment advisor (RIA)
Now there is a third option. Robo-advisors are new class of personal financial advisors that provides online, algorithm based portfolio management with minimal human intervention. Robo-Advisors going after the low-end of brokerage/RIA business with automated asset allocation using Modern Portfolio Theory.
The Robo-Advisors market leaders who are serving the mass affluent include are:
- Wealthfront (with over USD 2.6bn in assets under management (AuM) and 20,000 investors);
- Betterment (with over USD 1.4bn in AuM and 70,000 investors); and
- FutureAdvisor (With over $600 million in AUM).
The timing for this market shift coincides with three trends: consumerization, digital tools, and disillusionment with status-quo investment advisors. The gyrating stock market driven by program trading is increasingly bringing Robo-Advisors, algorithmic portfolio management to the forefront. Investors are getting disillusioned with traditional investment advisors who simply track the market indices (SPY, QQQ or Russell 2000) by purchasing ETFs at best.
Many banks and brokerage firms over the years have shifted their focus to serve ultra high net worth (UHNW) and high net worth (HNW) investors, leaving an opportunity for firms to target the “mass affluent” investors, or those with less than $1 million in investable assets. Younger investors are increasingly interested in online digital advice (trial-and-error bets), as opposed to hiring an adviser.