Enhancing the CIO’s Role with IT Process Automation

With the ever-increasing volume and complexity of data coming in (thanks in large part to trends like the IoT, BYOD and, of course, Big Data), the role of the CIO has also begun to expand. These individuals are now facing pressures to update and improve infrastructure, analyze and use the data available to them for the benefit of the organization and all while keeping internal networks, systems, applications and information secure. It’s no easy feat, but with IT process automation, it is entirely achievable.

Because of the heavy volume of data being shared today, integrating automated workflows and processes has become increasingly necessary in order to analyze and derive value from that data, and in a way that is as cost-effective as possible. If IT departments are to remain relevant, drive efficiency and support a profitable operation, it is imperative that they employ the use of IT process automation, and with the CIO as the key decision maker, it’s on him or her to ensure that the right tools are in place.

Just a few short years ago the general public was becoming aware of the IoT, but today organizations of every size and industry are capturing insight and achieving real, sustainable ROI from this advanced (and ever-evolving) technology. Furthermore, IT process automation is virtually revolutionizing everything from the SOC and NOC to the service desk and data center. Intuitive technology and artificial intelligence are being utilized to proactively monitor systems and devices, gather and evaluate complex data, remediate incidents and resolve issues – in many cases before any human worker is even made aware.

As a result of all of these changes, more basic requests, like password resets and system refreshes, which used to be handled almost exclusively by L1 support professionals are now being shifted to intelligent technology. Self-service portalsare empowering the end-user like never before while simultaneously alleviating IT personnel of the heavy burden associated with these routine, repetitive (but necessary) tasks.

Of course, this hasn’t necessarily made life perfect for IT professionals. Increased consumerization of IT has resulted in the services of many IT departments being compared and contrasted against that of external service providers. Expectations of faster service and the demand to take on more while also minimizing costs as much as possible continue to rise, subsequently increasing the pressures on top IT personnel, and perhaps no one is feeling the pressure more than the CIO. Embracing IT process automation is no longer an option, but a critical requirement.

At the very same time, the IT world is witnessing a significant change in responsibilities for the CIO, shifting from the old way of the maintenance and provision of physical infrastructure and devices to more of a data management role with an emphasis on innovating and creating value. Digitalization is now the focus, with CIOs playing a lead role in developing and implementing it throughout the entire enterprise. Paradoxically, these high-level IT professionals are being forced to orient and align themselves more with value creation than efficiency.

Data analytics is now being hailed as one of the primary contributors to driving this value, particularly given the ever-increasing pool of available information. It’s important to point out, however, that CIOs and other top IT managers must take the time necessary to understand what data is available to them, what that data equates to and, most importantly, how they can best leverage that information to improve operations across all functions of the organization. Savvy CIOs will leverage this data to obtain key insights that will support current and future business goals as well as identify new insight that will give the company competitive advantage.

Finally, the new role of the CIO will involve more engagement, inspiration and education of others than ever before. To fulfill these evolving duties, it’s absolutely essential that the CIO develops into a strong visionary and consistent innovator for the organization. Through better data analysis and the more widespread use of IT process automation, the person in this important role will begin to morph into the position of strategic advisor, driving the business onward and upward toward increasing and sustainable success well into the future.

Source: ayehu.com- Enhancing the CIO’s Role with IT Process Automation


The Robotics Industry Must Overcome the Awareness Gap

The Robotics Industry Must Overcome the Awareness Gap

Neil Kinson 

The robotics industry has come a long way. Where once we thought solely about mimicking human actions with machines, we are now capable of automating entire business processes to drive a business forward. Whether it’s a finance department, a HR team or a supply chain, automation can add an unprecedented level of value.

Unfortunately, public perception of this revolution has lagged. In a recent survey Redwood Software research conducted with Shared Services Link, 97% of respondents were confident that robots can automate the mundane stuff – data entry, and other manual tasks. When the level of complexity went up, confidence fell. Only 52% felt that robots could “understand basic finance processes,” and less than a third felt it was possible to automate 80% plus of company finance processes.

These stats are a wake-up call for the robotics industry. If we are going to realize the vast value of robotics, we need to address this education gap and overcome a longstanding myth: that robots will always replace people, and so can never move beyond the most basic tasks.


Let’s start with the basics: what is robotics? Everyone has an answer, but many are missing the big picture.

The Institute for Robotic Process Automation defines enterprise process robotics as the application of technology that enables employees to configure computer software, a “robot”, to catch and analyze existing applications for manipulating data, processing a transaction or triggering responses and communicating with other digital systems.

This definition is the beginning of what robotics can do, not the limit. Enterprise robotics doesn’t just switch a human being for a piece of software: it lets usreimagine an entire process. A human being is by definition tied to a user interface. By contrast, the next generation of software robots will communicate directly with applications at the server level and communicate directly with core ERP and other business system. That means that the new generation of software robots are able to see and understand a process from beginning to end, with complete knowledge of finance, supply chain and HR processes from the moment they are turned on.

For a service professional, that means they get a more complete, effective automation of their processes. If they’re looking to gain efficiencies, scale or reinvent their entire business, the software robot offers a degree a flexibility and skill that no human being could offer.

So why don’t people know about it?


For the services professional, skepticism about the value of process automation boils down to a simple point: they think it’s too good to be true. Sure, it’s possible that automation will yield great results – but isn’t there a massive risk that I’ll lose control of my department by incorporating automation? I know how to manage my team – how do I keep control of a faceless piece of software?

This is a natural concern, but it’s a misguided one. Automation of processes yields a vast amount of data, and allows the user to intervene at any step throughout the process. In other words, an automated process is significantly easier to control than one run by humans.

To take one example, look at the financial industry. End-to-end process robotics means you can capture all human, robotic and system activity, yielding a comprehensive audit trail, complete with documentation. Furthermore, built in business rules remove the need to micro-manage. Robotized processes are self-managed but users can still monitor progress, as well as review and trigger actions whenever necessary. For anyone who has had to run an accounting team, they know the value process robotics offers them in terms of relief from minor tasks, and lowering compliance risk.

As an illustration, Redwood recently worked with Royal DSM, a Dutch multinational focused on health and nutrition, to improve the efficiency and performance of its finance team. Each month, Royal DSM Financial Shared Services (FSS) team were carrying out a staggering 485,000 manual tasks for monthly close. Royal DSM worked with Redwood, targeting a 60% automation rate. By the end of the project, robotics had exceeded expectation, achieving an 89% automation rate. This not only improved efficiency, but it freed up the finance team for more strategic analysis.

Beyond the practical concern regarding control and efficiency, there is the more existential worry that robots will take away people’s jobs, leading to automated offices and legions of unemployed professionals. This techno-anxiety has existed for as long as there has been automation. The fact is that automated processes help drive economic growth and job creation. The economies of the United States and the United Kingdom show zero sign of a surge in unemployment, even as the robotics industry grows ever more sophisticated. Robotics will certainly displace some jobs, but they will create far more, and they may even yield greater job satisfaction, thanks to the move away from dull, repetitive processes.


Long gone are the days of manual time-consuming labor to ensure efficient core business processes are carried out. Enterprise process robotics provides accuracy, speed and consistency that businesses need in the competitive global marketplace of today, without unnecessary manual intervention. The organizations that will remain competitive in the fourth industrial revolution will be those who welcome the robotic revolution with open arms.

But the robotics industry can’t just sit back and wait for the business world to come to us. We must educate and evangelize to bust the myths and show what automated business processes can do. The technology is ready – now we have to show the world its value.

Source: roboticstomorrow.com-The Robotics Industry Must Overcome the Awareness Gap

CompuCom Survey: IT Pros View Lower Labor Costs as Biggest Benefit of Robotics Process Automation

According to a new survey from CompuCom Systems, Inc. (“CompuCom®”), a leading technology infrastructure services company, IT professionals see reduced labor costs (43 percent) as the biggest benefit of robotics process automation (RPA), over competitive edge (24 percent), better customer experience (20 percent) and employee job satisfaction (13 percent). The online poll collected responses from 600 IT professionals across multiple industries from May 26 – September 15, 2016.

“What do you see as the biggest benefit of Robotics Process Automation?”

RPA allows employees to configure software agents, or “robots,” to handle high-volume, repeatable, rules-based tasks that previously required a human to perform, such as entering data into systems or responding to messages and events. Valued at $183 million three years ago, the global RPA market is expected to be worth $4.98 billion by 2020, according to a report from Transparency Market Research, and most companies today are beginning by implementing just the lower-end capabilities of RPA.

CompuCom CTO Sam Gross noted, “There’s no question that RPA is transforming how we do business and we’re only seeing the tip of the iceberg with these capabilities, but do organizations win with RPA just by lowering costs? The real impact comes from empowering people to get their jobs done better, which leads to improved customer experiences – and a competitive edge.”

Respondents to the CompuCom poll answered the question, “What do you see as the biggest benefit of Robotics Process Automation?

Reduced labor costs – 43%
Competitive edge – 24%
Better customer experience – 20%
Employee job satisfaction13%
Total votes: 600

CompuCom is accelerating its presence in the automation space, with the recent acquisition of the Internet of Things (IoT) business of Extensys, a top provider of IoT solutions, and the integration of its core team. CompuCom also recently partnered with intelligent automation leader Arago to integrate Arago’s problem-solving artificial intelligence solution, HIRO™, into all of CompuCom’s managed services solutions for the data center – enabling incidents to be diagnosed and remediated more quickly, efficiently and with greater certainty.

Source: businesswire.com-CompuCom Survey: IT Pros View Lower Labor Costs as Biggest Benefit of Robotics Process Automation

What is Machine Learning?

Here’s a blog post covering some of the most frequently asked questions we get on Machine Learning and Artificial Intelligence, or Cognitive Computing. We start off with “What is Machine Learing?” and finish off with addressing some of the fears and misconceptions of Artificial Intelligence.

So, what is machine learning? A simple search on Google for the answer will yield many definitions for it that leave most non-analytical people confused and entering more “What is…” statements into Google. So, I asked our Head of Marketing to try his hand at defining Machine Learning in the most simplistic way he can: explain Machine Learning to someone you’ve just met at a social gathering. Here’s his definition – a “Machine Learning for Beginners’ ” definition if you will.

“To begin to understand Machine Learning you need to first understand what an algorithim is. An algorithm is a series of “If / Then” statements that essentially equate to actions and outcomes or results. For example, if I stick my hand into a fire, then my hand will burn. That is an “if / then” statement – an action and an outcome of that action. The more we repeat the action and the outcome is the same or similar, the more we are able to accurately predict the outcome if we do it again. We can then choose to do it again if the predicted outcome is positive, or change our action if the predicted outcome is negative. This is a basic way we as humans learn: we try different actions and remember the outcome. Now apply this method of learning to a computer, which creates the algorithms (“If / then” statements) from patterns observed in data and then analyses all of the data to predict the outcome of an action. This is Machine Leaning.”

How does Machine Learning relate to Artificial Intelligence?

To us, when we talk about Artificial Intelligence, we are referring to computational systems that behave like a human – i.e. they can understand the written and spoken language, they can converse and interact “like a human”, they can process complex concepts like sarcasm and humour. In developing this computational system, the computer needs to learn…a lot….and this is where Machine Learning comes in. The more we use Machine Learning to automate decision-making and actions taken the closer we get to Artificial Intelligence.

How does Machine Learning relate to Predictive Analytics?

Although most applications involve the requirement to predict something (like a response, a fraudulent application, an attrition account), there is a tranche of Machine Learning that does not predict anything specific. Customer segmentation is a good example, where one looks at the customer base and the requirement is to group the accounts into homogenous clusters without pointing to any specific target variable.

Anything that involves the prediction of something can be considered to be under the wider Predictive Analytics banner. There are two types of predictive Machine Learning: static (or batch) and dynamic (or incremental). The more stable and trusted approach would be to manually extract the relevant data, train the models with care, validate and then implement the models. With the computing systems and algorithms that are available today, full dynamic Machine Learning is possible which allows for the retraining process to be carried out automatically with little or no human intervention.

A good example of this is iPhone’s Siri which learns the lifestyle patterns of the user and automatically adjusts its recommendations accordingly. For most Machine Learning projects within the financial services industry, we do not advocate this black box approach, but rather the development of stable and explainable predictive models. Dynamic Machine Learning algorithms can then be applied on top of this solid foundation in order to bring in more recent patterns and trends, further lifting the performance of the predictive analytics system

Why has Machine Learning or Cognitive Computing become such a hot topic?

The reason for the increasing interest is due to the significant increase in data that is now available, which makes machine learning more relevant, accurate and more effective for more businesses than ever before. Machine Learning automates decisions by analysing large and diverse datasets at lightening speeds, predicting what would lead to a positive outcome and making or taking the recommended action.

The accuracy of Machine Learning depends on the volume and the reliability of the available data. With the data deluge happening now thanks to a combination of smart phones, the Internet of Things, the Internet, RFID technology and social media there is now an unprecedented amount of data being generated every second of the day for Machine Learning algorithms to analyse, search for and recognise patterns and trends in the data, and to then output a decision or recommended action.

What are the potential benefits of creating machines that can be programmed to learn?

Machine Learning automates decisions in a continuous improvement cycle. And machines automate actions. The combination of both greatly reduces the need for human intervention for a process to be completed. When we remove the reliance on human intervention we tend to achieve faster response times, a reduction in cost and a reduction in human error or bias for any process – from the granting of a loan to the landing of a plane.

Some examples of how Machine Learning is being used are:

  • to instantly identify fraudulent behaviour on an account as it happens and automatically sending an alert;
  • flagging customers who are most likely to unsubscribe or leave a business for a competitor based on real-time analysis of customer behaviour and transactional data and then taking appropriate action (or not as the case may be);
  • adjusting pricing on thousands of items at once based on availability and demand; and
  • making real-time recommendations of products or services based on a person’s purchase behaviour as they shop or complete a purchase.

Decisions based on Machine Learning get better or more accurate with time, as they learn what decisions and actions lead to a negative or positive outcome. As the algorithm is fed results of its actions and consequent outcomes, it learns and adjusts to do more of X to get the desired result instead of Y. It’s getting closer and closer to Artificial Intelligence every day.

The world is currently generating data more rapidly than we are able to analyse it, is Cognitive Computing the answer?

Yes. Although the human mind is complex and powerful, we are unable to compete with computers when it comes to the speed in which they are able to analyse large sets of data and recognise patterns and trends. And they are just getting faster and smarter. So, although the human race has been able to achieve some incredible things and there doesn’t seem to be any problem we cannot solve, in some cases, we lack the luxury of time to solve them. Cognitive computing could help us find answers or solutions to these problems faster.

How did you use Machine Learning to forecast the results of the 2015 Rugby World Cup matches with a high level of accuracy? How was this possible?

Sports fans make their own predictions on match results based on their own experience and observations, as well as gut feel and personal bias. With Machine Learning, we based our predictions primarily on data we took from 6,000 matches played by 99 teams since 1995. As we then progressed through the World Cup matches, we incrementally added the results from each match to our model, so the model could adjust its prediction based on what happened in the last few matches.

What was interesting was that the model didn’t look at who played the matches, but rather the characteristics of the teams that played the matches. So, there was no bias towards any team – it was just looking at the data or the characteristics of a specific in making its prediction. When we added that new data, the model adjusted and compared the characteristics of the teams whose data was added and the result of these teams’ matches and adjusted its expectations for teams with the same or similar characteristics.

We continually added new data and the model continually adjusted to this new data, increasing the accuracy of our prediction with every match played. Two interesting sets of data we included that added a bit of the human element to the prediction were the fantasy value of the players and the bookie odds. We didn’t look at fields, or influencers, such as referee and weather as this would have taken a lot more time and effort and would not necessarily have resulted in a large enough increase in accuracy of the prediction to justify the added effort. Understand, this was a side project our team of data scientists did in their spare time. Had this been a client project, the level of effort would of course been a lot higher and we may have run a number of “challengers” in order to explore the predictive power of alternate data elements.

What are the myths or misconceptions around Cognitive Computing and Artificial Intelligence?

I think the main one is that machines or robots running on artificial intelligence will one day take over the world and rule over humans, like the HAL computer in the movie 2001 or The Terminator. I think enough of us are aware of or share this same fear that we will always ensure that mechanisms are in place to ever prevent a machine from making a decision that could put someone’s life in danger. With that being said, however, the advent of self-driving cars highlights the conundrums that have to be solved: do the manufacturers programme a car to hit a pedestrian crossing the street if it means swerving to avoid the pedestrian could lead to the severe injury or death of the passenger? These are the kinds of scenarios – the kinds of scenarios where there isn’t a clear right or wrong answer – that must be thought of and addressed when programming artificial intelligence.

Source: insights.principa.co.za-What is Machine Learning?

In the future, it will be the rise of the machines

“As taxi drivers feel disenfranchised by Uber, fund managers and investors will feel disenfranchised by other fund managers who have access to AI.”

Trading and price setting in financial markets face disruption within a decade as artificial intelligence (AI) turns investors away and computers start to trade against each other.

The stark warning was made on Monday by Sam Sicilia, the highly respected chief investment officer of Hostplus, an industry superannuation fund catering to employees in the hospitality industry.

Mr Sicilia predicted that in less than 10 years, smart phones would be faster than the fastest computers today, flagging the extraordinary power that next generation computers will possess.

As a result of such rapid technological development, investors who tend to use intuition to help guide their decision-making will find that intelligent computers will be able to develop similar, if not superior intuition by studying and recognising patterns.

“They will stop trading. Why play the game when you are always going to lose? This is all uncharted territory.” Bloomberg

“In today’s market, those investors who use more intuition and fewer spread sheets will win. In the future, it will be the rise of the machines,” Mr Sicilia said on the sidelines of the Australian Institute of Superannuation Trustees investment conference in Cairns.

In an audience survey, 60 per cent of conference delegates said that humans would always play a role in the investment process, but Mr Sicilia said that would increasingly not be the case.

“AI will make better decisions,” he said, pointing to the victory earlier this year of a computer Go program developed by Google, over 18-time Go world champion Lee Sedol.

Go is a complex board game and the match was compared with the historic chess match between Deep Blue and chess champion Garry Kasparov in 1997.

One of the risks for individual and professional investors is that those investment companies that start to build AI into their processes will start to outperform other investors, making those investors increasingly reluctant to trade.

“Disruption is likely to come from an uprising of disenfranchised investors around the world who are losing to technology. As taxi drivers feel disenfranchised by Uber, fund managers and investors will feel disenfranchised by other fund managers who have access to AI,” Mr Sicilia said.

“They will stop trading. Why play the game when you are always going to lose? This is all uncharted territory,” he said.

Looking further ahead, markets face even greater disruption as intelligent computers trade against each other and, having studied the same patterns, want to buy and sell the same security at the same time, potentially causing trading to stop altogether.

“If you get two algorithms trading against each other, why would only one of them take one side of the trade?”

Mr Sicilia said that eventually capitalism could be at risk as price discovery in financial markets was disrupted altogether.

When it comes to implementing technological changes inside companies, Kyle Kung, managing director at GX Innovation Lab at State Street in Hong Kong, said managers should encourage employees to fail when trying out new ideas and systems – but to fail fast.

He said that when his division was trying new products or services, they would typically bring business analysts, technology and user experience designers together and put together a prototype within six weeks. Using teams in different countries was useful so the company could take advantage of staff working in different time zones.

Mr Kung said that in determining where to employ new technologies, it was useful to start with the organisation’s “pain points”.


Source: afr.com-‘In the future, it will be the rise of the machines’

Major IT outsourcing acquisition will have mixed impact

Information Service Group’s purchase of competitor Alsbridge creates a larger, more well-rounded outsourcing advisory, but will also reduce competitive pricing leverage for clients.

The announcement that outsourcing consultancy Information Services Group (ISG) will acquire competitor Alsbridge marked the biggest M&A announcement in the IT services advisory industry since KPMG bought EquaTerra in 2011. The two large independent outsourcing advisors are joining forces to create a 1,300-person firm with offices in 20 countries revenues targeted at between $285 and $300 million in 2017.

The combined firm, with its expanded services, data and market intelligence, could put pressure on the big consultancies who offer IT outsourcing advisory services. “ISG’s principle competitors — KPMG. Deloitte, EY and PwC — now have a bigger, badder ISG to contend with that can not only undercut them on fees but also can boast competencies in the emerging area of RPA, where the Big Four are currently winning out,” HfS Research CEO Phil Fersht recently wrote in a blog.

The upside and the downside of an ISG Alsbridge merger

But, more importantly, the joining up of these two major players will have an impact on buyers of IT outsourcing advisor services. On the plus side are those additional competencies ISG brings on board with its purchase of Alsbridge. “
“The combined capabilities of ISG and Alsbridge are formidable,” says Lee Coulter, senior vice president of Ascension and CEO of their shared services subsidiary “This deal adds a strong BPO and growing automation services capability to ISG. I think it is a good fit operationally and geographically.” ISG had made several smaller acquisitions over the last six years as well. “For loyal clients of both ISG and Alsbridge,” says Fersht, “most will have a larger pool of talent to help them. “

However, on the downside, this merger will remove one of the only remaining large competitors from the IT outsourcing advisory landscape, eliminating some competitive pricing pressure from the market. “Those who loved to trade off ISG and Alsbridge to get their fees lowered will have to resort to really small firms like Avasant and Aecus as alternatives, who are good at some things, but will often struggle to scale up to meet client needs,” Fersht says.

The new ISG will serve more than 700 clients, including 75 of the 100 largest enterprises in the world. Efficiencies gained by combining the two firms will produce an estimated $7 million in cost savings in the first 18 months, according to ISG. Some new or bolstered services that Alsbridge will bring include telecommunication sourcing, audit, and transformation services and robotic process automation (RPA) assessment and implementation. Of course, the larger firm will also continue to advise on outsourcing deals; ISG and Alsbridge are currentlyinvolved in approximation $18 billion of transactions globally.

IT and business service advisors will remain important to enterprises for some time, says Coulter. “As integrated IT and business services offerings become mainstream, companies are going to continue to need advisory to help them navigate the complexities of complex business services relationships,” he says. “The advisors will need to focus on growing their capabilities into helping organizations do the business process transformations necessary to take advantage of new super nimble cloud based business services. To continue to thrive, advisors will have to be competent across technology, security, business process and process automation and transformation.”

Source: Cio.com-Major IT outsourcing acquisition will have mixed impact

Robotic Process Automation (RPA) in Finance

Robotic Process Automation is now commonly used in many work environments to automate back office processes. But what can be said about its growing use in finance?

Robotic Process Automation or RPA is often used a buzz word by software companies to latch onto to make their tired software sound sexy and exciting.

In many instances we have seen various intelligent software applications in and around finance over the last 20 years, with spreadsheets, macros, ERP systems etc. So what is really new or old for that matter?

Well, effectively there are 3 levels of robotics, so when a software provider approaches you claiming to be a RPA player, make sure you know how they compare:

Level 1: Simple Process Replacement – The entry level to robotics which tends to be process automation or replacement of manual repetitive tasks by a software generated programme. In finance we have seen this for some time around Accounts Payable (AP) and the ability to OCR invoices (scrape data from a pdf document and create ability to speed up transaction matching, add to workflows etc.) This type of technology is not new and has been in a mature market for 10-15 years.

Level 2: Learning – Now this is where the software starts to not only replace data from documents, or add to a workflow, but can also start to learn and complete transactions in a programme. For example taking on board new customers and reading the fields in a document to know what information to populate and where. Also using an algorithm that the software can apply based on previous known transactions to allow software based decisions to be applied that could otherwise take a human a long time to complete or figure out.

Level 3: Cognitive – Now this is where real robotic software automation comes into its own. Not only can the software scrape data, add to workflows, replace repetitive keystrokes, use algorithm decisions etc. but it can now take in lots of different and complex data sets, using lots of rules, algorithms, previous history and start to not only learn but provide fast decisions that normally humans would never get to, well not in seconds!

So let’s identify common pain points and look at how robotics can be applied in the finance department.

Cash allocation or application is usually seen as the least value add process in the finance team and is frequently managed manually by teams that will download bank statements, receive remittances and then play a game of manual matching and keying to try and clear the cash as quickly as possible. Adding complexity to the model, single payments across multiple accounts, lack of remittance or bank reference data often means that simple matches are completed and the more difficult ones get posted to account (if known) and then managed and reconciled by credit controllers. This can be an expensive, inefficient and often complex process usually requiring multiple people processing cash which often requires support at month end from credit controllers.

Utilising a combination of all three levels of robotics, Rimilia software solutions transform the manual cash allocation process from the first day of implementation. Alloc8 can manage high volumes of incoming receipts and match them to the sales ledger without the need for manual intervention from start to finish. In reality, does this not make you want to transform your cash process into a smarter more proactive front end function to enable for greater growth for your business? We think it should.

Many global brands are already using Rimilia RPA solutions to improve finance processes in their daily routines by making them more efficient


Source: rimilia.com-Robotic Process Automation (RPA) in Finance

Gartner’s predictions — a look at the top 10 tech trends

Three of Gartner’s top 10 technology trends envision significant changes — and problems — with data centers.

The number of systems managed on premise is on decline, as more work is moved to cloud providers, SaaS vendors and others. But that trend doesn’t mean that an IT manager’s job is getting easier.

“IT shops are realizing that as we move more work off-premise, it makes the job more complex,” said David Cappuccio, the Gartner analyst who develops the research firm’s annual list. He presented it Monday at the this year’s Symposium/ITxpo here in Orlando.

The “Disappearing Data Center” was the top-ranked technology trend. But another point about data centers, “Stranded Capacity” — listed as No. 6 on the list — is closely related.

Gartner, through its user surveys, found that 28% of the physical servers in data centers are “ghost” servers, or what are often called “zombie” servers. These are systems that are in service but not running workloads.

Another problem Gartner found in data centers is that 40% of racks are underprovisioned. That means data center managers are wasting space by not utilizing racks, and might be able to shrink the size of their data centers through better management, said Cappuccio. Servers are also operating at 32% of their performance capacity.

Another data center-related trend, No. 5 on Gartner’s list, was the idea of Data Center-as-a-Service. Instead of thinking about the “data center” as the center of computing resources, managers are seeing their role as a deliverer of services to the business.

Other trends included interconnect fabrics, listed at No. 2, which are increasingly available in multi-tenant data centers. They provide networks that give users access to multiple services, such as the cloud services offered by Google, Amazon and Microsoft, as well as SaaS providers and analytics services. It gives users more flexibility to find the best platform and price, as well as redundancy.

The third top trend concerned the use of containers, microservers and application streams. Virtual machines need an operating system, but containers only require what’s needed to run a specific program. Containers can last weeks, days or seconds — “they drive new ways of looking at development,” said Cappuccio.

In fourth place is “business-driven IT.” Survey data shows that at least 29% of IT spending is outside the IT department. “Business is not willing to wait for IT,” said Cappuccio.

Two of the top 10 trends involved the internet of things (IoT), in particular emerging IoT platforms, which in many cases are incompatible. As for another trend, remote device management — “This could be a major headache,” said Cappuccio.

Micro and edge computing environments is next to last as a trend, and involves putting compute resources in places where they are most needed. That may include installing analytical capabilities at distant worksites that can be managed, for the most part, remotely.

The final trend, as pegged by Gartner, concerned the skills needed to manage emerging environments, including IoT architect, someone to manage cloud sprawl, and a capacity and resource manager.


Source: cio.com-Gartner’s predictions — a look at the top 10 tech trends

Rise of the automatons: Mary Lacity’s latest book tackles service…

Mary Lacity, Curators’ Distinguished Professor of Information Systems at UMSL, researches how robots and humans intersect in the workforce. (Photo by August Jennewein)

The exploration of how technology can spell promise or peril for mankind is a common trope in science fiction narratives such as “Star Trek,” “Blade Runner” and “The Matrix.”

Each series presents far-fetched story lines where human beings battle against sentient artificial intelligence bent on usurping mankind’s place in the world.

Co-authored with Leslie P. Willcocks, Mary Lacity’s latest book makes arguments for the efficacy of utilizing robots in industry and quells notions of a future where employment is scarce for humans. (Image courtesy of Mary Lacity)

Yet automation in current day industry is no science fiction — it has quickly become science fact, and concerns of employees being replaced by machines that can build cars, calculate insurance claims and win game shows have become all too real.

Despite worry over a jobless future, where the majority of the population would remain unemployed, Mary Lacity, Curators’ Distinguished Professor of Information Systems at the University of Missouri–St. Louis, is convinced that these fears may be overblown and inaccurate.

In her latest book, “Service Automation: Robots and the Future of Work,” she and co-author Leslie P. Willcocks from the London School of Economics analyze how new technology has changed, energized and grown within companies. In her estimation, automation is more boon than burden to society, allowing experienced professionals opportunities to develop new skill sets and advance their field.

“The predictions of jobs lost to automation rarely consider all the new jobs that will be created,” Lacity said. “Few knowledge-based occupations will go away, but rather the nature of those jobs will evolve. Accountants, auditors, doctors, professors, lawyers and other knowledge-based occupations will still exist – it’s just the composition of the tasks within those jobs that will be altered because of automation.”

Although AI programs such as IBM’s “Jeopardy!” winning Watson can process millions of pages of data in sub-second time, Lacity contends that machines – even incredibly fast ones – still just manipulate symbols.

According to Mary Lacity’s research, humans prove creative, original and empathetic – and can outperform even the most sophisticated computers in novel situations. (Image courtesy of Mary Lacity)
Always interested in math, Lacity pursued an undergraduate degree in quantitative business analysis at Penn State University during the early ’80s. Upon graduation, she took a job at Exxon where she was then trained in the emergent field of management information systems. The opportunity to work with cutting-edge technology offered her a revelation that would shape her career for decades to come.

“I soon realized that technologies offer no magic solutions — getting value from technology requires a massive amount of human agency and management,” Lacity said. “I became interested in studying how companies can successfully deploy technology, so I matriculated into a PhD program in MIS at the University of Houston to find answers.”

And now, as she nears 25 years as a professor at UMSL, Lacity’s research has yielded numerous publications, including a recent contribution to the MIT Sloan Management Review in which she and her co-author describe how human-robot teams are changing service industries for the better.

Through her research, Lacity has found that a cyborg-ruled, dystopian future ripped from the pages of science fiction thrillers is not likely. So far, software robots have been useful in taking over dreary, repetitive and highly structured tasks, liberating humans to perform other tasks requiring creativity, problem-solving and emotional intelligence – an outcome that, thankfully, mirrors the utopian possibilities of human advancement as seen in Star Trek.

“Across the Star Trek series, the Federation chose to use automation to liberate humanity from poverty and starvation so that humanity could focus on exploration and self-actualization,” Lacity said. “The Star Trek series underscores the idea of human agency and the importance of choice, which is important to consider as we seek to advance the use of new technology in thoughtful ways – otherwise we might all end up as Borgs.”

Source: blogs.umsl.edu-Rise of the automatons: Mary Lacity’s latest book tackles service…