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How Analytics Are Changing Frequent Flyer Programs

October 24, 2017 /

Airlines gather and analyze more data than ever before to improve operations and deliver better travel experiences. One of the most effective data-gathering tactics has been the establishment of frequent flyer programs.

In the beginning, those programs enabled the airlines to identify their “best” customers — those who flew more than others or spent more for seats than others. Today, frequent flyer status is determined by a much more sophisticated calculus that involves many data points , some of which are very creative. The data provides insight into customer behavior and preferences, as well as operational issues that need to be addressed for compliance or competitive reasons.

Like other businesses, airlines use increasingly sophisticated website analytics to better understand customer behavior and preferences. They also use mobile app data and social media data and they’ve structured partnerships with other airlines and businesses.

In short, airlines have more insight into travelers’ preferences and behaviors than ever before.

More Perks Mean More Data

Airlines offer branded credit cards because they provide insight into customers’ purchasing habits. Their partnerships with other airlines provide additional information.

Meanwhile, airlines continue to expand their frequent flyer programs beyond hotels and rental cars to include all sorts of things including flowers, a marriage proposal kit, wine and even a dog sled ride. Apparently one gentleman managed to rack up over 1.2 million miles purchasing massive amounts of pudding. If you’re interested in earning up to 250,000 Qantas Frequent Flyer program miles in a single purchase, buy a Jaguar.

Interestingly, Qantas Frequent Flyer members can use their miles to pay for the healthcare insurance Qantas offers. (Imagine that on the US Congressional agenda!) As part of its Wellness program, parents and their kids can earn points just by downloading the app and staying active.

Every swipe, every click, every soccer ball kick now matters.

Analytics Means Business for Some Airlines

Qantas’ Loyalty department is so effective, the company made the group available for hire so other businesses can maximize the ROI of their marketing and loyalty programs. Its strategy caused Virgin Airlines Australia to acquire an analytics company rather than building an internal capability.

Air Canada’s Aeroplan story is also interesting. In 2002, the airline spun off its loyalty program as a separate company. However, in 2020, Air Canada will launch its own loyalty program — again.

“The new program, launching in 2020, will offer additional earning and redemption opportunities, more personalized service and a better digital experience for Air Canada customers,” said Benjamin Smith, president, Passenger Airlines at Air Canada in a press release. “[B]y managing our own loyalty program, we will be able to take better care of our customers by making decisions in real time that address specific needs.”

Mr. Smith makes a good point. Fast data and combined data sources enable airlines to provide contextual experiences. Text alerts of flight delays or gate changes are just two examples. Airports have their own analytics infrastructures which feed certain information to airlines. Not surprisingly, airlines are also monitoring what’s happening in their member-only lounges so they can provide additional competitive benefits and improve operations.

HR Use of Social Media Grows, But Is the Data Reliable?

October 21, 2017 /

A recent CareerBuilder study of 2,300 hiring managers and human resources professionals shows that more employers are using social media to make hiring and retention decisions.

Drinking, partying and Kim Kardashian-like “break the Internet” posts are clearly unwise for anyone who wants to build a career or keep a job. According to a CareerBuilder press release, among employers who use social media networking sites as a source of information, 54% decided not to hire candidates based on their social media profiles, half of employers check employees’ social media profiles, and more than a third have reprimanded or fired an employee for inappropriate conduct. Seventy percent use social media to screen candidates.

Conversely, 57% are less likely to interview a candidate they can’t find online.

“The majority of employers are looking for information that supports their qualifications on the job [including] a professional persona, and what other people are posting about the candidate,” said Rosemary Haefner, chief human resources officer at CareerBuilder.

Employers want to know how well candidates are able to communicate and whether they exhibit prejudice against persons of a different race, gender or religion. They’re also interested in things candidates have to say about their previous employers, whether they’re lying about their qualifications, and more.

“Post at your own peril,” said Attorney James Goodnow, legal analyst. “Everything you put on Facebook, Instagram and Twitter is fair game for employers and often will have more of an impact on your employment prospects than what you say or do in a job interview. The reason: many employers consider what you post on social media to be the ‘real’ you.”

What if social media forces factual inaccuracies?

LinkedIn is the go-to place to find a person’s professional qualifications and work history, although abbreviated versions of the same information may appear in other social media profiles. What would happen if a person were kicked off one of the networks? Would it matter to the others? What would the person do?

Suppose that Facebook, relying on its famous algorithms questioned your authenticity after years of account activity. True, there is a grievance process. A person can send personally-identifiable documents, hoping to reactivate the account, which reportedly works for some people and not for others. If it doesn’t work, you could try to open another account on the site, but all of the data associated with the original account — email addresses, home town, educational background, and the like — might not be permitted under the new account. You become an unperson in the social media world.

Aside from the potential HR issues, another question is whether such an incident affects a person’s credit score.

Why social media doesn’t score at FICO

FICO looks at thousands of variables, but it tends to use less than a hundred when calculating a person’s credit scores. Apparently, the use of more variables leads to diminishing returns.

“Social media does not play into FICO scores in the U.S.,” said Sally Taylor-Shoff, Scores Vice President at FICO. “In the U.S., lenders use FICO scores to make lending decisions. Lending decisions are regulated, so the use of social media data will not meet the compliance requirements most lenders have to deal with.”

Past payment history is the most predictive indicator of whether a person will repay a loan. If the person doesn’t have a loan history, then FICO uses that person’s payment history of rent and cell phone bills, for example.

“We use a six-point test to evaluate whether that data should be used: whether it meets regulatory requirements, whether it has enough depth and breadth, enough scope and consistency in the data, and whether it’s predictive,” said Taylor-Shoff.

Accuracy also matters.

“It can’t be something consumers can just use or manipulate,” said Taylor-Shoff. “Credit data comes from creditors.”

Even though there may be some inaccuracies, lenders are legally required to have a grievance process consumers can pursue, and there’s no shortage of consumer protection information about what an aggrieved consumer can do.

In the social media world, bot decisions may be final, and there’s not necessarily a lot of transparency.

Want to Succeed at Data-Driven Transformation? Start Slow

September 21, 2017 /

Data-driven transformation efforts often fail because companies are moving too fast or too slow. If they’re racing into it, they’re probably not getting the details right. If they’re trying to get everything into a perfect state before doing anything, they’re wasting precious time. Either way, ROI suffers.

If you want to strike the right balance, you have to think carefully about what you want to do and why you want to do it. You also have to move at an appropriate speed which means balancing short-term and long-term goals.

Start small

Boston Consulting Group recently published an in-depth article about data-driven transformation. In it, the authors raise a lot of good points, one of which is taking small steps rather than giant leaps.

Taking small steps enables businesses to succeed more often, albeit on a smaller scale. If the project is a success, the money saved or earned can be used to fund subsequent steps. Small successes also help keep teams motivated. However, executing too many small projects simultaneously can be counterproductive.

“The first mistake I see is doing 200 proof of concepts. The second thing I see is people start to do a pilot, even at scale, but [they think] first we need a developer to reinvent the system and then we’ll get the value at the end,” said Antoine Gourévitch, a Senior Partner and Managing Director at BCG. “I’d rather find a quick and dirty way to connect the IT systems and get [some immediate value] rather than doing a full transformation. You can have a transformation over three to five years, but at least I need to do the connection between my pilot at scale and the dedicated systems for the data platform that’s needed to be of value as we go.”

The third challenge is prioritizing pilots or projects. Value is the criteria there. Without a prioritized roadmap, “cool” projects may take precedence over projects that deliver business value.

Three steps to better ROI

BCG offers a lot of good advice in its article, not the least of which is breaking short-term and long-term goals into a three-step process that enables quick wins while paving the way to data-driven transformation. The three steps are:

  • Use quick wins fund the digital journey and learn
  • Design the company-wide transformation
  • Organize for sustained performance
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Within those three steps, BCG specified actions companies should take. Upon close review, it’s clear that some of the recommended actions, such as “fail fast,” apply to more than one step. If you read BCG’s article and ponder the graphics, it will get you thinking about how to scale success in your organization.

BCG also presents a five-level transformation model that includes vision, use cases, analytics, data governance and data infrastructure. Gourévitch said data governance tends to be the most problematic because data isn’t viewed as a corporate asset, so data owners may hesitate to share it.

Bottom line

Companies often move too fast or too slow when becoming data-driven organizations. When they move too fast, they can overlook important details that cause initiatives to fail. When they move too slow, they risk losing competitive advantages.

Somewhere between 200 pilots and one massive transformation effort is a balance of short-term and long-term goals, investment and ROI. The challenge, as always, is finding the balance.

Why Advanced Analytics Is in Your Future

September 15, 2017 /

Basic reporting and analytics are now competitive table stakes across industries. As 2020 approaches, more companies are using sophisticated algorithms to drive higher levels of efficiency, reduce costs and risks, drive additional revenue, improve customer experience and more. If organizations want to become truly agile in today’s dynamic business environment, they have to continually improve their operations and evolve the ways they’re using analytics.

“If you’re not using advanced analytics yet, you’re in trouble,” said Bill Franks, chief analytics officer at the International Institute for Analytics (IIA). “Twenty years ago, if you were doing some type of analytics you had competitive advantage. Now if you’re not doing analytics, you’re falling behind. If companies don’t push to adopt the new stuff, it’s going to become a problem over time.”

What Is Advanced Analytics?

Advanced analytics, like data science, lacks a standard description, although characteristically, it involves prediction. Deep learning, neural networks, cognitive computing, and AI come to mind because the algorithms have capabilities traditional input/output systems just can’t provide.

“What’s commercially possible to do has expanded significantly,” said Chris Mazzei, chief analytics officer at professional services company EY. “Decreasing technology costs and the explosion of data changes what’s possible to do with analytics, and [the possibilities] are growing every year. That, combined with competitive pressures means if you’re not looking for ways to reduce costs, enhance customer experience, create new products and services, if you don’t want to manage risks radically different and better, you’re in trouble.”

Most companies start with basic analytics and then increase the level of sophistication as they begin to realize the limitations of their existing systems. Disruptors are an exception because they use advanced analytics early on in an attempt to outthink and outmaneuver the existing players.

Whether your company is trying to compete more effectively or just stay relevant, advanced analytics is in your future, sooner or later. The question is whether your company will lead or follow. Either way, now is the time to learn all you can about advanced analytics so you understand what benefits it can drive for your company.

Even Small Businesses Should Care

Not so long ago, only large companies could afford the tools and specialists necessary to take advantage of advanced analytics. However, as more capabilities are made available through cloud-based services and as more of the complexity is abstracted, more businesses are able to advantage of advanced analytics without spending millions of dollars and hiring data scientists.

For example, lawn care aggregator site LawnStarterstarted using prescriptive analytics about two years after the founders defined the business concept. The initial goal was to decrease customer churn.

“We have a customer risk model and a provider risk,” said Ryan Farley, co-founder or LawnStarter. “We have thousands of lawn care providers in our system and the number of jobs they have ranges from tens to hundreds. Sometimes they take on too much. Before we had predictive analytics, we had to wait for the problem to become obvious.” Now LawnStarter is able to operate in a proactive way rather than a reactive way.

In all fairness, Farley wasn’t a typical entrepreneur. Previously, he worked for Capital One, which has been using predictive analytics since the 1990s to improve the ROI of its direct mail campaigns. When LawnStarter was founded, the founders wanted to do “cool stuff” rather than follow the traditional method of starting a company, building a product, and writing code. Fortunately, LawnStarter and machine learning platform provider DataRobot were part of the same Techstars accelerator program, so LawnStarter became one of DataRobot’s beta customers.

“We were like, ‘This is so cool! There’s predictive capabilities in our data sets!” said Farley. “We started out doing it for fun, but then we realized there was actually business to be had there. Shortly thereafter, we started investing in the data infrastructure to where we can compile our different data together and make sure everything we’re collecting is consistent and accurate.

Tips for Getting Your Company Started with Analytics

September 1, 2017 /

n today’s fast-paced economy, businesses need access to insights faster than before. While periodic reporting still has its place, organizations are looking for deeper and more timely insights that can help them make better decisions, cut costs, improve efficiencies, reduce risks and drive more revenue.

It’s pretty obvious from all the hype around the topic that many powerful things can be done with analytics, but it isn’t always obvious where one should begin. We asked some experts — they or their firms presented in the Data & Analytics Track at Interop ITX this month — where they thought businesses should start, and here’s what they had to say.

Prove value, not concepts

Some organizations spend too much time trying to get everything in a perfect state before using analytics, which wastes valuable time and overcomplicates what could be a simple beginning. The best way to start is to choose a project that has the potential to demonstrate value without requiring a lot of extra work or heavy investment.

“When we build something that returns value to the organization right away, people start buying in because they see the ROI and the other types of value it provides like efficiencies in the workforce, short time to solution or short time to value,” said Kirk Borne, principal data scientist at Booz Allen Hamilton. “It can be something simple.”

For example, a financial services company managed to save $1 billion simply by analyzing web clicks, Borne said.

“They already had web analytics in place. They just needed to pay attention to what the signals were telling them,” said Borne. “It doesn’t have to be a complicated model or involve complicated data to prove value.”

Analytics can start anywhere

Analytics can begin at any level in an organization, whether it’s an executive who wants an answer a strategic question or a line of business manager or staff member who needs to solve a tactical problem.

Five years ago, the Association of Schools and Programs of Public Health(ASPPH) assigned data analytics as a part-time job to its current director of data analytics and another employee. The association had been sending members periodic reports, but as the volume of data grew, it became obvious ASPPH could provide more value to its members with analytics.

“We started out small, providing a few dashboards to our members,” said Christine Plesys, director of data analytics at ASPPH. “They didn’t have to wait four months to receive a report.”

Now ASPPH is teaching its members about the best practices in data analytics and how to use the data ASPPH provides for strategic planning and internal benchmarking purposes. In addition, its data analytics staff has grown to four full-time employees

Get an executive sponsor

First efforts can be difficult to get off the ground if no one at the executive level understands the potential value of the project. To minimize that challenge, it’s wise to have an executive involved who will help the project succeed.

“One of the things some people miss is it’s not just a chief data officer or a data scientist you should be talking to,” said Booz Allen Hamilton’s Borne. “Sometimes it’s the chief financial officer or chief marketing offer because those people hold the purse strings on the kinds of investments that need to be made.”

Expect the unexpected

When people receive reports, they often have more questions that require a new report to be built. Analytics dashboards enable individuals to explore data in a more iterative fashion which needs to be considered when launching a first attempt.

“A lot of people think analytics is a new word for data warehousing or business intelligence and then they try to run their [analytics] project the same way,” said Karen Lopez, senior project manager and architect at data project and data management consultancy InfoAdvisors. “At a base level, you’re building a Q&A system because you don’t know [all of the questions] you’re going to ask.”

It’s also difficult to anticipate what unexpected circumstances might arise, especially when launching an analytics project without the help of an expert. For example, it may be difficult to get the necessary data from IT or from another department because they don’t want to share it.

People new to analytics also tend to overlook data quality. Poor data quality can cause spurious analytical results and perfect data quality is virtually unattainable. It’s wise to understand the tradeoffs between data quality and the time and expense it takes to get it into a state that’s “good enough” for the purposes it will serve. An expert can help you find that balance.

Bottom line

Too often, first attempts are derailed by overcomplicating the problem or attempting to solve a problem that is too complex to be solved well with existing team members and tools. The best first analytics project is one that can demonstrate value quickly and cost-effectively. If you succeed, it will be easier to make a case for follow-on projects. If you fail, you’ll learn a lot without wasting months or years and several million dollars.

Analytics Ensure Safety in LA and White Plains

August 24, 2017 /

Security is top of mind when city CIOs think about the types of analytics they need. However, analytics is also enabling them to improve internal processes and the experience citizens and businesses have.

The City of White Plains , New York stores its data in a data center to ensure security. The City of Los Angeles has a hybrid implementation because it requires cloud-level scalability. In LA, 240 million records from 37 different departments are ingested every 24 hours just for cybersecurity purposes, according to the city’s CIO Ted Ross.

“We didn’t start off at that scale but [using the cloud] we’re able to perform large amounts of data analysis whether it’s cybersecurity or otherwise,” Ross said.

He thinks it’s extremely important that organizations understand their architecture, where the data is, and how data gets there and then put the appropriate security measures in place so they can leverage the benefits of the cloud without being susceptible to security risks.

“If you’re not doing analytics and you’re moving [to the cloud], it’s easy to think it will change your world and in certain [regards] it may. The reality is, you have to go into it with both eyes open and understand what you’re trying to accomplish and have realistic expectations about what you can pursue,” said Ross.

White Plains is on a multi-year journey with its analytics, as are its peers because connecting the dots is a non-trivial undertaking.

“Municipalities have a lot of data, but they move slowly,” said White Plains CIO Michael Coakley. “We have a lot of data and we are trying to get to some of the analytics [that make sense for a city].”

Departments within municipalities still tend to operate in silos. The challenge is eliminating those barriers so data can be used more effectively.

“It’s getting better. It’s something we’ve been working on the for the last few years which is knocking down the walls, breaking down the silos and being able to leverage the data,” said Coakley. “It’s for the betterment of citizens and businesses.”

Connecting data from individual departments improves business process efficiencies and alleviates some of the frustrations citizens and businesses have had in the past.

“If you’re a small business owner who bought a plot of land in White Plains and wants to [erect] a building, you could go to the department of Public Works to get a permit, the Building Department to get a permit and the Planning Department to get a permit and none of those departments know what you’re talking about,” said Coakley. “With the walls being broken down and each department being able to use the data, it makes the experience better for the business or home owner.”

The city is also connecting some of its data sets with data sets of an authority that operates within the city, but is not actually part of the city.

“There’s a reason for their autonomy, but it’s important to start the dialog and show them [how connecting the data sets] will benefit them,” said Coakley. “Once you show the department what they can provide for you, and ensure it’s not going to compromise the integrity of their data, they usually come along. They see the efficiencies it creates and the opportunities it creates.”

In those discussions, it becomes more obvious what kind of data can be generated when the data sets are used and shared and what kind of analytics can be done. The interconnection of the data sets creates the opportunity to get insights that were not previously possible or practical when the data generated in a department stayed in that department.

White Plains is trying to connect data from all of its departments so it can facilitate more types of analytics and further improve the services it provides citizens and businesses. However, cybersecurity analytics remain at the top of the list.

“Cybersecurity is number one,” said Coakley. “We have to worry about things like public safety, which is not just police, fire, emergency, public works, facilities, water, electrical, and engineering. There’s a lot of data and the potential for a lot of threats.

Why Automation and AI are Cool, Until They’re Not

August 15, 2017 /

Every day, there’s more news about automation, machine learning and AI. Already, some vendors are touting their ability to replace salespeople and even data scientists. Interestingly, the very people promoting these technologies aren’t necessarily considering the impact on their own jobs.

In the past, knowledge jobs were exempt from automation, but with the rise of machine learning and AI, that’s no longer true. In the near future, machines will be able to do even more tasks that have historically been done by humans.

Somewhere between doomsday predictions and automated utopia is a very real world of people, businesses and entire industries that need to adapt or risk obsolescence.

History isn’t simply repeating itself

One difference between yesterday’s automation and today’s automation (besides the level of machine intelligence) is the pace of change. Automating manufacturing was a very slow process because it required major capital investments and significant amounts of time to implement. In today’s software-driven world, change occurs very quickly and the pace of change is accelerating.

The burning existential question is whether organizations and their employees can adapt to change fast enough this time. Will autonomous “things” and bots cause the staggering unemployment levels some foresee a decade from now, or will the number of new jobs compensate for the decline of traditional jobs?

“I think there will be stages where we have the 10 percent digital workforce in the next two years and 20 percent in three to four years,” said Martin Fiore, Americas tax talent leader at professional services firm EY. “Some will say, ‘Wow, that’s scary.’ Others will say, ‘I see the light I’m going to upscale my capabilities.”

Businesses and individuals each need to change the way they think.

Angela Zutavern, VP at management consulting firm Booz Allen Hamilton and co-author of the forthcoming book, The Mathematical Corporation views intelligent automation as a new form of leadership and strategy as opposed to just technology.

“Companies who understand this and get on board with it will be way ahead and I believe that companies who either ignore it or don’t believe it’s real may go out of business,” she said. “I think it’s better to know about it, understand it, and be a part of making the change happen rather than getting caught off-guard and have it happen to you.”

An old company pioneers new tricks

Despite its 100-year history, EY is actively facilitating the adoption of Robotics Process Automation (RPA) and AI within its own four walls and among its customers.

Its RPA group employs a global team of 1,000 robotic engineers and analysts who are creating new applications. In past 18 months, more than 200 bots have been rolled out in tax operations, which includes work for clients. EY is also using RPA processes internally in core business functions to improve quality and performance while enabling a new sense of purpose among its employees.

“RPA helps us increase our ability to handle high levels of transaction volume (e.g.,tax returns), accelerate on-time delivery and improve accuracy,” said Fiore. “Over time, there will be a positive impact on our workforce model, and we’re planning for that now.”

Similarly, an EY innovation lab recently experimented to see if AI could help to analyze contracts faster and better than people.

“We thought we’d make headway and great progress in a year or two, but in the first 90 days [the machines were] three times more effective in the process,” said Jeff Wong, global chief innovation officer at EY. “You’ll see us increasing our efforts there radically in the next 12 to 18 months.”

Last year, EY “hired” 350 bots, although company spends about a half a billion dollars annually on employee training. Job rotation is also common at EY because the company wants to “teach people to learn how to learn.”

Education will change

Young people entering the workforce already need different skills than their predecessors, and the trend will continue. Param Singh, associate professor of business technologies at Carnegie Mellon University expects grade schools to teach fundamental programming skills and high schools to teach machine learning.

“Typically, managers [had] person management jobs. Increasingly those jobs will have to be good on the technology side,” said Singh. “Few people are good at deep learning, probably less than 5,000. We’ll needs hundreds of thousands when we see major adoption happening.”

Meanwhile, working professionals and their employers should not be complacent. As the levels of intelligent automation increase, individuals and companies will need to understand which jobs will be displaced and which jobs will be created, none of which is static.

“Cloud computers, data lakes and in the future, quantum computing are things that every leader should be conversant about or anyone who aspires to a leadership role in this machine learning age,” said Booz Allen Hamilton’s Zutavern. “People should understand what the possibilities are and know when to pull in the deep experts.”

DevOps Not Working? Here’s Why.

August 1, 2017 /

DevOps can help organizations get better software to market faster, when it’s working. When it’s not working, development and operations teams aren’t working as a cohesive unit.  They’re operating as distinct phases of a software development lifecycle.

Part of the problem may involve tools. Either the tools still operate as silos or they don’t provide the kind of cross-functional visibility that DevOps teams require. However, a bigger task may be getting development and operations working together.

What makes DevOps even more challenging is that there’s no one right way to do it.  Of course, there are better and worse ways to approach it, so here are a few suggestions to consider.

Think before automating. Automation is part of DevOps, but it’s not synonymous with DevOps. While it’s true that automating tasks saves time, automation also accelerates the rate at which mistakes can be propagated.

“If you just automate things and you haven’t built the skills to handle high speed, you’re putting yourself in a place where friction and accidents can happen,” said Sean Regan, head of growth, software teams at software development tool provider www.atlassian.com. “Before you automate everything, start with a culture. You’ll have happier developers, happier customers, and better software.”

Test automation is essential for DevOps, and to do that well, developers need to test their software in a production environment.

“DevOps is founded in automation. One of the first things organizations recognize is they need a dynamic infrastructure which most people think is cloud,” said Nathen Harvey, vice president, Community Development at DevOps workflow platform provider www.chef.io Chef Software. “It doesn’t have to be cloud, it means you have compute resources available to developers and the people who are running your production organization.”

With the help of automation and developer access to production environments, DevOps teams are delivering software in days or weeks instead of months.

Cultivate a DevOps culture. Software teams that have gone through an Agile transformation remember they had to change their culture for it to succeed. The same is true for DevOps.

“You need to get your teams collaborating in a way they haven’t done before,” said Harvey. “It becomes much less about a hand-off and more about understanding the common goals we’re working towards.”

One indication of DevOps maturity is whether the shipment of software is considered an end or a beginning. Atlassian used to celebrate after a product shipped, which used to be common for software companies. Now Atlassian celebrates milestones hit after the release, such as the number of customers using a new feature within a given time frame.

Take a hint from web giants. A decade ago, web companies were embracing DevOps and figuring out how infrastructure could be managed as code.  Meanwhile, other companies were operating in business-as-usual mode.

“If you’re coming from a more traditional organization, the idea of managing infrastructure as code may still be new,” said Chef Software’s Harvey. “I think the best way to achieve success is to pull together a cross-functional team that cares about driving a particular business outcome, such as how to deliver this one change out to our customer.”

 Cheat. Companies spend lots of time reinventing what works at other companies. Atlassian memorialized a lot of what it has learned in self-assessments and playbooks, so DevOps teams can identify and address the challenges they face.

“Customers are coming to us saying, ‘Give us playbooks, give us patterns, give us specific actionable ways to move toward DevOps,” said Regan.  “If you’re moving to DevOps, there’s usually an early stage where you wonder if you’re doing it right.”

16 Machine Learning Terms You Should Know

July 28, 2017 /

Advanced analytics is heating up. AI, machine learning, deep learning, and neural networks are just some of the terms we hear and should know more about. While most of us will never become statisticians or unicorn data scientists, it’s wise for us to understand some of the basic terms, especially since we’ll be hearing a lot more about machine learning in the coming years. Here are a few terms we should all know from some sites that have much more to offer:

Algorithm – a step by step procedure for solving a problem.

Attribute – a characteristic or property of an object.

Classification – to arrange in groups.

Clusters – groups of objects that share a characteristic that is distinct from other groups.

Correlation – the extent to which two numerical variables have a linear relationship.

Deep Learning – An AI function that imitates the workings of the human brain.

Decision Tree – a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

Natural Language Processing (NLP) – the automatic (or semi-automatic) processing of human language.

Neural Networks – a series of algorithms that attempts to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates.

Normal Distribution – symmetrical distributions that have bell-shaped density curves with a single peak.

Outlier – an observation that lies an abnormal distance from other values in a random sample from a population.

Regression – a statistical process for estimating the relationships among variables.

Statistical Model – a formalization of relationship between variables in the form of mathematical equations.

Supervised Learning – accomplished with training data that includes both the input and the desired results.

Unsupervised Learning – accomplished with training data that does not include the desired results.

Why IT is in Jeopardy

July 22, 2017 /

ome IT departments are struggling to prove their relevance as the pace of change continues to accelerate. On one hand, they’re responsible for their own predicament and on the other hand they’re not.

IT has been the master of change. On the other hand, what department wants to be responsible for its own its own demise?  IT as a function isn’t dead and it’s not going to be dead any time soon. However, IT is changing for good. Here’s why:

IT overpromised and under-delivered

Lines of business no longer want to wait for IT. They can’t. The competitive pressures are just too great to ignore. But, when something goes wrong with their tech purchases, who do they call?  IT.

“IT is in jeopardy because of the agreements or promises they’ve made to the business,” said David Caldwell, senior IT solutions consultant at Kaiser Permanente. “You can’t deliver on time, you can’t deliver what you promised and you can’t deliver reliable systems.”

What the business really wants is a dependable, enabling service that delivers what it promises.

Business expectations are too high

IT can’t be successful if the business leadership is viewing IT as a cost rather than an investment, which seems a bit strange, given the fact that today’s companies depend on technology for survival. Nevertheless, some businesses still have legacy cultural issues to overcome, one of which is realizing how value in their company is actually produced in this day and age.

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Worse, even C-level information and technology executives may not be viewed as equals among business leaders, so they’re left out of important meetings. Rather than having a partnership between IT and the business, the business may tell IT what it wants when without understanding the entire scope of the problem and how difficult or complex the solution to the problem may be.

“They don’t consider that IT leadership can help you decide how you’re going to strategically differentiate your business,” said Caldwell. “If you don’t let them in, you’re missing out on a lot of valuable input.”

A related issue is budget. If IT isn’t given enough budget to be successful, who can pin failures on IT?  Yet, over the past couple of decades IT has been told to do more with less to the point where the model itself breaks down.

IT has enabled its own demise

IT had a specific role to play before the cloud, SaaS and Shadow IT were fashionable. They were the keepers of hardware, software and networks.

“IT brought the wave of innovation in, [and yet,] IT is under the same assault of things they were the perpetrators of,” said Greg Arnette, CTO at cloud information archiving company, Sonian. “IT is going through a metamorphosis that reduces the need to have as many in IT as in previous history.”

The adoption of cloud architectures and SaaS were fueled by the economic downturn of 2008 and 2009 which forced companies to view IT in terms of operating expenses rather than capital expenses.

“It was a perfect storm,” said Arnette. “Shadow IT was driven by business unit managers frustrated with their IT departments [so they] used their credit cards to sign up for Salesforce.com or go buy ZenDesk or any of these popular SaaS apps that have become the new back office systems for most companies.”

Never mind who purchased what with which purchasing method — purchase order or credit card — when things go wrong, it’s IT’s job is to fix it. That’s one way to provide the business with services, but probably not the model IT had in mind.

The CIO/CTO role is changing

There are plenty of CIOs and CTOs, but some of them are being moved into new roles such as Chief Data Officer, Chief Analytics Officer or Chief Innovation Officer. Whether these roles are a reflection of The Brave New World or whether they’re ultimately too narrow is a debatable point.

“It’s not such a focus on information. It’s now analytics, data wrangling and a focus on innovation as a key way IT can help customers do more,” said Arnette. “I think that’s where IT will come back, but it won’t be the same type of IT department.”

Indeed. Traditional hardware and enterprise software management are being usurped by IaaS and SaaS alternatives. It’s true that a lot of companies have hybrid strategies that combine their own systems with virtualized equivalents and that some companies are managing all of their own technology, but the economics of the virtual world (when managed responsibly) are too attractive to ignore over the long term.

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