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Category: Analytics (Page 2 of 4)

Why You Business May Not Be Ready for Analytics

Artificial intelligence is on the minds of business leaders everywhere because they’ve either heard or believe that AI will change the way companies do business.

What we’re seeing now is just the beginning. For everyone’s sake, more thought needs to be given to the workforce impact and how humans and machines will complement each other.

Recently, professional services company Genpact and FORTUNE Knowledge Group surveyed 300 senior executives from companies in the North American, European and Asia-Pacific regions with annual revenues of $1 billion per year or more. According to the report, “AI leaders expect that the modern workforce will be comfortable working alongside robots by 2020.”

However, getting there will require a different approach to organizational change.

“A bunch of people are thinking about AI as a technology. What they’re not thinking about is AI as the enabler of new enterprise processes, AI as an augmenter of humans in enterprise processes,” said Genpact Senior Vice President Gianni Giacomelli. “Right now, 70% of the effort is spent on technology, 20% on processes and 10% on humans as a process piece. I think that’s the wrong way to look at it.”

What is the right way to think about AI? At one end of the spectrum, people are touting all the positive things AI will enable, such as tackling some of our world’s biggest social problems. On the other end of the spectrum are Elon Musk, Stephen Hawking and others who foresee a dark future that involves unprecedented job losses if not human extermination.

Regardless of one’s personal view of the matter, business leaders need to be thinking harder and differently about the impact AI may have on their businesses and their workforces. Now.

How to think about the problem

The future’s trajectory is not set. It changes and evolves with technology and culture. Since AI’s end game is not completely foreseeable, one way to approach the problem, according to the survey, is to begin with the desired outcome, think about the processes required to achieve that outcome and then ponder how machines and humans can complement each other.

“Generally, the biggest impediment we see out there is the inability to create a portfolio of initiatives, so having a team or a number of teams coming back and saying, ‘These are the 50 things I could do with AI based on what AI is able to do today and in the next 12 months,’ and then [it’s up to senior management to] prioritize them,” said Giacomelli. “You need to have people going through the organization, unearthing places where value can be impacted.”

Over the last three decades or so, business leaders have been setting strategy and then implementing it, which isn’t going to work moving forward. The AI/human equation requires a hypothesis-driven approach in which experiments can fail fast or succeed.

“It’s a lot more about collective intelligence than let’s get a couple of experts and let them tell us where to do this. There are no experts here,” Giacomelli said.

Focus on the workforce

AI will impact every type of business in some way. The question is, what are business leaders doing to prepare their workforce for a future in which part or all of their jobs will be done by AI? According to the survey, 82% of the business leaders plan to implement AI-related technologies in the next three years but only 38% are providing employees with reskilling options.

“I think HR functions are completely backwards on this one,” said Giacomelli. “They haven’t started connecting the dots with what needs to be done with the employees.”

Some companies are already working on workforce planning, but they view AI as a means of materially reducing the workforce, such as by 20% or 30%, which Giacomelli considers “a primitive approach.”

“There are jobs that will go away completely. For example, people who do reconciliation of basic accounts, invoices, that kind of stuff,” he said. “Most of the jobs that will be impacted will be impacted fractionally, so part of the job is eliminated and then you figure out how to skill the person who does that job so she can use the machine better.”

What would people do, though? It’s clear that most working professionals have various types of experience. The challenge for HR is to stop looking at a snapshot of what a candidate or employee is today and what prior experience has qualified them to do what they do today. Instead, they should consider an individual’s future trajectory. For example, some accountants have become sales analysts or supply chain analysts.

Looking for clues about what particular roles could evolve into is wise, but that does not provide the entire picture, since all types of jobs will either evolve or become obsolete in their current forms.

“I don’t feel that many people are looking at the human element of digital transformation and AI except fearful people,” said Giacomelli. “Every year, we will see people somewhere making sense of this riddle and starting to work in a different way. I think we need to change the way we look at career paths. We’ll have to look at them in a hypothesis testing way as opposed to have a super guru in HR who knows how AI will impact our career paths, because they don’t [know].”

The bottom line is that individuals need to learn how to learn because what AI can do today differs from what it will be able to do tomorrow, so the human-and-machine relationship will evolve over time.

Even if AI was just a science fiction concept today, the accelerating paces of technology and business underscore the fact that change is inevitable, so organizations and individuals need to learn how to cope with it.

Don’t dismiss the other guy

AI proponents and opponents both have valid arguments because any tool, including AI, can be used for good or evil. While it’s true AI will enable positive industrial, commercial and societal outcomes, the transition could be extremely painful for the organizations and individuals who find themselves relics of a bygone era, faster than they imagined.

AI-related privacy and security also need more attention than they’re getting today because the threats are evolving rapidly and the pace will accelerate over time.

An important fundamental question is whether humans can ultimately control AI, which remains to be seen. Microsoft’s Tay Twitterbot demonstrated that AI can adopt the most deplorable forms of human expression, quickly. In less than 24 hours, that experiment was shut down. Similarly, a Facebook chatbot experiment demonstrated that AI is capable of developing its own language, which may be nonsensical or even undecipherable by humans. So risks and rewards both need to be considered.

 

How to Get Educated About Analytics

Analytics education is anything but static. It’s so hot now that lots of education-focused organizations are offering traditional degree programs, online degree programs, certifications and courses.

As we well know, analytics capabilities and best practices are evolving, as is the application of analytics to business problems. In addition, there’s still a shortage of data-savvy people. All of that is creating demand for analytics-oriented education which is and has paved the way for so many new offerings. The question is, which option is best for you?

There are a number of ways to answer that question, some of which are more productive than others.

If you go online, you may find yourself overwhelmed by the choices. If you contact someone from any one of those organizations and ask their advice, they’ll probably tell you their program is the best.

In my view, there is no one best option. The answer depends on several factors which include the amount of time you have to dedicate to the program, the focus of the program and the cost (assuming your employer won’t cover the cost).

Probably the most frustrating approach is to look at the growing haystack using a Google search and start clicking on the endless stream of links. If you do that, you’ll probably be more confused than when you started your search. Lists of “top schools” may help narrow the focus as may a few suggestions below.

Degree or Certificate?

Data analytics degree programs didn’t exist until recently. So, if you lack an analytics degree, you’re in good company. Many universities offer formal BA/BS and/or MA/MS programs now, and they may have online degrees or certifications as well. For example, the W.P. Carey School of Business at Arizona State University offers MA and BA degrees in Analytics which can be earned online or as a full-time student. MIT Sloane School of Managementoffers a one-year Master’s degree program, an undergraduate degree, and a certification program, albeit for graduate students. However, MITx (an online MIT entity) has an online data science certificate program, which I can vouch for personally.

Degrees and certificates don’t carry the same weight, especially if you lack prior relevant work experience. A degree represents a level of mastery and years of dedication. However, employers are also well-aware that analytics degrees are new. Even if the degrees weren’t new, deep experience and a certificate or no certificate might be more valuable, depending on the candidate and position. A certificate also suggests a level of mastery, albeit the kind that can be earned in weeks or months versus years. If you have a some kind of degree, such as a business or information systems degree, analytics-related certification tends to indicate a dedication to continuous learning, which is a good thing.

There are a few other points to consider such as the school’s pedigree, how long they’ve had programs, what students and former students think of them, the companies that work with them, and the companies that hire from them. If you’re using education as a means to a promotion, find out what matters most to your employer.

Also realize that two programs with the same title can differ greatly in terms or course material, focus, faculty competency and how well faculty members communicate. Some professors or instructors are very articulate and easy to understand, even when they’re presenting highly technical material. Some are not.

In addition, understand your limitations because certification courses tend not to list prerequisites, like degree programs. For example, (and not surprisingly), a course may require students to use Microsoft Excel Professional Edition at a minimum. That’s may not a problem if you have access to the program at work, but if you don’t already have the software, be prepared to invest more than you anticipated in your certification. Similarly, some courses require a deeper background in math than others.

Self-study

Online courses tend to emphasize that the coursework can be accomplished on a “flexible” schedule but you may be required to repeat the course in a particular time frame to earn the certificate. You may think that’s not a problem because you’re psyched! Then life happens. If you suspend your study of such a program, you may be required to start from the beginning when the next class begins.

Part of the reason some self-study programs work within a given time frame is that it gives the “class” the ability to interact with the faculty and other students. EdX has many such programs, some from top universities, including MIT.

One of the cool things about EdX is you can take courses for free and only pay if you want the certificate associated with the class. The class may still start and stop at particular dates, but hey, the classes are free so you can get the syllabus, go through a module or two and see whether the course is suited to you. Right now, EdX has 122 online courses available that focus on analytics. Some of them are broad, such as the ColumbiaX Statistical Thinking for Data Science and Analytics. Others are narrow including the UC BerkeleyX https://www.edx.org/micromasters/berkeleyx-marketing-analytics course in Marketing Analytics.

It’s Your Choice

If you ask 10 people about which degree, certificate or course you should pursue, you’ll probably get 10 different answers. Ultimately, your path should align with your interests and career goals.

That said, if your choice requires a heavy investment in time, money or both, don’t make the decision in a vacuum. Consider some of the points above, talk to people and do some research so you have a better idea of what’s really right for you.

You’re Doing Analytics Wrong. Here’s Why.

Businesses across industries want to compete on insights, and yet many of them aren’t getting the ROI they expect. Somewhere between raw data and actionable insights, processes break down or they don’t exist. It’s not just a tool problem. Organizations need to change the way they think and behave.

Data is data

Every company has data, but not all organizations treat data as a valuable corporate asset.  If they did, they probably wouldn’t have multiple copies of customer records, none of which is “the golden record.”

“There’s no documentation so there’s no traceability,” said Ivan Chen, director of Enterprise Business Analytics at GPU manufacturer NVIDIA. “If you really want to get value out of your data, you have to put structure around it. We realized that, so we’re putting plans in place to provide a foundation for analytics.”

Chen is wrapping up the first month of a three-month pilot that will enable self-service analytics.  As a first step, his team is inventorying all of NVIDIA’s data assets, documenting them and putting them in a central repository.

“We’re codifying all the business logic in the repository so that people can see how the transformation that was done,” said Chen. “That way, there’s a baseline for discussion.”

In the next phase, analysts will get access to self-service analytics.

“The fundamental reason analytics is not successful is because different functions have different goals,” said Chen. “Business professionals are trying to answer questions about the market, but the market conditions keep changing so you have to keep enhancing the data in reports. IT is supposed to make those changes, but IT has other priorities, so the whole thing breaks down.”

NVIDIA dedicated three IT professionals to the pilot who are supporting Chen and his team. Before the pilot began, it took IT six months enhance the data in a report. Now it takes two or three days.

Analytics and outcomes aren’t aligned

When analytics and business outcomes aren’t linked, it’s impossible to understand the ROI. All too often, people believe analytics is the outcome, rather than a means to an outcome.

“Analytics are viewed as how many tools or dashboards you have, or how many reports you generate, ” said Isher Kaila, CEO of management consulting firm Sapphire Nine Consulting.  “No one is anchoring that to the amount of insights you are delivering, and by extension, what those insights translate to in terms of business outcomes.”

Visualizing data in different ways can help clarify what the data says. However, it’s also possible to visualize data over and over without producing any insights.

“You have to understand which insights are necessary for you to deliver an outcome, which people are attached to generating those insights, and who you will hold accountable for optimizing the insights linked to your outcomes,” said Kaila. “Most organizations do a pretty good job of managing their reporting tool environment, but someone needs to be accountable for transforming insights into outcomes.”

You’re stuck in the past

More companies are using predictive analytics to accomplish something proactively, although a lot of companies are still doing business through the rear-view mirror.

“If you’re just showing me the same data in a new way, how will that show me what I should be thinking about going forward? Is it challenging me to think about how I can optimize my business model? Are there processes I need to tweak? How much money am I leaving on the table? Are we cannibalizing our own revenue? Those are the kinds of questions you should be able to ask of your data,” said Kaila.

Some organizations have hired a chief analytics officer or a chief data officer to ensure that data can be used as a strategic asset. That person is responsible for bridging the gap between business and IT, orchestrating resources, and driving value from analytics.

“Leaders need to ensure accountability for insights,” said Kaila.  “If you do that, you’ll be able to align the definitions, the processes and ultimately how you operationalize predictive insights.”

[Analytics and advanced use of data in AI and machine learning will once again be featured during Interop ITX in 2018. The Interop team has issued a Call for Speakers. Practitioners and independent experts are invited to submit.]

Analytics work best when they’re executed as repeatable, sustainable processes that transcend any particular role. In many organizations, analytics are executed as a set of discrete functions and tasks, negatively impacting the potential value analytics can provide.

“Executives should be aware that they’re likely not even harnessing the value of their analytics spend today. There’s an analytics value chain in which outcomes are generated by insights, insights are generated by accountable business owners, and the accountable business owners have a strong partnership with their technical partners,” said Kaila. “Too often we see a lack of alignment around metrics and insights.”

Insurance Struggles with Lead Gen & Data Analytics

The International Institute for Analytics (IIA) recently published a report stating that the insurance industry was the least mature of 12 vertical markets it studied. Insurance companies have lots of data, but they’re having trouble making sense of it.

“We have this data, but we can’t make heads or tails of it because we have data integration problems and there’s no data governance,” said Samantha Chow, senior analyst at market research firm Aite Group. “[Insurance carriers] are hiring data analysts and data scientists, but it’s very fragmented. They don’t have the support they need [to improve] their targeting, products, pricing — all of these things they’re trying to do.”

Lead generation is a huge problem. Older agents are retiring and more business is transacted online, which means the approach to lead generation must evolve with the industry. At the present time, lead generation involves a complicated web of data, external partners, and internal systems, all of which need to be orchestrated into compelling offers that are relevant to individual consumers “in the moment.”

Insurance companies also want to improve their ability to act on “triggers” that suggest a prospect’s interest in a particular product. For example, multiple mortgage loan pulls on a credit report indicate that the prospective home buyer will probably need homeowners insurance. To get information they lack, insurance companies use third-party sources such as credit information provider Transunion, data company Lexis/Nexis, and partners who specialize in social media analytics.

Addressing lead generation

Aite Group recently published a report based on interviews with 80 lead generation vendors and more than 30 insurance company and agency lead-generation and marketing executives. Chow said orchestrating information is the biggest problem the insurance industry faces today, which is why some carriers turn to vendors such as data integration platform provider LeadCloud.

Meanwhile, data acquisition costs are rising because insurance companies don’t know how to target prospects younger than the Baby Boomer generation.

“Getting a 30-year-old to understand the value of life insurance is difficult,” said Chow. “Learning how to target them and speak to them adds to the acquisition costs.”

It doesn’t help that the information insurance companies provide to consumers can be more confusing than clarifying. Because consumers have trouble differentiating products, insurance companies such as Geico, Progressive, MetLife, and Allstate spend lots of money on radio, TV, and pay-per-click advertising promoting their brands.

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To reduce lead-generation costs, insurance companies need to improve their ability to use data and analytics.

“We’re seeing acquisition costs go down [among auto insurance carriers] but [for the rest] it’s going to be learning who your target market is, having the supporting data, and being able to hone in on that particular consumer. It’s not going to be easy,” said Chow.

Making sense of data still difficult

The insurance industry also has challenges with data access. Data quality is a problem because different systems say different things about the same person or issue.

Further complicating the matter is the number of systems insurance companies have. They have dedicated systems for claims, underwriting, new business, customer service, and policies. Worse, there are often duplicates of systems because one may cover claims from 1990 to 2000, while another covers claims from 2001 and later, for example.

“On average, some of the top tier carriers have over 26 or 27 legacy policy administration systems they’re running on at one time,” said Chow. “If you have a life insurance policy and an auto policy or a dental policy with [a particular carrier], they can’t get data from one policy to the next policy. They can’t merge that together so they can learn more about you.”

Even if insurance companies could unearth “the golden leads,” loyalty may be an issue for some types of insurance. Chow said most people won’t move their life insurance policy from one carrier to another. Yet, most consumers shop for auto insurance based on price. When it comes to health insurance, customer loyalty depends on the carrier’s willingness to make good on its promises and streamlining the claims process.

Perhaps the insurance industry lags behind in its intelligent use of data because its technology stack and business processes are complex and fragmented. Still, if insurance companies want to remain competitive, they must be able to use data more adeptly to quickly identify quality leads and compete more effectively.

How Customer Intelligence Impacts Customer Loyalty, Wallet Share

Many of today’s companies talk about getting a 360-degree view of their customers and how that will enable them to increase share of wallet and improve customer loyalty. As a consumer and an industry observer, I would argue that these “360-degree views” are aspirational at best. Striving for a holistic view of customers is a noble goal and a necessary one; however, achieving customer intelligence nirvana is easier said than done.

“The vast majority of the people we talk to aspire to get a 360-degree view of the customer, but the reality is they may not have closed the circle,” said Julio Hernandez, partner, global customer Centre of Excellence Lead and US Customer Advisory Practice Lead at KPMG . “A 360-degree view of the customer is really knowing who the customer is, what they’re doing and why they’re doing it. It’s also bringing in the right information sources, and the information sources are continuously evolving.”

Companies should also understand how valuable their customers are and where they are on their journeys. However, truly understanding customers and marketing to them appropriately is still difficult despite all the technology and data that’s available now.

Part of the problem is what Hernandez calls “The New Year’s Day” problem, which is saying one thing and doing another.

“It goes back to the 360-degree view and having multiple sources of data, combining them, and combining [different] types of analysis to get a better picture of what the customer wants and is doing,” said Hernandez. “You have to start with what you’re trying to achieve with customer insights. That drives how you harness the analytics and how you look at the data. If you expect to just look at all your data and [get] all the insights in the world, you’re going to come up short.”

Increasing Share of Wallet

Businesses purchase a lot of third-party data to better understand their customers’ economic means, what they’re buying and where they’re buying it to understand their share of wallet. If they have a loyalty program, they have insight into what their share of wallet looks like.

“You have to make some inferences about what’s your share in the marketplace is in different categories,” said Hernandez. “You can also triangulate and come up to a number about what I’m actually selling to that person versus the inferred wallet [because] you won’t know for sure exactly what their wallet is.”

Businesses should also consider the attributes of their best customers and then identify customers who share the same attributes but spend less. That way, the company can intervene with some sort of marketing campaign that encourages the latter group to spend more.

Improving Customer Loyalty

Businesses with loyalty programs get varied results depending on the benefits their programs provide and the degree to which companies leverage that information.

“Loyalty cards are interesting because they’re trying to [get] you to clearly state who you are when you’re using the card and then they can track your basket and your purchases,” said Hernandez. “But you have to step back and ask how do you as an organization define loyalty? Is it someone who stays with you on an ongoing basis? If so, that’s great, but if you’re a utility and I continue to business with [you], that doesn’t necessarily means that I’m loyal. It means I’m lazy or I don’t have substitutes.”

Money isn’t everything. If two customers spend the same amount of money, but one is a brand advocate on social media, the latter is considered more valuable now.

“When you think about loyalty, it’s also about what are they’re doing with their loyalty,” said Hernandez. “Are they engaging with your services? Are they proponents of it? Those are things that help you determine what kind of loyalty you have.”

Hotels Check In with More Analytics

Hotels continue to invest in analytics so they’re in a better position to optimize revenue, deliver better customer experiences and improve operations. Like other organizations, hotels realize they can improve their competitive position using data and analytics more effectively than others. To do that, they need to integrate data coming from different functional areas and connect internal data with external data to make material improvements across the board.

Revenue Optimization

Hotels are moving past historical data and current bookings to maximize room occupancy and profitability. To improve their effectiveness, they’re using competitive data, weather dataevent data, predictive capabilities, and more. Starwood Resorts is experimenting with machine learning and neural networks to change pricing dynamically, rather than twice or three times per day or seasonally.

The goal is to optimize the profitability of each room, not just hotels at large

Customer Loyalty

Customer loyalty programs have evolved with data analytics capabilities to provide guests with better, albeit different, experiences. For example, some guests care more about the quality of concierge services than Wi-Fi. Hotels must understand such differences to understand a guest’s preferences and cater to those preferences. After all, every customer has an individual expectation of what a “good” hotel experience is, not just the Premier or Platinum members. While the most profitable and loyal customers deserve exclusive benefits, catering to them should not be done at the expense of other guests.

To provide better experiences at all levels, hotels are attempting to understand their customers more holistically than they have in the past to provide a relevant experience. Having a “special requests” textbox in a booking application yields some information, so are customer requests and feedback recorded at the front desk. Another way to understand customer preferences is to slice and dice room offerings based on non-traditional amenities, such as allergen-filtering, in addition to the usual size, price, category and smoking/no smoking designations. Tracking a customer’s preferences over time, helps too in an effort to anticipate guests needs and desires.

These days, loyalty isn’t something that kicks in at check-in. Hotels are using search, online bookings, social media, call center data, front desk data and surveys to better understand customer journeys and what people want.

Moving up a level or two, some are targeting Millennials, which included Pokemons in the pool and on beds at Marriott hotels, clearly a non-traditional “benefit,” though an attractive benefit for those caught up in the Pokemon Go craze. The campaign happened to be a very smart marketing move from a social media point of view — free advertising.

The relationship among marketing, customer loyalty and revenue optimization enables a continuous feedback loop where insights from one bucket inform the others. And that’s not all.

Operations

Third party data can be very valuable from a predictive point of view when it impacts hotel occupancy and profitability. Weather and event data are two examples. Here in Sedona, Ariz., wildfires and heavy monsoon rains can cause massive hotel room cancellations. In other cities, popular concerts, sports games and flight cancellations cause a spike in demand. While those things may seem intuitive, actual data feeds can help hotels plan for the dips and spikes more accurately, so they can right size things like staff on hand and supply orders.

From an internal perspective, hotels need to monitor and constantly improve the efficiency of individual functions such as housekeeping, not only to reduce costs, but to keep up with competitors’ improvements. Some operational information is used to craft marketing messages such as Starwood Hotel’s “Smart check-in.”

Analytics is also providing insight into age-old issues such as sluggish room service. Is the problem too many simultaneous orders, too few members of the kitchen staff, poor kitchen management, something else or a combination of things? Operational analytics provides some insight as will guests’ social media posts, survey data, call center data, front desk data, etc.

Mobile

Hotel chains have mobile apps that give them even more insight into customer behavior, especially as they expand out from reservations made using a mobile device to keyless entry (using a smartphone), mobile food orders and more.

Some hotels are adopting “mobile first” strategies given the popularity of the devices and the fact that more hotel customers are using mobile devices instead of laptops to book rooms.

Hotels face many of the same problems enterprises face generally, not the least of which is connecting dots in a way that is valuable to their organizations and customers.

How Analytics Are Changing Frequent Flyer Programs

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 flowersa marriage proposal kitwine 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?

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.

Why Advanced Analytics Is in Your Future

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.

Analytics Ensure Safety in LA and White Plains

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.

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