How to build a high performing analytics team?


Having BI capabilities have proven to help organizations improve efficiency and stay ahead in overall competitiveness.  With digital transformation taking over sectors, businesses continue to evolve into data driven models. Each day more businesses are focusing on insights from data that drive better decisions and strengthen customer relationships.

Self-service tools such as Tableau, Alteryx, Qlikview, Power BI etc. have introduced a dash of ease into ways businesses convert their data into insights, making business intelligence (BI) initiatives go mainstream.

Choosing analytics providers, structuring an effective BI ecosystem is easy. The difficulty that decision makers face is building a good quality BI team. In the light of this talent crisis, an accelerating number of companies are motivated to hire any resource they can get, but those approaches mostly go dysfunctional. This not only incurs excessive cost burden for businesses but also makes room of inefficiency leading to the failure of BI endeavors.

Some challenges enterprises face while building BI teams:
•Lack of efficient analytics workforce makes way for inevitable competitive lag.
•It is tough for organizations to fulfil skill development needs of their large teams.
•Technical resources are masters of tools but lack the art of business storytelling with analytics.
•Hiring specific SMEs for varied functions of the BI lifecycle drains budget and keeps ROIs in doubt.
•Businesses waste time over manpower fulfilment when they could be leveraging a BI solution for business gains.
•Longer learning curves because of the diversity of tools and the stern need for accuracy in BI ecosystems
•No accountability for the inevitable technical or team related roadblocks that can appear amidst a BI lifecycle.

The options
Every business has its unique demands when it comes to building a data workforce. A different approach is needed for each instance and there are several ways, to begin with. The first step being gauging the magnitude of the BI initiative and the expectations from it. Once that’s in place businesses can start to make decisions whether to hire, outsource or augment.

Outsource – If it is right for you
Organizations leveraging data for certain projects or for improvements of business processes need not go that way. If BI is a support initiative, partnering with third parties to execute analytics initiatives is the best bet.

Having a full-time in-house analytics team an attractive proposition but is expensive to manage and especially when companies are new to BI and unsure of ROIs. Outsourcing is an easier, faster and cheaper way to jumpstart analytics endeavors for such businesses.

In-house teams – build and improve over time
Large enterprises having data and analytics as their core business strategy need to put all BI components i.e. people, processes, and platforms in place. Certain business models cannot allow data to seep out of the organization? Other than those reasons, if businesses know that once rolled out BI initiatives will not be taken back, they can go ahead and start hiring specialists to build in-house BI teams.

In-house data teams can determine and control data lineage, but how do businesses ensure a perfect team that is capable of translating data into success? The answer is – if candidates have the aptitude for analytics and know a tool well in the BI arena, they are potentially capable of evolving as BI needs of businesses do and make great hires that cannot be easily poached. The major takeaway here is hiring people who are masters of their tool and constantly provide opportunities to learn and grow so that the team is up to date, always.

Extended data teams – The best model so far
Finding a qualified match for any role in the IT industry is not an easy job but with the diversity of tools, processes, and evolution involved in BI presents some unique roadblocks in hiring the right talent for BI endeavors.

There’s a lot of maturation time in hiring an internal team and training them to attain optimum results from a BI initiative. An extended team can help chart a course through BI endeavors, with a flexibility that these teams can be involved at any stage of the BI lifecycle.

Extended teams came into the picture to free businesses of unruly time and monetary investments. They also eliminate the worries of hiring, training, and fear of losing seasoned BI experts. The best part is that as a customer you can call off the engagement, the minute the projects starts to derail, saving cost and time resources


The Vast, Secretive Face Database That Could Instantly ID You In A Crowd

Amid questions of accuracy and legality, the FBI and police can search images for millions of Americans’ faces, potentially in real time.


If you’ve been to the DMV to have your driver’s license photo taken, there’s a good chance your face is in a little-known group of databases that functions like a digital police lineup. Except, in this case, you needn’t even be suspected of a crime, and soon the searching could take place virtually anywhere, anytime.

It’s just one example of the exploding use of facial recognition technology in law enforcement, and it’s raising major concerns among U.S. lawmakers and privacy advocates.

“I’m frankly appalled,” Representative Paul Mitchell, a Republican from Michigan, told Kimberly Del Greco, the FBI’s deputy assistant director of criminal justice, during a House oversight committee hearing last week. “I wasn’t informed when my driver’s license was renewed my photograph was going to be in a repository that could be searched by law enforcement across the country.”

Mitchell’s face and those of more than 125 million Americans—more than half of the country’s adult population—are thought to be stored in a vast network of databases used by local and federal law enforcement to scan photos and videos of individuals. Many of these faces, which can be searched without reasonable suspicion, belong to people who have never been charged with a crime and have no idea they are being searched.

Yet there are few guidelines or legal rulings that govern exactly how face recognition, like a number of other new technologies, should be used by police. In a report last May on the FBI’s system, the Government Accountability Office (GAO) found the FBI had “not fully adhered to privacy laws and policies and had not taken sufficient action to help ensure accuracy of its face recognition technology.” In response to the report, which urged the FBI to conduct regular audits of the system, “the Department of Justice and the FBI disagreed with three recommendations and had taken some actions to address the remainder, but had not fully implemented them,” the GAO said.

“No federal law controls this technology, no court decision limits it,” said Alvaro Bedoya, executive director of Georgetown Law’s Center on Privacy and Technology, and the coauthor of “The Perpetual Lineup,” a report on the FBI and state face recognition databases. “This technology is not under control.”

While a few attempts to set limits are inching slowly through state legislatures, the technology is racing ahead. Advancements in machine vision and artificial intelligence are widening the scope of the lineup too: Via body-worn police cameras, which are rapidly proliferating, face searches could happen up-close, at street level and in real-time—anticipating a future in which anonymity in certain public places could disappear.

It’s this pairing of technologies in particular—the ability to scan and identify faces on the street—that is the “most concerning” from a privacy and First Amendment perspective, said Jason Chaffetz, Republican representative from Utah and chairman of the House Oversight committee. A 2014 Justice Dept. report also highlighted the combination, warning that using body cameras with “facial recognition systems and other new technologies like live feed and auto recording . . . may pose serious risks to public privacy.” In the legal vacuum surrounding their use, agencies exploring these technologies should “proceed very cautiously,” the report said.

“Imagine the world where the cops are going down the street and they’ve got Google Glass on and their body cameras are recognizing people,” says Barry Friedman, the director of the Policing Project at New York University School of Law. “And it’s not just recognizing them, but they’re getting their security scores at the same time, and people are getting colored based on how dangerous the algorithms think they are. That’s one scary world.”

Already law enforcement is pairing real-time face recognition software with footage from surveillance cameras, and police officers around the country are using face recognition apps on mobile phones to more quickly identify suspects they stop on the street. In New York, it emerged this week that police are beginning to acquire face recognition technology to scan the faces of all drivers commuting between the five boroughs.

Questions Over Legality And Accuracy

Of immediate concern to Congress was the legality of the FBI’s system. With the FBI’s Del Greco in the hot seat, a number of committee members asked why the agency hadn’t published a privacy report of its face recognition system until 2015, years after it first deployed the technology in public, in 2010.

Had anyone at the FBI been reprimanded for the delay? Rep. Mitchell asked.

“I have no knowledge,” Del Greco said. “There are days ignorance is bliss,” he fired back.

Del Greco said the FBI had advised its privacy attorney internally throughout the roll-out of the system.

“We don’t believe you,” Chaffetz said, “and you’re supposed to make it public.” He also alleged that the FBI “went out of its way” to exempt its facial recognition database from the Privacy Act.

“So here’s the problem,” said Chaffetz. “You’re required by law to put out a privacy statement and you didn’t and now we’re supposed to trust you with hundreds of millions of people’s faces.”


Del Greco defended the agency’s use of what she referred to as “face services,” saying it had “enhanced the ability to solve crime,” emphasized that privacy was of utmost importance at the FBI, and said that the system was not used to positively identify suspects, but to generate “investigative leads.” In one recent positive outcome for the technology, Charles Hollin, an alleged child molester, was caught after spending 18 years as a fugitive, thanks to a database that contained his passport photo.

Currently, 18 U.S. states let the FBI use face-recognition technology to compare suspected criminals to their driver’s license or other ID photos. These, in addition to criminal and civil mug shots, and photos from the U.S. State Department’s passport and visa records, are part of a set of databases used by the FBI and police across the country. Over the past decade and a half, 29 states have allowed police agencies and the FBI to search their repositories of drivers’ faces during investigations.

“I have zero confidence in the FBI and the [Justice Department], frankly, to keep this in check,” Rep. Stephen Lynch, a Democrat from Massachusetts, said.

After the hearing, Bedoya, a former chief counsel for Sen. Al Franken of Minnesota, noted that the discussion had inspired a rare moment of bipartisan agreement. “The opposition to the use of driver’s licenses was remarkably strong and remarkably uniform across party lines,” he said. “In my five years as a Senate staffer, I never saw anything like it on a similar privacy issue.”

In some ways, the FBI’s approach to face recognition resembles how police traditionally try to match fingerprints at a crime scene to those of criminals. In this case, however, the “prints” include the faces of millions of innocent people, often collected and scanned without their knowledge, according to the analysis by Bedoya and his colleagues. It estimated that around 80% of the faces searchable by the FBI belong to individuals who have never been charged with a crime.

In under half a decade, they found, the FBI searched drivers’ faces more than 36,000 times, without warrants, audits, or regular accuracy tests. In Florida, police are encouraged to use face recognition “whenever practical.” (Individually, dozens of states also use face recognition to crack down on fraudsters applying for duplicate driver’s licenses, for instance.)

To access the FBI’s networked databases of faces, an authorized, participating police agency need only show that its search is for “law enforcement purposes,” said Del Greco of the FBI. Those criteria are determined by individual states, she noted.

Imagine going to the DMV to renew your license, Bedoya wrote in a recent op-ed in the Washington Post. “What if you—and most other teens in the United States—were then asked to submit your fingerprints for criminal investigations by the FBI or state police?”

In scope, he argues, the face-matching system resembles the National Security Agency’s call-records program, which logged the metadata of all Americans’ phone calls. “This has never happened before—not with DNA or fingerprints, which are kept in smaller national networks made up mostly of known or suspected criminals. Yet law-enforcement face-recognition systems have received a fraction of the NSA’s oversight.”

The possibility of misidentification and false positives is also worrisome, especially because the FBI has not been keeping track of such failures. “It doesn’t know how often the system incorrectly identifies the wrong subject,” explained the GAO’s Diana Maurer. “Innocent people could bear the burden of being falsely accused, including the implication of having federal investigators turn up at their home or business.”

More worrisome, research shows that facial recognition appears to disproportionately impact minority communities. A report that was co-written by the FBI in 2012 found that the technology exhibited a higher number of failure rates with darker faces, a function of the kinds of data that humans input as they train the algorithms.

“If you are black, you are more likely to be subjected to this technology, and the technology is more likely to be wrong,” said Elijah Cummings, a congressman for Maryland. “That’s one hell of a combination. Just let that sink in.”

The FBI, like other law enforcement agencies, has argued that the algorithms are race-blind, and reiterates that face searches are only used as “investigatory” leads. “This response is very troubling,” Cummings noted. “Rather than conducting testing that would show whether or not these concerns have merit, the FBI chooses to ignore growing evidence that the technology has a disproportionate impact on African Americans.”

Friedman notes that racial concerns aren’t exclusive to face recognition. “A lot of these technologies, just because of how they’re deployed, come with racialized aspects. If you’re using license plate readers in more heavily policed neighborhoods, you’re picking up more data in those neighborhoods. That’s something we need to be very aware of and thoughtful about.”


A plethora of private companies already have your face in all of its biometric glory, provided it’s ever been uploaded to the servers of firms like Apple, Google, and Facebook. As these companies pile millions into AI research—in part, to better find you and objects in photos—a number of startups are also racing to automatically analyze the world’s video. One startup, Kairos, aims to let Hollywood and ad agencies study audiences’ emotional responses and help theme park visitors more easily find and purchase photos of themselves. Another, Matroid, launched this week by researchers at Stanford, focuses on analyzing television appearances and scanning surveillance video.

“Google can give you pictures of cats, but not cat with grandpa or cat with grandpa and Christmas tree or with your son,” Pete Sonsini, a general partner at New Enterprise Associates, which is funding Matroid to an unspecified tune, told Bloomberg. “It’s really powerful for any human to be able to create a detector that can identify any image or set of images or face from their dataset.”

Banks are using biometric technology to provide better personal verification, eventually allowing people to pay with their face. Airlines are imagining using biometrics to let passengers board an airline without a paper ticket. At a conference this week in Orlando, NEC, the Japanese company whose facial recognition algorithms are considered the most accurate by the Dept. of Homeland Security, released new features for its software suite which include a “virtual receptionist” and a system by which “age/gender recognition can trigger tailored advertisements/greetings and can trigger notifications to sales personnel for immediate follow-up and interaction,” said a press release.

Another app, installed on a hotel’s cameras, would notify hotel receptionist and concierge services when a VIP or high value customer arrives at any of the building’s entrances. “This will allow them to receive them in person, greet them by their name, and provide them a better service. Also this can be used to identify any staff that were terminated when they enter the property and take appropriate action.”

At Beijing’s Temple of Heaven Park, meanwhile, biometrics are already being used to ration toilet paper, CNN reported last week: “The facial recognition program keeps the dispenser from offering another round to the same person; if you need more, you reportedly need to sit tight—literally—for nine minutes.”

Still, the most profitable applications for biometrics lie in the fast-growing law enforcement and public safety sector. Industry executives and police experts say that the next stage of the technology—automatically scanning public places for faces and objects in real time—could help more quickly find armed and dangerous or missing persons, identify critical moments amid torrents of body and dash cam footage, or even perhaps identify biased policing.

In many communities, automatic license plate readers—brick-sized cameras mounted on the back of patrol cars—already do something similar with cars, archiving and cross-referencing every license plate they pass in real time to check for outstanding warrants or traffic violations.

These are also the most worrisome applications, privacy advocates say, given the general secrecy that surrounds them, the few restrictions on their use, and the ability to track individuals. Pairing face-matching algorithms with body cameras, said Bedoya, “will redefine the nature of public spaces.”

Even the mere prospect of the technology could have a chilling effect on people’s First Amendment rights, privacy advocates warn. In some cities, police are restricted from filming at protests and demonstrations—unless a crime is thought to be in progress—for this reason.

Other police departments, however, have routinely filmed protesters: In New York, for instance, the NYPD sent video teams to record Occupy and Black Lives Matter protests hundreds of times, and apparently without proper authorization, agency documents released this week show.

“Will you attend a protest if you know the government can secretly scan your face and identify you—as police in Baltimore did during the Freddie Gray protests?” Bedoya writes. “Do you have the right to walk down your street without having your face scanned? If you don’t, will you lead your life in the same way? Will you go to a psychiatrist? A marriage counselor? An Alcoholics Anonymous meeting?”

Rep. Chaffetz appeared to support one controversial application of face recognition: using it to find undocumented immigrants. The Dept. of Homeland Security stores the faces of every visitor in its own database, and is determined to better track those who overstay their visas; in 2015, the agency estimated 500,000 overstays.

“I think it is absolutely a concern that face recognition would be used to facilitate deportations,” Rachel Levinson-Waldman, senior counsel to the Brennan Center’s National Security Program at New York University School of Law, told The Intercept‘s Ava Kofman. “We’re seeing how this administration is ramping up these deportation efforts. They’re looking much more widely.”

Generally, however, Chaffetz urged firmer limits. The technology “can also be used by bad actors to harass or stalk individuals,” he said. “It can be used in a way that chills free speech and free association by targeting people attending certain political meetings, protests, churches, or other types of places in the public.”

“And then having a system, with a network of cameras, where you go out in public, that too can be collected. And then used in the wrong hands, nefarious hands… it does scare me. Are you aware of any other country that does this? Anybody on this panel? Anybody else doing this?”

Neither Del Greco nor other members of the panel responded.

Jennifer Lynch, a staff attorney at the Electronic Frontier Foundation, noted that “we don’t yet appear to be at point where face recognition is being used broadly to monitor the public.” But, she said, “it is important to place meaningful checks on government use of face recognition now before we reach a point of no return.”

Real-time face recognition is coming to law enforcement; the question is how the technology itself will be policed, and what role the public will have in dictating its use. “If we’re going there, then we better be going there together,” said Friedman, of the Policing Project. “Did we all discuss this and agree to this?”

To improve the use of face recognition, Bedoya and other privacy advocates urge well-defined limits. The FBI should only access mugshot databases with reasonable suspicion of a crime, and, if it’s a forensic use—a face recognition scan on surveillance footage, for instance, as opposed to a police officer photographing a person he has stopped—the technology should only be used in cases of felonies.

For databases containing drivers license photos, Bedoya says law enforcement should have express consent from state legislatures, and only search those databases when they have probable cause to implicate the subject for a serious offense, like the kind required for a wiretap. The FBI should also regularly scrub its databases to remove innocent people, be more transparent about how it uses the technology, and, as the GAO recommended, conduct audits to ensure the software meets privacy and accuracy requirements.

Policing veterans have also expressed discomfort with the use of the technology. In December, a guide to body cameras published by the Constitution Project, a nonpartisan think tank, and co-authored by a handful of retired police officials, warned that the privacy risks of video “tagging” technologies were “immense” because they “have the potential to end anonymity, catalog every person at a sensitive location or event, and even facilitate pervasive location tracking of an individual over a prolonged period of time.”

In an interview about the future of body cameras, Bill Schrier, the former chief information officer at the Seattle Police Department and a 30-year veteran of government technology, told me that “most reasonable people don’t want potentially dangerous felons or sex offenders walking around in public and would, I think, support such use” of real-time face recognizing body cameras in those cases.

But used to catch people wanted for “misdemeanors such as unpaid traffic tickets or pot smoking,” real-time face recognition could be dangerous. That, he said, could “seriously undermine faith in government, and start us down the road to a police state.”

Source: FastCompany

Robotic Process Automation + Analytics

“Looking to the future, the next big step will be for the very concept of the “device” to fade away. Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world.” — Sundar Pichai, CEO Google


•A global oil and gas company has trained software robots to help provide a prompt and more efficient way of answering invoicing queries from its suppliers.
•A large US-based media services organization taught software robots how to support first line agents in order to raise the bar for customer service.

Software agents or Robotic process automation (RPA) is becoming a mainstream topic at leading corporations. I have seen a massive uptick in corporate strategy work in this area as C-Suite execs look at new ways to do more with less.

Software robots ∼ Conversational-AI products like Apple Siri, Microsoft Cortana, IBM Watson, Google Home, Alexa, drones and driverless cars ∼ are now mainstream. What most people are not aware of is the rapidly advancing area of enterprise robots to create a “virtual FTE  workforce” and transform business processes by enabling automation of manual, rules based, back office administrative processes.

This emerging process re-engineering area is called Robotic process automation (RPA).

Machine Learning (ML) and graph processing are becoming foundations for the next wave of advanced analytics use cases. Speech recognition, image processing, language translation have gone from a demo tech to everyday use in part because of machine learning. Machine learning models, e.g., in driverless cars,  teaches itself how to discover relevant things like a stop sign with snow partially obscuring the sign.

The market opportunity of artificial intelligence has been expanding rapidly, with analyst firm IDC predicting that the worldwide content analytics, discovery and cognitive systems software market will grow from US$4.5 billion in 2014 to US$9.2 billion in 2019, with others citing these systems as catalyst to have a US$5 trillion – US$7 trillion potential economic impact by 2025.

RPA – What?
“Robotic automation refers to a style of automation where a machine, or computer, mimics a human’s action in completing rules based tasks.” – Blue Prism

RPA is the application of analytics, machine learning and rules based software to capture and interpret existing data input streams for processing a transaction, manipulating data, triggering responses and driving business process automation around enterprise applications (ERP, HRMS, SCM, SFA, CRM etc.).

RPA is not a question of “if” anymore but a question of “when.”  This is truly the next frontier of business process automation, enterprise cognitive computing, predictive analytics and machine learning. To make a prediction, you need an equation and parameters that might be involved.

Industrial robots are remaking blue-collar factory and warehouse automation by creating higher production rates and improved quality.  RPA, simple robots and complex learning robots, are revolutionizing white-collar business processes (e.g. customer service), workflow processes (e.g., order to cash), IT support processes (e.g., auditing and monitoring), and back-office work (e.g., data entry).

I strongly believe that as cognitive computing slowly but surely takes off, RPA is going to impact process outsourcers (e.g., call center agents) and labor intensive white collar jobs (e.g., compliance monitoring) in a big way over the next decade. Any company that uses labor on a large scale for general knowledge process work, where workers are performing high-volume, highly transactional process functions, will save money and time with robotic process automation software.


Making Predictive Analytics a Routine Part of Patient Care


Over the last five years, electronic health records (EHRs) have been widely implemented in the United States, and health care systems now have access to vast amounts of data. While they are beginning to apply “big data” techniques to predict individual outcomes like post-operative complications and diabetes risk, big data remains largely a buzzword, not a reality, in the routine delivery of health care. Health systems are still learning how to broadly apply such analytics, outside of case examples, to improve patient outcomes while reducing spending. From a review of the literature on health systems that have successfully integrated predictive analytics in clinical practice, we have identified steps to make predictive algorithms an integrated part of routine patient care.

Determine the clinical decision. There is now a plethora of data available for nearly every potential clinical outcome. And where you have data, there is a potential predictive algorithm. But while it may be easy to develop clinical algorithms, it is equally necessary to be specific about which specific clinical decision(s) that algorithm will inform.

For example, there are many algorithms predicting a patient’s risk of hospital readmission (although the vast majority performs poorly). But simply knowing the percentage risk of readmission does not answer the questions that physicians and nurses typically ask before a patient is discharged: Should I discharge this patient now? Should I assign this patient to a readmission prevention intervention? Should this patient go to a short-term rehabilitation facility? Does she need a home care visit in the next two days?

Parkland Health and Hospital System in Dallas, Texas, has developed a validated EHR-based algorithm to predict readmission risk in patients with heart failure. Patients deemed at high risk for readmission receive evidence-based interventions, including education by a multidisciplinary team, follow-up telephone support within two days of discharge to ensure medication adherence, an outpatient follow-up appointment within seven days, and a non-urgent primary-care appointment. In a prospective study, the algorithm-based intervention reduced readmissions by 26%. Parkland’s success stems from focusing its algorithm on a specific population and tying it to discrete clinical interventions.

Leverage the data from EHRs. Algorithms are only as reliable as the data they are based on. While algorithms for acute clinical issues (e.g., heart attack, septic shock) may not require large amounts of data to predict risk, algorithms that utilize greater amounts of clinical data have greater accuracy and potential clinical applications.

The Veteran’s Health Administration (VHA), the largest health system in the United States, has collected electronic data from its patients for over three decades. Beginning in 2006, the VHA built a corporate data warehouse as a repository for patient-level data across its national sites. The sheer amount of inpatient and outpatient data has allowed the VHA to create comprehensive algorithms that reliably predict meaningful outcomes such as risk of death and hospitalization. Nurse care managers use these scores to guide intensity of outpatient services, including end-of-life and palliative care, delivered by multidisciplinary teams. The VHA’s investment in an integrated EHR and data repository — 5% of its total health spending — is substantial. However, the ability to reliably predict outcomes to improve quality of care may explain why the VHA’s net return on EHR investment is over $3 billion.

Focus on low-value decision points. Uncertainty over a clinical decision often leads physicians to overtreat or undertreat patients. Predictive analytics can allow clinicians to steer high-cost interventions to those high-risk patients who actually need them.

Consider the use of antibiotics to treat newborns. While less than 0.05% of all newborns have infection confirmed by blood culture, 11% of them receive antibiotics. Kaiser Permanente of Northern California has used predictive analytics to reduce this overuse. Its researchers have developed an algorithm to accurately predict the risk of severe neonatal infection based on a mother’s clinical data and the baby’s condition immediately after birth. Using this algorithm OB/GYNs can better determine which babies need antibiotics, sparing up to 250,000 American newborns each year from receiving unnecessary antibiotics. This could reduce medication costs and side-effects among vulnerable newborns.

Source: Predictive Analytics Times

Data Scientist – best job in America, again

The popular job site Glassdoor published a list of 50 Best Jobs in America, and Data Scientist is again the no. 1 job in USA, with Job score 4.8 out of 5, $110,000 Median Base Salary, and 4,000 job openings.


Half of the top 10 jobs are related to Analytics, Big Data, and Data Science!

Rank Title Job Score Job Satisfaction Median Base Salary
1 Data Scientist 4.8 4.4 $110,000
2 DevOps Engineer 4.7 4.2 $110,000
3 Data Engineer 4.7 4.3 $106,000
5 Analytics Manager 4.6 4.1 $112,000
7 Database Administrator 4.5 3.8 $93,000


Compared to Glassdoor 2016 post where Data Scientist was also no. 1 job in USA, we note that the median Salary has declined from $117 to $110, but the number of listed job openings has increased from 1,700 to 4,200.

Indeed job trends also show continuing growth in the demand for Data Scientists:

You can also reach those talented but hard-to-find Data Scientists by placing a job ad on KDnuggets – email to See Details and posting info.

According to CareerCast recent report, Data Scientist is the most demanded job in 2017. Here are their top 10 jobs for 2017.

Profession Annual
Median Salary*
Data Scientist $128,240 16%
Financial Advisor $89,160 30%
General & Operations Manager $97,730 7%
Home Health Aide $21,920 38%
Information Security Analyst $90,120 18%
Medical Services Manager $94,500 17%
Physical Therapist $84,020 34%
Registered Nurse $67,490 16%
Software Engineer $100,690 17%
Truck Driver $40,260 5%


To determine the most in-demand professions, evaluates Bureau of Labor Statistics (BLS) data on growth outlook, as well as industry and profession hiring trends over the last decade; trade statistics; university graduate employment data; and the database of listings to determine the factors driving hiring needs.

*Median Annual Salary and Projected Hiring Growth by 2024 are via the U.S. Bureau of Labor Statistics.

Source: Predictive Analytics Times


How IT can empower the enterprise with self-service analytics at scale

Business intelligence used to be a top-down affair which IT often approached in the same manner as traditional IT projects. The business makes a request of IT, IT logs a ticket, then fulfills the request following a waterfall methodology.

While this approach centralized data and promoted consistency, it sacrificed business agility. There was a significant lag between question and answer. And this delay led to lackluster adoption and low overall business impact.

Fast-forward to today and IT finds itself at a crossroad with self-service BI as the new normal that can no longer be ignored. The business demands the agility that comes with self-service to drive and improve business outcomes through data-driven decision-making.

This presents IT with an important choice. Either embrace the demand for self-service BI and enable the broader use and impact of analytics, or ignore the trend and continue producing lower-value enterprise reporting stifled by the limitations of traditional tools. IT professionals who are ready to serve as the catalyst will deliver far greater value than those who choose to ignore the real needs of their business users and analysts.

As organizations begin the transition from a traditional approach driven by IT to a self-service approach enabled by IT and led by the business, a new framework is required. This means that past decisions supporting the core foundational components of a BI program—people, process, and platform—must be revisited.

Prioritize people and their needs
A successful transition to self-service analytics begins with people. In a traditional BI model, people were often considered last after platform and process. IT often took the “if you build it, they will come” approach.

But even after they built it, most people did not come. That’s because there was little to no collaboration between the business users and IT during the process of building the solution after an upfront requirements-gathering phase.

Collaboration between the business and IT is critical to the success of the implementation. IT knows how to manage data and the business knows how to use the insights to drive business decisions. Early collaboration will not only lead to the deployment of a platform that meets the needs of the business but also drives adoption and impact of the platform overall.

Reimagine your process
Self-service analytics does not mean end users are allowed unfettered access to all data. It means they have the freedom to explore pertinent business data that is trusted, secure, and governed.

This is where process comes into play. This is the component that requires the most significant shift in traditional IT thinking. A successful modern BI program can deliver both IT control and end-user autonomy and agility. A well-established process is required to strike this delicate balance.

A waterfall-based process limits access to only a few specialists who are expected to meet the needs and answer the questions of the many. This approach often fails to deliver on the promise of BI—to deliver tangible value through improved decision-making with minimal time, effort, and cost.

A modern analytics solution requires new processes and newly-defined organizational roles and responsibilities to truly enable a collaborative self-service-based development process. IT and users must collaborate to jointly develop the rules of the road.

IT’s success is highlighted, and its value to the organization realized, when the business can realize significant value and benefit from investments in analytics and BI.

Implement a platform that IT loves and the business trusts
Since BI has been historically viewed as an IT initiative, it is not surprising that IT drove virtually every aspect of platform evaluation, selection, purchasing, implementation, deployment, development, and administration.

But with drastic changes required to modernize the people and process components of a BI and analytics program, IT must change the criteria for choosing the technology to meet these evolving requirements. Perhaps the most obvious change is that IT must intimately involve business users and analysts from across the organization.

A modern platform must address a wide range of needs as well as the increased pace of business and the exponential growth in data volume and complexity. Organizations need a platform that can adapt to an evolving data landscape and insulate users from increased complexity and change.

The most critical aspect is the ability to meet these diverse needs in an integrated and intuitive way—without having to introduce separate products or modules to execute specific tasks along the way.

Become a strategic partner to the business
As organizations shift their approach to analytics, IT leaders should seize the opportunity to redefine their role. Adopting a collaborative approach to truly support self-service is the key to changing the perception of IT from a producer to a strategic partner and enabler for the organization.


5 Necessities of an Effective Closed-Loop Customer Feedback Program


If you’ve been keeping up with this series, you’re familiar with the idea of gathering continuous customer feedback. But it’s important to note the changing environment for how that feedback is handled.

Traditionally, customer satisfaction surveys have focused on collecting aggregate data. In the world of market research, this approach makes sense. It’s statistically accurate, high-level, and shows trending data—all great things for market researchers. But as customers have become more aware and their expectations have risen, this “open-loop” system falls short. Customers expect that if they take the time to provide personal feedback, then someone should take the time to provide personal follow-up.

A closed-loop customer feedback management system gives institutions the tools they need to take this personal follow-up and “close the loop” on each piece of customer feedback. Ideally, your system should work on two different but complementary levels:
1.Support follow-up with individual customers based upon their feedback (as depicted by the smaller circle in the diagram below), and
2.Deliver insights from aggregate feedback to drive action that will benefit all or a sub-set of the bank’s customers (as depicted by the larger circle):

Five Requirements for Effective Follow-up with Individual Customers:
These qualities should be present in any effective customer feedback management solution as it relates to following up with individual customers:

1.  Non-Anonymous
Patricia Smith, President of Wealth Management at First Interstate Bank, explains the difference between our ongoing Voice of the Customer program and prior efforts the bank had undertaken to listen to customers:

“The biggest component in our relationship with PeopleMetrics is that the surveys aren’t anonymous, and it is a continuous improvement process—meaning we are able to attach the responses, then look at the customer relationships and better understand what’s working well and where our areas of opportunity lie.”

When you’re trying to close the loop, you do not have to protect the identity of your customers who give you feedback. Two-way communication and dialogue is fundamental to a solid and healthy relationship with them.

2.  Action Triggers Built into Survey Design
Don’t fall into the trap of asking your customers about all the elements of the experience that only matter to the bank’s leadership, managers and employees. Did our staff greet you by name? Did you have to wait in line longer than 2 minutes? Were you able to easily find our mobile banking app?

If you impose such questions, but they aren’t tied to issues or important to your customers, you can do little with the insights. Instead, structure an extremely short survey that elicits what’s important to the customer, what happened to them that warrants sharing, provides room to elaborate in their own words, and points bank employees to what they should do next.

The survey questions used in a closed-loop feedback program should clearly point to the best next action the bank should take.

3.  Automated Notifications
It is possible now to trigger automatic notifications (e.g., email, SMS) to bank representatives that alert the employee to a piece of customer feedback requiring action.

These automatic notifications are a core element in a closed-loop customer feedback system. It helps branch managers hear from customers, quickly determine the context of feedback, and respond accordingly.

Common automatic notifications or Action Alerts include:
•Recover Notifications or Course Correct Alerts, when an unresolved problem is dissatisfying the customer;
•Improve or Innovate Alerts, when a customer has a suggestion for how the bank or the channel can get better; and
•Recognize or High Five Alerts, when an employee has gone above and beyond and is deserving of recognition.

Many of our banking clients are also adopting Grow or Opportunity Alerts, which help employees identify opportunities for cross-selling and up-selling to their clients. This soft lead generation tool is helping to shift the culture at one of our client’s banks from one that is purely about service to one where sales, in the name of helping the customer, is an essential and acceptable part of the experience.

4.  Case Management Tools
Asking managers to take action on real-time customer feedback requires work on their end. Strong closed-loop feedback programs provide tools to help managers delegate follow-up actions and track the progress of open cases. Case management tools make it easy to create transparency around the customer experience and ensure that no piece of feedback gets lost or overlooked.

5. Root Cause Tracking
As your managers or other employees close the loop with customers, they should document the root cause of the issue within the case management system. Perhaps customers are complaining about a lack of knowledge of staff at a given branch, and you know that branch has had high turnover and many new hires in recent months. Or perhaps numerous comments related to unexpected branch closings reveal a poor communications plan. By tracking root causes, you will be able to identify areas to improve your operations and eliminate the issues moving forward.