10 Ways to Use Analytics to Drive Innovation

Much of the focus on the potential for big data has centered around using analytics to boost sales and marketing.

While those areas are indeed ripe for innovation, companies can tap analytics for a slew of other novel improvements to outperform competitors, including identifying new profit models, designing new products and streamlining processes.

Tom Davenport, a professor at Babson College and an independent senior advisor to Deloitte Analytics, outlines the 10 ways companies can foster innovation with analytics in an article in Deloitte University Press.

The 10 ways companies can drive innovation with analytics are:

1. Profit model innovation: “There is certainly an analytics spin on this form of innovation, in that many companies in both online and offline businesses are attempting to make profits with new data and analytics-based products and services,” Davenport says. GE, Monsanto and several large banks are among the companies using analytics to identify new ways to monetize their offerings and assets.


2. Network innovation: Companies can use analytics to deliver new products, services or processes to their existing network of partners, such as suppliers. For example, a company could deliver analytics to suppliers and partners to help them make better decisions. “In another context, with the Internet of Things, companies almost always need to share sensor data with their ecosystems, and to define standards so that the information can be integrated and analyzed,” Davenport points out.


3. Structure innovation: Analytics can be used to organize company assets in new ways to create value. “Large banks, for example, have formed new business units to analyze customer data,” the article notes. “Similarly, other businesses create a centralized group of analysts, and then ‘embed’ many of them with key decision makers in business units and functions.”


4. Process innovation: While process improvement was one of the most common uses of analytics in the early days of the technology, companies are evolving from a narrow focus on supply chain and logistical processes to bolster processes in pricing, marketing, sales and manufacturing, Davenport points out.


5.  Product performance innovation: While product innovation has not traditionally involved analytics, that’s changing now that different devices come with the ability to track the physical movements of the wearer.


6. Product system innovation: A business and its network of partners can use analytics to sift through the vast amount of data created by sensors.


7. Service innovation: Analytics can drive or measure service innovation, the article notes. Companies that build complex products like vehicles, computer hardware or jet engines can use analytics to monitor how the machines are performing and predict when they may need maintenance.


8. Channel innovation: Analytics can measure the effectiveness of the different channels companies use to deliver products and services. “Today, the enormous challenge for many organizations is to understand customer relationships across all channels and touch points,” according to Davenport. “Even identifying the same customer across channels is often a problem, although analytics can make it much easier.”


9. Brand innovation: Analytics are key to knowing how a brand is performing.


10. Customer engagement innovation: Analytics are ideally suited for digging into customer behavior data to identify the most effective ways to profitably interact with them.

via 10 Ways to Use Analytics to Drive Innovation | TIBCO Spotfire’s Trends and Outliers.

10 Ways to Use Analytics to Drive Innovation

Companies use ‘Moneyball’ approach in hiring

 Big data helps HR get a more complete profile of job candidates.

Human resources departments finally are beginning to apply lessons from “Moneyball,” the 2003 book by Michael Lewis (later a Brad Pitt-starring movie) that chronicled the Oakland A’s use of big data to compete against richer baseball teams.

Convinced that slugging percentages and on-base percentages were more important than batting averages and stolen bases, the Oakland Athletics focused on recruiting under-the-radar college players with good statistics in those under-appreciated areas for its 2002 season.

Joe Brooks, CEO of recruiting analytics firm Zapoint, says many companies lack visibility into the skills of their workforces.

Despite having the third lowest salary budget in major league baseball ($41 million), the A’s went all the way to the playoffs, competing successfully against teams with more than twice its budget. It also produced a 20-game winning streak — the second longest in major league baseball history.

“Recruiting is an inefficient industry,” says Joe Brooks, CEO of the recruiting analytics firm Zapoint. But, unlike baseball, “many companies lack visibility into the skills of their workforces.” Instead, data is spread across multiple silos and pulling it together into a coherent profile is difficult and time-consuming without an overarching analytics application.

Analytics helps HR

The application of big data analytics alleviates the inefficiency, enabling HR to assemble profiles based on resumes, professional profiles, performance reviews, publications, speaking engagements, interests and opinions relevant to the job or industry.

The result is an up-to-date, more comprehensive portrait of a candidate that includes skills and capabilities that may not appear on resumes. With this, HR can broaden its focus — like the 2002 Oakland A’s — and build a more effective team.

High-tech recruiter Entelo, for example, combs the Internet regularly to identify updates to personal websites and professional profiles like those on LinkedIn or more specialized sites (like Inbound.org for marketers) for the 23 million people in its database.

“Individuals’ skills are evolving, and people apply for several different types of jobs. Often they have different resumes for each type of job. Finding the relevance is what’s important,” Brooks says.

Data analytics helps put individuals’ capabilities in the proper context. For example, a resume may show a candidate has six years’ sales experience, but if it was 10 years ago, it may not be relevant to his or her current skills and aspirations. Big data analytics also help provide checks and balances by showing how skills have been applied and finding recommendations that may not otherwise be obvious.

‘Personalized approach’

“Recruiting used to be based on gut feeling,” acknowledges Kyle Paice, senior director of marketing for Entelo. “Today, if we’re searching for a senior IT developer, we can do a Google search and see what candidates have written, the type of code they develop, and opinions and indicators of personal interest.” This helps recruiters craft more personalized approaches for candidates.

Entelo also tracks organizational changes, like acquisitions or downsizing notices. Combining this information moves recruiting beyond headhunters and resumes to help hiring companies target job candidates who are ready to move. “If you’re flagged by Entelo, you’re 30 percent more likely to change jobs in the next 90 days,” Paice says.

“You can look at individuals, but in reality it’s a team you’re trying to establish,” Zapoint’s Brooks emphasizes. “First understand what you have.”

He recommends identifying key performers and comparing them to the competencies of others in that role. Perhaps others need the same skills, but it’s also possible the team needs to be augmented with complementary capabilities to increase effectiveness and efficiency.

Challenging assumptions

For the Oakland A’s, this meant challenging the assumption that recruits with the most home runs should be on the team. Instead, it analyzed what made a successful team and recruited accordingly.

“Organizations list the talent shortage as a top challenge. But, those using data to inform their recruiting efforts say they experience fewer challenges,” notes Elissa Tucker, human capital management research program manager for business benchmarking advocate APQC. Therefore, “there’s a lot of experimentation in big data analytics for hiring among large companies.”

For example, APQC conducted a study for a leading cereal company several years ago to correlate employees’ performance and attrition rates to their universities. Based on the results, it now it targets its recruiting to certain schools.

The point of analytics is to help organizations make more objective, evidence-based hiring decisions that increase effectiveness and efficiency. But, as Brooks points out, “Analytics don’t replace the human component. Instead, it makes a pile of resumes easier to wade through.”

via Companies use ‘Moneyball’ approach in hiring.

Companies use ‘Moneyball’ approach in hiring

HR Technology - From Thought to Action

Companies have been talking about investing in HR technology for a long time. In 1999, the intent was clear, with 100% of this survey’s respondents planning to automate their HR systems (or upgrade their current automated systems). Before moving forward, however, most organizations were waiting for a new generation of technology to emerge that would support a more robust business case.

Fifteen years later, intent has become a reality: investment in HR technology is growing across all industries and geographies and HR technology solutions now exist to meet a wide range of needs and budgets.

HR Technology – From Thought to Action

The most recent evidence of the shift from thought to action is documented in Towers Watson’s 2014 HR Service Delivery and Technology Survey: a global survey of 1,048 companies. This survey found that, even where HR spending is being reduced in other areas, HR technology is flagged for increased spending. In fact, one in three respondents (33%) plan to spend more on HR technology in the coming year, with some respondents expecting that increase to exceed 20%. More importantly, only 15% plan to spend less in HR technology in the coming year as compared to the previous year.

Reasons cited for this increased commitment to HR technology include the more traditional drivers of cost reduction and efficiency; together with a number of emerging drivers, including improved data analytics as a contributor to strategy development, and providing greater accessibility to information via web-based portals and SaaS solutions.

HR Data Contributes to Strategy

Data obtained through effective use of HR technology is increasingly being used to delve into organizational health and to contribute insights toward strategy development. Towers Watson Study, for example, found:

• 63% of respondents regularly conduct engagement surveys and use the data to contribute to strategic people-related investments that impact their business;


• 33% of respondents use the results of these surveys to influence transformation within their organizations; and


• 30% feel they could do even more with this data.

According to Edward Piper of Stoel Rives, LLP, in his review of the White House report entitled, Big Data: Seizing Opportunities, Preserving Values:[1] “For employers, big data collection can have major benefits. It allows employers to assess the characteristics of their workforces in unprecedented detail and detect trends that, until very recently, were analytically invisible. The insights an employer gleans from that analysis may challenge long-held assumptions about the best ways to hire, promote, and fire. For example, which applicants help decrease employee attrition? What perks attract the best talent? What personality traits jive best with my organization’s culture? Big data can offer incisive—and often surprising—answers, which many employers are beginning to use to recalibrate their approach to human resources.”

HR technology is the vehicle which enables companies to gather, analyze and act on HR data and to realize the potential it offers for more insightful and strategic business management. The predictive analytics potential of HR data is a growing motivator for the adoption, expansion and restructuring of HR technology.

SaaS and HR Technology

It’s especially interesting to note the impact SaaS has had on the evolution of HR technology. In 1999, the conversation about HR technology was almost exclusively happening among the largest organizations and revolved around automating payroll and implementing core HRIS functionality. Today, HR technology offers a much broader array of tools to supplement traditional HR information management; from applicant tracking and onboarding tools to performance review automation, real-time feedback and mobile access. In addition to simplifying employee and manager self-service, a feature used by 71% of North American organizations[2], SaaS has made it possible to offer a full gamut of HR technology options, ranging from single purpose applications to more robust, integrated HCM systems, at a price point smaller companies can afford.

The SaaS landscape has also matured in the past 15 years and now includes some well-established players. While these larger players grow market share, a number of scrappy, innovative startups continue to push the boundaries and hold their own. In fact, some companies in the market for new HR technology prefer to work with these startups. They recognize the limits of HR technology are a long way from being reached, and that some of the most creative ideas are being developed by these agile contenders.

The evidence is in and the message is clear: HR technology has become a “must have” for business. While HR technology will never replace the human in HR, it provides a power-tool for administration, a 24-hour access channel for managers and employees, and a goldmine of insight for leaders.

via HR Technology – From Thought to Action.

HR Technology - From Thought to Action

Next Wave HR Tech Part 2: HR Data

You don’t need to read the latest story about NSA leaks to understand that people today leave an increasingly wide trail of data as they work their way through life. There’s the data found in HR systems, the data that is compartmentalized in departments (like sales or production), and external data channels like social media. Is there an opportunity for HR to harness people data across the entire spectrum of data sources to find the best utilization of people?

 Integrating Non HR Data

Like most functions, organizations and people, the HR Department is buried in new somewhat relevant data. The organization’s management will waste little time before they mandate the discovery of uses for this external data. Recruiting, which is always the most competitive of the HR silos is already trying to make sense of a world that violates our preconceptions.

What used to be private is now public. What used to be assumed is now measured. What used to be implied can now be made literal.

Today’s data tsunami seems to get much of its volume from social media. Services like Dice’s Open Web (or Gild, Entelo, TalentBin.com, HiringSolved, Swoop, RemarkableHire) aggregate social data much the way that Indeed aggregates job data. Relentlessly scraping social sites for information about people, these services claim to give the Recruiter deeper and better access to insight about a particular candidate.

LinkedIn sells the data. Monster, CareerBuilder and even Indeed sell data about candidates. The problem isn’t new or is it? We think that the volume of data coupled with our lack of ability to digest it all means that this is more than a bigger version of an old issue.

And, data about our people or their connections is just the beginning. The contemporary HR Department has to be prepared to incorporate data like the following:

• Investment data (including a clear picture of which employee owns how much of the company)


• Industry market trend data (for workforce planning)


• Labor Market Data (who are the competitors and what does supply look like?)


• Free or low cost Training available through various sources (YouTube, Khan Academy)


• BYOD device data (to assess risk when circumstances require it)


• Aggregate health data (from suppliers like Kaiser) to fill in Workforce


• Social Media data about current employees (not to mention the spillage from internal collaboration systems


• Supply Chain people data (for the management of the health of the ecosystem)

And, that’s just the beginning. Individual employees will increasingly be a part of the development of learning modules and/or figuring out what works from the marketplace.

In order to blend the flow of external data with the material we already have captured in our systems, new concepts will have to be forged. In order to fully deploy our people, we’ll need to know more about them. At the same time, we’ll be taking more of their input.

The mechanics of data integration are currently up in the air. Providers like Broadbean develop integration tools that give recruiting departments a multidimensional view of their performance. The process harnesses data that’s been lying around as well as data from new sources.

Data makes its own gravy. That means that each of the new data flows will also kick off powerful metadata (like anonymities health care data to help uninsured companies cover their employees). The more that external data is intertwined with internal data, the better our prognostics will be.

HR Data for Other Departments

As long as an organization is a comfortable tribal size (say, under 150 employees), it’s possible for everyone to know everyone. The distance between the top and the bottom is not great. Job descriptions are not work rules. Departmental lines are fuzzy.

Growth creates the need for policies, procedures and structured governance. It’s not long until the various boundaries between people and sub groups start to get rigid. By the time a company reaches 1,000 people, it’s the big time. It’s no longer possible for everyone to know everyone. Hierarchies are established to navigate the problems caused when most members of the organization are strangers to each other.

One of HR’s central roles is policeman. Someone has to enforce the governance structures required by size. While it would be great to have a world where strangers immediately understood each other’s boundaries, we build our organizations with human beings who have a limit of about 150 connections that they can manage well.

Much of HR’s work is designed to overcome the communications problems demonstrated in the telephone game. Just like any form of copying, the clarity of a message declines each time it is transmitted. (Basic internet protocols are designed to overcome this problem by including additional information so that the message content remains intact.) HR’s job (in interpersonal matters particularly) is to ensure that the organizations rules and boundaries are enforced and reinforced.

Social tools (like Honey, Yammer, Chatter and a host of others) are restructuring the way that communications work inside the company. (Here are the stories about that.) As a result, cultural norms form like crystals around seeds discovered in the social flow. The really interesting thing is that these social tools seem to impact the degree to which the telephone game disrupts communications.

What used to be delivered in a stale memo is now communicated in the flow/context of other data. It turns out that the more personal the data, the more it sticks. The memo was used to reinforce the message of policy; to prevent telephone game-like degradation. Much of that function can now be accomplished socially.

This is a significant unintended consequence of using social media in the organization. It enlarges and extends the span of control without resorting to enforcement or coercion.

So, HR needs to be a good bit smarter about using influence (think of peer pressure, not Klout) to move ideas through the organization. The interesting question here is whether or not social media creates too much homogeneity. The fact remains that social media reduces the work required from HR.

Meanwhile, the other departments in the company are getting hungry to use social data about employees as a way of getting things done. HR seems like the logical receptacle for the organization’s data on its people, doesn’t it? Since forever, individual departments have only been able to know a lot about the people within their boundaries. Today, they can easily discover things about the rest of the organization through social channels.

While you could be forgiven for forecasting a chaotic reality in which HR never stepped up to this responsibility, that future isn’t very likely. The forces that drive the requirements for HR in the first place haven’t been voided. HR’s opportunity horizon has expanded.

Here are some of things the rest of the organization would like to know about the workforce:

• Which employees are also customers? Which are not? Why


• Which employees are also investors? Which are not? Why?


• Which employees are stakeholders in the community (from elected to volunteer leadership)?


• Which employees would be good for beta testing programs? Who has already done this and what were the topics?


• Which employees would be good for focus groups? Who has already done this and what were the topics?


• What do we need to know about the people on the other department’s softball team?


• Which employees might be useful (by virtue of education, experience or avocation) in times of talent shortage?


• Which employees have connections that might be useful in a particular sales process?


• Which employees have connections that should be converted to leads?


• Is there something about our department that is causing communications problems? (see how our beliefs and values line up with another department)

And, that’s just the beginning. As more data about employees becomes available, there will need to be a central repository for the information and useful ways to sift through it.

In the very near term, HR will become a net publisher of data to other departments. By helping the organization know as much as it can about employee likes, dislikes, affiliations, hobbies, connections and other interests, HR will be able to step up to its mission of finding the best utilization of people.

via Next Wave HR Tech Part 2: HR Data | HR Examiner.

Next Wave HR Tech Part 2: HR Data

Google’s secret weapon for world domination: Data-based hiring

I have long advocated the use of hard scientific data in hiring as one of the most effective means to drive organisational success. It was a bit of an uphill battle at first. While all CEO’s recognised that the key to a successful business is having the best people, they were not using the best, most predictive methods for identifying the best people. But more and more I see technology meeting psychology in forward-thinking companies. I recently discovered a great article which explored the secret to the hiring success of one of the world’s most successful and innovative firms—Google.

Google has done away with the ages-old subjective decision-making approach in HR which relies on CV reviews and interviews, and replaced it with a highly innovative, data-powered approach it calls “people analytics.” Does it work? Well, considering that each Google employee generates over $1,000,000 in revenue every year, I would think the answer is yes. And given that every company is only as good as its people, I believe that a large part of Google’s extraordinary market success can be attributed to its data-based hiring approach.

The most powerful illustrations of Google’s “People Analytics” approach.

Google has identified the key mechanisms by which they can improve the effectiveness of their employees, here are my top 5 most powerful demonstrations of these:

8 key characteristics of effective managers– Google looked at internal data to identify the 8 key characteristics that are essential for great managers in terms of performance and retention. The key to being a successful leader is providing team members with frequent personalized feedback.

An effective hiring algorithm with only a 1.5% miss rate– Google approaches selection scientifically and has developed an algorithm for predicting which candidates have the highest probability of success. The algorithm analyzed rejected resumes for each large job family in order to identify top candidates who might have been missed. This algorithm found that their initial miss rate was only 1.5%.

What about retention? Google has developed a successful mathematical algorithm to predict which employees are most likely to turn over. This enables leaders to personalise coaching and act before it’s too late.

What about the value of top performers? Google has identified that an exceptional technologist can be as much as 300 times more effective than an average one. By proving the value of top performers, executives can be convinced to provide the resources essential to employing, keeping, and developing talent.

Here’s the data to prove it — Success is not a matter of opinion or qualitative judgment. Analytics teams look to pre-employment assessment and on-the-job metrics to prove it.

By using these methods Google has determined that minimal value was added by an extended interviewing process and was consequently able to dramatically shorten their time to hire.

Every smart business today is metrics-driven. But Google has taken the lead in applying an analytical, metrics-driven approach to the processes of employee hiring and staff development. By their own analysis, a data-based approach to people has generated exceptionally positive business results for them. And hey – if it’s good enough for Google, it probably would work for you too!

via on assessment | Google’s secret weapon for world domination: Data-based hiring.

Google’s secret weapon for world domination: Data-based hiring

Hiring and Big Data: Those Who Could Be Left Behind

Recruiters, HR managers, and investors have always sought better ways of identifying who fits and has potential, and how to allocate opportunities to them. Big data holds the promise of not only vastly improved efficiencies but also bringing greater objectivity to our very unconsciously biased human decision-making.  And without a doubt, predictive “people analytics” are starting to transform how employers hire, fire, and promote.  As a recent Atlantic article argues, “What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.”

But that’s just the tip of the iceberg.  One of the developments that will undoubtedly cement the relationship between big data and talent processes is the rise of massive open online courses, or MOOCs.  Business schools are jumping into them whole hog.  Soon, your MOOC performance will be sold to online recruiters taking advantage of the kinds of information that big data allows—fine distinctions not only on content assimilation but also participation, contribution to, and status within associated online communities.  But what if these new possibilities—used by recruiters and managers to efficiently and objectively get the best talent—only bake in current inequities? Or create new ones?

Lauded as purveyors of equality, the data not only show that most MOOC-takers are well-educated, employed, young and male —but that most of the teachers, especially the “stars,” are men.  And as a recent article entitled “Masculine Open Online Courses” warns, MOOCs may be taking academe back “to the days of huge gender gaps, when senior scholars overwhelmingly were men.” Yet who teaches us is important in more ways than one. Look at any piece of research about the subtle, systemic or “second-generation bias” holding back women and minorities in business and you will find lack of role models at the top of the list.  After all, who are among our first role models (after the parents, if we’re lucky): Our teachers.  Speaking from experience, I know that I would not have ended up a Yale PhD if my department head at the University of Miami, Dr. Robert Tallerico, hadn’t personally encouraged and mentored me from day one.

Far from democratizing education, critics argue that MOOCs will only reinforce those with power and weaken those without it. Early evidence from MOOCs suggests huge falloff rates. After Udacity founder Sebastian Thrun’s very public defection from the MOOC church, he was lambasted for conducting a for-profit “experiment” at San Jose State without thought to whether completion rates might differ across racial and class lines.  I can’t help but wonder what would have happened to me if my first year at university was all MOOC.  Did Thrun and his colleagues consider the possibility that the issue might not have been a “difficult neighborhood without good access to computers” but lack of contact and identification with the faculty?

And let’s look more closely at those online games that Don Peck reports on in his Atlantic piece. As more recruiters use gaming data for hiring decisions, are they inadvertently ensuring a homogenous workforce?   Males rack up many more hours of practice at these kinds of games than females, a recent Sex Roles study demonstrates. Gaming is also associated with less time spent doing homework, i.e., working hard—the essential ingredient girls, minorities and immigrants (I know, I tick all 3) rely on to get ahead. I cannot imagine my parents saying “honey, put away those textbooks and work on your games or you’ll never get anywhere in life.”

And yet recruiters are taking this data seriously.  “How long you hesitate before taking every action, the sequence of actions you take, how you solve problems,” says one purveyor of workforce analytics, “all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality.”   Even after only twenty minutes of play, you will generate several megabytes of data that “compose a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.”

There’s more. The Sex Roles study’s co-author says another possible contributor to girls’ lack of interest in gaming is the scarcity of women working in the game-design industry. “88 percent of game developers are male,” Heeter says, adding that “games designed to optimally appeal to women might minimize in-game performance pressure, provide real-world benefits such as stress relief, brain exercise or more quality time with family and friends, and be playable in short chunks of time.”

Which leads to another question: What if “in-game performance pressure” triggers stereotype threat? Decades ago psychologist Claude Steele showed that women and African Americans underachieved academically, and on standardized tests, not due to incapacity but rather due to stereotype threat—the fear that they would be stereotyped and underestimated on the basis of their race and gender.  Steele also discovered that the dropout rate for African American students was much higher than for their white peers, even though they were good students and had received excellent SAT scores.  As forms of online learning and screening get more sophisticated, adding more elements of participation and linking more explicitly to career gatekeepers, will we be plugging leaks in the diversity pipeline—or adding more?

The beauty (and danger) of big data is that it’s not limited to the tests a person takes voluntarily as part of the hiring process—it can also scour our digital traces to find leading indicators correlated with on-the job performance. The vast number of data points that miners marshall afford them surgical precision in discerning which attributes correlate best with success in different jobs. For instance, it turns out that what browser an applicant uses to take the online test matters a lot, especially for technical roles, because using the most sophisticated browsers requires “a measure of savvy and initiative to download them.” Other predictors are so troubling that companies don’t use them despite their power. One start-up that applies people analytics to screen job applicants found that distance between home and work is strongly associated with employee engagement and retention. Another finds that the strongest coders tend to be fans of a particular Japanese manga site.  What is the difference between the pattern recognition afforded by big data, and profiling on the basis of gender, race or class?

Even the person who’s never ventured beyond Statistics 101 knows that correlation is not causation, a truism acknowledged by the creators of the algorithms. Google, for example, stopped using puzzles and brainteasers in hiring after finding they did not predict work performance and leadership capacity. And, in many cases, unmeasured factors cause both the predictor variable and the outcome it is aiming to predict. What if what browser you use or what you read for fun in your spare time depends in part on your social network? It is well-documented, for example, that innovations diffuse via social networks that are notoriously “homophilous,” i.e., that connect people who are similar on demographics like class, race, and gender.  Will algorithms based on our social interactions not only digitally recreate but exponentially empower the “old (white) boys club?”

Big data offers the promise of greater predictability, but that should not be confused with objectivity.  As a researcher myself, I offer a word of caution: Before we go whole hog on our embrace of this evidence-based revolution, shouldn’t we follow its tenets by actually conducting some studies about the diversity dynamics of this brave new world of talent management?

via Hiring and Big Data: Those Who Could Be Left Behind – Herminia Ibarra – Harvard Business Review.

Hiring and Big Data: Those Who Could Be Left Behind

The Modern Data Scientist Skillset - Infographic

The Modern Data Scientist Skillset explained.  Infographic

 

The Modern Data Scientist Skillset - Infographic