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THERE’S AN ART TO DATA SCIENCE. CAN YOU SEE THE BIG PICTURE? DATA SCIENCE IN FINANCIAL SERVICES A Guide for the Modern Chief Risk Officer

MEET THE MODERN CHIEF RISK OFFICER Driving Data Science Strategies from the Boardroom

In the digital age, the role of the chief risk officer (CRO) is evolving fast— not least because of fundamental changes to the way that businesses gather and analyze data. Thanks to the emerging, innovative tools and technologies of data science, firms can now use data to not only explain and predict behavior but also learn from it. As a result, they can gain deeper insights into the opportunities and risks that face their business and shape its fortunes. For the CROs of today’s financial services firms, data science itself presents both an opportunity and a challenge. On the one hand, it gives them a chance to help their business get more value from the wealth of data at its disposal. But on the other, it means steering the firm through uncharted territories and avoiding a range of potential pitfalls. Whatever route you take as a CRO, your seat on the board and skills in analyzing data put you in a unique position to define and lead your company’s data science strategy. Leave the technical wizardry to the scientists themselves; if data science is also an art, it’s your role to see the big picture. You can, however, learn much from those that have gone before. Read on for our introductory guide to using data science in financial services—the why, the how and what to expect along the way.

FOR THE CRO s OF TODAY’S FINANCIAL SERVICES FIRMS, DATA SCIENCE ITSELF PRESENTS BOTH AN OPPORTUNITY AND A CHALLENGE.

DATA SCIENCE BASICS FROM A TO T An Overview for the C-suite

So, What Exactly Is Data Science? In the simplest terms, data science builds on traditional statistical analysis to extract deep, meaningful insights from raw data. It’s a broad field that sums up and brings together a number of processes, theories, concepts, tools, and technologies. And it’s come into its own by helping businesses make better decisions, develop products, analyze behavior—and forecast the future. 1 Many of the concepts that are part of data science will be more than familiar to CROs and their fellow board executives; others may need further clarification. Some certainly overlap—either with one another or related disciplines. As you start to develop your own deeper understanding of data science, let’s take a look at the terms you are most likely to encounter. 2

Algorithms: A set of instructions and rules for a computer to follow, in a particular order, to solve a problem or achieve an objective. Artificial intelligence (AI): A broad field of computer science in itself that uses software, algorithms and data inputs to help computers or robots understand speech, convey knowledge, reason through problems, and understand images. Big data: Information produced in vast quantities that is therefore difficult to store and analyze, contains high volumes of records (think billions or trillions), and covers a wide variety of formats (e.g. text, images, and numbers). Big data can vary in quality, with information either missing or inaccurate, and is typically produced at speed—making the transactions processed by a financial institution a good example. Deep learning: A method of machine learning that uses supervised learningalgorithms tocreateanartificial neural network, which essentially aims to mimic the structure and function of the human brain. The more data you give to a deep learning model to train its performance, the better it performs—so, it’s well suited to the supersize end of big data. Hadoop: A common database for storing data before it is analyzed, ideal for very large datasets.

Machine learning: An application of AI that allows computers to learn from experience, using the big data it typically processes to identify patterns, make decisions, and improve performance without further human inter- vention. Using software languages like R and Python, machine learning models can be “trained” to statistically test relationships between an outcome (e.g. sales) and any number of variables (e.g. customer demographics) to make predictions or better understand behavior. SQL: Another common database usually used by businesses that are either well established or don’t need to store high volumes of data. Supervised learning models: Models deployed in machine learning to understand the drivers of an outcome or simply make predictions. One of the downsides of supervised learning models is that outcomes must be observed and a sufficiently large amount of data must exist to train the model. ( Unsupervised learning models , by contrast, will find patterns in small data samples without knowing what the outcomes might be. So, in the retail space, for example, these are more effectively applied to products that have never been sold before.) Topological data analysis: A relatively new technique that is good at managing small data samples, larger numbers of predictors, missing data and errors in data capture. With helpful visualization tools that can simplify results, it is a perfect choice for unsupervised learning.

REAL WORLD USES Why Data Science Makes Sense for Financial Services

Data science’s ability to delve into massive quantities of unstructured data makes it especially appealing to the financial services industry. For buy- and sell-side firms alike, the big data flowing through financial systems is a valuable asset in itself. As a result, many firms rely increasingly on the tools and techniques of data science to help them make better use of their resources and improve decision making and performance. Here are six top use cases for data science that are currently proving their worth in financial services.

1 FRAUD

DETECT ION Data science in general and machine learning, in particular, are proving adept at detecting and preventing financial services fraud

2 REAL - T IME AND

PREDICT IVE ANALYT ICS Now, data science techniques are taking analytical insight to the next level.

4 RI SK

3 CUSTOMER EXPERI ENCE PERSONAL I ZAT ION Data science can transform your compliance burden into new business opportunities—by unlocking a treasure trove of intelligence on your customers.

MANAGEMENT Data science tools help financial organizations understand their risks more clearly.

CREDI T SCORING 6

5 PROCESS

Data science has helped financial lenders significantly reduce lending risk by rapidly analyzing a wider range of data.

AUTOMAT ION

Through data-science-driven process automation, firms have been able to increase productivity.

With worldwide losses to fraudulent activity now as high as $200 billion a year, 3 data science in general and machine learning, in particular, are proving adept at detecting and preventing financial services fraud—and, in turn, improving security for customers and staff. 4 While more traditional rule-based detection systems can spot obviously fraudulent activity, they subject customers to multiple, tortuous steps and involve lots of manual adjustment to different scenarios. Above all, they can easilymiss subtle or disguised behavior that may also indicate fraud. On all these fronts, machine learning makes a better alternative as a fraud detective. It finds hidden or implicit correlations in data, reduces verification measures, and automatically detects potential fraud in real time. 5 While rule-based systems are still prevalent, leading financial insti- tutions already use data science technology to combat fraud. For example, MasterCard has trained up and integrated machine learning and AI tools to track and process a range of variables across credit card transactions—from transaction size, location, and time to device and purchase data. By providing a real-time judgment on whether a transaction is fraudulent, MasterCard is helping reduce the number of false declines in merchant payments.

FRAUD DETECT ION

While rule-based systems are still prevalent, leading financial institutions already use data science technology to combat fraud.

As modern CROs will be well aware, analytics have already been instrumental in driving a more strategic approach to risk management — and helping the function play a more proactive role in supporting the bottom line. 6 Now, data science techniques are taking analytical insight to the next level. The timing couldn’t be better: just as data is becoming more readily available through digital channels analytics tools are becomingmore sophisticated and accurate. Data science effectively allows you to cut through the noise of big data and home in on truly relevant data to make smarter strategic decisions. More specifically, real-time analytics help you understand the problems that may be holding back your business, while predictive analytics shows you new ways to solve them. And by integrating both into your workflow, you can better avoid potential issues in advance. 7 For real-time analytics in action, see the way that PriceStats analyzes inflation trends around the world for major financial institutions, by collecting online prices of products across 22 economies throughout the day. Predictive analytics, meanwhile, help high-frequency traders fight off tough competition in a fast-moving environment by identifying specific market participants and anticipating their future actions. 8

REAL - T IME AND PREDICT IVE ANALYT ICS

Data science effectively allows you to cut through the noise of big data and home in on truly relevant data

Since the global financial crisis of 2008, a raft of new regulations have obliged financial institutions to gather, store, and report on ever-increasing amounts of data. The good news is that data science can transform your compliance burden into new business opportunities—by unlocking a treasure trove of intelligence on your customers. As a matter of course, digital services create terabytes of transac- tional and personal information that give data scientists all the raw materials they need to sculpt a personalized marketing program and online experience for each customer. Data analytics and data mining tools do the heavy lifting: isolating and processing only the most relevant data to identify individual needs and preferences. From here, data scientists can build models that will predict how customers will respond to particular products, promotions or offers—and offer the right product to the right person on the right device. 9 In this respect, data science can help meet an increasingly important priority for the financial sector. 28% of financial services firms cited “optimizing the customer experience” as the single most exciting opportunity for their organization in 2018, followed by “data-driven marketing that focuses on the individual” for a further 23%. 10

CUSTOMER EXPERI ENCE PERSONAL I ZAT ION

cited “optimizing the customer experience” as the single most exciting of financial services firms 28%

opportunity for their organization in 2018

Risk management covers a wide spectrum of disciplines for a financial institution and its CRO, with different types of organizations focusing more heavily on different areas of risk. For example, according to a report by The Economist, retail banks worry most about credit risk, commercial banks about market risk, and investment banks about operational risk. In the same survey, 80% of banks overall consider data science a viable option to measure and mitigate risks. While credit and liquidity risks scare the industry most as a whole, organizations are grasping the potential of big data analytics to link seemingly unconnected events and warn against a possible liquidity crisis. 11 With the power to make more accurate predictions by analyzing a broader set of data points, data science tools help financial organi- zations understand their risks more clearly, model them more effec- tively, and cope better with regulations. 12 Consequently, almost all banks are now investing in data science to improve risk management. Notably, commercial and investment banks especially are looking to not only expand but also centralize their data science operations to drive common standards and best practice across the organization. It’s a wise move, as the same big data infrastructure can empower banks to both mitigate risks and pursue new sources of revenue. 13

4 RI SK

MANAGEMENT

of banks overall consider data science 80%

a viable option to measure and mitigate risks.

The provision of financial services naturally involves a plethora of repetitive tasks that firms have traditionally managed manually. The digital age has already increased automation in the back office— but AI and machine learning tools are now helping turbo-charge administrative processes and further reduce human intervention. Through data-science-driven process automation, firms have been able to increase productivity, improve the customer experience and scale up their services. Successful use cases include chatbots, call-center automation, paperwork automation and even the gamification of staff training. And the success stories keep coming. JPMorgan Chase, for instance, has used natural language processing (NLP)—a form of AI that gets computers closer to a human understanding of language—to automate the processing of commercial loan agreements. The manual review of 12,000 contracts would typically take up around 360,000 labor hours, the bank’s new NLP-powered platform does the same job in just a few hours. 14

5 PROCESS

AUTOMAT ION

Data science has helped financial lenders significantly reduce lending risk by rapidly analyzing a wider range of data on potential customers and using it to provide a single representation of risk: the credit score. 15 Predictive models are typically trained using thousands of customer profiles, each made up of hundreds of data entries. After “learning” from a combination of historical data on customers, plus peer group and other data, the models are ready to perform the same credit- scoring tasks on real-life loan applications. By reliably predicting the likelihood of customers paying back their loans—or displaying other defined behavior in future—robotical- ly-charged credit scoring systems enable human staff to work much faster and more accurately. 16 At Provenir, we have expanded our own risk analytics and decisioning platform to support a wide range of programming languages including Python. With access to an ever-widening range of algorithms and data libraries, Python’s speed, flexibility, stability, and ease of integration with almost any information source have made it today’s go-to tool for data scientists. Because it works so well with AI, Python enables you to build self-sufficient non-linear models with a large number and variety of historic data variables. This ultimately helps our clients create more sophisticated statistical models at higher speeds, gain a more accurate picture of prospective customers, and drive faster, more reliable credit risk decisions.

CREDI T SCORING 6

Data science has helped financial lenders significantly reduce lending risk by rapidly analyzing a wider range of data on potential customers

CAN’T AFFORD A DATA SCIENCE TEAM

DIRTY DATA

In a survey of more than 16,000 data scientists, Kaggle identified the top ten barriers that data science strategies come up against. 18

LACK OF CLEAR QUESTION TO ANSWER

LACK OF MANAGEMENT/ FINANCIAL SUPPORT

SCIENCE TALENT

EXPLAINING DATA SCIENCE TO OTHERS

PRIVACY ISSUES

LACK OF DOMAIN EXPERT INPUT

RESULTS NOT USED BY DECISION MAKERS

DATA UNAVAILABLE OR DIFFICULT TO ACCESS

OBSTACLES TO SUCCESS What Stands in the Way of Data Science Strategies?

With such an impressive list of use cases, data science is clearly making its mark on the financial services industry. At the peak of its powers, its tools and technologies can help organizations significantly increase their competitive edge by improving customer engagement, reducing operational costs, and optimizing marketing outreach, risk management, and pricing. 17 What could possibly go wrong? As a discipline in its infancy, data science is an art that business is still learning to master. So, what stops big data analytics providing the true picture that organizations need of their opportunities, risks, and customers? In a survey of more than 16,000 data scientists, Kaggle identified the top ten barriers that data science strategies come up against. 18

LACK OF MANAGEMENT/ FINANCIAL SUPPORT

DIRTY DATA

49.4%

37.2%

Inaccurate, incomplete or inconsistent data sullies results—and cleaning it up can cost companies up to 25% of possible revenue. 19 With so much big data to work through, the need to cleanse dirty data—before even starting to analyze it—wastes the time and expertise of data scientists.

For a data science initiative to fulfill its potential, an organization must be willing to make significant investments in people, infrastructure and platforms. To get the most payback from its expenditure, though, it also needs senior management to get behind the initiative and make sure it is wholly in line with the firm’s overall business strategy. The CRO must play a leading role in encouraging executive buy-in and prioritizing projects that will offer the greatest return on investment. 21

SCIENCE TALENT

41.6%

The skills of data scientists are in high demand, but the world’s well-publicized shortage of qualified candidates is making posts increasingly hard to fill. With 59% of openings coming from the finance, insurance, professional services, and IT industries, it is forecast that the number of data science jobs available will increase by 364,000 by 2020, bringing the total to 2,727,000. 20

LACK OF CLEAR QUESTION TO ANSWER

30.2%

To get valuable, usable results from data science that solve a real business problem, your first task is to identify exactly what the problem is and define each aspect of it. Data scientists have the talents and tools to answer all kinds of questions; but without a clear definition of what the business needs, they can’t design an effective solution. 22

DATA UNAVAILABLE OR DIFFICULT TO ACCESS

PRIVACY ISSUES

30.2%

19.8%

The success of data science also relies on getting your hands on the right kind of data. With infor- mation often scattered across different lines of business, gaining access to a sufficient range of data in appropriate formats can challenge the most dogged data scientist. 23

Concerns about the security of data and the threat of identity theft, privacy invasion, and social media stalking have never been greater. So, it’s critical to determine if and how you can gain permission to use and process the data that might yield the most useful insights. 24

RESULTS NOT USED BY DECISION MAKERS

LACK OF DOMAIN EXPERT INPUT

24.3%

19.6%

Even the most instructive insights will go to waste if they aren’t acted upon by decision makers. That’s another reason for getting board-level support from the start and ensuring that business requirements guide your strategy at every stage, from what problem you are solving to how results will be used.

Data science is all about computer wizardry, right? Wrong: you need to underpin every data science strategy with sound knowledge of the business domain under scrutiny. Think of data scientists as a bridge between the IT department and top management, bringing together the business requirements of the strategy with the technical know-how to make it happen.

EXPLAINING DATA SCIENCE TO OTHERS

22.0%

CAN’T AFFORD A DATA SCIENCE TEAM

17.8%

There are two aspects to this particular obstacle. First, the business leaders that are paying for the data science strategy must appreciate the point of the project and the value it will deliver. Second, the results must always be presented in a way the business understands. In both cases, the CRO can help cut through the complex concepts and potentially baffling jargon of data science to show the concrete objectives and bottom-line benefits.

With their skills in short supply (see barrier 2), the ongoing rise in demand for data scientists can make them expensive to hire. Currently, in fact, they can earn base salaries up to 36% higher than other predictive analytics professionals. 25 But as many early adopters have shown, the business benefits can ultimately well outweigh the costs.

TIPS AND TRICKS Five Steps to Mastering the Art of Data Science as a CRO

Every data science strategy needs a senior sponsor to pull it into focus and make sure it never loses its business perspective. Few on the board will understand analytics as well as the CRO, nor be able to draw as clear a line between complex data models and game-changing decisions. Now, with a deeper understanding of the techniques applied and the challenges involved, it’s time to take a step back from the finer details of data science and see your strategy from an executive viewpoint. Here are the five priorities to keep at the top of your mind as you turn detailed plans into action.

DEAL WI TH THE DATA

Any data analysis work must start with a well-defined workflow that makes sure you identify the problem, design the right solution and follow a clear path through data collection, cleansing, aggregation, and analysis. For access to the right data, the business needs robust data management systems and data integration tools that support connectivity with

external data sources and absorb them seamlessly into your workflow and models. For the CRO, it is important to remember that data quality is an enterprise issue. Data governance tools can help you maintain accuracy and consistency, and manage formatting issues, but data quality managers should also be in place across the business to stamp out dirty data. 26

SECURE BUS INESS BUY- IN

Here, the CRO needs to lead the strategy from the top. Take the time to ensure that the rest of the board has at least a basic grasp of the processes that allow you to extract insights from your data— and, most importantly, the value they can add to your business. It is also vital to establish a strong working partnership between your senior business leaders and the data science team. These highly skilled practitioners are experts at gathering and manip- ulating data, but they must also understand the business whose information they are modeling—

its objectives, market, customers, processes, products, roadmap, and risks. What role will they play both from day-to-day and in the company’s long-term goals? By answering these questions and making data science an integral part of the business, the board can help the discipline fulfill its commercial potential. At the same time, the domain expertise, business vision and critical thinking of senior executives will provide valuable direction, give projects the right focus and help data scientists design models that meet key strategic objectives.

ANSWER THE RIGHT QUEST IONS

For truly actionable insights, data science needs to know what it’s looking for. According to Ken Schultz, VP of data science at Elevate Credit, “A potential issue with using data science in an organization is solving problems that don’t need to be solved, or solving irrelevant problems.” 27 In other words, to get the best from data science, you need to make it clear what stories you are looking for in the data and why. Again, this comes down to determining the business context and the specific problems that need to be solved. The key is to identify the story you need to know the ending of—and stick to it. Let’s say that two data scientists look at the same set of loan applications. Scientist A finds that appli-

cations peak in the morning; scientist B sees that applicants who meet certain criteria are more likely to default. Both are correct—the data science tools don’t lie—but scientist A’s findings are of no use to the business. Thecompany’s loanprocessingplatformis powerful enough to manage any spikes in volume, whenever they happen; what senior managers needed was a more accurate indication of who will or won’t pay back a loan. The problem is that nobody told scientist A what they were looking for. So, there’s moral to this story. The results of data science will only influence business decisions when they answer the right question. 28 Containerization technologies provide a more modern, workable alternative, allowing you to use native microservice software to meet your changing needs. A microservices architecture also limits any possible technical failures to isolated components, which allows you to take advantage of lightweight, cloud-based applications. Finally, it’s critical that models can scale up easily to the higher volumes of data and performance demands they can face in production. Again, a microservices-based approach can help—by letting you adapt quickly through simple changes to the configuration. 29

TAKE CARE OF TECHNOLOGY

A number of operational challenges can slow down the deployment of data science models, but they are easily overcome with the right technology. For example, the model developed by your data scientists may not automatically be compatible with your production environment, so may need to be recoded before IT can deploy it. The best way to avoid this scenario is to use an agnostic scoring engine, which can handle models created in any language and deploy them easily into production. Monolithic modeling platforms can pose another problem and limit the evolution of your models.

5 ASSEMBLE THE RIGHT TEAM

As the worldwide shortage of data science talent continues, PwC has suggested the following three-pronged strategy for building a team.

I. Look for competencies, not just skills.

II. Invest in existing employees.

III. Connect with your community.

If they are already domain experts, upgrading the skills of your statisticians or programmers could instantly widen your recruitment options and give you a significant return on your investment.

For example, while skills in certain coding languages suchasPython or R may be preferable right now, the candidate should also demonstrate the ability to learn new languages and programs. It may be more difficult to teach other skills on the job, like critical thinking and teamwork. It’s all about finding talent that can adapt to constantly changing requirements.

Join a local business forum or other organization focused on creating a competitive workforce for the modern age. Local workforce development programs can also help you understand how to attract the right skills and find candidates within your community. 30

CONCLUSION Get Data Science down to a Fine Art

Data science holds enormous potential for the financial services industry, some already tapped to great effect by the most progressive organizations. Its models, algorithms, and tools have the power to transform huge quantities of everyday data into meaningful business insight that drives competitive advantage. A science it may be, driven by automated technology, but this broad, complex discipline is also something of an art. And like all great art forms, it relies—ironically—on human intervention and real-world relevance to give it meaning. The CRO of a financial institution is ideally placed to play an intermediary role between the science and the art of big data analytics; the intricate models and the strategic thinking; the theoretical concepts and the business context; the technical minutiae and the bigger picture. But it’s a position of responsibility that inevitability comes with challenges. As our guide aims to show, however, the risks of running a data science project are usually well worth taking. And a clear action plan, a partnership approach, and an unwavering focus on strategic objectives will stand you in good stead to surmount the obstacles.

With an eye for detail but a head for business, are you ready to master the art of data science?

SOURCES:

1 Techopedia, What Is Data Science? Retrieved January 18, 2019, from https://www.techopedia.com/definition/30202/ data-science 2 As defined by Colleen M. Farrelly, The Executive Guide to Data Science and Machine Learning. Retrieved January 18, 2019, from https://www.kdnuggets.com/2018/05/executive-guide-data-sci- ence-machine-learning.html 3 DataRobot, 5 AI Solutions Every Chief Risk Officer Needs, 2018 4 Igor Brobiakov, Top 9 Data Science Use Cases in Banking. Retrieved January 18, 2019, from https://medium.com/activewiz- ards-machine-learning-company/top-9-data-science-use-cases- in-banking-6bb071f9470c 5 AltexSoft, Fraud Detection: How Machine Learning Systems Help Reveal Scams in Fintech, Healthcare, and eCommerce. Retrieved January 18, 2019, from https://www.altexsoft.com/ whitepapers/fraud-detection-how-machine-learning-systems- help-reveal-scams-in-fintech-healthcare-and-ecommerce/ 6 Christopher P. Skroupa, The Future of the Chief Risk Officer – What Will Drive the Change. Retrieved January 18, 2019, from https://www.forbes.com/sites/christopherskroupa/2017/11/16/ the-future-of-the-chief-risk-officer-what-will-drive-the-change/ 7 Igor Brobiakov, Top 9 Data Science Use Cases in Banking. Retrieved January 18, 2019, from https://medium.com/activewiz- ards-machine-learning-company/top-9-data-science-use-cases- in-banking-6bb071f9470c 8 Master’s in Data Science, Data Science in Finance. Retrieved January 18, 2019, from https://www.mastersindatascience.org/ industry/finance/ 9 Igor Brobiakov, Top 9 Data Science Use Cases in Banking. Retrieved January 18, 2019, from https://medium.com/activewiz- ards-machine-learning-company/top-9-data-science-use-cases- in-banking-6bb071f9470c 10 Econsultancy, Digital Intelligence Briefing: 2018 Digital Trends in Financial Services, June 2018 11 The Economist Intelligence Unit, Retail banks and Big Data: Big Data as the Key to Better Risk Management, 2014 12 Toolbox, How Can Data Science Improve Risk Management? Retrieved January 18, 2019, from https://it.toolbox.com/ blogs/emiliamarius/how-can-data-science-improve-risk-man- agement-032118 13 The Economist Intelligence Unit, Retail banks and Big Data: Big Data as the Key to Better Risk Management, 2014 14 Igor Brobiakov, Top 9 Data Science Use Cases in Banking. Retrieved January 18, 2019, from https://medium.com/activewiz- ards-machine-learning-company/top-9-data-science-use-cases- in-banking-6bb071f9470c 15 Natasha Mashanovich, Using Big Data and Predictive Analytics for Credit Scoring. Retrieved January 19, 2019, from https://dzone. com/articles/using-big-data-and-predictive-analytics-for-credit

14 Konstantin Didur, Machine Learning in Finance: Why, What and How. Retrieved January 19, 2019, from https://towards- datascience.com/machine-learning-in-finance-why-what-how- d524a2357b56 17 Cognizant, Bank(ing) on Data Science. Retrieved January 19, 2019, from https://www.cognizant.com/InsightsWhitepapers/ Banking-on-Data-Science.pdf 18 Kaggle, The State of Data Science and Machine Learning. Retrieved January 19, 2019, from https://www.kaggle.com/ surveys/2017 19 Thomas C. Redman, Seizing Opportunity in Data Quality. Retrieved January 19, 2019, from https://sloanreview.mit.edu/ article/seizing-opportunity-in-data-quality/ 20 Eva Short, What Can Be Done about the Data Science Skills Gap? Retrieved January 19, 2019, from https://www.siliconre- public.com/careers/data-science-skills-gap 21 Cognizant, Bank(ing) on Data Science. Retrieved January 19, 2019, from https://www.cognizant.com/InsightsWhitepapers/ Banking-on-Data-Science.pdf 22 IMS Proschool, Top 5 Challenges Faced by Data Scientists and How to Overcome Them. Retrieved January 19, 2019, from https://www.proschoolonline.com/blog/challenges-faced-by- data-scientists/ 23 IMS Proschool, Top 5 Challenges Faced by Data Scientists and How to Overcome Them. Retrieved January 19, 2019, from https://www.proschoolonline.com/blog/challenges-faced-by- data-scientists/ 24 Cognizant, Bank(ing) on Data Science. R etrieved January 19, 2019, from https://www.cognizant.com/InsightsWhitepapers/ Banking-on-Data-Science.pdf 25 Eve Neuner, I Can’t Afford to Hire a Data Scientist. Now What? Retrieved January 19, 2019, from https://blog.dataiku.com/i-cant- afford-to-hire-a-data-scientist.-now-what 26 IMS Proschool, Top 5 Challenges Faced by Data Scientists and How to Overcome Them. Retrieved January 19, 2019, from https://www.proschoolonline.com/blog/challenges-faced-by- data-scientists/ 27 bobsguide, Why Data Scientists Solve Irrelevant Problems. Retrieved January 19, 2019, from https://www.bobsguide. com/guide/news/2018/Nov/13/why-data-scientists-solve-irrele- vant-problems/ 28 bobsguide, Why Data Scientists Solve Irrelevant Problems. Retrieved January 19, 2019, from https://www.bobsguide. com/guide/news/2018/Nov/13/why-data-scientists-solve-irrele- vant-problems/ 29 Open Data Group, Technical Challenges of Model Deployment. Retrieved January 19, 2019, from https://www.opendatagroup. com/blog/technical-challenges-of-model-deployment 30 PwC, What’s Next for the Data Science and Analytics Job Market? Retrieved January 19, 2019, from https://www.pwc.com/ us/en/library/data-science-and-analytics.html

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