Data Scientist - Manager of the Discovery Lifecycle

Kurt Englmeier

and Fionn Murtagh

Faculty of Computer Science, Schmalkalden University of Applied Science, Blechhammer, Schmalkalden, Germany

Department of Computing and Mathematics, University of Derby, Derby, U.K.

Keywords: Data Science, Big Data, Information Discovery, Data Analysis, Data Mining, Text Mining, Information

Extraction, Information Usability, Semantic Layer.

Abstract: Data Scientists are the masters of Big Data. Analyzing masses of versatile data leads to insights that, in turn,

may connect to successful business strategies, crime prevention, or better health care just to name a few. Big

Data is primarily approached as mathematical and technical challenge. This may lead to technology design

that enables useful insights from Big Data. However, this technology-driven approach does not meet com-

pletely and consistently enough the variety of information consumer requirements. To catch up with the ver-

satility of user needs, the technology aspect should probably be secondary. If we adopt a user-driven approach,

we are more in the position to cope with the individual expectations and exigencies of information consumers.

This article takes information discovery as the overarching paradigm in data science and explains how this

perspective change may impact the view on the profession of the data scientist and, resulting from that, the

curriculum for the education in data science. It reflects the result from discussions with companies participat-

ing in our student project cooperation program. These results are groundwork for the development of a cur-

riculum framework for Applied Data Science.

1 INTRODUCTION

Data Scientists have the most attractive job, these

days, haven’t they? Not necessarily. In any case, they

have one of the most demanded and most demanding

jobs in IT. From health over finance and marketing to

crime prevention, in almost all sectors we see an

astonishingly rising need for data scientists. It is true,

with Big Data we have a lot more data to delve into

and data scientists can help us not to enjoy these data

and not to get drowned. Our smartphones turned into

a communication centre. We stay not only in touch

with our beloveds, colleagues, and business partners,

but also with society as a whole through Twitter and

the like. Apart from that, our phones can also com-

municate with all kinds of devices and trigger actions,

like turning on the light at home or the heating or tell-

ing us the nearest available parking lot in the area. A

deluge of sensor data, biomedical data, including lo-

cation data, retail transaction data, and the like merge

with all kinds of text messages and data on social me-

dia activities and form a yet unseen sea of data. It

hosts an equally unseen variety of information that

has the capability to give us astonishing insights

about the reality surrounding us.

We expect much from Big Data. To meet our ex-

pectations we count on approved disciplines, most

prominently data mining, on new tools, most promi-

nently Hadoop, and on new professions, most promi-

nently the data scientist. Data science has strong roots

in artificial intelligence. Education in data science

nowadays focusses on Bachelor and Master pro-

grammes developed around topics like Data Mining,

Neural Networks, Artificial Intelligence (AI), and

Machine Learning. However, is it really necessary

that the data scientist must be an expert in these

fields? Big Data roused expectations that seemed

dormant so far, despite Knowledge Mining, Predic-

tive Analysis, Machine Learning, etc. being around

for decades now. Since that time, we have analysts

and statisticians working hard with all kinds of min-

ing tools to provide us with insights emerging from

the data around us. Do we fear they may fail in Big

Data Analytics? Is Big Data too big for them? Why

do we yearn for a new kind of profession? Data Sci-

ence is certainly more than just the extension of data

mining, machine learning and the like towards un-

structured data and non-SQL databases. We propose

to take information discovery as the overarching par-

adigm. This broadens the perspective on data science

Englmeier, K. and Mur tagh, F.

Data Scientist - Manager of the Discovery Lifecycle.

DOI: 10.5220/0006393801330140

In Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), pages 133-140

ISBN: 978-989-758-255-4

133

and brings the expectations into focus that infor-

mation consumers conjoin with Big Data.

Through our student cooperation projects where

students develop practical solutions in close coopera-

tion with partners from the industry we learned that

information discovery is a prominent theme for pro-

fessionals and practitioners in industry when ap-

proaching the Big Data challenge. Viewing data sci-

ence from the perspective of information discovery

reveals the semantic and organisational dimension

that reaches beyond the mere AI focus. From this per-

spective we develop a job profile for data scientists

and give recommendations for a corresponding struc-

ture of a curriculum for the education in data science.

This article thus sketches the role of the data sci-

entist as enabler of actionable insights emerging from

Big Data. We see the data scientist’s responsibility

▪ in the design of an overarching semantic layer

addressing data and analysis tools,

▪ in identifying suitable data sources and data

patterns that correspond to the appearance of

structured and unstructured data, and

▪ in the management of the information discov-

ery lifecycle and discovery teams.

It is important to note that we rather consider infor-

mation discovery as a cooperative effort where discov-

ery processes and tools are continuously evaluated and

improved by discovery teams. These consist of experts

with statistical and computational knowledge and

skills, domain experts, and finally the data scientist.

Discovery improves through experience, and the more

intense the mutual exchange of discovery experiences

then the better the teams perform.

The article also highlights the semantic dimension

of data science with its ramifications into text mining,

opinion mining, sentiment analysis and the like. It

outlines the discovery lifecycle that is the process to

manage information discovery as a commodity on

workgroup or corporate level. Practical examples

from the economic analysis give a hint on the variety

of discovery and provide an impression why a user-

driven approach may lead to data analysis solutions

that meet the requirements of information consumers.

Finally, the article presents our recommendation for a

curriculum in the education of data science.

2 INFORMATION CONSUMER’S

VIEW ON DATA SCIENCE

The data scientist must not necessarily take the role

of a statistician, data analyst, database expert, content

manager, or programmer. We have all these profes-

sions around for decades. If it is for these professional

qualities, do we really require a new profession? Is it

because Big Data means more unstructured data and

more non-SQL access to data? Many university cur-

ricula in data science cover exactly these areas. Does

it mean, data scientists have to be professionals in all

these areas or does it suffice for them to know just a

bit of everything? Shall they be just all-rounders in

AI? There is no doubt, AI provides a lot of useful

techniques and methods that help us to master the Big

Data challenge. Nevertheless if we view data science

from the perspective of AI’s capabilities to contribute

to Big Data solutions, we may bypass some expecta-

tions whereby information consumers connect with

Big Data. Even though we cannot satisfy all their ex-

pectations it could be quite useful to view data science

from the information consumers’, perspective. In

general, they expect to discover information in data

that reveals insights to facts reflected in these data.

Furthermore, they expect the data scientists to take

care of their expectations (Englmeier and Murtagh,

2016). Decision makers must hold their own with

their theories, their views, and their practices. Mar-

keting managers, for instance, have to bolster their

decisions on a particular advertisement campaign,

that is, their theory on how many potential customers

they reach and how this translates into increasing

sales of a particular product. In the past, they filled

some Excel spreadsheets with sales data from their

company’s database and plugged in their assumptions

on customer behaviour in different segments and re-

gions. If their forecasts proved wrong, it was their as-

sumptions that were blamed first together with their

model on how the campaign impacts customers and

their purchases. This, in turn, negatively affected their

reputation. No wonder decision makers want to keep

an eye on their assumptions and their theory behind,

that is, on well-reasoned insights that help to make

their assumptions defensible. Therefore, they want

data scientists who understand their needs and dis-

cover useful information.

Data scientists are first of all professionals in in-

formation discovery. “More than anything, what data

scientists do is make discoveries while swimming in

data […] they are able to bring structure to large

quantities of formless data and make analysis possi-

ble.” (Davenport and Patil, 2012) They sketch, or-

chestrate, and control the discovery process. The

leading paradigm in this process is to find information

that promises the insights information consumers re-

quire. “We need to avoid the temptation of following

a data-driven approach instead of a problem-driven

one.” (Gudivada et al., 2015). Data scientists, thus,

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

134

should listen to people, understand their information

need, and manage the Information Discovery Lifecy-

cle that supports this need.

3 SEMANTIC DIMENSION OF

DATA SCIENCE

Data scientists are data explorers, discoverers who

detect and locate facts in–mostly unstructured–data

(mainly texts) and prepare them for data analysis. On

the other end, they have to ensure that the excitement

about discovery in Big Data pays off in well-reasoned

insights, that is, they have to ensure that decision

makers, or information consumers in general, get

solid information that leads to solid and reasonable

decisions. The data scientist’s challenge is to discover

facts–to reveal whereabouts and facets of information

in the sea of structured and (mainly) unstructured

data. This makes the process of information discovery

getting essential in data science. Each fact may take a

variety of forms of appearance in data and that may

vary even more over different data sources. They

range from different spellings in texts, over different

representations in database schemata, to special en-

codings on digital cards like the health insurance card

or the passport. If we recall how many forms even a

simple fact may take, then we get an impression that

handling the variety of appearances of facts is far

from being trivial. Without transforming all these dif-

ferent forms into one standardized representation we

cannot even feed the corresponding fact into our data

analytic systems. Information extraction and normal-

ization is thus a broad and important field the data

scientist has to manage.

The data scientist maps a certain variety of data

patterns of a particular information concept to its

standardized form reflecting the corresponding fact.

The facts map to superior concepts with relationships

among them. Data analysis takes facts and relation-

ships and tries to derive new information from the re-

sults. It is again the data scientist who takes all this

information together and presents it to the people who

require it. The information must be useful, that is, it

must enable to its consumers well-reasoned insights.

Information usability is the key indicator for the qual-

ity of the discovery process and it depends on the

needs of the information consumers. The data scien-

tists design, manage, and control the discovery pro-

cess, in short, they manage the Discovery Lifecycle.

Therefore, this new profession has a closer link to en-

terprise information integration and human-computer

interaction than to artificial intelligence. Data Science

is much more than just an extended version of data

mining and machine learning covering increased vol-

ume, variety, and velocity of data unleashed by Big

Data. It has to manage the semantic dimension of this

rich and volatile data ecosystem. This dimension is

not only important because the vast majority of Big

Data is text data. In fact, Big Data has a strong lan-

guage background. We want to discover facts and the

relationships among them that reveal the phenome-

nona we are interested in. That can include opinions,

sentiments, lines of arguments, etc. The facts are usu-

ally terms or phrases in combination with numerical

data. We need a strong semantic model that helps us

to describe the fabric of impact among the facts. This

model guides not only our exploring of the data envi-

ronment but also the discovery processes we use to

locate and extract the data. There are many recurring

elements these process models may have, but there

are no standard models. Usually, they need to be con-

stantly adapted to the changing data environment.

4 DISCOVERY LIFECYCLE

Big Data is not just one homogenous source of infor-

mation, it is masses of heterogeneous sources spawn-

ing avalanches of data. Discovering right sources and

valuable data is not a trivial thing. It emerges from a

combination of three knowledge areas, namely do-

main knowledge, technical knowledge and manage-

rial capabilities. The domain knowledge serves to get

an abstract conception of the information environ-

ment where discovery takes place. This abstract con-

ception helps the data scientist to develop an over-

arching mental model that, in turn, guides the devel-

opment of a semantic data layer on top of the data

sources. This layer is a building block for a classifi-

cation system that handles not only data but also anal-

ysis tools. It supports data scientists to determine

what tool and what kinds of data are best suited for a

specific purpose. The technical knowledge covers the

knowledge on how facts appear domain-inde-

pendently in data and how to standardize them.

Checking positive or negative comments about a

product, for instance, requires domain knowledge on

the characteristics of the product and its competitive

products, such as price, labels, etc., the full array of

qualities producers and consumers attribute to this

product. The knowledge on data patterns manifests

the data scientist’s technical knowledge. Product

characteristics like price, size, power, energy con-

sumption, for example, are expressed by specific data

patterns. They are domain-independent by nature.

The same holds for word patterns that turn a statement

into a positive or negative comment. Finally, the data

Data Scientist - Manager of the Discovery Lifecycle

135

scientist requires managerial knowledge to transform

this knowledge into a cooperative discovery strategy.

Managing the discovery lifecycle includes the manage-

ment of analytics teams, too. Decentralized teams can

be assigned to specific analysis tasks. At the same time,

they can be moved closer to domain experts or business

functions, for instance, to have them better meet their

specific needs. These three knowledge areas constitute

the proficiency we expect from data scientists.

The data scientist is more an information concep-

tualist who concentrates on definition, integration, and

sharing of information concepts and less on analytic

modelling. Even though information discovery re-

quires a bit of everything, from coding and querying

databases over statistical modelling to data mining, the

role of the data scientist starts beyond that, it targets

storytelling about discovery that may feature episodes

from all these fields. These stories are about how we

get insights from data, where to discover those data,

and how to merge them into suitable visualizations.

The data scientists’ mental models (Norman, 1987)

on data patterns reflect information concepts. Usually

there is a variety of data patterns that map onto a single

concept. Discovery starts with a hypothesis on the con-

cepts that meet the need of the information consumer.

Data scientists sketch information concepts by blue-

prints that guide the discovery process. Discovery en-

gines then execute these blueprints, that is, they locate

the data that map to each of the required information

concepts and extract and annotate these data. Discov-

ery blueprints are far from being programming instruc-

tions. They are pattern descriptions, that is, schemas of

how the required information may look like in data. For

example, a blueprint may address the different facets

of the mentioning of the most recent revenue of a cer-

tain company in tweets, newspaper articles, and the

like. It may also describe the mapping of geospatial lo-

cations to possible descriptions in crime reports. After

distilling—locating, extracting, and standardizing—

the data they can be fed into statistical tools, data min-

ing or text mining tools. A suitable visualization of the

results from analysis finally provides the insights the

decision maker—or the information consumer in gen-

eral—expects.

Data scientists barely produce blueprints alone

and in one step. It is an experimenting and iterative

process. They initiate it by a certain belief—predis-

position or bias reinforced by years of experience—

and gradually refine this belief through cycles of

gathering of instances of information concepts and

checking them against their hypotheses. The blue-

prints describe the sources of information (news

channel, location sensor, email, etc.) and the way,

how data related to the different concepts must be ex-

tracted. We can call this process “operationalization”.

The data scientist designs one or more patterns that

map data to a particular information concept and ap-

plies them to different data sources. Each of these

tests checks the coverage of each pattern, that is, the

potential of the pattern to detect all instances of the

information concept. After sufficient iterations, data

scientists operationalize their blueprints in their indi-

vidual environment and discuss them with their col-

leagues. The cooperative approach to this process is

necessary in order to incorporate reflections of other

data scientists and stakeholders about this particular

information discovery (Elbeshausen et al., 2014). Af-

ter further reflections on workgroup or corporate

Figure 1: Schema of operationalization and institutionalization of discovery blueprints.

DATA 2017 - 6th International Conference on Data Science, Technology and Applications

136

level they institutionalize their blueprints. It is obvi-

ous that each pattern may also be a composition of

existing patterns.

From a different angle, we can say the data scien-

tists are in charge with the corporate’s discovery gov-

ernance, which means, with the design, implementa-

tion, management, control, and improvement of in-

formation discovery. Discovery governance rests thus

on the following phases that are managed by the data

scientists (cf figure 1):

▪ Conceptualization: the data scientists design

and manage the information concepts in a cor-

porate thesaurus.

▪ Operationalization: they develop and maintain

discovery blueprints that serve mapping of data

onto information concepts.

▪ Institutionalization: they organize sharing and

discussing of blueprints as a corporate com-

modity.

▪ Transformation: they organize the application

of analytical tools/models to produce new in-

formation.

▪ Visualization: data scientists are also responsi-

ble to ensure that information consumers un-

derstand the findings, understand the “mes-

sage” in the results. Proper visualization of key

performance indicators, for instance, may

faster impart the performance of a marketing

campaign than mere numerical data. In general,

proper visualizations rise the information usa-

bility (Rosenbaum and Ramey, 2014).

In general, the process of discovery passes itera-

tively through some or all of these phases. At the end

of each phase, results are carefully checked for plau-

sibility and usefulness. Each phase is thus completed

by a quality gate where data scientists evaluate the

quality of the results achieved and approve or reject

the respective phase activities.

5 DISCOVERY LIFECYCLE IN

TEXT MINING

There is no general approach to information discov-

ery. It is highly individual, theme-specific, and vola-

tile. The data scientist may delegate some subpro-

cesses to machines, but there is no reason to think that

the machine’s algorithm could discover all the ingre-

dients needed to develop a clear picture on the facts

the information consumer expect. It is an illusion to

believe that discovery as a whole or even to a large

extent can be delegated to machines. Automatic

information discovery is worthless if it bypasses sub-

stantial ingredients of the facts to be discovered.

There are many discovery tasks that serve individual,

ad hoc, and transient purposes. Automatic discovery

can only focus on mainstream discovery that, in con-

trast, supports recurring discovery requests com-

monly shared by large user communities. Mainstream

discovery addresses data that appear in quite a stand-

ardized form like stock quotes from a ticker service

or weather data from a weather service. It works fine

when discovery tasks are well-defined and the data

patterns to look after are highly recurring over the-

matic domains.

Figure 2: Two text snippets showing the stance of German

people on fiscal solidarity (source: Langfeld, Kroh, 2016).

In practical data science, text mining has quite a

prominent position (Bedathur et al., 2010; Fan et al.,

2006). It starts with extracting statements from texts

that follow a certain pattern of appearance. The state-

ments are then normalized, that is, they are transformed

into a standardized form of representation or are anno-

tated by terms reflecting their content in a standardized

form. The combination of factual (numerical) data with

text data has its particular appeal. A statistical analysis

may come to a certain findings. Text mining can help,

in parallel, to find statements in articles, news, or Twit-

ter messages that underpin or refute these findings. Nu-

merical analysis, for example, may observe a certain

stock by applying time series analysis to measure the

probability that its value will rise or drop. Accompany-

ing text analysis sifts through texts and looks for sig-

nals that indicate whether this stock is about to take off

or drop in value. Identifying these signals and merging

Data Scientist - Manager of the Discovery Lifecycle

137

them with the numerical analysis rest on quite an array

of discovery tasks.

Discovering and comparing opinions on a partic-

ular issue is an interesting topic nowadays. Opinion

Mining (or sentiment analysis) tries to identify peo-

ple’s stance in favour or against politics, products,

persons, etc. (Feldman, 2013; Wright, 2009; Turney,

2002). It also tries to find facts (features) that under-

pin these opinions. Furthermore, fake news on social

media seem to become a growing threat. Even based

on false assumptions they can have an impact on pub-

lic perception. In particular during the most recent

election campaign in USA, much fake news appeared

in social media (for example: Rosenberg, 2016).

Opinion mining (Feldman, 2013; Evangelopou-

los, Visinescu, 2012) is a good example to highlight

the data scientists’ role in managing the semantic di-

mension in discovery. They have to organize the de-

velopment (or reuse) of discovery blueprints that

guide the discovery of the ingredients of a particular

opinion. The result takes the form of a semantic skel-

eton that guides the discovery engine. Opinion min-

ing requires discovery tasks but also integration tasks.

Both are relevant for listing in the data scientists’ job

description. They detect, for instance, different facets

of an opinion in a text, the relationship to a particular

opinion holder and the overall theme of the opinion.

We may take an opinion like “We expect that in about

five years, we will see about 50 percent of the refu-

gees in employment.” and relate it to the theme “Ref-

ugee Crisis in Germany” and the opinion holder “Jür-

gen Schupp”, a scientist at the German Institute for

Economic Research (example taken from Wittenberg,

2016). If we see the attributes of this opinion in rela-

tionship with related topics, such as education level,

language skills, etc. we can set up a heat map high-

lighting the essential concepts around the theme “em-

ployment of refugees”. The map helps follow this

opinion and all its thematic ramifications. The follow-

ing design of blueprints relates to the example shown

in figure 2. The opinion about solidarity with crisis

countries is reflected in objective sentences contain-

ing factual information. To discover the fact whether

a majority or minority is favouring solidarity we have

to look after clusters of facts that are tightly related.

We can discern the corresponding concepts for posi-

tive (“agreement”), negative (“opposition”), and neu-

tral stance (“no opinion”) over solidarity with crisis

countries. We also identify the respective quantifica-

tions together with the variations of the key expres-

sion “fiscal solidarity”. The second snippet uses in ad-

dition the concepts “increase” and “decrease” to ex-

press change in people’s opinion over time.

Discovering the facts is the result of a number of

text analysis processes:

▪ Identification of sentiment expressions (indi-

cated in red in figure 2).

▪ Identification of factual (theme independent)

information (indicated in blue)

▪ Identification of aspects or features (indicated

in green)

▪ Extraction of the facts

The examples show that text analysis can get

quite individual, hardly covered by mainstream dis-

covery. Users may have to analyze, for instance, from

time to time dozens of failure descriptions or com-

plaints, for instance. These appear in many different

facets over a variety of sources. Serving small-scale

requests would mean permanent system adaptation,

which is too intricate and prohibitively expensive in

most of these cases. The same holds for data science

as a service (DSaaS). They offer only standard dis-

covery and analytical processes that cannot cover the

broad variety of discovery required by information

consumers.

6 PROPOSAL FOR A

CURRICULUM IN DATA

SCIENCE

Industry and Science is demanding a new profession

that designs, orchestrates, and controls statistical and

computational skills and tools within an organization

to carry out information discovery on a large scale. Ed-

ucation in data science shall provide technical and

managerial knowledge and skills for this profession.

Inclined to the current layouts of curricula in data sci-

ence and considering the discussion of this article we

propose the following framework for a curriculum of

data science. It is structured along six knowledge areas.

The broad field of methods and tools from AI sup-

porting information discovery constitutes an integral

part of data science. However, is it really necessary

that the data scientists are expert in AI, down to all its

mathematical and statistical foundations? Doesn’t it

suffice if they roughly know features and capabilities

of AI tools and let the AI practitioners handle the par-

ticular problems that can be solved with these tools?

Is it even necessary that data scientists are experts in

Hadoop, R and the like, if there are correspondingly

trained programmers available? We may not forget

that building data mining or predictive models for AI

tools is time-consuming and quite difficult. It usually

involves testing and verifying different model hy-

potheses with a large array of variables, before the

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analytic results approve the underlying overall hy-

pothesis. The degree of difficulty ratchets up further

the more complex the hypothesis becomes. Further-

more, the findings need to be validated carefully. In

the end, the reputation of the whole discovery team is

at stake if the discovery project goes awry. If results

are approved too early it can damage the team’s cred-

ibility. The failure of the Google Flu Trends project

is a prominent example in this context (Lohr, 2014).

It takes experienced analysts to develop and verify the

core of complex analysis models. Knowledge and

skills required for predictive analysis, deep learning

and data mining are part of the formation in artificial

intelligence or domain-specific fields like economics

or statistics. It may be reasonable to employ data sci-

entists if the analysis models are less complex. In gen-

eral, however, we expect the data scientists to scruti-

nize model assumptions and to detect imperfections

of predictive models. They can support the discovery

team to focus on transparency. The data scientist need

to understand how an analysis model came to its con-

clusions. The degree of uncertainty needs to be com-

municated, too. Even if the team supposes 90% cer-

tainty, there are chances the outcome will turn out to

be false.

We think the main focus of the data scientists

should be more on managerial capabilities concerning

the information discovery lifecycle.

They should know

▪ where the required data come from,

▪ how they need to be transformed or standardized,

▪ how they must be semantically integrated,

▪ which kind of analytical model can be applied to

them, and

▪ how they are best visualized to the information

consumers.

We know that we cannot draw a clear line be-

tween the mathematical, statistical, and computa-

tional fundamentals a data scientist should know and

the more overarching managerial capabilities to or-

chestrate discovery processes. There may be situa-

tions when the data scientist needs to assist in the de-

sign of special analytic tools. However, this will be

more the exception than the rule.

Based on this profession profile of the data scien-

tist, we propose that education in data science should

address the following formation areas:

▪ Information Extraction and Standardization.

Information-related data must be identified, in

particular in unstructured data. The data scien-

tist should know what forms of appearance the

required information may take.

▪ Data Mining. This is the traditional and most

widely adopted area for data science. It may be

sufficient if the data scientist knows the nature

of the analytic models, the required input, and

the results they produce.

▪ Text Mining. It is currently underrepresented in

education related to data science. This is quite

strange, because the vast majority of Big Data

are text data. Furthermore, it is key for text

analysis like Sentiment Analysis, Opinion Min-

ing, and Fake News Detection, just to name a

few.

▪ Management of the Information Discovery

Lifecycle. It starts with analyzing the needs of

the information consumers. From their require-

ments the data scientist designs, guides, and

controls the discovery process. This includes

the composition of the discovery team to en-

sure that discovery is based on the necessary

domain and technical knowledge.

▪ Quality and Ethics in Data Science. Privacy is-

sues around big data will only become more

prominent in the years ahead. More and more

we adopt tools, like devices for connected

home, driving assistants, and fitness or health

trackers. They produce vast troves of interest-

ing data about us that could theoretically be

considered useful by criminal investigators,

health care institutions, or national security

agencies. Therefore, we expect the data scien-

tists to take care of privacy concerns. We also

expect them to check the assumptions of statis-

tical methods and tools applied in analysis and

to check the plausibility of their results.

Through the semantics of all of the context of

the work that is underway, to study and to fully

respect the individual’s role as well as the ag-

gregate and collective roles.

▪

Information Visualization helps to impart anal-

ysis results to information consumers. In gen-

eral, it shall ensure information usability. Con-

sumers expect that results are presented in a

way that helps them to understand them and to

translate them into suitable actions and deci-

sions.

7 CONCLUSION

There is no gold standard for the job profile of data

scientists. In general, we expect them to manage the

Data Scientist - Manager of the Discovery Lifecycle

139

discovery of information enabling us valuable in-

sights from Big Data. AI claims to be in the position

to provide information consumers with useful in-

sights. There is no doubt, data mining, machine learn-

ing, deep learning, etc. are essential in data science.

Their methods and tools produce a lot of useful data,

which, in turn, lead to the expected insights. How-

ever, focusing exclusively on AI would restrict us to

insights that we may gain from the results of AI tools.

The capabilities of the available AI tools determine

the range of insights.

By viewing data science as the discipline of infor-

mation discovery performed along the requirements

of information consumers and managed by data sci-

entists we bring humans into the loop of technology

design and use. The user-centred approach shifts the

focus from a purely data-driven approach towards a

problem-driven approach. We see practical data sci-

ence rather as the result of a cooperative effort of dis-

covery team representing domain and technical

knowledge and managed by the data scientist. In this

article we sketched the profession of the data scientist

from this perspective that conceives the data scien-

tists rather as a master of the information discovery

lifecycle than data mining expert or the like.

Data scientists do not construct models for all

kinds of analysis tools and reasoning systems but

should clearly be aware of the information those sys-

tems presume to produce. They should constantly

check the results for false positives, in particular,

when personal data are involved and/or expectations

are high on the outcome of the data analysis. The data

scientist is a scientist. She or he should take care that

information is put to good use. We can expect that she

or he assumes ethical responsibility and makes sure

that information provided is reliable. The “everything

is possible” attitude that shines through in many Big

Data discussions is dangerous and rises false expec-

tations. The polls during the recent elections in USA

showed that sometimes even the whole statistical and

data mining machinery of an entire country can fail

quite embarrassingly. Data Scientists need to develop

a sound sensation of plausibility that helps them to

rise doubts and to prompt a closer look when the re-

sults of data analysis seem too questionable to them.

Finally, we advocate for the education in data sci-

ence a stronger focus on text mining, information vis-

ualization, ethics aspects rised by Big Data, and the

management of information discovery.

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