Where should you put your data scientists?

•

4 likes•13,138 views

Daniel Tunkelang

I delivered this keynote at the Fast Forward Labs Data Leadership Conference on April 28, 2016. You can find related materials in the following publications: https://www.oreilly.com/ideas/where-should-you-put-your-data-scientists http://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/

Where should you put
your data scientists?
Daniel Tunkelang

Review: why you hire data scientists

To improve products

To improve decisions

Organizational structure matters

stand-alone
embedded
integrated

The stand-alone model

Pro: gives team autonomy

Pro: positions data as ﬁrst-class

Con: risks marginalizing team

Con: other teams like self-sufﬁciency

The embedded model

Pro: ensures utilization

Pro: ﬂexible allocation

Con: team resents lack of autonomy

Con: management challenges

The integrated model

Pro: organizational alignment

Pro: long-term investment

Con: dilution of data science

Con: lack of reuse

stand-alone: autonomy, data as ﬁrst class
embedded: utilization, ﬂexible allocation
integrated: alignment, long-term investment

stand-alone: marginalization, isolation
embedded: resentment, dual management
integrated: dilution, lack of reuse

You can mix and match

Adapt as company evolves

Remember the mission

Thank you.
Daniel Tunkelang
dtunkelang@gmail.com
: @dtunkelang

More Related Content

Similar to Where should you put your data scientists?

PDC 2008 Toward participatory organizations.

PDC 2008 Toward participatory organizations.

PDC 2008 Toward participatory organizations.

Modeling Framework to Support Evidence-Based Decisions

Modeling Framework to Support Evidence-Based Decisions

Modeling Framework to Support Evidence-Based Decisions

Case

Kolekar Mahadeo

Business Case for eLearning

Business Case for eLearning

Business Case for eLearning

E12+Analytics+Workshop+ppt.pptx

E12+Analytics+Workshop+ppt.pptx

E12+Analytics+Workshop+ppt.pptx

Organisation Effectiveness Assessment looks at some diagnostic models that can help diagnose the problems of organisations and how to find solutions to such problems. Consults need to have skills in how to assess organisations for change, efficiency or funding in the case of non-profit organisations. The Slides cover: 1. What is an organisation? 2. The Open Systems Model 3. What is organisational effectiveness?. 4. Approaches to measuring organisational effectiveness 5. What is organisational Analysis? 6. Organisational Analysis Cycle 7. Purposes and perspectives of organisational analysis. 8. Data Collection Methods. (Imagery; questionnaire; interviews;Observation;Focus groups; Secondary material, etc. 9. Advantages and Disadvantages of Data Collection Methods 10.Use of appropriate organizational diagnostic models to assess organisations. a. Kurt Lewin’s Force Field Model; b. Likert’s Management System Model; c. McKenzie’s 7S Model; d. Weisbord 6-Box Model 11. Organisational Capacity Assessment Tools (OCAT). a. Why conduct Organisational Capacity Assessment? b. Organisational Capacity Assessment Process; c. Application of an OCAT Tool Writing an Organisational Assessment Report.

ORGANISATIONAL EFFECTIVENESS ASSESSMENT SLIDES (1).pptx

ORGANISATIONAL EFFECTIVENESS ASSESSMENT SLIDES (1).pptx

ORGANISATIONAL EFFECTIVENESS ASSESSMENT SLIDES (1).pptx

Practical Leadership Change

Practical Leadership Change

Practical Leadership Change

When building a data team from scratch or inheriting an existing team, there are plenty of questions to ask when thinking about how to successfully deliver on our mission to the company. Should data engineering be part of the data organization or does it sit better with the engineering team? Data scientist is a job title that means a lot of different things to different companies, what does it mean to us? Are we aligned around platforms or functions? What's our strategy around data governance and compliance? And that's just to name a few. This talk will present some insights from prior experience on structuring data teams, both at startups and larger legacy organizations, covering examples that have been both successful and not so successful, and lessons learned in each case.

Building a successful data organization nov 2018

Building a successful data organization nov 2018

Building a successful data organization nov 2018

Alejandro Cantarero

Knowledge Management

Knowledge Management

Knowledge Management

The Project Management Process - Week 11 Contemporary Issues

The Project Management Process - Week 11 Contemporary Issues

The Project Management Process - Week 11 Contemporary Issues

WEBINAR : PeopleWiz-Reconfig

WEBINAR : PeopleWiz-Reconfig

WEBINAR : PeopleWiz-Reconfig

PeopleWiz Consulting

Using Collaboration To Improve Effectiveness

Using Collaboration To Improve Effectiveness

Using Collaboration To Improve Effectiveness

Km ver 1.0 student

Km ver 1.0 student

Km ver 1.0 student

A Big Picture in Research Data Management

A Big Picture in Research Data Management

A Big Picture in Research Data Management

© P ix m an n/ Im ag ez oo / Ge tty Im ag es 12 Restructuring Organizations learning objectives Describe the most common organization structures used today and understand their strengths and weaknesses. Present the process of downsizing. Describe and evaluate the reengineering intervention. In this chapter, we begin to examine techno-structural interventions—change programsfocusing on the technology and structure of organizations. Increasing global competition and rapid technological and environmental changes are forcing organizations to restructure themselves from rigid bureaucracies to leaner, more flexible designs. These new forms of organizing are highly adaptive and innovative, but require more sophisticated managerial capabilities to operate successfully. They often result in fewer managers and employees and in streamlined work flows that break down functional barriers. Interventions aimed at structural design include moving from more traditional ways of dividing the organization’s overall work, such as functional, divisional, and matrix structures, to more integrative and flexible forms, such as process, customer-centric, and network structures. Diagnostic guidelines help determine which structure is appropriate for particular organizational environments, technologies, and conditions. Downsizing seeks to reduce costs and bureaucracy by decreasing the size of the organization. This reduction in personnel can be accomplished through layoffs, organization redesign, and outsourcing, which involves moving functions that are not part of the organization’s core competence to outside contractors. Successful downsizing is closely aligned with the organization’s strategy. Reengineering radically redesigns the organization’s core work processes to give tighter linkage and coordination among the different tasks. This workflow integration results in faster, more responsive task performance. Reengineering often is accomplished with new information technology that permits employees to control and coordinate work processes more effectively. 12-1 Structural Design Organization structure describes how the overall work of the organization is divided into subunits and how these subunits are coordinated for task completion. Based on a contin- gency perspective shown in Figure 12.1, organization structures should be designed to fit with at least four factors: the environment, organization size, technology, and 339 organization strategy. Organization effectiveness depends on the extent to which its structure is responsive to these contingencies.1 Organizations traditionally have structured themselves into one of three forms: functional departments that are task specialized; self-contained divisional units that are oriented to specific products, customers, or regions; or matrix structures that combine both functional specialization and self-containment. Faced with accelerating changes in com- petitive environments and t ...

© PixmannImagezooGetty

© PixmannImagezooGetty

© PixmannImagezooGetty

LesleyWhitesidefv

Resilient Design Management

Resilient Design Management

Resilient Design Management

BUS137 Chapter 6

BUS137 Chapter 6

BUS137 Chapter 6

Deborah Oronzio

Tips to help bypass the top five learning industry roadblocks. Whether you’re trying to keep afloat in today’s economy or your Learning & Development organization is sailing through smooth waters, you probably face daily challenges to improve training products within budget. Listed on the following page are five challenges learning leaders throughout our industry have pinpointed as their top roadblocks.

Conquering Corporate Challenges

Conquering Corporate Challenges

Conquering Corporate Challenges

Michaels & Associates

1 STRATEGIC GROUPING FOR TOYOTA CORPORATION Milestone 2: Strategic Grouping for Toyota Corporation Richard K. Hairston GM543.01 Organizational Diagnosis and Design 12/15/17 Professor Dr. Rebecca Herman Introduction Strategic grouping provides an analysis to understand the strategy employed within an industry better. This makes it easy for strategists to understand the industry better in which they operate in. in addition to that, it helps in the identification of any gaps or areas where a shift in the strategy could help in building rewards (Drejer, 2002). The rewards can be built through capitalizing on an opportunity in a less crowded market. Toyota Corporation can have different business segments catering to different markets. Strategic groups can be varying in an industry such that big businesses may have a much higher bargaining power than the small businesses. Organizational Archetype Organizational archetype is distinguished by one feature that defines it. There are four types of the organizational archetype. The first type is the simple archetype. Where an organization is more structured like the traditional pyramid. Employees in an organization report to the manager who then reports to the CEO all the way to the top. The second type is the professional bureaucracy archetype. The professionals in the organization who have undergone some type of training are the given the responsibility of making decisions. Companies like financial agencies and regulatory bodies use this type of archetype. The third type is the divisional archetype. As stated above, many small organizations use simple organizational archetype while large organizations use divisional archetype (Kesler, & Kates, 2010). A Simple organizational archetype cannot be applied to larger organizations since each manager is only mandated to manage a small or a limited number of employees. In divisional archetype, the employees in an organization are grouped into divisions. Even though large organizations use this type of archetype, they may end up implementing the same differences as that of smaller organizations. The last type is the flexible archetype. This type of archetype is characterized by lack of formal structure. Such organizations also tend to indulge external influence. Since Toyota Corporation is a large organization, it requires an archetype that will support its size. There are different divisions in Toyota such as the geographic divisions and product-based divisions. It has got 8 geographic divisions and four product-based divisions. Therefore, the archetype that fits Toyota is the divisional archetype. This is because each manager can only manage a limited number of employees due to its size. Therefore, employees are group into divisions that can be easily managed. The managers of the divisions make choices for how the employees in a certain division will undertake activities. Analysis Four Classic Design Options Advantages and Disadvantages ...

1STRATEGIC GROUPING FOR TOYOTA CORPORATIONMilestone 2 Strat.docx

1STRATEGIC GROUPING FOR TOYOTA CORPORATIONMilestone 2 Strat.docx

1STRATEGIC GROUPING FOR TOYOTA CORPORATIONMilestone 2 Strat.docx

felicidaddinwoodie

Knowing - Doing Gap

Knowing - Doing Gap

Knowing - Doing Gap

Similar to Where should you put your data scientists? (20)

PDC 2008 Toward participatory organizations.

PDC 2008 Toward participatory organizations.

PDC 2008 Toward participatory organizations.

Modeling Framework to Support Evidence-Based Decisions

Modeling Framework to Support Evidence-Based Decisions

Modeling Framework to Support Evidence-Based Decisions

Case

Business Case for eLearning

Business Case for eLearning

Business Case for eLearning

E12+Analytics+Workshop+ppt.pptx

E12+Analytics+Workshop+ppt.pptx

E12+Analytics+Workshop+ppt.pptx

ORGANISATIONAL EFFECTIVENESS ASSESSMENT SLIDES (1).pptx

ORGANISATIONAL EFFECTIVENESS ASSESSMENT SLIDES (1).pptx

ORGANISATIONAL EFFECTIVENESS ASSESSMENT SLIDES (1).pptx

Practical Leadership Change

Practical Leadership Change

Practical Leadership Change

Building a successful data organization nov 2018

Building a successful data organization nov 2018

Building a successful data organization nov 2018

Knowledge Management

Knowledge Management

Knowledge Management

The Project Management Process - Week 11 Contemporary Issues

The Project Management Process - Week 11 Contemporary Issues

The Project Management Process - Week 11 Contemporary Issues

WEBINAR : PeopleWiz-Reconfig

WEBINAR : PeopleWiz-Reconfig

WEBINAR : PeopleWiz-Reconfig

Using Collaboration To Improve Effectiveness

Using Collaboration To Improve Effectiveness

Using Collaboration To Improve Effectiveness

Km ver 1.0 student

Km ver 1.0 student

Km ver 1.0 student

A Big Picture in Research Data Management

A Big Picture in Research Data Management

A Big Picture in Research Data Management

© PixmannImagezooGetty

© PixmannImagezooGetty

© PixmannImagezooGetty

Resilient Design Management

Resilient Design Management

Resilient Design Management

BUS137 Chapter 6

BUS137 Chapter 6

BUS137 Chapter 6

Conquering Corporate Challenges

Conquering Corporate Challenges

Conquering Corporate Challenges

1STRATEGIC GROUPING FOR TOYOTA CORPORATIONMilestone 2 Strat.docx

1STRATEGIC GROUPING FOR TOYOTA CORPORATIONMilestone 2 Strat.docx

1STRATEGIC GROUPING FOR TOYOTA CORPORATIONMilestone 2 Strat.docx

Knowing - Doing Gap

Knowing - Doing Gap

Knowing - Doing Gap

More from Daniel Tunkelang

Query Understanding and Ecommerce

Query Understanding and Ecommerce

Query Understanding and Ecommerce

Daniel Tunkelang

Semantic Equivalence of e-Commerce Queries

Semantic Equivalence of e-Commerce Queries

Semantic Equivalence of e-Commerce Queries

Daniel Tunkelang

Behavioral economics transformed how we think about human decision making, rejecting expected utility maximization for the real world of heuristics, biases, and satisficing. In this talk, I'll argue that our thinking about search engines needs a similar transformation. I will compare the Probability Ranking Principle to expected utility maximization and offer ways that AI can help searchers satisfice through query understanding. This was an invited talk given at the 2023 Walmart AI Summit. Speaker Bio Daniel Tunkelang is an independent consultant specializing in search, machine learning / AI, and data science. He completed undergraduate and master's degrees in Computer Science and Math at MIT and a PhD in computer science at CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired in 2011. He then led engineering and data science teams at Google and LinkedIn. He has written a book on Faceted Search, and he blogs on Medium about search-related topics — particularly query understanding. He has worked with numerous tech companies, retailers, and others, including Algolia, Apple, Canva, Coupang, eBay, Etsy, Flipkart, Home Depot, Oracle, Pinterest, Salesforce, Target, Yelp, and Zoom.

Helping Searchers Satisfice through Query Understanding

Helping Searchers Satisfice through Query Understanding

Helping Searchers Satisfice through Query Understanding

Daniel Tunkelang

MMM, Search! An opinionated discussion of search metrics, models, and methods. Presented to the Wikimedia Foundation on April 27, 2020. About the Speaker Daniel Tunkelang is an independent consultant specializing in search, discovery, machine learning / AI, and data science. He was a founding employee of Endeca, a search pioneer that Oracle acquired. After 10 years at Endeca, he moved to Google, where he led a local search team. He then served as a director of data science and search at LinkedIn. After leaving LinkedIn in 2015, he became an independent consultant. His clients have included Apple, eBay, Coupang, Etsy, Flipkart, Gartner, Pinterest, Salesforce, and Yelp; as well as some of the largest traditional retailers. Daniel completed undergraduate and master's degrees in Computer Science and Math at MIT and a Ph.D. in computer science at CMU. He wrote a book on Faceted Search, published by Morgan & Claypool, and he blogs on Medium about search-related topics -- particularly about query understanding. He is also active on Twitter, LinkedIn, and Quora.

MMM, Search!

Daniel Tunkelang

Enterprise Intelligence: Putting the Pieces Together http://enterpriserelevance.com/kdd2016/keynote.html These slides are for a keynote presentation delivered at the Workshop on Enterprise Intelligence, held in conjunction with the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016). About the author: Daniel Tunkelang is a data science and engineering executive who has built and led some of the strongest teams in the software industry. He studied computer science and math at MIT and has a PhD in computer science from CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired for $1.1B. He led a local search team at Google. He was a director of data science and engineering at LinkedIn, and he established their query understanding team. Daniel is a widely recognized writer and speaker. He is frequently invited to speak at academic and industry conferences, particularly in the areas of information retrieval, web science, and data science. He has written the definitive textbook on faceted search (now a standard for ecommerce sites), established an annual symposium on human-computer interaction and information retrieval, and authored 24 US patents. His social media posts have attracted over a million page views. Daniel advises and consults for companies that can benefit strategically from his expertise. His clients range from early-stage startups to "unicorn" technology companies like Etsy and Pinterest. He helps companies make decisions around algorithms, technology, product strategy, hiring, and organizational structure.

Enterprise Intelligence

Enterprise Intelligence

Enterprise Intelligence

Daniel Tunkelang

Query Understanding: A Manifesto

Query Understanding: A Manifesto

Query Understanding: A Manifesto

Daniel Tunkelang

Data Science: A Mindset for Productivity Keynote at 2015 Ronin Labs West Coast CTO Summit https://www.eventjoy.com/e/west-coast-cto-summit-2015 Abstract Data science isn't just about using a collection of technologies and algorithms. Data science requires a mindset that solves problems at a higher level of abstraction. How do we model utility when we think about optimization? How do we decide which hypotheses to test? How do we allocate our scarce resources to make progress? There are no silver bullets. But I'll share what I've learned from a variety of contexts over the course of my work at Endeca, Google, and LinkedIn; and I hope you'll leave this talk with some practical wisdom you can apply to your next data science project.

Data Science: A Mindset for Productivity

Data Science: A Mindset for Productivity

Data Science: A Mindset for Productivity

Daniel Tunkelang

My Three Ex’s: A Data Science Approach for Applied Machine Learning Daniel Tunkelang (LinkedIn) Presented at QCon San Francisco 2014 in the Applied Machine Learning and Data Science track https://qconsf.com/presentation/my-three-ex%E2%80%99s-data-science-approach-applied-machine-learning Abstract This talk is about applying machine learning to solve problems. It’s not a talk about machine learning — or at least not about the theory of machine learning. Theoretical machine learning requires a deep understanding of computer science and statistics. It’s one of the most studied areas of computer science, and advances in theoretical machine learning give us hope of solving the world’s “AI-hard” problems. Applied machine learning is more grounded but no less important. We are surrounded by opportunities to apply classifiers, learn rules, compute similarity, and assemble clusters. We don’t need to develop new algorithms for any of these problems — our textbooks and open-source libraries have done that hard work for us. But algorithms are not enough. Applying machine learning to solve problems requires a data science mindset that transcends the algorithmic details. In this talk, I’ll communicate the data science mindset by describing my three ex’s: express, explain, and experiment. These three activities are the pillars of a successful strategy for applying machine learning to solve problems. Whether you’re a machine learning novice or expert, I hope you’ll leave this talk with some practical wisdom you can apply to your next project.

My Three Ex’s: A Data Science Approach for Applied Machine Learning

My Three Ex’s: A Data Science Approach for Applied Machine Learning

My Three Ex’s: A Data Science Approach for Applied Machine Learning

Daniel Tunkelang

Web Science: How is it different? Daniel Tunkelang, LinkedIn Keynote Address at ACM Web Science 2014 Conference The scientific method of observation, measurement, and experiment may be our greatest achievement as a species. The technological innovation we enjoy today is the product of a culture of systematized scientific experimentation. But historically scientific experimentation has been expensive. Experiments consumed natural resources, took a long time to conduct, and required even more time and labor to analyze. In order to be productive, scientists have had to factor these costs into their work and to optimize accordingly. Web science is different. Not, as some have speciously argued, because big data has made the scientific method obsolete. The key difference is that web science has changed the economics of scientific experimentation. Thus, even as web scientists apply the traditional scientific method, they optimize based on very different economics. In this talk, I'll survey how web science has changed our approach to experimentation, for better and for worse. Specifically, I'll talk about differences in hypothesis generation, offline analysis, and online testing. Bio Daniel Tunkelang is Head of Query Understanding at LinkedIn, where he previously formed and led the product data science team. LinkedIn search allows members to find people, companies, jobs, groups and other content. His team aims to provide users with the best possible results that satisfy their information needs and help to get insights from professional data. Tunkelang has BS and MS degrees in computer science and math from MIT, and a PhD in computer science from CMU. He co-founded the annual symposium on human-computer interaction and information retrieval (HCIR) and wrote the first book on Faceted Search (Morgan and Claypool 2009). Prior to joining LinkedIn, Tunkelang was Chief Scientist of Endeca (acquired by Oracle in 2011 for $1.1B) and leader of the local search quality team at Google, mapping local businesses to their home pages. He is the co-inventor of 20 patents.

Web science - How is it different?

Web science - How is it different?

Web science - How is it different?

Daniel Tunkelang

Better Search Through Query Understanding Presented as a Data Talk at Intuit on April 22, 2014 Search is a fundamental problem of our time — we use search engines daily to satisfy a variety of personal and professional information needs. But search engine development still feels stuck in an information retrieval paradigm that focuses on result ranking. In this talk, I’ll advocate an emphasis on query understanding. I’ll talk about how we implement query understanding at LinkedIn, and I’ll present examples from the broader web. Hopefully you’ll come out with a different perspective on search and share my appreciation for how we can improve search through query understanding. About the Speaker Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.

Better Search Through Query Understanding

Better Search Through Query Understanding

Better Search Through Query Understanding

Daniel Tunkelang

Keynote at CIKM 2013 Workshop on Data-driven User Behavioral Modelling and Mining from Social Media Social Search in a Professional Context Daniel Tunkelang (LinkedIn) Social networks bring a new dimension to search. Instead of looking for web pages or text documents, LinkedIn members search a world of entities connected by a rich graph of relationships. Search is a fundamental part of the LinkedIn ecosystem, as it helps our members find and be found. Unlike most search applications, LinkedIn's search experience is highly personalized: two LinkedIn members performing the same search query are likely to see completely different results. Delivering the right results to the right person depends on our ability to leverage our each member's unique professional identity and network. In this talk, I'll describe the kinds of search behavior we see on LinkedIn, and some of the approaches we've taken to help our members address their information needs.

Social Search in a Professional Context

Social Search in a Professional Context

Social Search in a Professional Context

Daniel Tunkelang

Find and Be Found: Information Retrieval at LinkedIn SIGIR 2013 Industry Track Presentation http://sigir2013.ie/industry_track.html LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also the most valuable entities in our corpus, which consists of people, companies, jobs, and a rich content ecosystem. Our members use LinkedIn to satisfy a diverse set of navigational and exploratory information needs, which we address by leveraging semi-structured and social content to understanding their query intent and deliver a personalized search experience. In this talk, we will discuss some of the unique challenges we face in building the LinkedIn search platform, the solutions we've developed so far, and the open problems we see ahead of us. Shakti Sinha heads LinkedIn's search relevance team, and has been making key contributions to LinkedIn's search products since 2010. He previously worked at Google as both a research intern and a software engineer. He has an MS in Computer Science from Stanford, as well as a BS degree from College of Engineering, Pune. Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.

Find and be Found: Information Retrieval at LinkedIn

Find and be Found: Information Retrieval at LinkedIn

Find and be Found: Information Retrieval at LinkedIn

Daniel Tunkelang

Search as Communication: Lessons from a Personal Journey by Daniel Tunkelang (Head of Query Understanding, LinkedIn) Presented at Etsy's Code as Craft Series on May 21, 2013 When I tell people I spent a decade studying computer science at MIT and CMU, most assume that I focused my studies in information retrieval — after all, I’ve spent most of my professional life working on search. But that’s not how it happened. I learned about information extraction as a summer intern at IBM Research, where I worked on visual query reformulation. I learned how search engines work by building one at Endeca. It was only after I’d hacked my way through the problem for a few years that I started to catch up on the rich scholarly literature of the past few decades. As a result, I developed a point of view about search without the benefit of academic conventional wisdom. Specifically, I came to see search not so much as a ranking problem as a communication problem. In this talk, I’ll explain my communication-centric view of search, offering examples, general techniques, and open problems. -- Daniel Tunkelang is Head of Query Understanding at LinkedIn. Educated at MIT and CMU, he has his career working on big data, addressing key challenges in search, data mining, user interfaces, and network analysis. He co-founded enterprise search and business intelligence pioneer Endeca, where he spent a decade as its Chief Scientist. In 2011, Endeca was acquired by Oracle for over $1B. Previous to LinkedIn, he led a team at Google working on local search quality. Daniel has authored fifteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval.

Search as Communication: Lessons from a Personal Journey

Search as Communication: Lessons from a Personal Journey

Search as Communication: Lessons from a Personal Journey

Daniel Tunkelang

Enterprise Search: How Do We Get There From Here? by Daniel Tunkelang (Head of Query Understanding, LinkedIn) Keynote at 2013 Enterprise Search Summit We've been tackling the challenges of enterprise and site search for at least 3 decades. We've succeeded to the point that search is the gateway to many of our information repositories. Nonetheless, users of enterprise search systems are frustrated with these systems' shortcomings. We see this frustration in surveys, but, more importantly, most of us experience it personally in our daily work life. We all dream of a world where searching any information repository is as effective as searching the web—perhaps even more so. A world where we find what we're looking for, or quickly determine that it doesn't exist. Is this Utopia possible? If so, how do we get there from here? Or at least somewhere close? In this talk, Tunkelang reviews the track record of enterprise search. He talks about what's worked and what hasn't, especially as compared to web search. Finally, he proposes some paths to bring us closer to our dream. -- Daniel Tunkelang is Head of Query Understanding at LinkedIn. Educated at MIT and CMU, he has his career working on big data, addressing key challenges in search, data mining, user interfaces, and network analysis. He co-founded enterprise search and business intelligence pioneer Endeca, where he spent a decade as its Chief Scientist. In 2011, Endeca was acquired by Oracle for over $1B. Previous to LinkedIn, he led a team at Google working on local search quality. Daniel has authored fifteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval.

Enterprise Search: How do we get there from here?

Enterprise Search: How do we get there from here?

Enterprise Search: How do we get there from here?

Daniel Tunkelang

Big Data, We Have a Communication Problem by Daniel Tunkelang Presented on April 30, 2013 at the TTI/Vanguard Conference on Ginormous Systems http://www.ttivanguard.com/conference/2013/ginormous.html It's a cliché that we live in a world of Big Data. But the bottleneck in understanding data is not computational. Rather, the biggest challenge is designing technical solutions that effectively leverage human cognitive ability. Data analysis systems should augment people's capabilities rather than replace them. This argument is as old as computer science itself: in 1962, Doug Engelbart said that the goal of technology is “the enhancement of human intellect by increasing the capability of a human to approach a complex problem situation.” Algorithms extract signal from raw data, but people fill in the gaps, creating models and evaluating analyses. Empowering people to understand data is not just a surface problem of building better interfaces and visualizations. We need to interact with data not only after performing computational analysis, but throughout the analysis process in order to improve our models and algorithms. In order to do so, we need tools and processes specifically designed to offer people transparency, guidance, and control. Human-computer information retrieval has been revolutionizing our approach to information seeking -- no modern search engine limits users to black-box relevance ranking and ten blue links. We need to take similar steps in our analysis of big data, making people the center of the analysis process and developing the technical innovations that enable people to fulfill this role.

Big Data, We Have a Communication Problem

Big Data, We Have a Communication Problem

Big Data, We Have a Communication Problem

Daniel Tunkelang

How To Interview a Data Scientist Daniel Tunkelang Presented at the O'Reilly Strata 2013 Conference Video: https://www.youtube.com/watch?v=gUTuESHKbXI Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy. At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work. In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.

How to Interview a Data Scientist

How to Interview a Data Scientist

How to Interview a Data Scientist

Daniel Tunkelang

Information, Attention, and Trust: A Hierarchy of Needs

Information, Attention, and Trust: A Hierarchy of Needs

Information, Attention, and Trust: A Hierarchy of Needs

Daniel Tunkelang

Data By The People, For The People Daniel Tunkelang Director, Data Science at LinkedIn Invited Talk at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012) LinkedIn has a unique data collection: the 175M+ members who use LinkedIn are also the content those same members access using our information retrieval products. LinkedIn members performed over 4 billion professionally-oriented searches in 2011, most of those to find and discover other people. Every LinkedIn search and recommendation is deeply personalized, reflecting the user's current employment, career history, and professional network. In this talk, I will describe some of the challenges and opportunities that arise from working with this unique corpus. I will discuss work we are doing in the areas of relevance, recommendation, and reputation, as well as the ecosystem we have developed to incent people to provide the high-quality semi-structured profiles that make LinkedIn so useful. Bio: Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, created the annual workshop on human-computer interaction and information retrieval (HCIR), and participated in the premier research conferences on information retrieval, knowledge management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data Mining). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.

Data By The People, For The People

Data By The People, For The People

Data By The People, For The People

Daniel Tunkelang

Content, Connections, and Context Daniel Tunkelang, LinkedIn Keynote at Workshop on Recommender Systems and the Social Web At 6th ACM International Conference on Recommender Systems (RecSys 2012) Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context. Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate. I'll talk about how we use these three kinds of signals in LinkedIn's recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.

Content, Connections, and Context

Content, Connections, and Context

Content, Connections, and Context

Daniel Tunkelang

Keynote at 2012 Semantic Technology and Business Conference Scale, Structure, and Semantics Daniel Tunkelang, LinkedIn Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000. Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data. In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.

Scale, Structure, and Semantics

Scale, Structure, and Semantics

Scale, Structure, and Semantics

Daniel Tunkelang

More from Daniel Tunkelang (20)

Query Understanding and Ecommerce

Query Understanding and Ecommerce

Query Understanding and Ecommerce

Semantic Equivalence of e-Commerce Queries

Semantic Equivalence of e-Commerce Queries

Semantic Equivalence of e-Commerce Queries

Helping Searchers Satisfice through Query Understanding

Helping Searchers Satisfice through Query Understanding

Helping Searchers Satisfice through Query Understanding

MMM, Search!

Enterprise Intelligence

Enterprise Intelligence

Enterprise Intelligence

Query Understanding: A Manifesto

Query Understanding: A Manifesto

Query Understanding: A Manifesto

Data Science: A Mindset for Productivity

Data Science: A Mindset for Productivity

Data Science: A Mindset for Productivity

My Three Ex’s: A Data Science Approach for Applied Machine Learning

My Three Ex’s: A Data Science Approach for Applied Machine Learning

My Three Ex’s: A Data Science Approach for Applied Machine Learning

Web science - How is it different?

Web science - How is it different?

Web science - How is it different?

Better Search Through Query Understanding

Better Search Through Query Understanding

Better Search Through Query Understanding

Social Search in a Professional Context

Social Search in a Professional Context

Social Search in a Professional Context

Find and be Found: Information Retrieval at LinkedIn

Find and be Found: Information Retrieval at LinkedIn

Find and be Found: Information Retrieval at LinkedIn

Search as Communication: Lessons from a Personal Journey

Search as Communication: Lessons from a Personal Journey

Search as Communication: Lessons from a Personal Journey

Enterprise Search: How do we get there from here?

Enterprise Search: How do we get there from here?

Enterprise Search: How do we get there from here?

Big Data, We Have a Communication Problem

Big Data, We Have a Communication Problem

Big Data, We Have a Communication Problem

How to Interview a Data Scientist

How to Interview a Data Scientist

How to Interview a Data Scientist

Information, Attention, and Trust: A Hierarchy of Needs

Information, Attention, and Trust: A Hierarchy of Needs

Information, Attention, and Trust: A Hierarchy of Needs

Data By The People, For The People

Data By The People, For The People

Data By The People, For The People

Content, Connections, and Context

Content, Connections, and Context

Content, Connections, and Context

Scale, Structure, and Semantics

Scale, Structure, and Semantics

Scale, Structure, and Semantics

Recently uploaded

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA. In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability. In this session, participants gained insights about the following: Most common types of AI categories and use cases; Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives; Taxonomy and ontology design considerations and best practices; Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and Tools, roles, and skills to design and implement AI-powered search solutions.

IAC 2024 - IA Fast Track to Search Focused AI Solutions

IAC 2024 - IA Fast Track to Search Focused AI Solutions

IAC 2024 - IA Fast Track to Search Focused AI Solutions

Enterprise Knowledge

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

How to convert PDF to text with Nanonets

How to convert PDF to text with Nanonets

How to convert PDF to text with Nanonets

MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

Histor y of HAM Radio presentation slide

Histor y of HAM Radio presentation slide

Histor y of HAM Radio presentation slide

Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024. Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights. Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer. Learn about: - The essence and purpose of taxonomies and ontologies in information and knowledge management; - Advantages of semantic layers leveraging organizational taxonomies; and - Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

Enterprise Knowledge

GenAI Risks & Security Meetup 01052024.pdf

GenAI Risks & Security Meetup 01052024.pdf

GenAI Risks & Security Meetup 01052024.pdf

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Principled Technologies

Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

Scaling API-first – The story of a global engineering organization

Scaling API-first – The story of a global engineering organization

Scaling API-first – The story of a global engineering organization

GenCyber Cyber Security Day Presentation

GenCyber Cyber Security Day Presentation

GenCyber Cyber Security Day Presentation

Michael W. Hawkins

BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver

08448380779 Call Girls In Civil Lines Women Seeking Men

08448380779 Call Girls In Civil Lines Women Seeking Men

08448380779 Call Girls In Civil Lines Women Seeking Men

Delhi Call girls

Finology Group – Insurtech Innovation Award 2024

Finology Group – Insurtech Innovation Award 2024

Finology Group – Insurtech Innovation Award 2024

The Digital Insurer

Recently uploaded (20)

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

Presentation on how to chat with PDF using ChatGPT code interpreter

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

IAC 2024 - IA Fast Track to Search Focused AI Solutions

IAC 2024 - IA Fast Track to Search Focused AI Solutions

IAC 2024 - IA Fast Track to Search Focused AI Solutions

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

How to convert PDF to text with Nanonets

How to convert PDF to text with Nanonets

How to convert PDF to text with Nanonets

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Histor y of HAM Radio presentation slide

Histor y of HAM Radio presentation slide

Histor y of HAM Radio presentation slide

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

GenAI Risks & Security Meetup 01052024.pdf

GenAI Risks & Security Meetup 01052024.pdf

GenAI Risks & Security Meetup 01052024.pdf

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

presentation ICT roal in 21st century education

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

Scaling API-first – The story of a global engineering organization

Scaling API-first – The story of a global engineering organization

Scaling API-first – The story of a global engineering organization

GenCyber Cyber Security Day Presentation

GenCyber Cyber Security Day Presentation

GenCyber Cyber Security Day Presentation

08448380779 Call Girls In Civil Lines Women Seeking Men

08448380779 Call Girls In Civil Lines Women Seeking Men

08448380779 Call Girls In Civil Lines Women Seeking Men

Finology Group – Insurtech Innovation Award 2024

Finology Group – Insurtech Innovation Award 2024

Finology Group – Insurtech Innovation Award 2024

Where should you put your data scientists?

1. Where should you put your data scientists? Daniel Tunkelang

2. Review: why you hire data scientists

3. To improve products

4. To improve decisions

5. Organizational structure matters

6. stand-alone embedded integrated

7. The stand-alone model

8. Pro: gives team autonomy

9. Pro: positions data as ﬁrst-class

10. Con: risks marginalizing team

11. Con: other teams like self-sufﬁciency

12. The embedded model

13. Pro: ensures utilization

14. Pro: ﬂexible allocation

15. Con: team resents lack of autonomy

16. Con: management challenges

17. The integrated model

18. Pro: organizational alignment

19. Pro: long-term investment

20. Con: dilution of data science

21. Con: lack of reuse

22. stand-alone: autonomy, data as ﬁrst class embedded: utilization, ﬂexible allocation integrated: alignment, long-term investment

23. stand-alone: marginalization, isolation embedded: resentment, dual management integrated: dilution, lack of reuse

24. You can mix and match

25. Adapt as company evolves

26. Remember the mission

27.

28. Thank you. Daniel Tunkelang dtunkelang@gmail.com : @dtunkelang