The document discusses the differences between data-driven companies and data-justified companies. Data-driven companies start with a business question and use a scientific process to capture data, form hypotheses, run tests, make recommendations, and implement changes based on the results. Data-justified companies start with pre-existing beliefs and cherry-pick data that supports those beliefs while discarding other data. The document advocates that analytics professionals should only work for genuinely data-driven companies and outlines strategies for identifying data-justified versus data-driven companies and managers.
Acceptance, accessible, actionable and auditableAlban Gérôme
Many a stakeholder presented with actionable insight have expressed doubts about data quality, its relevance or potential impact. In other cases, the stakeholders will want a data analyst who also commands knowledge of the business or a similar unicorn. The web analytics practitioners would rise to the challenge, and the stakeholders will then want their own Hans Rosling and his dazzling data visualisation and raconteur sills. Your stakeholders are silently experiencing the data transformation like a conservatorship. How can you help them perceive your efforts like a temporary guardianship from which they will emerge as ready to face your data-driven competitors?
The Business Analyst as a Professional Problem SolverNUS-ISS
Presented by Dr Matt. Fourie, CEO & Author of Thinking Dimensions Global at ISS-IIBA Seminar: The Business Analyst as a Problem Solver, on 21 May 2015.
Talent Sleuthing in the Intelligence Community - Jo Weech; recruitDC Spring 2018RecruitDC
How do you recruit for people with clearances? In 2016, I won the HRLA Leadership Excellence award for successfully growing a software engineering firm by 30% with zero attrition on contract. All had to have full scope polys. I will share all of my secret sauces so that you can be inspired to borrow mine or create your own!
Build & Track an Amazing End to End Candidate ExperienceRecruitDC
n addition to his job as head of employer brand at software giant CA technologies, Craig Fisher also consults with other major organizations such as GM, HSBC, Microsoft, and many more. He will show you best practices from around the world to build and document an amazing candidate experience to help gain rabid fans of your company's employer brand, culture, and people. He'll show you how you can quickly humanize your company's employer brand, by sharing your culture through your people and shrink time to fill and cost per hire. This isn't just mile high level information. This is real strategy, tools, and hacks that you can implement today.
Data-Driven Requirements for User-Stories on JustAnswerVlad Mysla
Process of switching to Data-Driven Requirements for User-Story creation. It has information about internal JA tools, which isn't useful for anyone outside the company.
Bridging the Gap Between Data Science & Engineer: Building High-Performance T...ryanorban
Data scientists, data engineers, and data businesspeople are critical to leveraging data in any organization. A common complaint from data science managers is that data scientists invest time prototyping algorithms, and throw them over a proverbial fence to engineers to implement, only to find the algorithms must be rebuilt from scratch to scale. This is a symptom of a broader ailment -- that data teams are often designed as functional silos without proper communication and planning.
This talk outlines a framework to build and organize a data team that produces better results, minimizes wasted effort among team members, and ships great data products.
Workforce Intelligence: How HR Can Make Data-Driven Decisions That Move the N...Human Capital Media
The idea of data-driven HR has been a top topic and trend for several years now, yet the vast majority of HR organizations are still underserved with insights. While many organizations are thinking about workforce analytics, few have truly put them to work. Indeed, as Josh Bersin of Bersin by Deloitte aptly described, most HR organizations are “stuck in neutral” with workforce analytics, unsure how to get started with this complex topic.
How can you get out of neutral with workforce analytics? How can you and your organization overcome the data hurdles and technical complexity -- despite having little or no experience in analytics? How can you get to workforce insights that will help you understand with precision what it takes to recruit, retain, and motivate the best workforce -- and drive measurable business outcomes?
In this webinar, analytics expert Ian Cook will provide direct examples of you can take to:
Improve recruitment success and more efficiently find expertise needed at the right time for the best price.
Retain star performers more cost effectively.
Connect employee engagement to business outcomes.
Decode workforce planning and understand the “cost” side of the workforce.
LavaCon Conference Business Case PresentationTechWhirl
This workshop explores how technical communicators can build the business case(s) needed to take a more active visible role in product development and business strategy by speaking directly to management’s needs and agenda.
Acceptance, accessible, actionable and auditableAlban Gérôme
Many a stakeholder presented with actionable insight have expressed doubts about data quality, its relevance or potential impact. In other cases, the stakeholders will want a data analyst who also commands knowledge of the business or a similar unicorn. The web analytics practitioners would rise to the challenge, and the stakeholders will then want their own Hans Rosling and his dazzling data visualisation and raconteur sills. Your stakeholders are silently experiencing the data transformation like a conservatorship. How can you help them perceive your efforts like a temporary guardianship from which they will emerge as ready to face your data-driven competitors?
The Business Analyst as a Professional Problem SolverNUS-ISS
Presented by Dr Matt. Fourie, CEO & Author of Thinking Dimensions Global at ISS-IIBA Seminar: The Business Analyst as a Problem Solver, on 21 May 2015.
Talent Sleuthing in the Intelligence Community - Jo Weech; recruitDC Spring 2018RecruitDC
How do you recruit for people with clearances? In 2016, I won the HRLA Leadership Excellence award for successfully growing a software engineering firm by 30% with zero attrition on contract. All had to have full scope polys. I will share all of my secret sauces so that you can be inspired to borrow mine or create your own!
Build & Track an Amazing End to End Candidate ExperienceRecruitDC
n addition to his job as head of employer brand at software giant CA technologies, Craig Fisher also consults with other major organizations such as GM, HSBC, Microsoft, and many more. He will show you best practices from around the world to build and document an amazing candidate experience to help gain rabid fans of your company's employer brand, culture, and people. He'll show you how you can quickly humanize your company's employer brand, by sharing your culture through your people and shrink time to fill and cost per hire. This isn't just mile high level information. This is real strategy, tools, and hacks that you can implement today.
Data-Driven Requirements for User-Stories on JustAnswerVlad Mysla
Process of switching to Data-Driven Requirements for User-Story creation. It has information about internal JA tools, which isn't useful for anyone outside the company.
Bridging the Gap Between Data Science & Engineer: Building High-Performance T...ryanorban
Data scientists, data engineers, and data businesspeople are critical to leveraging data in any organization. A common complaint from data science managers is that data scientists invest time prototyping algorithms, and throw them over a proverbial fence to engineers to implement, only to find the algorithms must be rebuilt from scratch to scale. This is a symptom of a broader ailment -- that data teams are often designed as functional silos without proper communication and planning.
This talk outlines a framework to build and organize a data team that produces better results, minimizes wasted effort among team members, and ships great data products.
Workforce Intelligence: How HR Can Make Data-Driven Decisions That Move the N...Human Capital Media
The idea of data-driven HR has been a top topic and trend for several years now, yet the vast majority of HR organizations are still underserved with insights. While many organizations are thinking about workforce analytics, few have truly put them to work. Indeed, as Josh Bersin of Bersin by Deloitte aptly described, most HR organizations are “stuck in neutral” with workforce analytics, unsure how to get started with this complex topic.
How can you get out of neutral with workforce analytics? How can you and your organization overcome the data hurdles and technical complexity -- despite having little or no experience in analytics? How can you get to workforce insights that will help you understand with precision what it takes to recruit, retain, and motivate the best workforce -- and drive measurable business outcomes?
In this webinar, analytics expert Ian Cook will provide direct examples of you can take to:
Improve recruitment success and more efficiently find expertise needed at the right time for the best price.
Retain star performers more cost effectively.
Connect employee engagement to business outcomes.
Decode workforce planning and understand the “cost” side of the workforce.
LavaCon Conference Business Case PresentationTechWhirl
This workshop explores how technical communicators can build the business case(s) needed to take a more active visible role in product development and business strategy by speaking directly to management’s needs and agenda.
A Different Data Science Approach - StampedeCon AI Summit 2017StampedeCon
This session will focus on how to execute Data Science caliber efforts by creating teams with the attributes of Data Science to deliver meaningful results. As Data Scientists are harder to find and keep, this session should appeal to anyone who is either seeking an alternative approach to executing Data Science delivery or augmenting their current Data Science model with additional options.
Going Beyond ‘What Success Looks Like’ – Using Data to Achieve Successful Pro...Jamie Clouting (CSPO)
Delivering value is at the heart of the Business Analyst role, but how easy is it to identify tangible value and prove the success of a project or program?
In agile projects we’ll often define a “definition of done” or ask the question “what does success look like”. At LateRooms.com, we’ve developed a toolkit for our Business Analysts to support the business in using data to define what success looks like, and track it throughout the project lifecycle.
This presentation will look at the ways LateRooms.com collects, analyses and uses data to better define the problem space, setup up KPI driven Critical Success Factors and present Benefits Realisation.
"How To Build and Lead a Winning Data Team" by Cahyo Listyanto (Bizzy.co.id)Tech in Asia ID
Cahyo is a data geek, gamer and comic nerd.
Excel and Database are his favorite since his middle school.
Having graduated from a Vocational High School of Informatics and Technology
made him able to start his career early and led many DWH BI projects at his early 20.
He currently leading a data team in bizzy.co.id as the Head of Data Analytics.
Previously he worked for Microsoft Indonesia as Data Platform Technology Specialist where he provides strategic technical leadership supporting Microsoft customers and partners to adopt, deploy, and support solutions based on SQL Server and Data Platform related technologies.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
Insight and Action: Making Your Data Work for YouCARMA
Insight and Action: Making Your Data Work for You, a webinar sponsored CARMA and co-hosted by Arment Dietrich's Laura Petrolino and CARMA's Jen Zingsheim Phillips, covers how to analyze data and gain insight.
The session explores examples of companies using insight to make decisions, how to ask the right questions about data, and data-driven assumptions versus data-driven decisions.
Technology increasingly permeates every facet of modern business, from communications to CRM systems and customer analytics. IT and Digital have been drawn from a background support function and re-positioned as a core driver of strategy, value creation and competitive edge. This tectonic shift has placed senior technologists at the heart of the organisation, making them integral to decision making and leadership.
The DIGIT Leader Summit will explore this evolution of Information Technology as a discipline, discussing the increasing role of senior technologists in driving innovation and efficiency and shaping business strategy within their organisation. The programme will also consider some of the crucial components of leadership, looking at culture, vision, team building, up-skilling and communication.
The Summit is geared for senior IT and Digital leaders and is designed to promote knowledge exchange, best practice and collaboration in a friendly open forum. The event will be held at Dynamic Earth in Edinburgh on 24th May 2017 and will be free for delegates to attend.
Collaborate 2018: How to Get Cross Functional Reporting with an Enterprise Da...Datavail
Many organizations not only lack the ability to look at their data across the organization as whole, but often have no lens into the metrics that they need to report against or manage the business of their own departments.
How beneficial would it be to have a central data information repository – we call it an Enterprise Data Warehouse – from which to retrieve accurate data from across all aspects of your business? This presentation explains how this, and more, can be a reality for your business, in a relatively short amount of time.
Guidance for the small business owners & entrepreneurs on how to create a great customer experience by listening to your customer. User Research & Design methods, affordable online tools, and tips and tricks are shared in this presentation.
Clinical Site intelligence provides sponsors and CROs with a method to analyze data, measure performance, and more confidently predict results on investigative sites for clinical studies.
It draws upon the perspectives of Christy Gilchrist and Todd Tullis, as well as questions common asked by sponsors and CROs.
Addressing topics like...
- the predictive approach to site selection
- measuring and sharing metrics during startup
- evaluating performance vs plan
...and many more!
The CSI document is presented simply and clearly, and is elaborated more fully in each successive slide, providing a rich set of questions for people who are interested in asking the right questions when assessing sites.
Panelists from a large company, a small company and a software consulting firm will share insights on how their companies are tackling the arena of Big Data and how to leverage a variety of data sources for strategic decision-making.
Earning more as a Digital or Web AnalystAlban Gérôme
The salaries have dropped in the UK for Digital and Web Analysts, regardless of whether they are permanent hires or contractors. Could this happen outside the UK? Why are recruitment agencies letting this happen? What can we do as Digital and Web Analysts do to buck the trend
Is it just me, or the C-suite doesn't care about data?Alban Gérôme
With my MC Lyon session, I started a series of presentations on what I consider to be faulty assumptions in web analytics. The MC North America session was about how emotions, rather than making decision-making irrational, are essential to get buy-in. This session will be about exploring whether the C-suites are really as supportive of data-informed decision-making as they claim.
Cracking trading cards packs and web analyticsAlban Gérôme
We often hear the same names in web and digital analytics such as Daniel Kahneman or Hans Rosling. If they were sports athletes whose kids collect and swap the trading cards of, we would find that we all have the same players.
Time to crack open some new packs, and add new names into our metaphorical albums. Let's see how neurosciences challenge long-held assumptions in web and digital analytics.
Spicy javascript: Create your first Chrome extension for web analytics QAAlban Gérôme
Adobe Launch has a monitoring hooks API that provides more details about the rules that passed or failed. That's a great excuse for writing a Chrome extension. This will benefit you even if you have no need or experience with Adobe Launch.
The us vs the uk web analytics job slideshareAlban Gérôme
Why the US job market for web analytics seems to be so busy, offer plenty of senior roles, would even consider remote workers but not from the UK? It could be a win-win deal!
Acceptance, Accessible, Actionable and AuditableAlban Gérôme
A model for the digital transformation and excellence in analytics
Presented at Google Academy for Data Festival London 2018. This is an updated version of my AAAA model presentation for WAW Copenhagen in October 2017
Become an artisan web analytics practitioner by building your own analytics QA tool. For Adobe Analytics but you could do the same with Google Analytics, A/B testing, tag management, VOC tools and many other analytics tools
La creation d'un programme analytics cause des déséquilibres dans les conseils d'administrations. Comment vaincre les résistances dans les services censés béneficier d'une approche basée sur les données.
This an update on my previous presentation on Kermit. This was presented at the Brighton SEO conference on April 7th 2017. This is for Kermit v0.8 and this client-side Javascript framework will help you with:
- tagging page views including virtual page views generated by single page applications using AngularJS for example
- tagging interactions (covered in the previous presentation)
- managing your cookie consent, in particular which elements will provide implicit consent
- catch potential losses of analytics reporting caused by code changes made by your developers before these changes go live
Want to move your career forward? Looking to build your leadership skills while helping others learn, grow, and improve their skills? Seeking someone who can guide you in achieving these goals?
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Learn how you can make a difference in the project management community and take the next step in your professional journey.
About Hector Del Castillo
Hector is VP of Professional Development at the PMI Silver Spring Chapter, and CEO of Bold PM. He's a mid-market growth product executive and changemaker. He works with mid-market product-driven software executives to solve their biggest growth problems. He scales product growth, optimizes ops and builds loyal customers. He has reduced customer churn 33%, and boosted sales 47% for clients. He makes a significant impact by building and launching world-changing AI-powered products. If you're looking for an engaging and inspiring speaker to spark creativity and innovation within your organization, set up an appointment to discuss your specific needs and identify a suitable topic to inspire your audience at your next corporate conference, symposium, executive summit, or planning retreat.
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Exploring Career Paths in Cybersecurity for Technical CommunicatorsBen Woelk, CISSP, CPTC
Brief overview of career options in cybersecurity for technical communicators. Includes discussion of my career path, certification options, NICE and NIST resources.
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Explore Careers and College Majors is a new online, interactive, self-guided career, major and college planning system.
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6. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
7. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
8. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
9. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
10. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
11. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
This what data-driven
looks like
12. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
This what data-driven
looks like
• Start with beliefs and the idea you
want to support
13. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
This what data-driven
looks like
• Start with beliefs and the idea you
want to support
• Request more data than what you
really need
14. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
This what data-driven
looks like
• Start with beliefs and the idea you
want to support
• Request more data than what you
really need
• Cherry-pick the data that justifies
your prior beliefs, discard the rest
15. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
This what data-driven
looks like
• Start with beliefs and the idea you
want to support
• Request more data than what you
really need
• Cherry-pick the data that justifies
your prior beliefs, discard the rest
• Make your business case
16. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
This what data-driven
looks like
• Start with beliefs and the idea you
want to support
• Request more data than what you
really need
• Cherry-pick the data that justifies
your prior beliefs, discard the rest
• Make your business case
• Implement changes
17. Data-driven vs data-justified
• Start with a business question
• Brainstorm about the data we will
need
• Implement the data capture
• Formulate an hypothesis
• Run a test
• Make a recommendation
• Implement changes
This what data-driven
looks like
• Start with beliefs and the idea you
want to support
• Request more data than what you
really need
• Cherry-pick the data that justifies
your prior beliefs, discard the rest
• Make your business case
• Implement changes
This is what data-justified
looks like
27. Analytics talent shortage
Let’s build an analytics team with internal employees
• Great understanding of the business
• Poor data literacy and objectivity
28. Analytics talent shortage
Let’s build an analytics team with internal employees
• Great understanding of the business
• Poor data literacy and objectivity
Let’s hire external talent
29. Analytics talent shortage
Let’s build an analytics team with internal employees
• Great understanding of the business
• Poor data literacy and objectivity
Let’s hire external talent
• Hard to find, expensive
30. Analytics talent shortage
Let’s build an analytics team with internal employees
• Great understanding of the business
• Poor data literacy and objectivity
Let’s hire external talent
• Hard to find, expensive
• Lack of domain knowledge
42. Making your own luck
• Data-justified companies can only recruit by taking advantage of ill-
informed analysts
43. Making your own luck
• Data-justified companies can only recruit by taking advantage of ill-
informed analysts
• The analysts, sick of boritoring, start building networks and
44. Making your own luck
• Data-justified companies can only recruit by taking advantage of ill-
informed analysts
• The analysts, sick of boritoring, start building networks and exchange
opinions and information about better places to work for and
45. Making your own luck
• Data-justified companies can only recruit by taking advantage of ill-
informed analysts
• The analysts, sick of boritoring, start building networks and exchange
opinions and information about better places to work for and how
much they are worth
46. Making your own luck
• Data-justified companies can only recruit by taking advantage of ill-
informed analysts
• The analysts, sick of boritoring, start building networks and exchange
opinions and information about better places to work for and how
much they are worth
• Experienced analytics practitioners know which managers have a
proven data-driven record
47. Making your own luck
• Data-justified companies can only recruit by taking advantage of ill-
informed analysts
• The analysts, sick of boritoring, start building networks and exchange
opinions and information about better places to work for and how
much they are worth
• Experienced analytics practitioners know which managers have a
proven data-driven record. Anybody else, the answer is нет (nyet)
50. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
51. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
• Geeks deemed as unsuitable
candidates for managerial roles
52. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
• Geeks deemed as unsuitable
candidates for managerial roles
• Hard to replace an analyst once
promoted because of talent
scarcity
53. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
• Geeks deemed as unsuitable
candidates for managerial roles
• Hard to replace an analyst once
promoted because of talent
scarcity
I can’t become a head of analytics?
Oh well, hello data science!
54. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
• Geeks deemed as unsuitable
candidates for managerial roles
• Hard to replace an analyst once
promoted because of talent
scarcity
I can’t become a head of analytics?
Oh well, hello data science!
• Every year Big Four consultants
look for client-side manager roles
55. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
• Geeks deemed as unsuitable
candidates for managerial roles
• Hard to replace an analyst once
promoted because of talent
scarcity
I can’t become a head of analytics?
Oh well, hello data science!
• Every year Big Four consultants
look for client-side manager roles
• They will then rotate every couple
of years until a CXO role
opportunity comes
56. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
• Geeks deemed as unsuitable
candidates for managerial roles
• Hard to replace an analyst once
promoted because of talent
scarcity
I can’t become a head of analytics?
Oh well, hello data science!
• Every year Big Four consultants
look for client-side manager roles
• They will then rotate every couple
of years until a CXO role
opportunity comes
• Therefore prior analytics
experience is irrelevant and
perhaps even bad
57. Managers with analytics skills?
• Many top-performing employees
fail their transition to
management
• Geeks deemed as unsuitable
candidates for managerial roles
• Hard to replace an analyst once
promoted because of talent
scarcity
I can’t become a head of analytics?
Oh well, hello data science!
• Every year Big Four consultants
look for client-side manager roles
• They will then rotate every couple
of years until a CXO role
opportunity comes
• Therefore prior analytics
experience is irrelevant and
perhaps even bad
Head of analytics? What the heck is
that? I will rotate in 2 years, right?
60. Expert leadership
More and more experienced analytics practitioners are finally getting
promoted Head of Analytics and implement a genuinely data-driven
approach and transform the analytics department into a profit centre
61. Expert leadership
More and more experienced analytics practitioners are finally getting
promoted Head of Analytics and implement a genuinely data-driven
approach and transform the analytics department into a profit centre
Expert leaders are a great motivator for more junior analysts who can
look up to someone who was just like them 5 or 10 years ago
62. Expert leadership
More and more experienced analytics practitioners are finally getting
promoted Head of Analytics and implement a genuinely data-driven
approach and transform the analytics department into a profit centre
Expert leaders are a great motivator for more junior analysts who can
look up to someone who was just like them 5 or 10 years ago
In cities where flats are ridiculously expensive, expert leadership could
help a mid-weight analyst stop renting and get a mortgage instead
65. Remember this?
What if all the web and data analysts worked only for data-driven
companies?
66. Remember this?
What if all the web and data analysts worked only for data-driven
companies?
If you are working in a data-justified department
67. Remember this?
What if all the web and data analysts worked only for data-driven
companies?
If you are working in a data-justified department, this
department only exists
68. Remember this?
What if all the web and data analysts worked only for data-driven
companies?
If you are working in a data-justified department, this
department only exists because you and your
colleagues took their job
69. Remember this?
What if all the web and data analysts worked only for data-driven
companies?
If you are working in a data-justified department, this
department only exists because you and your
colleagues took their job instead of the same job but
at a data-driven company
72. Nobody wants to work for us?
• I told him “That’s how we do web analytics here”. A week later, he
handed me his resignation, he had three job offers elsewhere. He was
still in his probation period
73. Nobody wants to work for us?
• I told him “That’s how we do web analytics here”. A week later, he
handed me his resignation, he had three job offers elsewhere. He was
still in his probation period
• I don’t understand what’s going on, I’m only getting junior candidates
from the career pages and the recruiters say that nobody is interested
74. Nobody wants to work for us?
• I told him “That’s how we do web analytics here”. A week later, he
handed me his resignation, he had three job offers elsewhere. He was
still in his probation period
• I don’t understand what’s going on, I’m only getting junior candidates
from the career pages and the recruiters say that nobody is interested
• I thought the interview went well, she was a strong candidate. Then
the recruiter said she told him after that I could not name one single
thought-leader in analytics and she won’t work for us
79. Identify data-justified companies
• Find other people in analytics
• Figure out how much you are really worth
• Identify the companies and managers who are data-driven in
our field
80. Identify data-justified companies
• Find other people in analytics
• Figure out how much you are really worth
• Identify the companies and managers who are data-driven in
our field
• When a company is hiring, try to find the name of the
manager and check their credentials and reputation
81. Identify data-justified companies
• Find other people in analytics
• Figure out how much you are really worth
• Identify the companies and managers who are data-driven in
our field
• When a company is hiring, try to find the name of the
manager and check their credentials and reputation
• A company had Adobe Analytics and migrated to Google
Analytics = symptom of a company that could not deliver
value from analytics
84. At your next interview, ask them
• So, what’s your definition of analytics?
85. At your next interview, ask them
• So, what’s your definition of analytics?
• Can you name one thought-leader in the field of analytics?
86. At your next interview, ask them
• So, what’s your definition of analytics?
• Can you name one thought-leader in the field of analytics?
• What’s the last analytics blog or book you have read in the
past 3 months?
87. At your next interview, ask them
• So, what’s your definition of analytics?
• Can you name one thought-leader in the field of analytics?
• What’s the last analytics blog or book you have read in the
past 3 months?
If they answer wrong, they fail the interview