Building a Data-Driven Culture by Olof Hoverfält discusses how to build a data-driven culture at Sanoma Games. Key points include:
1) Being data-driven requires an organization that supports lean development, a data-driven culture with accessible tools, shared goals, and management that fosters self-direction.
2) A data-driven culture is built through intrinsic motivation by making the benefits of data visible, not through coercion. Transparency, autonomy and ownership are important.
3) Continuous hypothesis-driven testing should be the standard approach across functions to gain insights and steer development initiatives toward business goals.
You Can Hack That: How to Use Hackathons to Solve Your Toughest ChallengesBooz Allen Hamilton
“Hackathon” has become a trendy word in today’s business vernacular, and for good reason. The word “hackathon” comes from both “hack” and “marathon.” If you think of a “hack” as a creative solution and “marathon” as a continuous, often competitive event, you’re at the heart of what a hackathon is about. Hackathons enable creative problem solving through an innovative and often competitive structure that engages stakeholders to come up with unconventional solutions to pressing challenges. Hackathons can be used to develop new processes, products, ways of thinking, or ways of engaging stakeholders and partners, with benefits ranging from solving tough problems to broader cultural and organizational improvements.
This playbook was designed to make hackathons accessible to everyone. That means not only can all kinds of organizations benefit from hackathons, but that all kinds of employees inside those groups—executives, project managers, designers, or engineers—should participate and can benefit, too. Use this playbook as a reference and allow the best practices we outline to guide you in designing a hackathon structure that works for you and enables your organization to achieve its desired outcomes. Give yourself anywhere from six weeks to a few months to plan your hackathon, depending on the components, approach, number of participants, and desired outcomes.
Contact Director Brian MacCarthy at MacCarthy_Brian2@bah.com for more information about Booz Allen’s hackathon offering.
What are top industry experts saying about privacy regulations, the future of digital analytics, and improving data quality?
What are other leading analytics teams doing to foster success?
What strategies can you implement to improve your analytics implementations?
Answers to these questions help analysts and organizations improve their data quality to create better user experiences, expand their brand influence, and increase revenue.
The best part, you can find answers in this ebook from leaders like James McCormick from Forrester, Adam Greco and Michele Kiss from Analytics Demystified, Krista Seiden from Quantcast, and many others. You will also gain insights from other analytics teams who have shared their personal tips and tricks to hack the analytics problems analysts face daily. You’ll discover how to:
Implement strategies to put the customer first to create better user experiences.
How to improve your data intelligence maturity to increase ROI.
Getting executive buy-in to increase the importance of data quality within your organization.
And so much more.
This document summarizes a roundtable discussion about breaking down silos between primary research and platform analytics teams within organizations. Key discussion points included that 1) traditional organizational structures designed for the industrial age are preventing companies from leveraging all of their data, 2) there is often overinvestment in technology but underinvestment in developing employee skills, and 3) creating a truly data-driven culture requires openness, collaboration, and experimentation across departments beyond just research and analytics. The roundtable participants explored how blending skills and exposing employees to different business functions can help companies overcome data silos.
COVID-19 has had a wide-ranging impact on the economy. Chisel Analytics surveyed over 100 leaders in analytics for their perspectives and the adjustments they're making in response.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
Workfront - 9 Experts on How to Align IT's Work to Company StrategyMighty Guides, Inc.
IT teams often struggle to align with business priorities due to over-reliance on technical subject matter experts and lack of business skills. IT leaders must transition teams to focus on being business-minded problem solvers rather than technical experts. This involves adopting a service-aligned model and ensuring the right people fill product management roles to interface with the business. By changing the skills and mindset of those in IT, leaders can better position teams to strategically support organizational goals.
This document discusses how data and analytics can enable better decision making across businesses. It notes that while data-driven companies are more likely to report improved decision making, only 1 in 3 executives say their organization is highly data-driven. It also discusses challenges such as barriers related to skills and understanding data, and how most companies have not matured in their data analytics capabilities. The document advocates combining data science with business experience and judgment to make the best decisions.
Building a Data-Driven Culture by Olof Hoverfält discusses how to build a data-driven culture at Sanoma Games. Key points include:
1) Being data-driven requires an organization that supports lean development, a data-driven culture with accessible tools, shared goals, and management that fosters self-direction.
2) A data-driven culture is built through intrinsic motivation by making the benefits of data visible, not through coercion. Transparency, autonomy and ownership are important.
3) Continuous hypothesis-driven testing should be the standard approach across functions to gain insights and steer development initiatives toward business goals.
You Can Hack That: How to Use Hackathons to Solve Your Toughest ChallengesBooz Allen Hamilton
“Hackathon” has become a trendy word in today’s business vernacular, and for good reason. The word “hackathon” comes from both “hack” and “marathon.” If you think of a “hack” as a creative solution and “marathon” as a continuous, often competitive event, you’re at the heart of what a hackathon is about. Hackathons enable creative problem solving through an innovative and often competitive structure that engages stakeholders to come up with unconventional solutions to pressing challenges. Hackathons can be used to develop new processes, products, ways of thinking, or ways of engaging stakeholders and partners, with benefits ranging from solving tough problems to broader cultural and organizational improvements.
This playbook was designed to make hackathons accessible to everyone. That means not only can all kinds of organizations benefit from hackathons, but that all kinds of employees inside those groups—executives, project managers, designers, or engineers—should participate and can benefit, too. Use this playbook as a reference and allow the best practices we outline to guide you in designing a hackathon structure that works for you and enables your organization to achieve its desired outcomes. Give yourself anywhere from six weeks to a few months to plan your hackathon, depending on the components, approach, number of participants, and desired outcomes.
Contact Director Brian MacCarthy at MacCarthy_Brian2@bah.com for more information about Booz Allen’s hackathon offering.
What are top industry experts saying about privacy regulations, the future of digital analytics, and improving data quality?
What are other leading analytics teams doing to foster success?
What strategies can you implement to improve your analytics implementations?
Answers to these questions help analysts and organizations improve their data quality to create better user experiences, expand their brand influence, and increase revenue.
The best part, you can find answers in this ebook from leaders like James McCormick from Forrester, Adam Greco and Michele Kiss from Analytics Demystified, Krista Seiden from Quantcast, and many others. You will also gain insights from other analytics teams who have shared their personal tips and tricks to hack the analytics problems analysts face daily. You’ll discover how to:
Implement strategies to put the customer first to create better user experiences.
How to improve your data intelligence maturity to increase ROI.
Getting executive buy-in to increase the importance of data quality within your organization.
And so much more.
This document summarizes a roundtable discussion about breaking down silos between primary research and platform analytics teams within organizations. Key discussion points included that 1) traditional organizational structures designed for the industrial age are preventing companies from leveraging all of their data, 2) there is often overinvestment in technology but underinvestment in developing employee skills, and 3) creating a truly data-driven culture requires openness, collaboration, and experimentation across departments beyond just research and analytics. The roundtable participants explored how blending skills and exposing employees to different business functions can help companies overcome data silos.
COVID-19 has had a wide-ranging impact on the economy. Chisel Analytics surveyed over 100 leaders in analytics for their perspectives and the adjustments they're making in response.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
Workfront - 9 Experts on How to Align IT's Work to Company StrategyMighty Guides, Inc.
IT teams often struggle to align with business priorities due to over-reliance on technical subject matter experts and lack of business skills. IT leaders must transition teams to focus on being business-minded problem solvers rather than technical experts. This involves adopting a service-aligned model and ensuring the right people fill product management roles to interface with the business. By changing the skills and mindset of those in IT, leaders can better position teams to strategically support organizational goals.
This document discusses how data and analytics can enable better decision making across businesses. It notes that while data-driven companies are more likely to report improved decision making, only 1 in 3 executives say their organization is highly data-driven. It also discusses challenges such as barriers related to skills and understanding data, and how most companies have not matured in their data analytics capabilities. The document advocates combining data science with business experience and judgment to make the best decisions.
A presentation about a few current consumer trends and how they may apply to charities, covering AR (but not VR), data and AI, transparency and growth hacking.
Change Management: Expanding OMS Use Within Your OrganizationCartegraph
People are naturally resistant to change, so how do you expand Cartegraph OMS use within your organization? Uncover strategies for tracking and improving your organization’s OMS use, and learn how to establish and evaluate your own organizational standards with Cartegraph data.
1) Capturing and sharing lessons learned from past projects is challenging with traditional methods. Lessons are often lost once projects end and teams disperse to new work.
2) An AI/ML system could automatically capture and codify lessons from project data to provide knowledge continuity across projects. This helps prevent the same problems from reoccurring.
3) Providing easy access to insights from past similar projects could help project managers address challenges more effectively than relying only on their own experience.
The document summarizes key points from a business analytics conference. It discusses how analytics has become more important and useful due to increased data and new tools. While analytics is helping organizations, clear business needs and leadership support are still needed to ensure insights are properly used. There is a shortage of analytics talent, and on-the-job training is critical for developing skills. India has a large share of the analytics outsourcing market but can move further up the value chain through faster delivery and challenging itself.
Digital Employee Experience Breakfast - 28th March BrisbaneSquiz
A great digital employee experience provides many benefits such as improved productivity, higher employee retention rates and technology that adapts to each workforce. However, making this a reality is easier said than done.
Learn about:
How increasing employee digital literacy helps organisations to remain competitive, up skill their workforce for the future and incorporate new technologies into the organisation
The benefits of allowing employees to choose the way they work with technology, resulting in employee acquisition, satisfaction and retention
How the digital employee experience impacts the customer experience
The importance of a digital workplace to foster employee collaboration and engagement, make searching for people and files easy, while integrating with existing internal systems
CUSTOMER JOURNEY
Der Weg des Kunden zum Produkt umspannt Touchpoints in Online, Mobile, In-Store, Kundencenter, sozialen Netzen, Werbung im TV, Print, auf Plakaten, im Radio – einfach gesagt: Er sieht alles und das überall. Wie man dem Konsumenten trotzdem ein sinnvolles Bild der Marke gibt und ein unverwechselbares Angebot schafft. Wir begleiten den Konsumenten auf seinem Weg zum Kauf.
Matthew has a consistent record of under-achievement. His professional incompetence, intellectual torpor and general sloth are complemented by appalling communication skills. He is widely regarded as the most dysfunctional and unpopular member of any team he has ever worked in.
Through a combination of incompetence and corruption on the part of the admissions board, he was elected to a scholarship at Magdalen College Oxford, where he read English and Modern Languages. His undistinguished academic career was a surprise and disappointment to all except those who knew him.
His initial career in the textile industry was marked by a series of over-hyped marketing transformation initiatives, which delivered no business value and alienated customers and employees alike. On the back of these disastrous projects, there was nowhere to hide but the software industry.
His subsequent career in software has been one of spectacular failure and meteoric descent. In a series of ill-judged and unimaginative moves, he has helped destroy the value of marketing applications right across the industry. It is difficult to gauge which has been less impressive, his time in product management or in presales consulting. At every level, he has exuded arrogance and incompetence.
His promotion to Vice President at Oracle in December 2011 was welcomed by close colleagues as ‘an utter travesty’ and hailed as ‘a clear mistake’. With his new broader focus on Customer Experience Management solutions, across a wide range of industries – encompassing all of Oracle applications and technologies – he has the opportunity to wreak havoc on a truly global scale. It is a worrying new juncture in a dismal career.
Matthew’s self-knowledge is his only redeeming feature.
This document summarizes the key findings from the 2016 O'Reilly Data Science Salary Survey, which collected responses from 983 data professionals. Some of the main findings include: Python and Spark contribute most to salary; those who code more earn higher salaries; SQL, Excel, R and Python are the most commonly used tools; attending more meetings correlates with higher pay; women earn less than men for the same work; and geographic location, as measured by GDP, serves as a proxy for salary variation. The report also clusters respondents based on their tool usage and tasks to identify subgroups.
1) Effective campaign execution requires clarity, collaboration, and communication. Marketing teams must have a clear understanding of campaign goals, audiences, and objectives.
2) Collaboration is important from the early strategy phase by incorporating diverse viewpoints. This helps balance best practices with innovation and prevents disruptions.
3) Consistent communication is needed internally with stakeholders and externally by setting expectations and providing regular updates. This helps ensure alignment and flawless execution.
The document discusses how modern data analytics are transforming business. It introduces the topic and explains that data is doubling every two years and analytics are becoming more valuable. The rest of the document is organized into five sections that will discuss topics like how analytics are changing business models, new technology platforms, industry examples, research, and marketing. The introduction of each section provides a brief overview of what essays in that section will cover. The overall goal is to provide insights from different perspectives on how analytics are rapidly evolving and playing an increasingly important role.
Case Study: Analytics at CMC Markets: from measuring clicks to driving businessJohn Sinke
CMC Markets struggled with inconsistent analytics across websites and channels that led to double counting. They implemented Google Analytics for web analytics and campaign tracking across their portfolio for consistent measurement. They also implemented a single channel analytics provider to de-duplicate marketing costs and provide attribution modeling. This improved their understanding of customer acquisition channels, lifetime value, and drove business decisions to improve sales.
This document provides a summary of presentations from a conference on information management. It includes 20 brief presentations on various topics such as collaboration strategies, challenges of implementing automated retention, generational differences in technology adoption, and lessons learned from digital transformation projects. The document closes by thanking attendees and saying see you in Orlando in 2017, suggesting it is a summary of a past conference.
This takes a look at the architectural constructs that are used for building business intelligence systems and how they are used in business processes to improve marketing, better serve customers, and maximize organizational efficiency.
- Data and analytics capabilities have advanced significantly in recent years due to growing data volumes, more powerful algorithms, and improved computing power. However, most companies have captured only a fraction of the potential value identified in 2011.
- Organizational challenges are the biggest barriers to extracting more value from data. Talent shortages, especially of "business translators" who can apply analytics, also limit progress.
- Data and analytics are changing the basis of competition by enabling new business models and winner-take-all dynamics in some industries. Leading companies use analytics not just to improve operations but to disrupt entire industries.
The document provides findings from a survey of 150 data leaders about leveraging big data at their companies. Key findings include:
- Nearly all respondents believe their company is headed in the right direction with big data due to investments in new talent, technologies, and alignment on usage. However, only about half say big data is extensively leveraged across all business units currently.
- Respondents face challenges like data silos, legacy storage systems, and a lack of standardized measurement for big data initiatives. Finding skilled analytics talent is also difficult.
- Resources needed for success vary but include better tools, centralized talent management, and more information on the business value of big data.
Three key points from the document:
1) Self-direction requires more than just autonomy and trust - it requires a work environment that enables employees to improve, a data-driven culture, operational self-direction, and management that fosters self-direction.
2) Building a self-directed organization is challenging but has significant benefits like increased engagement and adaptability if successful. It requires a long term view due to initial performance risks.
3) In an increasingly changing environment, a flexible constellation of talented humans is better suited than static organizational structures. Efforts should optimize human capital yield by reflecting value creation opportunities.
This document summarizes a webinar hosted by the OR Society on making sense of data. It provides an agenda for the webinar including a welcome, a presentation on top tips for making sense of data, and a Q&A session. The presentation provides tips on deciding what to measure based on objectives, following measurement rules of recording and taking action, examining data distributions, separating vital from trivial factors, using moving averages for time-series data, and considering what the data does not show. It also discusses different types of averages, Pareto analysis, and examples of simple data collection tools. The webinar promotes the Pro Bono OR program which connects volunteers with charities for operational research projects.
This report proposes implementing a collaboration solution to improve information sharing, communication, and productivity within the company. It identifies key needs like increased workforce productivity, faster product development timelines, and streamlined communication. The current system of information storage in directories and informal knowledge sharing is inefficient. A collaboration environment could capture knowledge, facilitate project management and processes, and integrate with existing applications. This would help onboard new employees more quickly, reduce time spent searching for information, and empower employees and teams to work more effectively together.
7 Dimensions of Agile Analytics by Ken Collier Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself. Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities. The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This deck will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
sophisticated analytics techniques, plus
lean learning principles, plus
agile delivery methods, plus
so-called "big data" technologies
Learn:
The analytical modeling process and techniques
How analytical models are deployed using modern technologies
The complexities of data discovery, harvesting, and preparation
How to apply agile techniques to shorten the analytics development cycle
How to apply lean learning principles to develop actionable and valuable analytics.
Marcus Baker: People Analytics at Scale
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Cost & benefits of business analytics marshall sponderMarshall Sponder
The document discusses turning data into useful business insights through business intelligence and data enablement approaches. It advocates starting with departmental BI systems and linking them together, while also taking an "enablement" approach to integrate data from different silos. The document recommends conducting a data enablement audit to map data sources, identify measurement gaps, and develop standardized reporting to provide insights for objectives like sales, lead generation, and brand awareness. It emphasizes selecting the right team and approach to optimize the degree of insights that can be gained from enterprise data.
Bob Selfridge - Identify, Collect, and Act Upon Customer Interactions; Rinse,...Julia Grosman
This document discusses building a customer intelligence practice using an agile process. It recommends starting with reference data and supplementing it with transactional and subjective data. The agile process involves continuously defining needs, designing specifications, developing solutions, and delivering working solutions to measure return on investment. Key aspects of the agile approach include collaboration over negotiation, responding quickly to change, valuing working solutions over documentation, and frequent delivery of software.
A presentation about a few current consumer trends and how they may apply to charities, covering AR (but not VR), data and AI, transparency and growth hacking.
Change Management: Expanding OMS Use Within Your OrganizationCartegraph
People are naturally resistant to change, so how do you expand Cartegraph OMS use within your organization? Uncover strategies for tracking and improving your organization’s OMS use, and learn how to establish and evaluate your own organizational standards with Cartegraph data.
1) Capturing and sharing lessons learned from past projects is challenging with traditional methods. Lessons are often lost once projects end and teams disperse to new work.
2) An AI/ML system could automatically capture and codify lessons from project data to provide knowledge continuity across projects. This helps prevent the same problems from reoccurring.
3) Providing easy access to insights from past similar projects could help project managers address challenges more effectively than relying only on their own experience.
The document summarizes key points from a business analytics conference. It discusses how analytics has become more important and useful due to increased data and new tools. While analytics is helping organizations, clear business needs and leadership support are still needed to ensure insights are properly used. There is a shortage of analytics talent, and on-the-job training is critical for developing skills. India has a large share of the analytics outsourcing market but can move further up the value chain through faster delivery and challenging itself.
Digital Employee Experience Breakfast - 28th March BrisbaneSquiz
A great digital employee experience provides many benefits such as improved productivity, higher employee retention rates and technology that adapts to each workforce. However, making this a reality is easier said than done.
Learn about:
How increasing employee digital literacy helps organisations to remain competitive, up skill their workforce for the future and incorporate new technologies into the organisation
The benefits of allowing employees to choose the way they work with technology, resulting in employee acquisition, satisfaction and retention
How the digital employee experience impacts the customer experience
The importance of a digital workplace to foster employee collaboration and engagement, make searching for people and files easy, while integrating with existing internal systems
CUSTOMER JOURNEY
Der Weg des Kunden zum Produkt umspannt Touchpoints in Online, Mobile, In-Store, Kundencenter, sozialen Netzen, Werbung im TV, Print, auf Plakaten, im Radio – einfach gesagt: Er sieht alles und das überall. Wie man dem Konsumenten trotzdem ein sinnvolles Bild der Marke gibt und ein unverwechselbares Angebot schafft. Wir begleiten den Konsumenten auf seinem Weg zum Kauf.
Matthew has a consistent record of under-achievement. His professional incompetence, intellectual torpor and general sloth are complemented by appalling communication skills. He is widely regarded as the most dysfunctional and unpopular member of any team he has ever worked in.
Through a combination of incompetence and corruption on the part of the admissions board, he was elected to a scholarship at Magdalen College Oxford, where he read English and Modern Languages. His undistinguished academic career was a surprise and disappointment to all except those who knew him.
His initial career in the textile industry was marked by a series of over-hyped marketing transformation initiatives, which delivered no business value and alienated customers and employees alike. On the back of these disastrous projects, there was nowhere to hide but the software industry.
His subsequent career in software has been one of spectacular failure and meteoric descent. In a series of ill-judged and unimaginative moves, he has helped destroy the value of marketing applications right across the industry. It is difficult to gauge which has been less impressive, his time in product management or in presales consulting. At every level, he has exuded arrogance and incompetence.
His promotion to Vice President at Oracle in December 2011 was welcomed by close colleagues as ‘an utter travesty’ and hailed as ‘a clear mistake’. With his new broader focus on Customer Experience Management solutions, across a wide range of industries – encompassing all of Oracle applications and technologies – he has the opportunity to wreak havoc on a truly global scale. It is a worrying new juncture in a dismal career.
Matthew’s self-knowledge is his only redeeming feature.
This document summarizes the key findings from the 2016 O'Reilly Data Science Salary Survey, which collected responses from 983 data professionals. Some of the main findings include: Python and Spark contribute most to salary; those who code more earn higher salaries; SQL, Excel, R and Python are the most commonly used tools; attending more meetings correlates with higher pay; women earn less than men for the same work; and geographic location, as measured by GDP, serves as a proxy for salary variation. The report also clusters respondents based on their tool usage and tasks to identify subgroups.
1) Effective campaign execution requires clarity, collaboration, and communication. Marketing teams must have a clear understanding of campaign goals, audiences, and objectives.
2) Collaboration is important from the early strategy phase by incorporating diverse viewpoints. This helps balance best practices with innovation and prevents disruptions.
3) Consistent communication is needed internally with stakeholders and externally by setting expectations and providing regular updates. This helps ensure alignment and flawless execution.
The document discusses how modern data analytics are transforming business. It introduces the topic and explains that data is doubling every two years and analytics are becoming more valuable. The rest of the document is organized into five sections that will discuss topics like how analytics are changing business models, new technology platforms, industry examples, research, and marketing. The introduction of each section provides a brief overview of what essays in that section will cover. The overall goal is to provide insights from different perspectives on how analytics are rapidly evolving and playing an increasingly important role.
Case Study: Analytics at CMC Markets: from measuring clicks to driving businessJohn Sinke
CMC Markets struggled with inconsistent analytics across websites and channels that led to double counting. They implemented Google Analytics for web analytics and campaign tracking across their portfolio for consistent measurement. They also implemented a single channel analytics provider to de-duplicate marketing costs and provide attribution modeling. This improved their understanding of customer acquisition channels, lifetime value, and drove business decisions to improve sales.
This document provides a summary of presentations from a conference on information management. It includes 20 brief presentations on various topics such as collaboration strategies, challenges of implementing automated retention, generational differences in technology adoption, and lessons learned from digital transformation projects. The document closes by thanking attendees and saying see you in Orlando in 2017, suggesting it is a summary of a past conference.
This takes a look at the architectural constructs that are used for building business intelligence systems and how they are used in business processes to improve marketing, better serve customers, and maximize organizational efficiency.
- Data and analytics capabilities have advanced significantly in recent years due to growing data volumes, more powerful algorithms, and improved computing power. However, most companies have captured only a fraction of the potential value identified in 2011.
- Organizational challenges are the biggest barriers to extracting more value from data. Talent shortages, especially of "business translators" who can apply analytics, also limit progress.
- Data and analytics are changing the basis of competition by enabling new business models and winner-take-all dynamics in some industries. Leading companies use analytics not just to improve operations but to disrupt entire industries.
The document provides findings from a survey of 150 data leaders about leveraging big data at their companies. Key findings include:
- Nearly all respondents believe their company is headed in the right direction with big data due to investments in new talent, technologies, and alignment on usage. However, only about half say big data is extensively leveraged across all business units currently.
- Respondents face challenges like data silos, legacy storage systems, and a lack of standardized measurement for big data initiatives. Finding skilled analytics talent is also difficult.
- Resources needed for success vary but include better tools, centralized talent management, and more information on the business value of big data.
Three key points from the document:
1) Self-direction requires more than just autonomy and trust - it requires a work environment that enables employees to improve, a data-driven culture, operational self-direction, and management that fosters self-direction.
2) Building a self-directed organization is challenging but has significant benefits like increased engagement and adaptability if successful. It requires a long term view due to initial performance risks.
3) In an increasingly changing environment, a flexible constellation of talented humans is better suited than static organizational structures. Efforts should optimize human capital yield by reflecting value creation opportunities.
This document summarizes a webinar hosted by the OR Society on making sense of data. It provides an agenda for the webinar including a welcome, a presentation on top tips for making sense of data, and a Q&A session. The presentation provides tips on deciding what to measure based on objectives, following measurement rules of recording and taking action, examining data distributions, separating vital from trivial factors, using moving averages for time-series data, and considering what the data does not show. It also discusses different types of averages, Pareto analysis, and examples of simple data collection tools. The webinar promotes the Pro Bono OR program which connects volunteers with charities for operational research projects.
This report proposes implementing a collaboration solution to improve information sharing, communication, and productivity within the company. It identifies key needs like increased workforce productivity, faster product development timelines, and streamlined communication. The current system of information storage in directories and informal knowledge sharing is inefficient. A collaboration environment could capture knowledge, facilitate project management and processes, and integrate with existing applications. This would help onboard new employees more quickly, reduce time spent searching for information, and empower employees and teams to work more effectively together.
7 Dimensions of Agile Analytics by Ken Collier Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself. Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities. The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This deck will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
sophisticated analytics techniques, plus
lean learning principles, plus
agile delivery methods, plus
so-called "big data" technologies
Learn:
The analytical modeling process and techniques
How analytical models are deployed using modern technologies
The complexities of data discovery, harvesting, and preparation
How to apply agile techniques to shorten the analytics development cycle
How to apply lean learning principles to develop actionable and valuable analytics.
Marcus Baker: People Analytics at Scale
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Cost & benefits of business analytics marshall sponderMarshall Sponder
The document discusses turning data into useful business insights through business intelligence and data enablement approaches. It advocates starting with departmental BI systems and linking them together, while also taking an "enablement" approach to integrate data from different silos. The document recommends conducting a data enablement audit to map data sources, identify measurement gaps, and develop standardized reporting to provide insights for objectives like sales, lead generation, and brand awareness. It emphasizes selecting the right team and approach to optimize the degree of insights that can be gained from enterprise data.
Bob Selfridge - Identify, Collect, and Act Upon Customer Interactions; Rinse,...Julia Grosman
This document discusses building a customer intelligence practice using an agile process. It recommends starting with reference data and supplementing it with transactional and subjective data. The agile process involves continuously defining needs, designing specifications, developing solutions, and delivering working solutions to measure return on investment. Key aspects of the agile approach include collaboration over negotiation, responding quickly to change, valuing working solutions over documentation, and frequent delivery of software.
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
The document introduces the Data Skills Framework, a tool from the Open Data Institute (ODI) that helps users understand the range of skills needed to work with data effectively. It analyzes current data skills within an organization, identifies any imbalances, and addresses gaps. The framework balances technical data skills with other important skills like service design, innovation, and leadership. Users can apply the framework to assess their organization's current skills strategy and data literacy training programs in order to better develop a balanced set of data skills to meet strategic goals.
How to Modernize Your Data Strategy to Fuel Digital TransformationBrainSell Technologies
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https://alandix.com/academic/papers/synergy2024-epistemic/
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3. WORLDWIDE BUSINESS BUSINESS TO GO CREATIVE SOLUTIONS
WORLDWIDE BUSINESS BUSINESS TO GO CREATIVE SOLUTIONS
What my Friends Think I Do What my Mom Thinks I Do What Society Thinks I Do
What my Boss Think I Do What I Think I Do What I Actually Do
Misconceptions about Data Scientists
3
5. Open Source
& New Tools
Profits Steady ,
Adding Products
Report to VP
Marketing
Non Technical
Culture
First Data
Scientist
What does the organization do
best? How does it relate to
data and technology?
What is the business
core competencies?
What are existing tools,
processes, and code? Do you
have a budget for new tools and
resources?
What Tools are
Available ?
This is both a team members
and expectations related
question.
Where is your Team?
What is the mood of the
organization? How are they
solving problems? Why are they
adding DS/A into the
organization?
What is the State of
the Organization?
Who are the stakeholders?
How is data able to contribute
to their goals and
expectations?
Who has the
Influence On the
Roadmap?
Context for Presentation
Case Study: Startup in Digital Media
5
6. Effectively
Implement
Solutions
Maximize
Impact &
Commun-
ication
Set a Blueprint that
promotes flexibility,
iteration, and
scalability. It facilities
agile-oriented
mindsets for data
practices and it crucial
for implementation.
Build a Roadmap
from Blueprint to
shape data practices
and implement goals
from stakeholders,
company, as well as
strong DS/A
foundations.
Develop key
qualitative and
quantitative
milestones.
Communicate
consistently and
frequently to the
organization.
Influence
Expectations
Influence from both
angles, yours and
stakeholders
expectations. Find
explicit and implicit
goals and bridge the
gaps that you find.
6
Key Drivers Integrating Data Culture
Create an
Agile Data
Science
Stack
Non-technical focused
9. Explicit Goals & Expectations
Structured, straight-forward, logical, and safe
inquiries
Document, share, and openly discuss with team
members and stakeholders.
Jungwoo Hong @ Unsplash
15. IDENTIFY
BUILD SYS &
MODELS
- Select Appropriate Models
- Build Models and Pipelines
for Scalability
- Evaluate and refine Models
ACQUIRE
DATA
- Identify the “right” source
- Import data and set up
remote / local storage
- Determine tools to work
with selected sources
CREATE PROBLEM
STATEMENT
- Identify business, data,
product objectives
- Brainstorm potential
solutions
- Create questions and
identify people/stakeholders
to help
PARSE & MINE DATA
- Determine distribution of
data and necessary
transformations
- Format, clean, splice, etc
- Create new derived data
PRESENT RESULTS
- Summarize Findings
- Add Storytelling aspects
- Identify next questions
and additional analysis
- For teams and
stakeholders
15
AGILE BY PROCESS
Blueprint approach from workflow perspective
ACQUIRE PARSE & MINE PRESENTBUILD DEPLOY
16. IDENTIFY
BUILD SYS &
MODELS + DEPLOY
Leverage platforms that document
models, pipelines, and feature
iterations. Collaboration is a plus.
- Sklearn pipelines
- DS/ML platforms: Yhat,
domino labs, anaconda
ACQUIRE DATA
Curate data from existing sources that
is cleaned, reliable, and automated,
where ETL can be skipped
- Segement.io
- Zapier
- CrowdFlower
- Open Data
CREATE PROBLEM
STATEMENT
Keep most attributes of
this section in-house and
within your team
PARSE & MINE DATA
For the data that cannot be
automated or acquired
cleanly, sklearn pipelines or
open source Luigi
(Spotify) or airflow
(AirBNB) can mitigate this
process.
PRESENT RESULTS
Adopt platforms that allow for
iterations and data mining/
parsing process to feed into
reports and presentations
- Ipython Jupyter
Notebooks
- Dashboards: Looker,
RJMetrics, Tableau
16
SaaS & PaaS Integration
Customize as the Process Increases in Complexity
ACQUIRE PARSE & MINE PRESENTBUILD DEPLOY
18. Burn Rate
Most companies do not widely
broadcast but transparency can put
decisions into perspective for the
organization. Time and urgency can
also be of the essence.
Customer
Acquisition
Cost (CAC)
Illustrates market competitiveness
with your products, services, and
market saturation. Social media ad
platforms can make up a large portion
of these costs.
19. Gross
Profit &
Revenue
Actual revenue & profit after
expenses, investors, and
ongoing costs. If the business
model and product are viable
then the company will be able
to stand on its own without
external capital.
Active Users
Measure the ongoing stickiness
of a service or product. Clearly
define “active” to not
overcompensate first-time, new,
and experimental users. Can
the company move beyond
early adopters and fans?
20. Churn Rate &
Retention
How many people are leaving or
become inactive after a certain
period of time? When in the
customer’s lifetime is churn more
likely to occur? The higher the
expected churn rate, then the
more the company has to spend
on acquiring new customers.
Cumulative
Growth
Cumulative growth puts a long
term and sustainable
perspective to just month over
month growth. Short-term
growth can unabashedly take
over and cause decision
makers to lose sight of an
organization’s mission and
goals.
21. Response
Time
The amount of time teams take
to respond and complete tasks,
which includes bug fixes,
technological improvements,
product upgades, and customer
service. Responsiveness
demonstrates staff and team
dedication, effective allocation of
resources, operational
effectiveness, and no tech debt.
Customer
LIfetime
Value (CLV)
Total dollars from a customer
during the lifetime relationship
with that customer. Intersection
of frequency of customer
purchases, revenue per
customer, acquisition costs.
This measure can have
predictive qualities
23. "Leadership is the art of giving people
a platform for spreading ideas that
work."
-Seth Godin
23
24. Evaluate milestones,
iterate and grow
Month 12
Blueprint for Agile
Data Science and
Analytics Stack
Day 30
Establish clear
measures for success
as widespread as
possible
Day 90
Good first
impressions. Listen
and Learn!
Day 1
Celebrate improvements
to workflow,
effectiveness, and
access
Day 60
Democratize data
access and streamline
measures to external
and internal teams
Month 6
Communicate, Strategize, Communicate...
Connect the Dots
24
25. Anything Else Reporting &
Urgent
Requests
Data
Acquisition,
Cleaning
Exploration &
Analysis,
Reports, &
Presentation
20% 80% 80% 20%
25
Allocate Time & Resources Effectively
Business as Usual Allocation New Data Science Allocation
28. 6
1
2
5Central to the ability to
juggle and balance
responsibility of being the
first/lead data scientist.
Agile Data Science
& Analytics Stack
3
4
Active
Listeni
ng
Influen
ce
Collabora
te with
Metrics
Explore
Implement
Grow
Actionable Agile DS/A Stack is Key to
Success
28