Data has been a hot top for a number of years and, with GDPR looming, it will continue to be a top priority for businesses. But its not just about wrangling big data, creating a unified or single view of the customer, its about building a data-driven culture.
During Effectiveness Week back in November 2016, I spoke at the DMA's “Creating a Data Culture” on the merits and challenges of building effective data-driven cultures within different types of organisations; offering perspectives from working with big brands, agencies and startups.
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Baking analytics into the culture of an organization is not always the easiest thing because it doesn't come intuitively to humans. This presentation was given at Kumpul co-working space in Sanur, Bali and it involves a sharing of my team's experience in building a data-driven culture at TradeGecko.
A Workflow is an automated series of actions that produces a specified outcome. At certain points, it requires actions from users in the form of tasks.
Workflows help people collaborate on assets, automate processes, ...
A workflow is useful in case of:
- Asset approval
- Asset intake
- Issue management
- Escalation by default
- User on-boarding
The design of a workflow starts with defining a process definition. Collibra Data Governance Center uses the Activiti Workflow engine to manage its process definitions.
In this first lesson, I’ll show you how you have to set up the Activiti Workbench.
Targeting towards the health and human services communities, this presentation covers the importance of a data-driven culture, how to identify areas where data can be used to innovate and how to recognize the operational processes you must have in place to fully utilize your data.
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Forget Big Data. It's All About Smart DataAlan McSweeney
This proposes an initial smart data framework and structure to allow the nuggets of value contained in the deluge of largely irrelevant and useless data to be isolated and extracted. It enables your organisation to ask the questions to understand where it should be in terms of its data state and profile and what it should do to achieve the desired skills level across the competency areas of the framework.
Every organisation operates within a data landscape with multiple sources of data relating to its activities that is acquired, transported, stored, processed, retained, analysed and managed. Interactions across the data landscape generate primary data. When you extend the range of possible interactions business processes outside the organisation you generate a lot more data.
Smart data means being:
• Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including organisation structures, roles, devolution and delegation of decision-making, processes and automation
• Smart in being realistic, pragmatic and even skeptical about what can be achieved and knowing what value can be derived and how to maximise value obtained
• Smart in defining an achievable, benefits-lead strategy integrated with the needs business and in its implementation
• Smart in selecting the channels and interactions to include – smart data use cases
Smart data competency areas comprise a complete set of required skills and abilities to design, implement and operate an appropriate smart data programme.
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Baking analytics into the culture of an organization is not always the easiest thing because it doesn't come intuitively to humans. This presentation was given at Kumpul co-working space in Sanur, Bali and it involves a sharing of my team's experience in building a data-driven culture at TradeGecko.
A Workflow is an automated series of actions that produces a specified outcome. At certain points, it requires actions from users in the form of tasks.
Workflows help people collaborate on assets, automate processes, ...
A workflow is useful in case of:
- Asset approval
- Asset intake
- Issue management
- Escalation by default
- User on-boarding
The design of a workflow starts with defining a process definition. Collibra Data Governance Center uses the Activiti Workflow engine to manage its process definitions.
In this first lesson, I’ll show you how you have to set up the Activiti Workbench.
Targeting towards the health and human services communities, this presentation covers the importance of a data-driven culture, how to identify areas where data can be used to innovate and how to recognize the operational processes you must have in place to fully utilize your data.
Slides zum Impuls-Vortrag "Data Strategy & Governance" - BI or DIE LEVEL UP 2022
Aufzeichnung des Vortrags: https://www.youtube.com/watch?v=705DfyfF5-M
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Forget Big Data. It's All About Smart DataAlan McSweeney
This proposes an initial smart data framework and structure to allow the nuggets of value contained in the deluge of largely irrelevant and useless data to be isolated and extracted. It enables your organisation to ask the questions to understand where it should be in terms of its data state and profile and what it should do to achieve the desired skills level across the competency areas of the framework.
Every organisation operates within a data landscape with multiple sources of data relating to its activities that is acquired, transported, stored, processed, retained, analysed and managed. Interactions across the data landscape generate primary data. When you extend the range of possible interactions business processes outside the organisation you generate a lot more data.
Smart data means being:
• Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including organisation structures, roles, devolution and delegation of decision-making, processes and automation
• Smart in being realistic, pragmatic and even skeptical about what can be achieved and knowing what value can be derived and how to maximise value obtained
• Smart in defining an achievable, benefits-lead strategy integrated with the needs business and in its implementation
• Smart in selecting the channels and interactions to include – smart data use cases
Smart data competency areas comprise a complete set of required skills and abilities to design, implement and operate an appropriate smart data programme.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
The growth in the use of technology has led organizations to generate data for which they need Data Analytics to analyze the data to make business decisions.
The presentation includes the following topics:
- Introduction to Data Analytics
- Data Analytics Process
- Data Analytics Skills
- Certifications Information for Data Analytics
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
The Chief Data Officer Agenda: Metrics for Information and Data ManagementDATAVERSITY
Welcome to The Chief Data Officer Agenda, a DATAVERSITY monthly webinar focused on the emerging priorities of the Chief Data Officer (CDO). What issues are CDOs facing now, and what should be on their Agenda. The webinar series is moderated by DATAVERSITY CEO and Founder, Tony Shaw, who will be joined each month by guest experts to discuss the requirements and demands on the burgeoning CDO role.
This month in the series:
The value proposition of enterprise information management is founded on Information being treated as an Asset. Information management professionals concur, but CxOs will say "So what?" In most organizations, they are both right! The conflict starts with one group thinking metaphorically, and the other literally. CDOs know that “Information asset” needs to be more than a metaphor…it has to be actionable. When you’re in charge of the application and value of data, how do you measure that? How do you measure progress? What types of metrics are there and which ones actually work? There is a lot more to measuring the value of information than common ROI.
This presentation will give you some starting points for real information asset management and information economics. You’ll learn some of the techniques being used successfully today, and considerations for quantifying the value and progress of information management. There is a means of reconciliation between the metaphors and reality, and this talk will outline a vision for the future, but with practical steps to help you get there.
Partnerships, People & Proprietary Technology: The 3P’s of the Future Brand B...Pipa Unsworth
Faced with clients moving key services in-house, or working directly with media and marketing technology platforms, what can agencies do to remain relevant and deliver value?
Accelerate Your Career: Creating & Communicating Your Unique Personal BrandPipa Unsworth
You will, throughout your career, have many opportunities to present yourself to new people. People you want to impress (your boss, a new employer). So how do you make the right first impression? Well, it starts way before you ever meet someone. To articulate yourself succinctly, in a meaningful and memorable way, you need to know who you are and what value to have to offer the world. That’s essentially the power of personal branding.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
The growth in the use of technology has led organizations to generate data for which they need Data Analytics to analyze the data to make business decisions.
The presentation includes the following topics:
- Introduction to Data Analytics
- Data Analytics Process
- Data Analytics Skills
- Certifications Information for Data Analytics
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
The Chief Data Officer Agenda: Metrics for Information and Data ManagementDATAVERSITY
Welcome to The Chief Data Officer Agenda, a DATAVERSITY monthly webinar focused on the emerging priorities of the Chief Data Officer (CDO). What issues are CDOs facing now, and what should be on their Agenda. The webinar series is moderated by DATAVERSITY CEO and Founder, Tony Shaw, who will be joined each month by guest experts to discuss the requirements and demands on the burgeoning CDO role.
This month in the series:
The value proposition of enterprise information management is founded on Information being treated as an Asset. Information management professionals concur, but CxOs will say "So what?" In most organizations, they are both right! The conflict starts with one group thinking metaphorically, and the other literally. CDOs know that “Information asset” needs to be more than a metaphor…it has to be actionable. When you’re in charge of the application and value of data, how do you measure that? How do you measure progress? What types of metrics are there and which ones actually work? There is a lot more to measuring the value of information than common ROI.
This presentation will give you some starting points for real information asset management and information economics. You’ll learn some of the techniques being used successfully today, and considerations for quantifying the value and progress of information management. There is a means of reconciliation between the metaphors and reality, and this talk will outline a vision for the future, but with practical steps to help you get there.
Partnerships, People & Proprietary Technology: The 3P’s of the Future Brand B...Pipa Unsworth
Faced with clients moving key services in-house, or working directly with media and marketing technology platforms, what can agencies do to remain relevant and deliver value?
Accelerate Your Career: Creating & Communicating Your Unique Personal BrandPipa Unsworth
You will, throughout your career, have many opportunities to present yourself to new people. People you want to impress (your boss, a new employer). So how do you make the right first impression? Well, it starts way before you ever meet someone. To articulate yourself succinctly, in a meaningful and memorable way, you need to know who you are and what value to have to offer the world. That’s essentially the power of personal branding.
The Rise of the Data-Driven, Customer-Focused BusinessLinkedIn
Exploring Themes from 'The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits' by LinkedIn's Russell Glass & Sean Callahan. Check out a full review on our blog: http://linkd.in/1xNrwCe
The True Meaning of Customer Success - More than Just Usage DataGainsight
Customer Success is About Value, Not Data
Have you considered deploying Customer Success technology but are intimidated by the condition of your data (or lack thereof)?
You’re not alone – some of the biggest hesitations to moving a Customer Success project forward involve the state of customer data, including:
1) Our data is dirty – we’re not ready to touch that beast.
2) Our data is everywhere – I’m not sure where to begin.
3) We don’t have any data.
Well, luckily for all of us, the world has changed. Join Gainsight Chief Customer Officer, Dan Steinman, on Thursday 9/11 for a 50-minute webinar on how Customer Success technology can help your team get proactive about your customer relationships, standardize touch points, and build a scalable foundation for your company’s Customer Success operations.
Specifically, we’ll cover:
Consolidate risks and opportunities to consistently resolve with Customer Lifecycle Cockpit
Communicate centrally to ensure nothing slips through the cracks with Calls to Action
Collaborate across all internal stakeholders with Customer360 Sales, Support, and Mobile views
[@IndeedEng Talk] Diving deeper into data-driven product designindeedeng
Video available at: http://www.youtube.com/watch?v=i8MGTZ3KWmc
At April’s @IndeedEng Talk we introduced Indeed’s philosophy and practice of A/B testing. In this talk, two Indeed product managers will discuss how we used data-driven opportunity analysis and iterative testing to build two products. From product vision to product success, we’ll describe what we tested, how it performed, and what we learned from it. Product managers, designers, and engineers who want to learn how to prioritize product and feature ideas, iterate through tests, or optimize a funnel will find valuable insights to apply to their own products.
Graham Davis is a Senior Product Manager for Employer Products at Indeed. Prior to Indeed, Graham previously worked for several startups and got an MBA from Harvard Business School.
Donald Wysocki is Product Director for Job Search at Indeed. Prior to Indeed, Donald worked at frog design and Microsoft.
Data Driven Healthcare That Work: A Physician Group PerspectiveHealth Catalyst
Crystal Run Healthcare shares their story about using proven strategies to care for patients in an accountable care model by using data to drive those strategies. Gregory A. Spencer, MD, FACP, CMO, and CMIO at Crystal Run Healthcare discusses why they moved towards analytics and data warehousing as well as the 6 requirements their health system had as they searched for a partner: 1) The solution needed to hit the ground running. 2) The solution needed to provide quick, actionable data. 3) There needed to be a library of analytical applications. 4) The healthcare data model needed to be able to evolve. 5) They needed to be taught how to fish for the data. 6) A long-term relationship with the vendor was important
4 Customer Success Data Hacks to Identify your Ideal Customer ProfileGainsight
This slide deck - from a presentation given by Customer Success Evangelist at Gainsight, Lincoln Murphy - focuses on what goes into creating an Ideal Customer Profile and how to use existing Customer Success Data to surface customers most likely to be successful, acquired profitably, with expansion potential, or those most likely to be an advocate for you.
The most successful Enterprise SaaS or subscription companies - in fact, any company that values a long-term and expanding relationship with their customers - know that growing revenue only through new customer acquisition is the less efficient way to scale. Rather, they understand that growing revenue within your existing customer base - through up-sells, cross-sells, and expanded use - is the most profitable way to scale.
In fact, Enterprise SaaS companies that grow revenue - and company valuation - by expanding revenue within their existing customer base also know the key to making this work is to focus on - and operationalize - Customer Success.
A critical guide to selecting metrics to define a data-driven customer success strategy. Here is the table of contents:
- Metrics are for Decisions
- The Nature of Metrics
- Metrics Can Be Difficult
- Customer Success Metrics
Customer Lifetime Value (CLV)
Customer Churn Rate
Net Promoter Score (NPS)
Customer Health Score
Support Ticket Volume
Customer Log-in Counts Customer Acquisition Cost (CAC)
Product Activity Score
CSM Subjective Score
Customer Newsletter CTR
Background Signals
Christopher Penn - Build a Data-Driven Customer JourneyINBOUND
Do you understand the path your customers take toward conversion? Would you know if your customer journey is out of sync with your marketing operations? In this session, you'll learn what the best practice customer journey is for your industry, how to find your specific customers' desired path to conversion, and what next steps to take to optimize your marketing for maximum ROI.
This template will help you codify your customer success strategy. You can read the blog post and download the template here: http://blog.preact.com/customer-success-strategy-template
From customer success metrics to the customer journey, and from the customers' critical path to your customer success team, this template enables you to present your vision to your executive team.
Now that you’ve developed your strategy to optimize customer usage, loyalty and renewal, use this Customer Success Action Plan template to outline the activities you and your team will implement in order to reach your goals.
Acquire Grow & Retain customers - The business imperative for Big DataIBM Software India
The emergence of Big Data and Analytics has changed the way marketing decisions are made. Marketing has moved away from traditional ‘generalisation’ practices such as customer segmentation, geographical targeting etc. and is focussing more on the individual – the ‘Chief Executive Customer’.
Data Driven Culture with Slalom's Director of AnalyticsPromotable
Everyone wants to capture the benefits of big data by making better data driven decision. We are inundated by analytical tools that deliver "insights" and process information quickly.
Although often overlooked, creating a data driven culture is as important as finding the right tools to make data driven decisions. Organizations who skip this foundational element often find their investment in Data tools and personal don't yield the benefits that becoming Data Driven is supposed to unlock.
In this talk you'll learn about why creating a Data Driven culture is vital to every organization and the starting point for ensuring your data strategy generates strong impact and ROI.
Takeaways:
What is a data driven culture? Where does it start?
What happens when you implement tools (tableau, power BI, Machine Learning, etc) without first having a data driven culture
Stages of a Data Driven Culture?
How to get started?
Your Instructor: Kevin Chapin is the Practice Director for Data and Analytics at Slalom Consulting.
To see the full talk, click here: https://www.youtube.com/watch?v=7xNLgiK31Is
The purpose of this whitepaper is to enable businesses to leverage data and insights to increase efficiency, provide seamless experiences, build a data-driven culture, empower automation, data utilization at scale and use programmatic advertising to laser target relevant audience. Incorporating winning strategies, this research paper will allow you to better organize, analyze and apply data in every operation.
The enterprise marketer's playbook: Building an integrated data strategy.
An integrated data strategy can help any business see customer journeys more clearly ― and then give customers more relevant ads and experiences that get results. So why doesn't everyone have such a strategy? We look at what sets the marketing leaders apart.
Let marketing data be your guide
If you've ever felt too swamped by data to find the customer insights you need, you're not alone. But there's a new and better approach to gaining deeper audience insights: building an integrated data strategy.
Read this report to learn how:
86% of senior executives agree that eliminating organizational silos is critical to expanding the use of data and analytics in decision-making.
75% of marketers agree that lack of education and training on data and analytics is the biggest barrier to more business decisions being made based on data insights.
Leading marketers are 59% more likely to use digital analytics to optimize the user experience in real time.
Data set Improve your business with your own business dataData-Set
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
-Gain an understanding of how to take advantage of the existing data you already have
-Comprehend the location of where internal data already lies within your company
-Improve your knowledge on how data can help build your brand
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. DMA: Creating a Data Culture | 3 November 2016
Pipa Unsworth @peepa @verveiq
2.
3. AGENCIES
We work with digital,
integrated and media
agencies looking to create
or upgrade their customer
engagement and CRM
capabilities.
BRANDS
We work with innovative
brands looking to transform
their business and
modernise their CX/CRM
strategy and data-driven
marketing activities.
3
STARTUPS
We work with pioneering,
early-stage companies that
are seeking to find their
product-market fit, engage
with their audience and
scale their business.
VerveIQ is strategic growth consultancy that helps brands, agencies and startups build meaningful
propositions and valuable relationships that deliver sustainable growth.
www.verveiq.com
4. The most significant culture shift today for marketing teams is
adopting a data-driven marketing approach.
Companies may be thinking differently about their data but are
they acting differently based on what the data is telling them?
We’ll spend the next 20 mins looking at different approaches to
building a data-driven culture and how agencies, brands and
start-ups can all learn from each other.
DATA CULTURES: AGENCIES, BRANDS & STARTUPS
6. THE STATE OF PLAY
Many marketers admit their firms has yet to fully embrace the ‘criticality of data’:
§ 35% have a data strategy in place - but it is not embraced by the entire team
§ 23% said that no strategy exists at all
§ 43% have yet to fully embrace data as a critical operational requirement
§ 43% said it was too hard to get the entire organization to agree on a data strategy
Source: http://www.fiercecmo.com/data-analytics/23-businesses-lack-data-centric-marketing-strategy
7. Sour e: http://www.forbes.com/sites/emc/2014/06/06/5-steps-to-a-data-driven-culture
Limited access to data
Difficulties in performing analysis
Lengthy delays inherent in their analytic systems
Data is siloed in multiple departments
Many tools used to generate insights are not intuitive
Insights are often delivered too late, reducing their value
TYPICAL CHALLENGES
9. WHAT DOES GOOD LOOK LIKE?
Data-driven have several things in common:
1. Data-oriented mindsets and to processes to support (and use) KPIs
2. Data is up-to-date, organised and centralised
3. Formal policies that govern data access
4. Data access is widely available but layered
5. Analytics are integrated into innovative and intuitive tools
* Better employee understanding of the value of data & how to apply it to decision-making
* Widespread commitment to backing up ideas with data & measuring outcomes
Source: http://www.forbes.com/sites/emc/2014/06/06/5-steps-to-a-data-driven-culture
Source: http://www.ngdata.com/creating-a-data-driven-culture
10. “NOT EVERYTHING THAT CAN BE COUNTED COUNTS.
AND NOT EVERYTHING THAT COUNTS CAN BE COUNTED.”
Albert Einstein
12. AGENCIES
Winning new clients
Data as a differentiator
Make it easy
(product vs bespoke)
Use stories to
convey data insights
Growing client revenues
Provide dashboards as part
of account management
Platform + People
Add value through
benchmarks
Better work
Humanise the data to
inform the creative brief
Project/campaign
post-mortems
Shared learnings
13. BRANDS
Digital transformation
Using data to change
the status quo
KPIs shared across
the business
Widespread access to
intuitive information
Customer experience
Use data to break down silos
Reward collaboration
Drive results from data
(personalisation)
Unified customer view
Invest in the right tools,
process and people
Standardise taxonomies
Appoint data champions
14. STARTUPS
Soure:
Getting traction
Data vs. gut instincts
Build in tracking
from the start
Automate reporting
(real time dashboards)
Getting growth
Actionable insights
Justify product development
& prioritise roadmap
Keep growing team focused
on performance
Getting investment
Know your numbers
Solid business case
Model growth scenarios
and ROI
15. LEARNINGS
#1 A data culture leverages an organisation’s two greatest assets; its people and its data.
#2 Focus on data that supports decision-making and improving performance.
#3 Humanise the data. People always want to know,“What does this mean for me?”
#4 Find quick wins and champions to get buy-in and demonstrate value.
#5 Visualisation goes a long way. So does automation.
#6 Be patient; culture change takes time.