Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
Big-Data-The-Case-for-Customer-ExperienceAndrew Smith
This document discusses how big data has evolved from data warehousing in the 1990s to today's focus on big data to better understand customers. It argues that many organizations fail to leverage big data to improve customer experience and gain business insights. To succeed with big data, organizations must develop a clear strategy to deliver business value, such as increasing customer retention and growth. The document recommends that organizations focus big data initiatives on improving the customer experience through integrating customer data and feedback and providing frontline employees with easy access to customer information.
Social Data Intelligence: Webinar with Susan EtlingerSusan Etlinger
This webinar covers the findings from the Altimeter Group report, Social Data Intelligence, which lays out the imperative for organizations to integrate social data with other data streams in the enterprise. Includes best practices and frameworks, as well as a maturity map to enable organizations to make the best and most strategic use of social data.
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
This document discusses how businesses can use big data analytics to gain competitive advantages. It explains that big data refers to the massive amounts of data being generated every day from a variety of sources. By applying advanced analytics to big data, businesses can gain deeper insights into customer behavior and operations. The document provides examples of how industries like telecommunications, insurance, and entertainment are using big data analytics to improve customer service, detect fraud, and optimize marketing. It also outlines some of the key technologies that enable businesses to capture, store, and analyze big data at high volumes, velocities, and varieties.
Information 3.0 - Data + Technology + PeopleHubbard One
The document provides an overview of big data and its transformational value. It discusses how big data can drive value through case studies in technology and collaboration between CTOs and CMOs. It also identifies impediments to realizing big data's transformational value and provides recommendations to overcome these impediments through enhanced data policies and security, infrastructure improvements, organizational change, access to data, and CTO-CMO collaboration.
Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
Big-Data-The-Case-for-Customer-ExperienceAndrew Smith
This document discusses how big data has evolved from data warehousing in the 1990s to today's focus on big data to better understand customers. It argues that many organizations fail to leverage big data to improve customer experience and gain business insights. To succeed with big data, organizations must develop a clear strategy to deliver business value, such as increasing customer retention and growth. The document recommends that organizations focus big data initiatives on improving the customer experience through integrating customer data and feedback and providing frontline employees with easy access to customer information.
Social Data Intelligence: Webinar with Susan EtlingerSusan Etlinger
This webinar covers the findings from the Altimeter Group report, Social Data Intelligence, which lays out the imperative for organizations to integrate social data with other data streams in the enterprise. Includes best practices and frameworks, as well as a maturity map to enable organizations to make the best and most strategic use of social data.
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
This document discusses how businesses can use big data analytics to gain competitive advantages. It explains that big data refers to the massive amounts of data being generated every day from a variety of sources. By applying advanced analytics to big data, businesses can gain deeper insights into customer behavior and operations. The document provides examples of how industries like telecommunications, insurance, and entertainment are using big data analytics to improve customer service, detect fraud, and optimize marketing. It also outlines some of the key technologies that enable businesses to capture, store, and analyze big data at high volumes, velocities, and varieties.
Information 3.0 - Data + Technology + PeopleHubbard One
The document provides an overview of big data and its transformational value. It discusses how big data can drive value through case studies in technology and collaboration between CTOs and CMOs. It also identifies impediments to realizing big data's transformational value and provides recommendations to overcome these impediments through enhanced data policies and security, infrastructure improvements, organizational change, access to data, and CTO-CMO collaboration.
The document discusses integrating social and enterprise data for competitive advantage. It summarizes interviews with organizations using social and enterprise data together. While challenges exist, organizations are making progress in viewing social data as a strategic asset. Those with more mature approaches see measurable benefits in identifying opportunities/risks and improving customer experience. The document outlines a social data integration maturity model and provides case studies of organizations using integrated data.
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
1) Organizations want to achieve business value from data-derived insights in four key ways: efficiency/cost reduction, growth of existing business streams, growth through new revenue streams from market disruption, and monetization of data itself through new business lines.
2) Most organizations are adopting an incremental approach to realizing this value, first proving value through use cases, then expanding to pilots in a line of business, and eventually achieving enterprise-wide adoption. This allows them to set a strategic direction while delivering value incrementally.
3) Current business intelligence technology like enterprise data warehouses are not meeting organizations' needs to democratize access to data and analytics. Decision-makers need the ability to rapidly create insights aligned with
This document discusses key components of developing a big data strategy, including:
1. Big data initiatives are unique and will likely transform businesses, technologies, and organizations.
2. Companies should identify potentially valuable internal and external data sources, and generate innovative ideas for using big data.
3. Both business and IT strategies are needed to ensure infrastructure is adequate, skills are available, risks are managed, and analytics capabilities are expanded.
Why Master Data Management Projects Fail and what this means for Big DataSam Thomsett
This document discusses why Master Data Management (MDM) projects often fail and the implications for big data initiatives. Some key reasons for MDM project failures include a lack of enterprise thinking and executive sponsorship, weak business cases, treating MDM as an IT solution rather than business solution, unrealistic roadmaps, and poor communications planning. The document argues that establishing a data governance strategy, enterprise reference architecture, and prioritized project roadmap are important for MDM and big data success.
1) The document discusses how digital intelligence powered by data and analytics can provide competitive advantages for organizations. It argues that to fully benefit, organizations need to be able to easily access, analyze, and act on both structured and unstructured data from various sources.
2) It describes how cognitive systems can understand data in new ways, allowing organizations to explore more types of information and generate new insights. This helps organizations overcome limitations that currently prevent them from utilizing much of their available data.
3) Empowering various roles across an organization to access and analyze data independently can accelerate innovation and improve business outcomes. Developers, data scientists, business professionals, CIOs, and others need tools and technologies that make the most of
Here in a single document is a compilation of my learnings and observations working with real customers over the past couple of years. My thought in consolidating these posts from LinkedIn was to provide an easy hyperlinked reference for leaders interested in breaking through the clutter to learn ways to leverage data for competitive advantage into 2017 and beyond.
1) The document discusses how organizations need to develop data-driven decision making skills to capitalize on big data. MIT panelists said that relying on empirical data rather than intuition is important for success with big data.
2) The document outlines three basic business rules for capitalizing on big data according to Gartner analysts: define the value of big data for your company, take an inventory of your company's data sources, and adopt and adapt good big data ideas from other industries.
3) Education in statistical analysis and inference is important for making effective data-driven decisions with big data, but decisions should also be pushed closer to front-line workers where possible.
Big Data for Marketing: When is Big Data the right choice?Swyx
Chief Marketing Officers (CMOs) without plans for Big Data may be putting themselves and
their companies at a competitive disadvantage. Big Data is already being widely deployed to enhance marketing responsibilities, although the small number of widely-touted success stories might be masking a significant number of failed implementations. When correctly planned and implemented, however, Big Data can create significant value for CMOs and their organisations. In this paper, we focus on describing specific examples of how Big Data can support CMO responsibilities and developing frameworks for identifying Big Data opportunities.
The document discusses big data, including what it is, its history, current considerations, and importance. It notes that big data refers to large volumes of structured and unstructured data that businesses deal with daily. While the term is relatively new, collecting and storing large amounts of information for analysis has existed for a long time. Big data is now defined by its volume, velocity, and variety. Businesses can gain insights from big data analysis to make better decisions and strategic moves.
Disruptive Data Science Series: Transforming Your Company into a Data Science...EMC
Big Data is the latest technology wave impacting C-Level executives across all areas of business, but amid the hype, there remains confusion about what it all means. The name emphasizes the exponential growth of data volumes worldwide (collectively, 2.5 Exabytes/ day in the latest estimate I saw from IDC), but more nuanced definitions of Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In the current developmental-phase of Big Data, CIOs are investing in platforms to “manage” Big Data.
Big data is growing rapidly due to new sources of customer data from online platforms, mobile devices, and machine-to-machine communication. This creates challenges for companies around managing increasing data volumes, varieties, and velocities. The document discusses how some companies are using big data to better understand customers, increase profits, and gain a competitive advantage. It also notes that big data initiatives require business leadership and clear use cases to be successful.
The document discusses how investing in data quality can provide a significant return on investment for companies. It outlines five tenets that leading companies embrace to realize this ROI from quality data: 1) view data quality as a business issue, not just an IT issue, 2) establish an explicit data governance strategy, especially at the point of data entry, 3) use a third-party data provider to consolidate and cleanse data, 4) address the challenge of maintaining accurate data given the rapid rate of data changes, and 5) strive for a 360-degree view of customers and suppliers across the organization.
The document discusses key themes for organizations to focus on in achieving personalization and a single customer view: customer experience, data culture, and strategic vision. For customer experience, success requires solving clear customer needs, selecting relevant data, and making data accessible. For data culture, organizations must achieve a cross-channel view of data, prioritize analytics talent, and promote data advocacy. Strategic vision involves leveraging partnerships, complying with privacy regulations, and incrementally expanding technology capabilities.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
The document discusses key trends in data management identified by global research. It finds organizations are increasingly focused on understanding customers as individuals to offer personalized service. However, inaccurate and incomplete data undermines customer experience for many. Experts recommend using data to develop a single view of each customer by linking all available information. This would allow real-time insights and responses tailored to individual customers, improving relationships and sales. Achieving accurate and comprehensive customer data remains a challenge for most organizations.
ClickZ/Fospha: The State of Marketing Measurement, Attribution, and Data Mana...Clark Boyd
This report covers:
The data challenges marketers are confronting today
The business impact of a complex (and oft-misunderstood) data culture
The role of marketing intelligence software in a modern organization
How to define and use metrics like customer lifetime value
The features marketers wish their current technologies had
How to assess your own company’s data maturity
A new approach to agile, accessible marketing measurement
Magenta advisory: Data Driven Decision Making –Is Your Organization Ready Fo...BearingPoint Finland
It’s nice to have loads of data. Nevertheless, many managers start to sweat when it comes to genuinely fact-based decision making. This study reveals the keys to leveraging big data successfully.
Capitalize On Social Media With Big Data AnalyticsHassan Keshavarz
This document discusses how companies can capitalize on social media through big data analytics. It notes that while social media promises benefits, most companies struggle to measure the true value and impact. To leverage social media effectively, the entire business must be aligned in their interactions. The document also discusses how analyzing large datasets through big data analytics can provide strategic insights for success, maximize product performance, and deliver real business value. It emphasizes the need for companies to measure social media's impact on key metrics and business goals.
The document discusses integrating social and enterprise data for competitive advantage. It summarizes interviews with organizations using social and enterprise data together. While challenges exist, organizations are making progress in viewing social data as a strategic asset. Those with more mature approaches see measurable benefits in identifying opportunities/risks and improving customer experience. The document outlines a social data integration maturity model and provides case studies of organizations using integrated data.
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
1) Organizations want to achieve business value from data-derived insights in four key ways: efficiency/cost reduction, growth of existing business streams, growth through new revenue streams from market disruption, and monetization of data itself through new business lines.
2) Most organizations are adopting an incremental approach to realizing this value, first proving value through use cases, then expanding to pilots in a line of business, and eventually achieving enterprise-wide adoption. This allows them to set a strategic direction while delivering value incrementally.
3) Current business intelligence technology like enterprise data warehouses are not meeting organizations' needs to democratize access to data and analytics. Decision-makers need the ability to rapidly create insights aligned with
This document discusses key components of developing a big data strategy, including:
1. Big data initiatives are unique and will likely transform businesses, technologies, and organizations.
2. Companies should identify potentially valuable internal and external data sources, and generate innovative ideas for using big data.
3. Both business and IT strategies are needed to ensure infrastructure is adequate, skills are available, risks are managed, and analytics capabilities are expanded.
Why Master Data Management Projects Fail and what this means for Big DataSam Thomsett
This document discusses why Master Data Management (MDM) projects often fail and the implications for big data initiatives. Some key reasons for MDM project failures include a lack of enterprise thinking and executive sponsorship, weak business cases, treating MDM as an IT solution rather than business solution, unrealistic roadmaps, and poor communications planning. The document argues that establishing a data governance strategy, enterprise reference architecture, and prioritized project roadmap are important for MDM and big data success.
1) The document discusses how digital intelligence powered by data and analytics can provide competitive advantages for organizations. It argues that to fully benefit, organizations need to be able to easily access, analyze, and act on both structured and unstructured data from various sources.
2) It describes how cognitive systems can understand data in new ways, allowing organizations to explore more types of information and generate new insights. This helps organizations overcome limitations that currently prevent them from utilizing much of their available data.
3) Empowering various roles across an organization to access and analyze data independently can accelerate innovation and improve business outcomes. Developers, data scientists, business professionals, CIOs, and others need tools and technologies that make the most of
Here in a single document is a compilation of my learnings and observations working with real customers over the past couple of years. My thought in consolidating these posts from LinkedIn was to provide an easy hyperlinked reference for leaders interested in breaking through the clutter to learn ways to leverage data for competitive advantage into 2017 and beyond.
1) The document discusses how organizations need to develop data-driven decision making skills to capitalize on big data. MIT panelists said that relying on empirical data rather than intuition is important for success with big data.
2) The document outlines three basic business rules for capitalizing on big data according to Gartner analysts: define the value of big data for your company, take an inventory of your company's data sources, and adopt and adapt good big data ideas from other industries.
3) Education in statistical analysis and inference is important for making effective data-driven decisions with big data, but decisions should also be pushed closer to front-line workers where possible.
Big Data for Marketing: When is Big Data the right choice?Swyx
Chief Marketing Officers (CMOs) without plans for Big Data may be putting themselves and
their companies at a competitive disadvantage. Big Data is already being widely deployed to enhance marketing responsibilities, although the small number of widely-touted success stories might be masking a significant number of failed implementations. When correctly planned and implemented, however, Big Data can create significant value for CMOs and their organisations. In this paper, we focus on describing specific examples of how Big Data can support CMO responsibilities and developing frameworks for identifying Big Data opportunities.
The document discusses big data, including what it is, its history, current considerations, and importance. It notes that big data refers to large volumes of structured and unstructured data that businesses deal with daily. While the term is relatively new, collecting and storing large amounts of information for analysis has existed for a long time. Big data is now defined by its volume, velocity, and variety. Businesses can gain insights from big data analysis to make better decisions and strategic moves.
Disruptive Data Science Series: Transforming Your Company into a Data Science...EMC
Big Data is the latest technology wave impacting C-Level executives across all areas of business, but amid the hype, there remains confusion about what it all means. The name emphasizes the exponential growth of data volumes worldwide (collectively, 2.5 Exabytes/ day in the latest estimate I saw from IDC), but more nuanced definitions of Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In the current developmental-phase of Big Data, CIOs are investing in platforms to “manage” Big Data.
Big data is growing rapidly due to new sources of customer data from online platforms, mobile devices, and machine-to-machine communication. This creates challenges for companies around managing increasing data volumes, varieties, and velocities. The document discusses how some companies are using big data to better understand customers, increase profits, and gain a competitive advantage. It also notes that big data initiatives require business leadership and clear use cases to be successful.
The document discusses how investing in data quality can provide a significant return on investment for companies. It outlines five tenets that leading companies embrace to realize this ROI from quality data: 1) view data quality as a business issue, not just an IT issue, 2) establish an explicit data governance strategy, especially at the point of data entry, 3) use a third-party data provider to consolidate and cleanse data, 4) address the challenge of maintaining accurate data given the rapid rate of data changes, and 5) strive for a 360-degree view of customers and suppliers across the organization.
The document discusses key themes for organizations to focus on in achieving personalization and a single customer view: customer experience, data culture, and strategic vision. For customer experience, success requires solving clear customer needs, selecting relevant data, and making data accessible. For data culture, organizations must achieve a cross-channel view of data, prioritize analytics talent, and promote data advocacy. Strategic vision involves leveraging partnerships, complying with privacy regulations, and incrementally expanding technology capabilities.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
The document discusses key trends in data management identified by global research. It finds organizations are increasingly focused on understanding customers as individuals to offer personalized service. However, inaccurate and incomplete data undermines customer experience for many. Experts recommend using data to develop a single view of each customer by linking all available information. This would allow real-time insights and responses tailored to individual customers, improving relationships and sales. Achieving accurate and comprehensive customer data remains a challenge for most organizations.
ClickZ/Fospha: The State of Marketing Measurement, Attribution, and Data Mana...Clark Boyd
This report covers:
The data challenges marketers are confronting today
The business impact of a complex (and oft-misunderstood) data culture
The role of marketing intelligence software in a modern organization
How to define and use metrics like customer lifetime value
The features marketers wish their current technologies had
How to assess your own company’s data maturity
A new approach to agile, accessible marketing measurement
Magenta advisory: Data Driven Decision Making –Is Your Organization Ready Fo...BearingPoint Finland
It’s nice to have loads of data. Nevertheless, many managers start to sweat when it comes to genuinely fact-based decision making. This study reveals the keys to leveraging big data successfully.
Capitalize On Social Media With Big Data AnalyticsHassan Keshavarz
This document discusses how companies can capitalize on social media through big data analytics. It notes that while social media promises benefits, most companies struggle to measure the true value and impact. To leverage social media effectively, the entire business must be aligned in their interactions. The document also discusses how analyzing large datasets through big data analytics can provide strategic insights for success, maximize product performance, and deliver real business value. It emphasizes the need for companies to measure social media's impact on key metrics and business goals.
Business Intelligence for Consumer Products: Actionable Insights for Business...FindWhitePapers
While historically consumer packaged goods (CPG) organizations have made significant investments in data collection and integration, much of the data stored in their IT infrastructures has not been analyzed or deployed to further the firms business performance.
Tuesday's Leaders. Enabling Big Data, a Boston Consulting Group Report.BURESI
The document discusses six key capabilities that are essential for companies to successfully leverage big data: 1) Identifying innovative opportunities through a culture of experimentation and collaboration between data and business experts, 2) Building trust with consumers by being transparent about data use and providing control/benefits, 3) Laying a technical foundation with scalable, flexible platforms that support both existing and new data applications, 4) Shaping the organization through a center of excellence and linking data specialists to business units, 5) Participating in emerging big data ecosystems through strategic partnerships, and 6) Making relationships work by creating an open culture for partnering and data sharing. Speed is critical, as companies that quickly build these capabilities can realize big data's potential faster
Big & Fast Data: The Democratization of InformationCapgemini
Moving from the Enterprise Data Warehouse to the Business Data Lake
Is it possible that ubiquitous analytics represents the next phase of the information age? New business models are emerging, enabled by big data that business leaders are eager to adopt in order to gain advantage and mitigate disruption from start-ups and parallel industries. The winners are likely to be those that master a cultural shift as well as a technology evolution.
Our view is this will be realized through the alignment of a business-centric big data strategy, combined with democratization of the analytical tools, platforms and data lakes that will enable business stakeholders to create, industrialize and integrate insights into their business processes.
Innovative approaches are needed to free up data from silos whilst encouraging both the sharing and the continuous improvement of insights across the business. While it will be evolution for some, revolution for others; the risk of status quo is not just the loss of opportunity but also a widening gap between business and the internal technology functions.
https://www.capgemini.com/thought-leadership/big-fast-data-the-democratization-of-information
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.
The document discusses the importance of developing a big data plan. It states that while exploiting big data is an important source of competitive advantage, many companies struggle due to technical and organizational challenges. It recommends that companies craft a big data plan that focuses on three elements: assembling and integrating data from various sources, selecting analytic models that can optimize operations and predict business outcomes, and creating intuitive tools that help employees make use of the analytic outputs. Developing such a plan will help companies prioritize investments and initiatives to harness big data effectively.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Data as a Service (DaaS): The What, Why, How, Who, and WhenRocketSource
Data as a Service (DaaS) is one of the most ambiguous offerings in the "as a service" family. Yet, in today's world, data and analytics are key to building a competitive advantage. We're clearing up the confusion around DaaS and helping your company understand when and how to tap into this service.
The document discusses a survey of 300 enterprise organizations about data ownership and big data initiatives. It finds that marketing and sales are most involved in purchase decisions, but sales, business development, and insights/analytics have the most influence. Most functions see their involvement peaking late in the purchase process. Organizations need strategies to align functional areas and determine influence. Data initiatives are being driven by needs for better analytics, marketing intelligence, and predictive capabilities rather than just data quality issues.
The document discusses building an integrated data strategy for marketing. It describes the challenges of accessing and integrating large amounts of customer data from various online and offline sources. An integrated data strategy can help marketers gain a complete view of customer journeys across channels to deliver more personalized experiences. The document outlines three pillars of an effective integrated data strategy: having the right data, culture, and technology. It emphasizes using data to guide marketing decisions rather than relying solely on intuition.
The rising collection and analysis of data has shifted the way companies do business. Four key ingredients to develop a data strategy, how to leverage next-generation technologies, and three essential steps for rolling out implementation are included. The Data Ecosystem will show you how to develop and implement the strategies that will meet the needs of your business.
The document discusses how established companies can become more data-driven through a strategic transformation. It provides examples of how the Spanish hotel chain Ilunion and Transport for London used data analytics to improve decision making. The key steps for companies include linking data initiatives to business goals, creating a data-driven culture where all employees use data in their work, and implementing technology infrastructure to make relevant data and insights accessible. Becoming truly data-driven requires addressing cultural and technical barriers and viewing data as a strategic asset.
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
The document discusses how big data is changing marketing by providing unprecedented tools to understand consumer behavior with more precision. Marketers who use big data at least 50% of the time are more likely to exceed their goals and see benefits like improved ROI and insights into customer behavior compared to those using big data less. While executives believe they are using big data sufficiently, the data shows room for more use of big data in marketing decisions. Machine learning systems that can quickly generate insights from changing consumer data will become increasingly important for marketing success.
This document contains an introduction and 6 articles related to the future of data and data specialists in Singapore.
The introduction discusses how data analytics is becoming integral to the media industry. It notes that while agencies have implemented analytics, clients want to understand how it relates to their business issues. The 6 articles then explore topics like how agencies can better sell analytics solutions rather than just products; the need to value data talent and help them build skills; and how agencies can collaborate more effectively.
Similar to Colab 2019 Making Sense of the Data That Matters (20)
Get Control of Your Video Assett WorkflowIan Gibbs
The document outlines seven critical issues facing practitioners in video asset workflow:
1. Sourcing creative is a frustrating and complex process that often results in campaign delays.
2. Communication between stakeholders relies too heavily on email, which is an inadequate system that can't provide proper versioning or audit trails.
3. Distribution inaccuracies, like sending the wrong files to the wrong places, are common and delay campaigns while hurting consumer trust in advertising.
4. The many variables involved in different video formats and platforms introduces complexity and errors into the workflow.
The document discusses harnessing data from JICMAIL, a currency for direct mail and door drops in the UK, to improve econometric analysis of advertising mail campaigns. It provides an overview of JICMAIL and its growing role in measuring mail interactions and effectiveness. The document also summarizes a roundtable discussion on challenges with measuring mail and how JICMAIL data could help address gaps. Key recommendations include using JICMAIL data to convert mail pieces to impressions, analyze mail behavior trends, measure a wider range of mail effects beyond direct sales, and assess brand-specific and competitive mail campaigns. The overall aim is to offer practitioners new ways to evaluate and account for mail in their econometric models.
The document discusses how 73% of 16-34 year olds' time is spent on digital devices versus other activities. Younger generations are spending most of their free time engaged with technology like smartphones, tablets, and computers rather than other hobbies or socializing in person. Digital natives in their late teens and twenties are particularly reliant on being constantly connected online.
This document outlines key stages in a customer funnel from awareness to purchase, including consideration and influence. It describes common digital marketing tactics used at each stage like display/video ads and branded content. Metrics for measuring the impact of these tactics are provided, such as brand awareness, favorability, purchase intent, sales uplift and ROI. The document notes some challenges in interpreting metrics like self-reported purchase intent.
The document discusses 5 publishing trends for 2019: 1) The increasing use of social media "stories" formats by publishers, 2) Ongoing debates around data usage and ad targeting in light of privacy regulations, 3) Claims of moving to a "post-literate" media environment dominated by images and video, 4) Emerging new revenue models for news organizations, and 5) Evolving consumer preferences around short and long form content consumption across different platforms. The document provides commentary arguing that some predictions overstate shifts away from text and premium ad environments, and that opportunities remain for collaboration and building reader relationships.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Data Stories: a year in measurement - digital, media, ads - dec 17Ian Gibbs
The document discusses considerations for digital media, advertising, and marketing metrics in 2018. It notes a disconnect between clicks and long-term value, and the need to measure true brand impact beyond clicks. It also addresses challenges in measuring return on investment for digital spending, reconciling different ROI studies, and ensuring digital optimization. Finally, it emphasizes the importance of setting specific, measurable goals and using accurate, thorough data aligned with business objectives.
Getting smart with ad data and measurement nov 16Ian Gibbs
Data is transforming marketing for the better and worse. What can publishers, brands and marketers do to make sure they're planning and measuring using the data that matters?
Measuring a deeper impact... and why ad measurement matters more than ever be...Ian Gibbs
The online advertising ecosystem has been built around an unsustainable preoccupation with clicks. Why is brand impact measurement so important and how do you do it in a scaleable cost effective way?
This document outlines three principles for building commercial influence: 1) Attention pays - gaining attention through compelling content is important for influence, 2) Follow the water - focus on where audiences spend their time both online and through apps, and 3) Collaborate - partnering with other influential organizations can help spread ideas to larger audiences totaling over 100 million people globally. The document provides examples of how The Guardian newspaper has successfully built influence through trustworthy news coverage and unstoppable stories that engage audiences across different platforms.
This document discusses the importance of trust in business and how media brands can build trust through paid, owned, and earned channels. It notes that trust is the most important currency in a networked world. The Guardian is highlighted as the most trusted media brand in the UK. The document advocates building trust by staying true to brand values across paid advertising placements, owned content on platforms like mobile, and earned engagement on social media.
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1. SPONSORED BY
MAKING SENSE OF
THE DATA THAT MATTERS
KEY CONSIDERATIONS FOR
DATA DRIVEN ORGANISATIONS
OCTOBER 2019
2. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
INTRODUCTION
The data organisation is locked in the grip of profound change, and nowhere is
this more true than in the media owner space. The digital era was once thought
to answer all of our data woes. By generating terabytes of behavioural data
points that could be seamlessly integrated with multiple other data sources and
delivered in insightful real time dashboards, business units can become
empowered to optimise performance.
The truth, however, is a little more nuanced.
Data drives organisational change, and it is unlikely you’d find a business in the
FTSE 100 which wouldn’t describe itself as a data driven organisation. Yet at the
same time, the proliferation of data and the ubiquity of the tools used to tease
insight from it, has prompted fragmentation of data ownership across multiple
business units.
The challenge with this fragmentation is that where there is not joined up
thinking at an organisational level on how data should be integrated, analysed
and actioned, at worst the business’s use of data becomes aspirational only. It
becomes fundamentally backward facing, reporting and analysing and justifying
what has been, rather than trying to impact future performance.
Those business which are truly transformative in their application of data are
fundamentally forward facing. They use data to forecast and make the most of
predicative capabilities to optimise performance. This paper will explore the
experiences and opinions of a range of media industry experts in relation to the
business critical challenge of making sense of the data that matters.
1
INTRODUCTION
2
EXECUTIVE SUMMARY
3
KEY ISSUES IN MAKING
SENSE OF THE DATA
THAT MATTERS
4
CONSIDERATIONS
FOR PUBLISHERS
AND VENDORS
5
ACKNOWLEDGMENTS
2
“Analytics is not data-driven if its findings are never seriously considered or
acted upon. If they are unread, ignored and the boss is going to do whatever
he or she wants, then they are ineffectual.”
Carl Anderson
Creating a Data Driven Organisation
3. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
Source: Revenue Operations Barometer Pro Survey H1 2019, CoLab
REVENUE OPERATIONS
AND THE NEW CUSTODIANS
OF ORGANISATIONAL DATA
Revenue operations teams increasingly find themselves the custodians of digital audience data that was once
owned by business insights and research teams. The ad tech tools which enable revenue operations teams to build
and monetise digital audiences are converging with martech tools to generate strategic audiences insight that has
applications across all areas of the modern data organisation.
CoLab Consulting’s Revenue Operations Barometer, launched in April 2019, sought to gain the perspective of this
often under-analysed group by interviewing rev ops professionals from over fifty leading global publishers. While
business priorities and challenges varied by organisational type, the one consistent finding highlighted was that
improved analytics was seen as the top priority over the coming year.
3
Analytics is a broad field however and covers a range of uses,
from the descriptive and exploratory to the predictive and causal.
To unpick this question further and to shine a light on how leading
publishers are currently making sense of the data that matters, a
roundtable of industry experts was convened in September 2019.
The key themes that emerged are summarised in the following
paper and provide a vital insight for any business in the broader
publishing ecosystem.
How will you prioritise the following areas of the business
over the next 12 months? % High Priority
60%
Improved Analytics
31%
Sales Order Management
23%
Team Size and Structure
15%
DSP
19%
SSP
31%
Consent Management
21%
DMP
10%
Ad Server
James Gilkes
Global Pricing and Inventory Director
BBC
David Evans
Head of Data and Insight
CNBC International
Adjani Nicholson
Sales Manager EMEA
IDG Solutions
JoaoFelizardo
Head Program Manager
IDG UK
Brett Gibson
Client Advisory and Value Consulting Director
Domo
Rupert Staines
Digital Marketing, Data and Technology Expert
Duncan Arthur
Partner
CoLab Consulting
Anne Goodman
Associate
CoLab Consulting
Ian Gibbs
Associate
CoLab Consulting
Rebecca Rangeley
Head of Business Insight
Freewheel
AlbertoSantangelo
Director of Multiplatform Ad Operations
Viacom
Danny Doyle
Head of Ad Operation
Hearst UK
David Hayter
Programmatic, Data and Technology Director
Stylist Group
Roundtable Expert Panel
The roundtable took place on the morning of the 10th September 2019 in London, UK and attendees covered a range
of revenue operations, digital product and business insight roles:
Publisher
Do the key themes and
challenges ring true for
your business and what
are you doing to make
sense of the data that
matters?
Vendors
How can you help
publisher make sense
of the data that matters?
4. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
SEVEN KEY THEMES FOR
DATA DRIVEN ORGANISATIONS
EXECUTIVE SUMMARY
1. Data fragmentation is a key challenge for all. Fragmentation of data sets and
analytics professionals creates organisational inefficiencies and works
against data driven decision making.
2. What do we really mean by data integration? Whether it be the centralisation
of analytics resource or the integration of discreet data sets, it must be
a process baked into organisation culture if it is to succeed.
3. Automation increases human resource burden. Perhaps counter-intuitively,
data automation can result in greater business interest in analytics,
increasing demands on analyst time for higher level analytical work.
4. Centralisation is favoured over shared ownership of data. The centralisation
of data after the expansion of cloud business applications aims to govern
and expand access without restricting the decision-making process.
5. Senior management needs help understanding the business case for
data integration. Without senior management buy-in, data integration
and analytics initiatives will have little longevity or may fail due to
underinvestment.
6. Marketers are hooked on over-abundant behavioural data in campaign
reporting. This is useful data for campaign reporting, but it tells us little
of medium- or long-term value for advertisers.
7. The first-party future. Dependable behavioural data and declared profile
data will help publishers maximise yields in the long run.
4
5. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
1.
DATA FRAGMENTATION IS
A KEY CHALLENGE FOR ALL
The fragmentation of data and the tools used to draw insight from and monetise it, was a key talking point in our
discussion. Fragmentation can take many forms, but primarily was seen to manifest as:
5
“The difficulty is fragmentation… lots of fragmentation of data [across] different data points. Data is dirty: although
there are rich data sets, finding the truth is really quite difficult”
Roundtable Attendee
In the case of point A, the focus of many expert
panellist’s businesses has been to harness multiple data
sets through data warehouses, DMPs and data lakes, yet
this is a process that takes up varying levels of resource
depending on organisational size. In the words of one
panellist the search for perfection in this area (i.e. all
organisational data perfectly linked in one single,
accessible, system) can often result in a poor effort
vs reward trade off when generating business insight.
In the case of fragmented and siloed data ownership,
the downside to the business is organisational
inefficiency. There will be a commonality of metrics
across ad servers, web analytics systems, DMPs and
third party analytics tools which will not only result in
overlap of effort, but will invariably differ from one
another too – creating multiple versions of the truth and
creating confusion in the process of how to action data.
The most valuable data to revenue operations and data teams requires little processing and additional manipulation
and is tangibly linked to ad dollars, both for delivery optimisation and selling the value of ads more broadly. However,
in a world of data fragmentation, the challenges in getting to this point are abundant.
A.
Discrete data sets existing in separate tools
and systems
B
Data and systems ‘owned’ across multiple
business siloes
6. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
2.
WHAT DO WE REALLY
MEAN BY DATA INTEGRATION?
If the solution to data fragmentation is data integration,
then consensus must be reached on what exactly this
means within the organisation.
From a technical data point of view, does data
integration mean the linkage of multiple data sets at
their rawest form, enabling once discreet data points to
be cross referenced and cross analysed from a single
source in a data warehouse? Or is it enough to make
discreet data sets accessible via a common portal,
allowing business areas to view and analyse data in
parallel using a common data vocabulary?
6
“The data warehouse where raw data [is organised]
into buckets, where you just push a button and say
“I would like to know this” is the dream. But the
dream would be that it would not just be one person
pushing the button… everyone is able to do it. You
give people access to the reporting so they become
independent and autonomous.”
Alberto Santangelo
Director of Multiplatform Ad Operations, Viacom
The latter point in particular speaks to the power of
data visualisation managed within business units, with
many panellists discussing their successes and failures
in implementing dynamic and engaging systems for
presenting data to the business. On the one hand such
systems democratise data by opening it up to the entire
organisation, but such shared usage does not suit
everyone, and it is acknowledged that there are some
people who will always have their own unique
requirements and dependence on older systems due to
either loyalty or technical debt. There was some debate
as to whether it was reasonable to expect that a single
source of centrally managed and curated data could be
agile enough to meet the diverse demands of analysis
across the business.
The expert panel was of the view that while many
tech vendors sell the promise of truly integrated
organisational data, many fail to live up to the promise.
This was particularly true of data that is used by more
than just one team and therefore might be depended
upon for multiple purposes, as revenue systems are not
oriented to provide capability to answer marketing or
financial questions despite having some commonality
of the underlying data. A stark warning in terms of
vendor credibility in this space.
3.
AUTOMATION INCREASES
HUMAN RESOURCE BURDEN
A core benefit of data integration, management and
the development of self -serve analytics tools for the
business is that it removes the burden of day-to-day data
reporting and queries from data analysts and, in the case
of our expert panel, revenue operations people. Their time
can instead be freed up to shift their focus towards
analysis and insight generation, answering strategic
rather than tactical business queries.
There was a distinct sense amongst our expert panel,
that processes that automate data delivery may not
ultimately reduce the burden on human resource. In fact
in many instances the burden increased as more in depth
insight generation sparked more in depth follow up
queries from an insight-hungry business. However the
demand was addressed, whether through increased
resource or some level of unmet demand, the resulting
business value was significantly greater from being able
to ask questions rather than battling to tame data
preparation, quality and integration challenges.
Data -driven organisation must be ready for the increased
demands for their time that their success will generate
and in so doing must acknowledge that there are two
distinct skill sets required in terms of human resource
when making the most of data assets: Those who extract
the data vs those who derive insight from it.
“There are two separate skill sets. One is extracting
data and one is extracting value from the data.”
David Evans
Head of Data and Insight, CNBC International
7. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
4.
CENTRALISATION IS
FAVOURED OVER SHARED OWNERSHIP OF DATA
While the physical and virtual integration of data sources goes some way to overcoming the hurdles associated
with fragmentation, our expert panel was almost unanimous in their belief that centralised data teams are also
a vital component of the contemporary data driven organisation.
Pockets of data, insight and reporting currently exist within multiple business units – from marketing, to product
teams, to revenue operations and business intelligence teams. The resulting inefficiencies are often borne out of an
overlap in resource and datasets, but potentially more damaging to the business are the competing and sometimes
conflicting data narratives that results from different sources and analytical techniques.
7
“There’s data from many sources in any organisation.
The immediate issue is that the data in itself,
regardless of where it comes from, is an organisation
within the organisation. If you ask everyone around
the table who have different operational
responsibilities [for data], even the CEO, you’re going
to be asking for different sets of data.”
Rupert Staines
Digital Marketing, Data and Technology Expert
Our panel highlighted that a centralised data team’s core
purpose should be to define how the organisation needs
to perform in terms of data driven decision making and
enable access to data even if not centrally provisioned.
There is a fine balance between democratizing data
throughout an organisation to empower business units
with relevant insight accessible at their fingertips, and
ensuring a consistency of quality in how that data is
applied strategically. A centralized data team should
not necessarily look to “own” all organisational data,
but rather define best practice in how it is analysed and
deployed. It recognises that different team structures,
skills and the complexity and cadence of business
questions means that a one-size-fits-all approaches
won’t work for all requirements.
Ultimately centralisation should improve data access
and data decision making for the entire organisation.
8. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
5.
SENIOR MANAGEMENT NEEDS HELP UNDERSTANDING
THE CASE FOR DATA INTEGRATION
This was a theme echoed by our expert panel and one
which ultimately manifested itself in two ways.
1. Business processes relating to data and analytics
projects were often deemed to be sub-standard.
More than one expert panellist cited examples of a
poor brief at project kick off stage resulting in
inadequate analysis that failed to get to the heart of
the business’s burning strategic questions. An
inability to iterate through the process locks in any
early gaps in knowledge.
2. When being asked for budgets to conduct large scale
data infrastructure initiatives such as building a data
warehouse (a vital and powerful tool in enabling
organisations to understand relational data and
driving decision making), CEO’s, CFO’s and CIO’s
often do not have a true understanding of what they
are being asked to sign off.
Scoping data management initiatives up front in terms
of tangible business value is critical to help the C-Suite
understand the business case for enhanced data
projects. Multi-year data lake and warehouse projects
should not be undertaken without clearly defining exactly
what return the organisation will gain from the initiative
(return from both a data perspective and an ROI
perspective) and a timeline of when benefits can be
expected.
8
“We are successfully building diverse revenue streams” 52%
AGREED
“We have an effective suite of technology to support
all advertising operations”
48%
AGREED
“The company invests adequately in ad
operations technology”
46%
AGREED
“Senior management understand this area of the
business well and give it the attention it deserves”
37%
AGREED
“We will require new ad operations technology to maximise revenue” 63%
AGREED
With a range of businesses on our expert panel, it was
clear that the younger digital-era businesses were more
capable of being nimble in this regard, scoping and
executing on data initiatives in weeks rather than months
and providing insight in to the return for the business in a
far shorter time frame than larger organisations.
To truly drive success in data initiatives our panel felt
that the resulting data processes must be baked into the
sales organisation – for example, tied to performance
and bonuses. Furthermore, self-service should be
mandatory for certain types of data queries if the
organisational analytics tools have been built, enabling
the centralised data team to focus on higher level,
specialist, analytics output.
It is this process of linking data initiatives to individual
performance that will drive understanding of data
management at a board level.
To what extent do you agree or disagree with the following statements? % High Priority
The CoLab Revenue Operation’s barometer
reveals only 37% of leading publishers
agree that senior management understands
their area of the business well and gives it
the attention it deserves.
9. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
6.
MARKETERS ARE HOOKED ON OVER-ABUNDANT
BEHAVIOURAL DATA IN CAMPAIGN REPORTING
Given the professional interest area of our expert panel, the topic of campaign and digital advertising effectiveness
reporting was top of mind when discussing the data that matters most to publishers.
For revenue operations and sales professionals, a large amount of their time is used to generate post campaign
reports for clients, often aggregating campaign data from multiple sources (ad server, web analytics and social
data for example) in what is often and time consuming and cumbersome task.
While there was clear demand therefore, for tools that integrated multiple campaign data sources, it was also very
telling that the many agencies and advertisers and still overly-dependent on behavioural data such as clicks, likes
and shares, rather than data points that enable true ROI reporting (such as sales uplift), or report on longer term
brand building goals. This is a well reported problem that has driven much of the over-commoditization of digital
ad inventory in the past decade.
9
“One thing we still struggle with is that manual
process. Clients have access to all of this data and
all these campaign reports, but are calling us and
saying ‘I’m not getting what I paid for what are you
doing about it?’ … We have this great tool that’s going
to give you loads of insight and is going to tell you a
great story, but I still need someone to tell that story.”
Joao Felizardo
Head Program Manager, IDG UK
The expert panel discussed the notion that rather
than being solely dependent on short term behavioural
metrics, digital marketing effectiveness should be
viewed through a short/medium/long-term lens to truly
understand its value to the organisation and unlock
further revenue potential for publishers.
While this lens will naturally vary by industry (product
purchase cycles vary dramatically for high and low
consideration products for example), data and systems
that explore the effectiveness of digital beyond short
term direct response goals will have profound effects
of the digital ecosystem.
7.
THE FIRST PARTY FUTURE
While third party data still has a degree of value to
publishers while brands and digital buyers place value
in it, the longer term and more exciting opportunity for
publishers comes from leveraging first party data assets
for campaign targeting, optimisation and reporting.
Our expert panel discussed the differing value exchange
that takes place for varying types of first party data –
from behavioural page level data, to potentially more
valuable user profile information. The latter data set is
declared and probabilistic and does not rely on the same
deterministic modelling techniques that third-party
audience segments do.
First party data results in a smaller data set consisting
of higher quality data points and safeguards publishers
from the challenges associated with third party
measurement methodology changes.
“We’ve almost got away from using any third party data
at all… The Safari ITP moment [for example]: we have
no problems with that because we can collect data on
those users. We can collect data from those places
where cookies generally don’t work.”
David Hayter
Programmatic, Data and Technology Director, Stylist Group
With large proportions of publisher audiences now
existing on platforms where first party data plays a far
greater role (such as mobile apps and safari), data driven
organisations must align their systems and processes to
capitalise on this shift - one which is more relevant than
ever in a post-GDPR world.
10. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
CONSIDERATIONS FOR PUBLISHERS AND
VENDORS
FOR
PUBLISHERS
FOR
VENDORS
1
DATA FRAGMENTATION
Is data fragmentation impacting your
business and do you have strategies in
place to mitigate against it?
How can you help publishers with the
systems and processes required to break
down data siloes?
2
DATA INTEGRATION
How integrated do you want your data
to be?
Discreet data sets in one system or truly
joined up, queryable single-source data?
Can you deliver on the promise of true
data integration for a sometimes-cynical
publisher market?
3
DATA AUTOMATION
Is your data organisation sufficiently
resourced to meet the demand that
improved analytics will result in?
How can you help publishers create seamless
processes and tools to manage data and
insight demand?
4
DATA CENTRALISATION
Define how much “control” you want your
centralised team to have. Defining how
the business interacts with data is critical.
Systems that enable data democratization
across the entire organisation are compatible
with centralised data teams.
5
DATA BUSINESS CASE
Poor briefing and definition of scope
will increase the likelihood of data
initiative failure.
Build the business case for data integration
with your clients and prospects.
6
DATA FOR CAMPAIIGN
REPORTING
Centralised systems and dashboards to
enable campaign reporting are powerful
tools, but metrics that focus on real ad
effectiveness must not be ignored.
Can you help publishers report on campaign
ROI or brand impact – i.e. move beyond simple
behavioural metrics?
7
FIRST PARTY DATA
Are you collecting sufficient data to
realise the potential of the first party
data opportunity?
How can you help publishers collect, manage,
integrate, monetise and draw insight from first
party data?
10
11. MAKING SENSE OF
THE DATA THAT MATTERS
SPONSORED BY
ACKNOWLEDGEMENTS
We would like to extend our thanks to all participants
in the “Making sense of the data that matters” industry
roundtable, and to those who have given permission
for attributable quotes to be used.
ABOUT OUR SPONSOR
Domo is the fully mobile, cloud-based operating
system that unifies every component of your business
and delivers it all, right on your phone. Domo brings
together all your people, data, and systems into one
place for a digitally-connected business. For more
information about Domo, please contact
EMEAMarketing@domo.com ; visit www.domo.com or
call +44 203 8791006
11
ABOUT COLAB MEDIA CONSULTING
CoLab is a specialist media consulting and market
research business.
We provide insights and leadership at the intersection of
media and technology. Our consultants are digital media
veterans based around the globe who have held senior
positions at major media organisations including the
BBC, The Guardian and Microsoft.
We work collaboratively with our clients to deliver deep
sector knowledge, an impartial view on the best way
forward, and a plan rooted in practise not theory.