SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS World

Slides from my talk at MeasureCamp VII (London) in September 2015. Some key findings about Data Layers and how they are integrated with tag management solutions and organisations.

These are my slides from SMX München 2016. Content engagement is a tricky thing to measure, especially how it changes over time, but in this article I give some ideas for how to enhance your content measurement process within your organization.

My slides about how to do Advanced Form Tracking in Google Tag Manager. I presented these at Conversion Conference London, in October 2014.

Simo Ahava presentation at Digital Marketing Conference – iLive 2015, Riga, Latvia iLive conference is the place to be for actionable sessions on SEO, Google analytics, social media, community building and brand development in the online environment. Follow us on Facebook & Twitter & Instagram: Facebook: http://www.facebook.com/iLivelv Twitter: http://www.twitter.com/iLivelv Instagram: http://www.instagram.com/iliveconference

Google Tag Manager is an incredibly powerful tool and one you're likely not using to its full potential. In my talk from MozCon 2016, I delivered 29 rapid-fire tips intended to empower marketers to overcome the insurmountable odds and circumnavigate road blocks using this incredibly powerful marketing tool.

This document discusses Google Tag Manager and provides examples of how it can be used. Google Tag Manager allows tags and code snippets to be quickly updated on websites and mobile apps. It works by injecting JavaScript and can be used to track analytics, conversions, remarketing and more. The document provides examples of how Google Tag Manager can be used to track external link clicks, file downloads, form engagement, scroll tracking, and more. It also discusses triggers, variables, and version control within Google Tag Manager.

The document discusses Firebase Analytics, a tool for capturing user data across an app stack. It automatically captures events like app opens and purchases. Developers can choose from predefined events or customize their own. The data is then accessible in Firebase's dashboard for analyzing metrics like active users, revenue, and retention. Firebase Analytics seamlessly integrates with other Firebase tools to build audiences and send tailored notifications. It aims to help developers better understand users and improve the app experience at each stage of the user journey.

Skill Session about Google Analytics with a lot of tools and tips and tricks how to get a lot of value from Google Analytics and your data.

Google Analytics with an Intro to Google Tag Manager for Austin WordPress Meetup. This was an intermediate session where we took a deeper look into Google Analytics. We also introduced Google Tag Manager as a better way to run tracking code on a website.

This document contains questions and answers about configuring and using Google Tag Manager. It covers topics like how Tag Manager can help manage website tags, when tags should fire, setting up triggers, using built-in and custom variables, and setting up tags for Google Analytics tracking and Google Ads conversions/remarketing. The assessments contain multiple choice questions testing understanding of Tag Manager fundamentals, implementing the data layer, and using Tag Manager for analytics and advertising integrations like dynamic remarketing.

This document summarizes a presentation about Google Tag Manager for beginners. The presentation covers introducing Google Tag Manager and what it is used for. It then discusses setting up a basic Google Analytics tracking implementation using Google Tag Manager, including creating a container, tag, and rule. Finally, it discusses enhancing the implementation with a data layer, including defining macros to map to tags and using the data layer to trigger tags on interactions.

At Erudite we like to conduct our own R and D so that we truly understand the competitive landscape. We analysed the Lighthouse speed metrics of 5,000 of the UKs top websites, and categorised them by channel, so that we can better understand mobile site speed in the context of competition.

The document discusses how to run SEO experiments to test changes on websites. It recommends bucketing pages into control and treatment groups to test elements like title tags, meta descriptions, and content. It provides steps for designing an experiment, waiting 2-4 weeks for results, analyzing traffic differences between groups, concluding if results are significant, and iterating on new experiments. It also includes an example of testing alternative title tags on a company's community pages.

While content is essential to many agencies, creating an efficient content production system can be challenging—so let us show you how. In this presentation, Seeker founder, Gareth Simpson, demonstrates how to scale your content marketing efforts by using smart systems and automation. By taking a process-driven approach, you can scale your creative output and keep creatives happy. It’s a win-win.

Sites with any level of content production quickly build up pages that are outdated and left unmanaged, crawl budget can be wasted on low quality pages, penalties may be incurred and organic search visibility can be lost for the pages that matter most on a site. Sam will be sharing a new framework for conducting regular content audits that make use of many data sources but that are time efficient to implement and put you in the best position to make decisions on how to deal with the content on the sites you manage. Sam will examine the different data sources that you can bring together from commonly used tools and how to anchor this with crawl data in unique and original ways to assess onsite engagement and performance in search.

This document discusses optimizing a website's crawl budget for a mobile-first index. It recommends auditing the mobile site, understanding how mobile crawlers view it, and optimizing things like pruning content and internal redirects. It then discusses growing organic revenue as a case study, noting one site migrated to mobile-first and saw a 46% increase in organic traffic over 8 weeks after crawl budget optimization.

1. The document discusses optimizing website speed and performance to meet users' expectations. It outlines techniques like prioritizing above-the-fold content, progressive enhancement, and tailoring experiences based on device and connection. 2. Speed testing tools are recommended to analyze performance like PageSpeed Insights, WebPageTest, Chrome DevTools, and GTmetrix. 3. Future optimizations discussed include using resource hints, the Network Information API, and code improvements like preloading and graceful degradation. Developers should be consulted to learn more.

Here's my list of 10 JavaScript (related) concepts that I think all web analysts should understand at least on a basic level. A solid grasp of JavaScript is a base requirement for anyone working with the web browser.

My slides from the Searchlove Boston conference in May 2016. The presentation covers actionable tips and tricks for working with Google Tag Manager and Google Analytics.

Slides from my first talk at SMX München on March 17, 2015. The talk was about inspiring a critical approach to the metrics and dimensions we access through tools like Google Analytics. Sometimes we have to tweak the data collection mechanism to get more relevant results in our tools. In fact, I want to say that the quality of data in these platforms is directly proportional to your understanding of how the data is collected and aggregated. So be critical! Make the most of the metrics and dimensions, and ensure that the data you're using to grow your business is relevant.

The document discusses how weather data can be collected from website visitors and analyzed. It describes a system that uses a weather API to determine the weather based on a visitor's IP address, then tracks metrics like sessions, revenue and average order value according to the weather type. Implementation details are provided, such as using Google Tag Manager and storing the weather data in a custom dimension for analysis in Google Analytics. Some caveats of the system are also mentioned, such as it only capturing the current weather for a visitor's location.

My slides from the Emerce Conversion 2015 conference. Here's a nice method of reconfiguring a data collection platform such as Google Analytics so it gives you best possible data for YOUR business alone.

Here's the slides from my MeasureCamp presentation on Google Tag Manager, the data layer, and the tool-specific data model. Here's an accompanying blog post as well: http://www.simoahava.com/analytics/google-tag-manager-data-model/

The slides from my talk at GPeC Summit, Romania, on 11 May 2015. I introduce the Enhanced Ecommerce reports for Google Analytics, but before I do, I outline my ideology for using Enhanced Ecommerce. It's not just a flashy set of reports, it's an optimization tool and a hypothesis machine. I'm less interested in successful transactions and more in things like abandonment and lack of engagement. Enhanced Ecommerce lets us expand the somewhat broken concept of a session-based conversion rate, and granularly investigate its components and particles. This way we can analyze not only transactions, visits, and visitors, but the products themselves, too.

Slides from my talk at Google Analytics User Conference in Amsterdam. Some preaching about data collection and then a list of my favorite ways to make GTM and GA data more meaningful to your organization and your unique business goals.

The document discusses Google Analytics and strategies for making metrics more meaningful. It proposes treating blog content like online products by tracking things like page views, scroll depth, and dwell time to measure user engagement. Specific strategies covered include using the Page Visibility API to determine meaningful page views and modeling content engagement metrics after ecommerce metrics like product impressions, add to cart, and purchase. The tips provided emphasize designing data collection with analysis in mind.

The slides from my second talk at SMX München (18 March 2015). I've used Enhanced Ecommerce, implemented via Google Tag Manager, to analyze the content and user funnels on my website, and how people interact with different pieces of content. In these slides, I explain the methodology and the reasoning for such an unconventional approach. It's such a fun experiment, but it also leads to a lot of new insights for content optimization.

Slides from our webinar on "Exploring Big Data value for your business." Delivered by Andy Ormsby. V

Value proposition of open government data - presentation to International Open Government Data Conference by Alexander Howard, Government 2.0 Correspondent, O'Reilly Media

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-compology For more information about embedded vision, please visit: http://www.embedded-vision.com Ben Chehebar, co-founder of Compology, delivers the presentation "Using Vision to Improve Waste Collection Efficiency" at the December 2016 Embedded Vision Alliance Member Meeting. Chehebar describes a novel vision-based solution that is dramatically improving the efficiency of trash collection.

This document compares healthcare in the Industrial Age to the Information Age. In the Industrial Age, healthcare was professional-focused and encouraged tertiary and secondary care over primary care and self-care. The Information Age encourages individual self-care, friends/family support, self-help networks, and professionals as facilitators rather than authorities. The document suggests we are currently in a transition period between these two models of healthcare.

We studied impact of the Big Data phenomenon on SMBs. We conducted interviews among 30 SMBs to check: - Big Data understanding by SMBs - Adoption level of Big Data services - Value creation and go-to-market channels - Pain points in adopting Big Data

Presentazione utilizzata da Gijs Langeveld, Dutch Waste Management Association, durante il suo intervento alla conferenza internazionale Milano Recycle City, che si è svolta il 6 giugno 2014 presso la Fabbrica del Vapore di Milano

The document discusses Dominique Guinard's research into making products smart and connected through the Internet of Things. It describes his development of a Web of Things architecture including layers for device accessibility, findability, sharing, and composition. This research led to the founding of EVRYTHNG to provide an IoT backend platform. The company uses agile development practices and has clients in various industries tagging physical objects and assets.

This presentation presents a comprehensive model and the relations between Innovation, Business Model, Business/Industry, Open Data and Entrepreneurship. Later slides presents Open Data Business Model (the 6-Value Model) and Open Data Capability Matrix.

The Industrial Data Space is a strategic initiative driven by industry and supported by the German Federal Government. It aims at supporting the secure exchange and easy combination of data within ecosystems.

The document describes Adnologies' data management platform (DMP) which allows brands and agencies to better manage consumer data across multiple touchpoints and devices. The DMP provides a single consumer profile, supports real-time segmentation and activation of audiences, and offers data management and advertising activation features. Using the DMP helps improve marketing efficiency, create deeper customer insights, and increase commercial success through more personalized advertising.

The content of the document, "Implementing Data Mesh: Six Ways That Can Improve the Odds of Your Success," is a whitepaper authored by Ranganath Ramakrishna from LTIMindtree. The whitepaper introduces the concept of Data Mesh, a socio-technical paradigm that aims to help organizations fully leverage the value of their analytical data.

Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning. In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others. Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long. With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.

Keyrus is a data analytics consultancy that helps customers make data-driven decisions. It provides services including big data solutions, data management strategies, data integration, business intelligence dashboards, predictive analytics, and data science consulting. Keyrus has expertise in structured and unstructured data, data discovery visualization tools, and building end-to-end analytics solutions. Sample projects include building Hadoop environments for large telecom data and creating risk monitoring dashboards for investment banks.

Keyrus is a data analytics consultancy that helps customers make data-driven decisions. It provides services including big data solutions, data management strategies, data integration, machine learning, predictive analytics, and data visualization dashboards. Keyrus consultants have skills in databases, data modeling, programming, and business requirements. For example, for a bank, Keyrus built interactive dashboards from multiple databases to provide regulators with risk monitoring dashboards.

Businesses make critical decisions using key data assets, but stakeholders often find it difficult to navigate the complex data landscape to ensure they have the right data and understand it correctly. Companies are dealing with a number of different technologies, multiple data formats, and high data volumes, along with the requirements for data security and governance.

On November 6th, we got together at Google Campus to talk about Mesos and DC/OS. Ignacio Mulas, Sparta & Spark Product Owner at Stratio, explained how to build an environment that can secure and govern its data for operational and analytical applications on top of DC/OS platform. He showed that analytical and machine learning pipelines can be combined with operational processes maintaining the security and providing governing tools to manage our data. He focused on the architecture and tools needed to achieve an ecosystem like this and we will show a demo of it. He also explained how we can develop our pipelines interactively with auto-discovered data catalogs and explore our results. Find out more: https://www.stratio.com/events/discover-how-to-deploy-a-secure-big-data-pipeline-with-dcos/

Title DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed. Synopsis ● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure. ● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities. ● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product. ● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills. ● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products. ● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.

This document discusses how a big data fabric can enable machine learning and artificial intelligence by providing a flexible and agile way for users to access and analyze large amounts of data from various sources. It explains that a big data fabric, powered by data virtualization, allows organizations to build a modern data ecosystem that provides governed access to both structured and unstructured data stored in different systems. This helps users develop new production analytics and insights. The document also provides an example of how Logitech used a big data fabric and data virtualization to improve their customer analytics.

Many organizations fail to build a proper foundation for their martech-empowered use cases, in particular around customer data. They’ll often end up with huge third-party dependencies, paying a steep premium on the total cost of ownership while getting outcomes below their expectation. Dealing with customer data like a pro is essential to gain deep insights into customer journeys, and significantly improve user acquisition, conversion, and retention. But it doesn’t end there: Influencing and personalizing the customer experience in real-time is equally important. Join Snowplow to learn how organizations can build a sustainable first-party approach to martech with higher ROIs that goes beyond betting everything on third-party solutions like Google Analytics or CDPs. After this session, you’ll be able to: *Empower your organization to create a scalable foundation for martech that delivers high ROIs *Leverage best-in-class behavioral customer data to skyrocket your Customer 360 and further advanced use cases *Maintain control, ownership, and full compliance at all times

The document discusses master data management (MDM). It defines MDM as combining data governance practices with software tools to achieve a single version of the truth across systems. It then lists several market trends driving increased adoption of MDM, including MDM in the cloud, growing MDM software sales, rising information volumes, increased recognition of data's importance, and costs of poor data quality. The document also outlines how MDM can generate value in areas like customer/supplier relationships, engineering productivity, inventory costs, and procurement costs. Finally, it discusses common data issues that MDM can help solve and provides examples of potential solutions.

Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020. Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms. Data lakes will be built in cloud object storage. We’ll discuss the options there as well. Get this data point for your data lake journey.

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Information Builders provides the industry’s most scalable software solutions for data management and analytics. We help organizations operationalize and monetize their data through insights that drive action. Our integrated platform for BI, analytics, data integration, and data quality, combined with our proven expertise, delivers value faster, with less risk. We believe data and analytics are the drivers of digital transformation, and we’re on a mission to help our customers capitalize on new opportunities in the connected world. Information Builders is headquartered in New York, NY, with global offices, and remains one of the largest privately held companies in the industry.

William McKnight, President of McKnight Consulting Group and Information Builders’ Jake Freivald discuss the tools needed for a successful modern data integration.

The document discusses how a semantic "data lake" can help organizations extract meaning and insights from large amounts of digital data. A data lake combines data from different sources and uses semantic models, tagging, and algorithms to help users more quickly find relevant data relationships and insights. It describes how semantic technology plays a key role in data ingestion, management, modeling of different views, querying, and exposing analytics as web services to create personalized customer experiences.

Our architecturally solid stool requires three legs: people, process, and technologies. This webinar looks at the most misunderstood of these three components: technology. While most organizations begin with technologies, it turns out that technologies are the last component that should be considered. This webinar will survey a range of Data Management technologies that can be used to increase the productivity of Data Management efforts.

Major systems integrators and boutique consultancies will focus on developing data governance frameworks in 2018-2019, while large enterprises struggle to implement effective enterprise-wide data governance. Data governance is crucial for master data management project success but currently many organizations only have basic, reactive governance capabilities. By 2020, major vendors hope to provide more proactive governance solutions integrated with data management technologies. Effective data governance requires balancing people, processes, and tools across the organization.