Fully embracing a BI tool can mean the difference between the full payoff of your data analytics and returns that are just so-so. Learn how to avoid BI pitfalls and boost BI adoption to become a truly data driven organisation.
5 Essential Practices of the Data Driven OrganizationVivastream
The document discusses five essential practices of data-driven organizations: 1) defining key performance indicators, 2) deploying analytics tools expertly across channels, 3) analyzing results and making recommendations, 4) creating changes based on data, and 5) measuring results continuously. It emphasizes the importance of standardization, governance, accuracy, and having a repeatable process for using data to optimize digital properties and drive business goals.
Reaktor is a data science company with 8 PhDs and expertise in using data to optimize business challenges through personalization, recommendations, marketing impact analysis, and other uses. They follow an agile data science project model of optimizing actions using big and small data from various sources to generate insights and information for business drivers through iterative modelling, data wrangling, and testing. Some ideals of being data-driven include being curious, active in testing solutions rather than just observing, understanding uncertainties, acting on evidence courageously but also learning quickly through an agile process while maintaining transparency.
Creating a Data-Driven Organization (Data Day Seattle 2015)Carl Anderson
Creating a Data-Driven Organization
The document discusses how to create a data-driven organization. It argues that being data-driven requires having strong analytics, a data-focused culture, and using data to drive impact and business results. Some key aspects of a data-driven culture discussed are having a testing mindset, open data sharing, self-service analytics access for business units, broad data literacy, and visible data leadership. The presentation provides examples of actions organizations can take to promote a data-driven culture, such as improving analyst competencies and linking metrics to strategic goals. It cautions that becoming complacent once progress is made can undermine data-driven efforts, as demonstrated by Tesco's experience.
From insight to action - data analysis that makes a difference! - Heena JethwaIBM SPSS Denmark
Presentation from an IBM Business Analytics seminar, held the 22th of november 2012 at IBM Client Center Nordic.
Description:
Global competition has increased, and the need to meet customer demands has never been more important. It is essential that all parts of the company work efficiently to achieve success. IBM SPSS Predictive Analytics can help you increase efficiency and reduce costs at every stage of your operational processes. Predictive Analytics helps your organization to capture structured and textual data, so you can better manage its assets, maintain the infrastructure and capital equipment, as well as maximize the performance of your people, processes and assets.
Heena Jethwa, Program Director - Predictive Analytics Market Strategy, IBM
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
Creating a data-driven organization requires developing a data-driven culture. Key aspects of a data-driven culture include having a strong testing culture that encourages hypothesis generation and experimentation, an open and sharing culture without data silos, a self-service culture where business units have necessary data access and analytical skills, and broad data literacy across all decision makers. Ultimately, an organization is data-driven when it uses data to drive impact and business results by pushing data through an analytics value chain from collection to analysis to decisions and actions. Maintaining a data-driven culture requires continuous effort as well as data leadership from a chief data or analytics officer.
Creating a Data-Driven Organization, Data Day Texas, January 2016Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
This document discusses the development of a data strategy for an organization. It begins by introducing the presenter and organization. It then covers why a data strategy is needed to address common data issues. The strategy should define what the data team will and will not do. Developing the strategy requires gathering information, consulting other teams, and linking it to the organization's mission. Key aspects of the strategy include objectives, principles, delivery areas, and ensuring it is concise enough to be accessible and remembered.
5 Essential Practices of the Data Driven OrganizationVivastream
The document discusses five essential practices of data-driven organizations: 1) defining key performance indicators, 2) deploying analytics tools expertly across channels, 3) analyzing results and making recommendations, 4) creating changes based on data, and 5) measuring results continuously. It emphasizes the importance of standardization, governance, accuracy, and having a repeatable process for using data to optimize digital properties and drive business goals.
Reaktor is a data science company with 8 PhDs and expertise in using data to optimize business challenges through personalization, recommendations, marketing impact analysis, and other uses. They follow an agile data science project model of optimizing actions using big and small data from various sources to generate insights and information for business drivers through iterative modelling, data wrangling, and testing. Some ideals of being data-driven include being curious, active in testing solutions rather than just observing, understanding uncertainties, acting on evidence courageously but also learning quickly through an agile process while maintaining transparency.
Creating a Data-Driven Organization (Data Day Seattle 2015)Carl Anderson
Creating a Data-Driven Organization
The document discusses how to create a data-driven organization. It argues that being data-driven requires having strong analytics, a data-focused culture, and using data to drive impact and business results. Some key aspects of a data-driven culture discussed are having a testing mindset, open data sharing, self-service analytics access for business units, broad data literacy, and visible data leadership. The presentation provides examples of actions organizations can take to promote a data-driven culture, such as improving analyst competencies and linking metrics to strategic goals. It cautions that becoming complacent once progress is made can undermine data-driven efforts, as demonstrated by Tesco's experience.
From insight to action - data analysis that makes a difference! - Heena JethwaIBM SPSS Denmark
Presentation from an IBM Business Analytics seminar, held the 22th of november 2012 at IBM Client Center Nordic.
Description:
Global competition has increased, and the need to meet customer demands has never been more important. It is essential that all parts of the company work efficiently to achieve success. IBM SPSS Predictive Analytics can help you increase efficiency and reduce costs at every stage of your operational processes. Predictive Analytics helps your organization to capture structured and textual data, so you can better manage its assets, maintain the infrastructure and capital equipment, as well as maximize the performance of your people, processes and assets.
Heena Jethwa, Program Director - Predictive Analytics Market Strategy, IBM
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
Creating a data-driven organization requires developing a data-driven culture. Key aspects of a data-driven culture include having a strong testing culture that encourages hypothesis generation and experimentation, an open and sharing culture without data silos, a self-service culture where business units have necessary data access and analytical skills, and broad data literacy across all decision makers. Ultimately, an organization is data-driven when it uses data to drive impact and business results by pushing data through an analytics value chain from collection to analysis to decisions and actions. Maintaining a data-driven culture requires continuous effort as well as data leadership from a chief data or analytics officer.
Creating a Data-Driven Organization, Data Day Texas, January 2016Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
This document discusses the development of a data strategy for an organization. It begins by introducing the presenter and organization. It then covers why a data strategy is needed to address common data issues. The strategy should define what the data team will and will not do. Developing the strategy requires gathering information, consulting other teams, and linking it to the organization's mission. Key aspects of the strategy include objectives, principles, delivery areas, and ensuring it is concise enough to be accessible and remembered.
This webinar discussed the purpose of data analytics and how it can be a light in the darkness for your organization to make better decisions for the future. The webinar covered the purpose of data analysis and its definition, the fundamental steps to take to perform data analysis to problem solve, and closed with next steps that attendees can take to further develop data analysis and business intelligence within their organizations.
During this webinar, attendees learned about the following:
- How data analytics functions to help your organization improve.
- The process for using data analytics to solve problems.
- Next steps to take to build data analysis within your organization.
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.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
Building a Data-Driven Culture by Olof Hoverfält discusses how to build a data-driven culture at Sanoma Games. Key points include:
1) Being data-driven requires an organization that supports lean development, a data-driven culture with accessible tools, shared goals, and management that fosters self-direction.
2) A data-driven culture is built through intrinsic motivation by making the benefits of data visible, not through coercion. Transparency, autonomy and ownership are important.
3) Continuous hypothesis-driven testing should be the standard approach across functions to gain insights and steer development initiatives toward business goals.
Five steps to launch your data governance officeDATAVERSITY
About the Webinar
DATAVERSITY and OONdada have teamed up to create a new, innovative Data Governance tool called DATAVERSITY DGO. This webinar will educate you on the philosophy behind that tool as it contains the processes, instructions, and guidance from the Data Governance Institute's Framework and Methodology.
In this webinar, you will learn the five fundamental steps to launching a Data Governance Office:
1. Establish Goals
2. Engage Stakeholders
3. Align Governance
4. Exercise Governance
5. Communicate Success
About the Speaker
Max Gano has an extensive background in data management, serving as Chief Data Architect for a Fortune 500 Bank. He is passionate about contributing to the increasing maturity of data management and governance practices as critical capabilities for any organization that seeks sustainable prosperity and growth. As the Principle Data Strategist for the Data Governance Institute, he worked with a range of major corporations and industry thought leaders to formulate successful strategies for governing and managing information and the operational processes required to achieve those objectives. With that goal in mind, Max has now turned his attention to establishing OONdada as a leader in online collaboration services.
Data Driven Culture with Slalom's Director of AnalyticsPromotable
Everyone wants to capture the benefits of big data by making better data driven decision. We are inundated by analytical tools that deliver "insights" and process information quickly.
Although often overlooked, creating a data driven culture is as important as finding the right tools to make data driven decisions. Organizations who skip this foundational element often find their investment in Data tools and personal don't yield the benefits that becoming Data Driven is supposed to unlock.
In this talk you'll learn about why creating a Data Driven culture is vital to every organization and the starting point for ensuring your data strategy generates strong impact and ROI.
Takeaways:
What is a data driven culture? Where does it start?
What happens when you implement tools (tableau, power BI, Machine Learning, etc) without first having a data driven culture
Stages of a Data Driven Culture?
How to get started?
Your Instructor: Kevin Chapin is the Practice Director for Data and Analytics at Slalom Consulting.
To see the full talk, click here: https://www.youtube.com/watch?v=7xNLgiK31Is
In times of digitalization, every aspect of our life is connected to data. To leverage this data, companies need to understand and master analytics. In this presentation, Leo Marose will guide you through the world of big data & data science and show you his approach of how to build a data-driven organization.
Data is becoming an engine for many businesses in the information age, and every company needs to consider look at how that feels in their business model.
This an introductory guest lecture for students at Stockholm School of Entrepreneurship.
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
This document outlines a presentation on developing a data-centric strategy and roadmap. It discusses the importance of aligning data management goals to business needs through frameworks like Porter's competitive strategies and operating models. Metrics and success criteria must be defined by collaborating with business partners to measure improvements in specific opportunities. An example shows how a chemical company measured reductions in testing time and increases in researcher productivity after implementing a solution to integrate data across disparate systems.
The document discusses why 87% of data science projects fail to make it into production. It identifies three main reasons for failure: data is inaccurate, siloed and slow; there is a lack of business readiness; and operationalization is unreachable. To address these issues, the document recommends establishing data governance, defining an organizational data science strategy and use cases, ensuring the technology stack is updated, and having data scientists collaborate with data engineers. It also provides tips for successful data science projects, such as having short timelines, small focused teams, and prioritizing business problems over solutions.
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
Develop and Implement an Effective Data Management Strategy and Roadmap Info-Tech Research Group
Treat data as an asset and gain a competitive advantage.
Your Challenge
Despite the growing focus on data, many organizations struggle to develop an effective strategy for their data assets. This is due to their intangible nature and varying use across the business.
Data Management is a business process managed by IT. This creates a challenge for IT as it is required to create and manage complex systems of operations that link closely to integral business operations.
Our Advice
Critical Insight
Data Management is not one size fits all. Cut through the noise related to Data Management and create a strategy and process that is right for your organization.
Have the business drive your Data Management project.
It all starts and ends with Data Governance. At a minimum, invest in Data Governance initiatives.
Impact and Result
Coordination between IT and the business will create a Data Management strategy that understands and satisfies the data requirements of the business.
Data Management requirements and initiatives will be derived from the following: business goals and strategic plans, current capability assessments, business drivers for data, understanding of market and technology opportunities, and a clear understanding of the business’s drivers regarding data.
Creating a clear Data Management Strategy and developing a roadmap of initiatives will allow IT to create a plan for how to bridge the gap between IT and the business and create a Data Management framework that supports the business’s immediate and long-term data requirements.
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
A presentation about a few current consumer trends and how they may apply to charities, covering AR (but not VR), data and AI, transparency and growth hacking.
The document discusses developing an analytics strategy to drive healthcare transformation. It begins by outlining signs an analytics strategy is needed, such as having dashboards but no improvement. It then discusses components of an effective analytics strategy, including understanding business context, stakeholders, processes and data, tools and techniques, team and training, and technology. The strategy ensures analytics align with goals and avoids just collecting reports. Developing the strategy involves understanding requirements, identifying gaps, and executing the plan. The strategy provides a framework to guide analytics development and ensure optimal use of resources.
How do you get a job in data science? Knowing enough statistics, machine learning, programming, etc to be able to get a job is difficult. One thing I have found lately is quite a few people may have the required skills to get a job, but no portfolio. While a resume matters, having a portfolio of public evidence of your data science skills can do wonders for your job prospects. Even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is important. This is a talk based on my original blog on Building a Data Science Portfolio: https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c
The document discusses how to turn data into actionable insights through a multi-step process. It outlines two case studies where this process was applied. For the first case of increasing low-performing store performance, the process identified cross-selling as a hypothesis, tested it with store data, and found opportunities to improve layout, staffing, and skills. For the second case of finding new fitness center locations, the process developed a model to estimate revenues in different catchment areas and identified optimal new locations based on potential revenues.
Wikibon Big Data Capital Markets Day 2014Jeff Kelly
This document discusses the big data market and winners and losers. It finds that traditional companies like Oracle, Teradata, and SAP are under pressure as newer big data technologies like Hadoop and NoSQL have seen rapid growth. While the big data market is expected to be worth over $50 billion by 2017, practitioners have faced barriers adopting big data within their organizations. Overall, practitioners are seen as the biggest winners from leveraging big data, though the market remains in early stages.
This webinar discussed the purpose of data analytics and how it can be a light in the darkness for your organization to make better decisions for the future. The webinar covered the purpose of data analysis and its definition, the fundamental steps to take to perform data analysis to problem solve, and closed with next steps that attendees can take to further develop data analysis and business intelligence within their organizations.
During this webinar, attendees learned about the following:
- How data analytics functions to help your organization improve.
- The process for using data analytics to solve problems.
- Next steps to take to build data analysis within your organization.
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.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
Building a Data-Driven Culture by Olof Hoverfält discusses how to build a data-driven culture at Sanoma Games. Key points include:
1) Being data-driven requires an organization that supports lean development, a data-driven culture with accessible tools, shared goals, and management that fosters self-direction.
2) A data-driven culture is built through intrinsic motivation by making the benefits of data visible, not through coercion. Transparency, autonomy and ownership are important.
3) Continuous hypothesis-driven testing should be the standard approach across functions to gain insights and steer development initiatives toward business goals.
Five steps to launch your data governance officeDATAVERSITY
About the Webinar
DATAVERSITY and OONdada have teamed up to create a new, innovative Data Governance tool called DATAVERSITY DGO. This webinar will educate you on the philosophy behind that tool as it contains the processes, instructions, and guidance from the Data Governance Institute's Framework and Methodology.
In this webinar, you will learn the five fundamental steps to launching a Data Governance Office:
1. Establish Goals
2. Engage Stakeholders
3. Align Governance
4. Exercise Governance
5. Communicate Success
About the Speaker
Max Gano has an extensive background in data management, serving as Chief Data Architect for a Fortune 500 Bank. He is passionate about contributing to the increasing maturity of data management and governance practices as critical capabilities for any organization that seeks sustainable prosperity and growth. As the Principle Data Strategist for the Data Governance Institute, he worked with a range of major corporations and industry thought leaders to formulate successful strategies for governing and managing information and the operational processes required to achieve those objectives. With that goal in mind, Max has now turned his attention to establishing OONdada as a leader in online collaboration services.
Data Driven Culture with Slalom's Director of AnalyticsPromotable
Everyone wants to capture the benefits of big data by making better data driven decision. We are inundated by analytical tools that deliver "insights" and process information quickly.
Although often overlooked, creating a data driven culture is as important as finding the right tools to make data driven decisions. Organizations who skip this foundational element often find their investment in Data tools and personal don't yield the benefits that becoming Data Driven is supposed to unlock.
In this talk you'll learn about why creating a Data Driven culture is vital to every organization and the starting point for ensuring your data strategy generates strong impact and ROI.
Takeaways:
What is a data driven culture? Where does it start?
What happens when you implement tools (tableau, power BI, Machine Learning, etc) without first having a data driven culture
Stages of a Data Driven Culture?
How to get started?
Your Instructor: Kevin Chapin is the Practice Director for Data and Analytics at Slalom Consulting.
To see the full talk, click here: https://www.youtube.com/watch?v=7xNLgiK31Is
In times of digitalization, every aspect of our life is connected to data. To leverage this data, companies need to understand and master analytics. In this presentation, Leo Marose will guide you through the world of big data & data science and show you his approach of how to build a data-driven organization.
Data is becoming an engine for many businesses in the information age, and every company needs to consider look at how that feels in their business model.
This an introductory guest lecture for students at Stockholm School of Entrepreneurship.
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
This document outlines a presentation on developing a data-centric strategy and roadmap. It discusses the importance of aligning data management goals to business needs through frameworks like Porter's competitive strategies and operating models. Metrics and success criteria must be defined by collaborating with business partners to measure improvements in specific opportunities. An example shows how a chemical company measured reductions in testing time and increases in researcher productivity after implementing a solution to integrate data across disparate systems.
The document discusses why 87% of data science projects fail to make it into production. It identifies three main reasons for failure: data is inaccurate, siloed and slow; there is a lack of business readiness; and operationalization is unreachable. To address these issues, the document recommends establishing data governance, defining an organizational data science strategy and use cases, ensuring the technology stack is updated, and having data scientists collaborate with data engineers. It also provides tips for successful data science projects, such as having short timelines, small focused teams, and prioritizing business problems over solutions.
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
Develop and Implement an Effective Data Management Strategy and Roadmap Info-Tech Research Group
Treat data as an asset and gain a competitive advantage.
Your Challenge
Despite the growing focus on data, many organizations struggle to develop an effective strategy for their data assets. This is due to their intangible nature and varying use across the business.
Data Management is a business process managed by IT. This creates a challenge for IT as it is required to create and manage complex systems of operations that link closely to integral business operations.
Our Advice
Critical Insight
Data Management is not one size fits all. Cut through the noise related to Data Management and create a strategy and process that is right for your organization.
Have the business drive your Data Management project.
It all starts and ends with Data Governance. At a minimum, invest in Data Governance initiatives.
Impact and Result
Coordination between IT and the business will create a Data Management strategy that understands and satisfies the data requirements of the business.
Data Management requirements and initiatives will be derived from the following: business goals and strategic plans, current capability assessments, business drivers for data, understanding of market and technology opportunities, and a clear understanding of the business’s drivers regarding data.
Creating a clear Data Management Strategy and developing a roadmap of initiatives will allow IT to create a plan for how to bridge the gap between IT and the business and create a Data Management framework that supports the business’s immediate and long-term data requirements.
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
A presentation about a few current consumer trends and how they may apply to charities, covering AR (but not VR), data and AI, transparency and growth hacking.
The document discusses developing an analytics strategy to drive healthcare transformation. It begins by outlining signs an analytics strategy is needed, such as having dashboards but no improvement. It then discusses components of an effective analytics strategy, including understanding business context, stakeholders, processes and data, tools and techniques, team and training, and technology. The strategy ensures analytics align with goals and avoids just collecting reports. Developing the strategy involves understanding requirements, identifying gaps, and executing the plan. The strategy provides a framework to guide analytics development and ensure optimal use of resources.
How do you get a job in data science? Knowing enough statistics, machine learning, programming, etc to be able to get a job is difficult. One thing I have found lately is quite a few people may have the required skills to get a job, but no portfolio. While a resume matters, having a portfolio of public evidence of your data science skills can do wonders for your job prospects. Even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is important. This is a talk based on my original blog on Building a Data Science Portfolio: https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c
The document discusses how to turn data into actionable insights through a multi-step process. It outlines two case studies where this process was applied. For the first case of increasing low-performing store performance, the process identified cross-selling as a hypothesis, tested it with store data, and found opportunities to improve layout, staffing, and skills. For the second case of finding new fitness center locations, the process developed a model to estimate revenues in different catchment areas and identified optimal new locations based on potential revenues.
Wikibon Big Data Capital Markets Day 2014Jeff Kelly
This document discusses the big data market and winners and losers. It finds that traditional companies like Oracle, Teradata, and SAP are under pressure as newer big data technologies like Hadoop and NoSQL have seen rapid growth. While the big data market is expected to be worth over $50 billion by 2017, practitioners have faced barriers adopting big data within their organizations. Overall, practitioners are seen as the biggest winners from leveraging big data, though the market remains in early stages.
Data-Driven Innovation: 3 Ways to Create a New Level of Performance in Your O...Travis Barker
The document discusses 3 ways that organizations can use data-driven innovation to improve performance: 1) business process optimization through strategic alignment, automation, and risk mitigation; 2) enhanced customer intimacy by improving customer satisfaction, loyalty, and upselling; and 3) product and service innovation such as reducing costs, creating new revenue streams, and enabling open government collaboration. The role of finance is expanding to provide strategic insights and CFOs need skills in areas beyond finance like IT and commercial skills. Data-driven innovation is an opportunity for the finance function.
Create your Big Data vision and Hadoop-ify your data warehouseJeff Kelly
The document discusses big data market trends and provides advice on how organizations can develop a big data strategy and implementation plan. It outlines a 5 step approach for modernizing an organization's data warehouse with new big data technologies: 1) enhancing the data warehouse with unstructured data, 2) extending it with data virtualization, 3) increasing scalability with MPP databases, 4) accelerating analytics with in-database processing, and 5) creating an operational data store with Hadoop. The document also provides tips for selecting big data vendors, such as evaluating a vendor's ability to integrate with existing systems and make analytics accessible to both power users and business users.
The document is an introduction to big data presentation by Mohammed Guller. It discusses key big data concepts like volume, variety and velocity of data. It introduces big data technologies like Hadoop and Spark and how they address challenges of storage, processing and extracting value from large datasets. Specific technologies covered include Kafka for messaging, HDFS and MapReduce in Hadoop, and Spark's speed and programming model. The presenter's background and a book on big data analytics are also mentioned.
This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.
Becoming Data-Driven Through Cultural ChangeCloudera, Inc.
We've arrived at a crossroads. Big data is an initiative every business knows they should take on in order to evolve their business, but no one knows how to tackle the project.
This is the first in a series of webinars that describe how to break down the challenge into three major pieces: People, Process, and Technology. We'll discuss the industry trends around big data projects, the pitfalls with adopting a modern data strategy, and how to avoid them by building a culture of data-driven teams.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
Lecture on Data Science in a Data-Driven Culture Johan Himberg
The document discusses the importance of a data-driven culture for businesses. It provides the following key points:
1. Research has shown that companies that emphasize data-driven decision making have 5-6% higher productivity and output than comparable companies. This relationship also appears in other financial metrics like return on equity.
2. Data science draws from various fields like operations research, probability theory, analytics, and computer science. It is used for optimal decision making, handling uncertainties, generating insights from data, and implementing analytical solutions.
3. When adopting a data-driven approach, companies should focus on specific business goals and KPIs rather than just collecting data. Iterative testing is also important to measure impact
This presentation by Gartner discusses big data industry insights and trends. It provides an overview of organizations' investments in big data technology, the challenges they face in adoption, the types of big data being analyzed now and planned for the future, and examples of how different industries are using big data to address key business problems.
Consumer Insights: Finding and Guarding the Treasure TroveCapgemini
Consumer Product (CP) companies operate in an industry where the fundamental rules of the game are changing. The growth of e-commerce, the ability to bypass retailers, the rise of private labels, and the advent of niche CP startups are just some of the trends that are reshaping the sector.
But one significant change that stands out in particular is the direct connection that CP companies today have to the needs and aspirations – the ‘pulse’ – of consumers. This is, to a large extent, thanks to the rise of digital channels.
The customer journey could essentially be divided into 7 elements. We’ll touch upon the issue of ‘Privacy’ and how one balance social and commercial value. Practical examples of
customer analytics at its best will be discussed as well as the importance of the eco-system.
This document outlines a five-stage process for building a data-driven marketing strategy. The stages are: 1) Make data a habit by defining key performance indicators; 2) Audit your current data landscape to understand what data you have; 3) Identify gaps in your data and strategies to fill them; 4) Commit to improving data quality; and 5) Leverage technology to turn raw data into insights. Following these stages will help organizations avoid common pitfalls and create an effective data-driven marketing strategy.
Read this blog to understand agile development and its digital transformation deeply, as agile digital transformation occur through continuous innovation.
Vertex aims to establish an analytical data repository and business intelligence program to extract value from information silos. The summary proposes a strategic framework with the following elements:
1. Establish a BI Competency Center to provide leadership and governance over the program.
2. Implement a BI Foundation consisting of standards, skills, processes, and technologies to evolve the organization from being data-constrained to information-enabled.
3. Take an incremental approach, first addressing current needs while building capabilities to support more advanced, strategic analytics and proactively manage the business over time.
Occam - Building Your Own Data-driven Marketing StrategyRoger Stevens
This document outlines a five-stage strategy for building a data-driven marketing strategy. The stages are: 1) Make data a habit by defining key performance indicators; 2) Analyze your data landscape by auditing what data you have; 3) Fill data gaps by gathering needed data while respecting customer privacy; 4) Commit to data quality by investing in people, processes and technology; 5) Leverage technology to turn raw data into insights. Implementing this strategy in a careful, step-by-step manner can help marketers avoid common pitfalls and ensure their data delivers actionable insights to inform decisions.
This document discusses how business intelligence (BI) can help mid-sized organizations improve decision making. It provides examples of signs an organization may need BI, such as disagreements over data or inability to perform in-depth analysis. BI allows organizations to integrate data from various sources to get a complete view. It can be used to track key metrics, identify trends, and ensure regulatory compliance. The document outlines important components and benefits of BI, as well as factors to consider when selecting BI products and vendors, such as ease of use, scalability, and training capabilities.
BUSINESS INTELLIGENCE (BI) - Definition, Process, and BenefitDipstrategy
have you known what business intelligence is? the process? and also the benefit for your decision making. business intelligence become completely useful information to be a supporting system for decision making
The best preforming companies know they have to translate the abstract into concrete every day principles relying on their own uniquely developed talent and competencies. These organisations design and build their own specific skills to set them apart from competitors. They then bring those capabilities to scale.
This document provides information on becoming a data-driven business, including recognizing opportunities where big data can benefit a company. It discusses integrating big data by identifying opportunities, building future capability scenarios, and defining benefits and roadmaps. It also outlines six data business models: product innovators, system innovators, data providers, data brokers, value chain integrators, and delivery network collaborators. An example is given for each model.
Understanding the Meaning and Benefits of Staff Augmentation in Today's Busin...augmentation World
staff augmentation has become a revolutionary strategy for companies to succeed in the face of constantly shifting customer expectations. It is a crucial instrument for fostering growth and adaptability due to its versatility and capacity to meet certain demands. By accepting staff augmentation, businesses may access a variety of skill sets without making a long-term commitment, promoting flexibility and efficiency. Collaboration, information exchange, and creative problem-solving are encouraged when augmented workers are seamlessly integrated into current teams. Utilizing the potential of staff augmentation becomes a strategic requirement to remain ahead of the curve and achieve sustainable success as the business landscape continues to change.
Are you making the most of your information assets?
What's the Issue?
Local authorities are still under pressure to deliver a multitude of quality services and facilities to the community with a declining workforce and ever tightening budget constraints.
Manual paper-based processes, lack of real-time information and onerous information governance slows the pace of these business processes.
How do you become more efficient with your existing resources?
What's the Answer?
By developing an organisation wide Information Management Strategy to identify and extract real business value from their information assets and through the streamlining and automation of their document centric business processes, local government organisations are able to increase their efficiency immediately.
Discover how your organisation could increase efficiencies by adopting Objective's agile approach to better Information Management, which will allow your organisation to move towards its goal in four small, manageable steps.
Discover more at www.objective.co.uk/foursteps
There's a gap in #nonprofits...and building infrastructure can support creating a tech-centric sector.
In the 2014 Nonprofit Technology Network's study on technology investments, we learn, "nonprofits feel relatively confident that they have the tools to do their every-day work, but are less confident about having enough skilled staff or training to effectively use their technology for their work." This session will discuss the challenges organizations face in closing the infrastructure gap, creating a tech-centric culture and how leaders can make the shifts. We will also cover the specific skills needed in various areas of technology, opportunities for enhancing data usage, and how to expand collaborations within our sector.
How to successfully implement Business Intelligence into your organisation.
A completely agnostic and independent view from a market leader in delivering technology transformation.
Details on how to build a strategy to successfully execute on and more importantly how to get the business to adopt Business Intelligence into their day to day role.
Essential tool kit for any organisation looking to invest in Business Intelligence.
This article describes 10 Architecture Solution Design principles to help organization focus their solution architecture teams around simple but effective design criteria.
Key Advantages of Data Analytics Outsourcing - By DataToBizKavika Roy
Outsourcing data analytics can speed up the data transformation journey of a business. Here are the key advantages of outsourcing data analytics.
To Read the Full Article: https://www.datatobiz.com/blog/advantages-of-outsourcing-data-analytics/
Information Rich, Knowledge Poor: Overcoming Insurers’ Data ConundrumDeloitte United States
The ability to effectively harvest and harness data across the enterprise is quickly emerging as a competitive differentiator in the financial services industry. In the insurance sector specifically, a number of pioneers are already making healthy strides toward mastering information management, but for most companies that have not yet fully invested in this transformation, growing market mania around "Big Data" and looming regulatory changes that demand increased data transparency continue to generate considerable anxiety.
While many insurers have already spent and continue to spend heavily on core-system and technology modernization, most still find their efforts have fallen short of expectations and needs when it comes to information management. If data is expected to be realized as a strategic asset, insurers can no longer continue to merely tweak existing systems and business models to clear this data management hurdle.
However, operationalizing information management enterprise-wide is neither an easy nor short-term exercise, as demonstrated by programs already under way at companies that have pioneered the effort. But for many, the potential benefits to be derived from successfully organizing, governing, consuming and analyzing available data assets — both internal and external — are likely well worth the investment.
Still, to achieve holistic data fluency, optimize data exploitation and realize a positive ROI, insurers will need to dismantle numerous roadblocks embedded in their current infrastructure, hardware and software, corporate culture, and business models.
Information rich, knowledge poor explores challenges and potential solutions to mastering information management and realizing data as a strategic asset.
5 keys to digital transformation for small businessesSameerShaik43
Digital transformation is crucial for small businesses to overcome competition and derive benefits like reduced costs, improved efficiency and profits. There are five keys to effective digital transformation for small businesses: (1) Provide data to empower employees to make better decisions, (2) Break down silos and boost collaboration across departments, (3) Involve all levels of employees to develop the right business culture and vision, (4) Integrate business systems seamlessly for improved workflows and collaboration, (5) Partner with technology experts to optimize resources and implement the right strategies.
Similar to Becoming a Data Driven Organisation (20)
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
2. 1. CREATE A CULTURE OF
TRANSPARENCY
Spread the data across the
organisation
Improve information sharing between employees
and eradicate information silos by having all data
available to the whole organisation.
1
3. 2. CREATE A BUY-IN
FROM THE TOP
Encourage Top-Down Leadership
For this to be successful, Executives have to be on
board and embrace this strategy. Their influence,
example and leadership will help increasing the
commitment among the employees.
2
4. 3. SET MEASURABLE
GOALS
Prepare for what is about to come
The way your company works and its managing
processes is about to change. It will be easy to loose
sight from the final goal. Start by drawing a concise
BI and analytical plan with conceivable objectives
before implementing the strategy.
3
5. 4. GET THE NECESSARY
IT SUPPORT
Make what seems complex, simple
Show the benefits of using a BI tool to the
employees, and provide constant support in order to
guarantee the tool’s usage and its efficiency. Plus,
make sure the tool is correctly functioning and
available to all departments and individuals.
4
6. 5. ADOPT THE RIGHT
TECHNOLOGY
The BI Tool is the core of the whole
process
There are numerous BI Tools in the market and
choosing the right one is an important step to boost
adoption. Keep in mind the IT skills and the needs of
end users and understand the efforts that this new
tool will demand from your team. 5
7. 6. INTEGRATE DATA IN THE
DAILY OPERATIONS
Place data at the heart of business
When starting to benefit from data and its analysis by
making or questioning business decisions based on
analytics, perfomance and reliability might increase
across departments.
6
8. 7. ENCOURAGE THE USE
OF DATA BY EVERYONE
Data is a mean of powering growth
Shared data should be used by as many employees
as possible. The company should train, motivate and
ackowledge employees so they can become more
data literate.
7
9. 8. COMMITMENT
For something to be done correctly
there must be willpower.
Make data-collection a primary activity across
departmetns and be sure the BI Strategy is overseen
to guarantee the intended outcome.
8
10. Want to see Wizdee in action?
Visit wizdee.com and watch a demo!
9
Causing a cultural switch inside a company is not a simple or quick process. Becoming a
Data-Driven Enterprise takes time and a companywide commitment to make sure the overall
strategy is being implemented.
The good news is that today’s BI innovations allow people to simply speak or type queries and
get instant visual answers on any device. See for yourself!