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.
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”.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Overview of Business Analytics and career lessons learnt / advice. Presentation delivered to Melbourne Business School - Masters of Business Analytics - July 2016.
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.
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”.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Overview of Business Analytics and career lessons learnt / advice. Presentation delivered to Melbourne Business School - Masters of Business Analytics - July 2016.
Four Key Considerations for your Big Data Analytics StrategyArcadia Data
Learn 4 of the key things to consider as you create your big data analytics strategy from John Meyers (Enterprise Management Associates) and Steve Wooledge (Arcadia Data).
Your smarter data analytics strategy - Social Media Strategies Summit (SMSS) ...Clark Boyd
The volume and velocity of available data brings with it a huge amount of new opportunities for marketers. However, without the analytics know-how to avail of this data, these are opportunities that are often missed. Moreover, the variety of different data sources and analytics platforms only add to this complexity.
This presentation covers:
- How to define and communicate an analytics framework
- How to set up analytics dashboards for a range of stakeholders
- The people and skills you need for an optimal analytics team
- Practical tips for improving your campaign measurement
World of Watson 2016: Journey to Cognitive Excellence - Harness the Force of ...Julie Severance
Becoming a cognitive business is a journey, not a destination. A cognitive analytics culture is not something you can just buy or install. Although the right technology is crucial, its true value arises when the organizational mindset changes. Many organizations have learned to embrace analytics, but embracing cognitive is another step entirely, and it’s one that may be even more challenging. However, the possibilities are endless and the potential rewards make it worthwhile.
How to make data-driven interactive PowerPoint presentations for operationsGramener
Interactive data-driven presentations are a new way of presenting data. They allow the presenter and the audience to engage actively and drill into the data within PowerPoint.
Author: S. Anand - CEO, Gramener
Check out the full webinar on the topic: https://info.gramener.com/interactive-powerpoint-for-operations
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
Today, about 80% of companies considers data as an essential part of their strategy. However, although most of these companies are taking models into production, they still have trouble turning their data and insights into valuable AI solutions. With businesses heavily invested in data and AI, what is it that actually makes the difference for being successful with AI?
In this talk, I will argue that the extent to which AI is embedded in the organisation is crucial to success. Furthermore, I will show why the Analytics Translator is the designated person to drive AI adoption by the business and what his or her tasks should look like. The insights shared come from our own experience as consultants as well as interviews with top Dutch enterprises about their AI maturity.
Business Analytics to solve your Business ProblemsVishal Pawar
Business Analytics Solution in 12 Steps
What is Business Analytics ?
Why we need it ?
Identify your Focus Area and Target Applications
Importance of Business Analytics with different Roles
Confirming your Business Goal with value of Business Analytics
Differentiating Business Analysis , Analyst & Intelligence
What world is doing for Business Analytics Problems
Segregate solution with Data Discovery , Analytics & Science
Gathering information with All Available Data Source
Developing Business Analytics Framework & Components
Developing Visualization with Best User Experience
Improvising Maturity Level of Business Analytics
Get Connected with Expert Team , Who know technology !
Quantify your impact: how to measure the ROI of your talent brand | Talent Co...LinkedIn Talent Solutions
Today’s talent leaders are more empowered than ever to quantify the connection between talent brand and talent acquisition success. However, with so much data at your fingertips, it’s easy to get overwhelmed. Join us to learn how to pinpoint the key metrics & data points that matter most when measuring your talent brand and use them to tell a cohesive ROI story. Check out the best of Talent Connect: http://bit.ly/1MBqz6m
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
Measuring Success introduces nonprofit professionals to proven techniques on how to move from anecdotal to data-driven decision making and steer your organization to success. Gain insights on how to focus your limited organizational time and energies on the issues that are supported by data instead of anecdotes. Learn techniques for using data to track and measure progress over time, report impact to stakeholders, and manage toward success.
Analytics Staffing Models of Health Systems That Compete Well Using DataThotWave
Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytic leaders are facing unprecedented pressure as expectations from the digitization of health drives questions from every corner of the enterprise. Along with the operational and workflow changes that come with digital health, we are seeing greater demand for data to support care transformation, risk contracting and organizational performance.
The time is right to consider how analytics can support organizational strategies and how we can ensure alignment across the organization. As part of the strategic alignment exercise we often see organizations consider how to best deliver advanced analytic capabilities and then ask themselves the question “how should we organize our analytic teams?” Often, an effective way to increase that efficiency, improve morale and achieve economy of scale is to consider changes to how analytics teams are organized.
The most appropriate organizational structure will vary based on the health system size, culture, and analytics (and data) maturity. Should the analytics capabilities be centralized, decentralized, or should we consider an alternative, hybrid staffing model? Should analytics sit under IT or medical leadership?
In our Data4Decisions talk, we will review the common models employed by leaders in healthcare, and describe how they align with business strategy. Further, we will outline common challenges as well as share success secrets via case studies from across the US healthcare landscape. The goal of this presentation is to provide the audience with a strong foundation for understanding the healthcare analytics staffing models used across the industry.
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
Lecture notes on being Data-Driven and doing Data Science Johan Himberg
Visiting lecture held at Aalto University School of Business on prof. Pekka Malo's course "Data Science for Business". Lecture given by Johan Himberg and Jaakko Särelä (@ReaktorNow)
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
NEED FOR CHANGE: Data is changing the world. We all know that. The real challenge will be to keep up with those changes by hiring the right team to help you take on the data that is already in your organization.
STAFF FOR SUCCES: Make sure you have an executive sponsor that has a vision for how the organization can become data-driven; hire experienced team members to lead the data engineering, and architecture teams; and adopt agile methodologies to allow for quick experimentation and quick failures.
SKILL UP: In a recent survey focused on Spark, over 60 percent indicated that the skills/training gap was their biggest organizational challenges with Spark, but 65% of respondents indicated that they either did not know or had no future plans for training. Cloudera University to get them ramped up quickly. Cloudera University helps organizations tackle the skill gaps issue they encounter when growing their teams and helps them stay up to date on the latest supported technologies.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
#IBMInsight Session presentation "Transforming your Enterprise to Get Value from BigData and Analytics: How to Get Started".
Transforming Your Enterprise to Get Value from Big Data
and Analytics: How to Get Started
The Journey, The Value Analytics Drives, Analytics Leadership and Governance, Analytics Case Studies, Best Practices for Getting Started
More at ibm.biz/BdEPRs
Four Key Considerations for your Big Data Analytics StrategyArcadia Data
Learn 4 of the key things to consider as you create your big data analytics strategy from John Meyers (Enterprise Management Associates) and Steve Wooledge (Arcadia Data).
Your smarter data analytics strategy - Social Media Strategies Summit (SMSS) ...Clark Boyd
The volume and velocity of available data brings with it a huge amount of new opportunities for marketers. However, without the analytics know-how to avail of this data, these are opportunities that are often missed. Moreover, the variety of different data sources and analytics platforms only add to this complexity.
This presentation covers:
- How to define and communicate an analytics framework
- How to set up analytics dashboards for a range of stakeholders
- The people and skills you need for an optimal analytics team
- Practical tips for improving your campaign measurement
World of Watson 2016: Journey to Cognitive Excellence - Harness the Force of ...Julie Severance
Becoming a cognitive business is a journey, not a destination. A cognitive analytics culture is not something you can just buy or install. Although the right technology is crucial, its true value arises when the organizational mindset changes. Many organizations have learned to embrace analytics, but embracing cognitive is another step entirely, and it’s one that may be even more challenging. However, the possibilities are endless and the potential rewards make it worthwhile.
How to make data-driven interactive PowerPoint presentations for operationsGramener
Interactive data-driven presentations are a new way of presenting data. They allow the presenter and the audience to engage actively and drill into the data within PowerPoint.
Author: S. Anand - CEO, Gramener
Check out the full webinar on the topic: https://info.gramener.com/interactive-powerpoint-for-operations
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
Today, about 80% of companies considers data as an essential part of their strategy. However, although most of these companies are taking models into production, they still have trouble turning their data and insights into valuable AI solutions. With businesses heavily invested in data and AI, what is it that actually makes the difference for being successful with AI?
In this talk, I will argue that the extent to which AI is embedded in the organisation is crucial to success. Furthermore, I will show why the Analytics Translator is the designated person to drive AI adoption by the business and what his or her tasks should look like. The insights shared come from our own experience as consultants as well as interviews with top Dutch enterprises about their AI maturity.
Business Analytics to solve your Business ProblemsVishal Pawar
Business Analytics Solution in 12 Steps
What is Business Analytics ?
Why we need it ?
Identify your Focus Area and Target Applications
Importance of Business Analytics with different Roles
Confirming your Business Goal with value of Business Analytics
Differentiating Business Analysis , Analyst & Intelligence
What world is doing for Business Analytics Problems
Segregate solution with Data Discovery , Analytics & Science
Gathering information with All Available Data Source
Developing Business Analytics Framework & Components
Developing Visualization with Best User Experience
Improvising Maturity Level of Business Analytics
Get Connected with Expert Team , Who know technology !
Quantify your impact: how to measure the ROI of your talent brand | Talent Co...LinkedIn Talent Solutions
Today’s talent leaders are more empowered than ever to quantify the connection between talent brand and talent acquisition success. However, with so much data at your fingertips, it’s easy to get overwhelmed. Join us to learn how to pinpoint the key metrics & data points that matter most when measuring your talent brand and use them to tell a cohesive ROI story. Check out the best of Talent Connect: http://bit.ly/1MBqz6m
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
Measuring Success introduces nonprofit professionals to proven techniques on how to move from anecdotal to data-driven decision making and steer your organization to success. Gain insights on how to focus your limited organizational time and energies on the issues that are supported by data instead of anecdotes. Learn techniques for using data to track and measure progress over time, report impact to stakeholders, and manage toward success.
Analytics Staffing Models of Health Systems That Compete Well Using DataThotWave
Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytic leaders are facing unprecedented pressure as expectations from the digitization of health drives questions from every corner of the enterprise. Along with the operational and workflow changes that come with digital health, we are seeing greater demand for data to support care transformation, risk contracting and organizational performance.
The time is right to consider how analytics can support organizational strategies and how we can ensure alignment across the organization. As part of the strategic alignment exercise we often see organizations consider how to best deliver advanced analytic capabilities and then ask themselves the question “how should we organize our analytic teams?” Often, an effective way to increase that efficiency, improve morale and achieve economy of scale is to consider changes to how analytics teams are organized.
The most appropriate organizational structure will vary based on the health system size, culture, and analytics (and data) maturity. Should the analytics capabilities be centralized, decentralized, or should we consider an alternative, hybrid staffing model? Should analytics sit under IT or medical leadership?
In our Data4Decisions talk, we will review the common models employed by leaders in healthcare, and describe how they align with business strategy. Further, we will outline common challenges as well as share success secrets via case studies from across the US healthcare landscape. The goal of this presentation is to provide the audience with a strong foundation for understanding the healthcare analytics staffing models used across the industry.
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
Lecture notes on being Data-Driven and doing Data Science Johan Himberg
Visiting lecture held at Aalto University School of Business on prof. Pekka Malo's course "Data Science for Business". Lecture given by Johan Himberg and Jaakko Särelä (@ReaktorNow)
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
NEED FOR CHANGE: Data is changing the world. We all know that. The real challenge will be to keep up with those changes by hiring the right team to help you take on the data that is already in your organization.
STAFF FOR SUCCES: Make sure you have an executive sponsor that has a vision for how the organization can become data-driven; hire experienced team members to lead the data engineering, and architecture teams; and adopt agile methodologies to allow for quick experimentation and quick failures.
SKILL UP: In a recent survey focused on Spark, over 60 percent indicated that the skills/training gap was their biggest organizational challenges with Spark, but 65% of respondents indicated that they either did not know or had no future plans for training. Cloudera University to get them ramped up quickly. Cloudera University helps organizations tackle the skill gaps issue they encounter when growing their teams and helps them stay up to date on the latest supported technologies.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
#IBMInsight Session presentation "Transforming your Enterprise to Get Value from BigData and Analytics: How to Get Started".
Transforming Your Enterprise to Get Value from Big Data
and Analytics: How to Get Started
The Journey, The Value Analytics Drives, Analytics Leadership and Governance, Analytics Case Studies, Best Practices for Getting Started
More at ibm.biz/BdEPRs
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
In the recent past, we have learnt that data is the lifeline of any business and it is really important to collect data, more and more of it. But no one is telling us what to do with large volumes of data.
Shailendra has successfully delivered over One Billion Dollars in incremental value and will spend 30 minutes in showcasing how many large organisations are using data to their advantage by creating value through generating incremental revenue and optimising costs using analytics techniques.
Key Takeaways:
(i) Demystify the myths of analytics
(ii) Walkthrough a step-by-step approach to delivering successful projects that created an incremental value of hundreds and millions of dollars.
(iii) Three use cases where large organisations are using analytics to their advantage by creating value by generating incremental revenue and optimising costs.
Dynamic Talks: "Data Strategy as a Conduit for Data Maturity and Monetization...Grid Dynamics
Organizations need to tap into the huge potential of their vast volumes of data, but a use case tactical approach is not going to work. Instead, they need to work in the definition of a data strategy linked to the most relevant goals for the enterprise.
At Axtria, we provide world-class training, support and growth prospects - all crafter to build on your unique skills and outline your success. You will be in a highly collaborative culture among a bunch of the most talented and visionary folks in the industry.
These slides--based on the webinar hosted by leading IT analyst firm Enterprise Management Associates (EMA) and Digitate--provide insights into the impact of machine learning on managing workload automation.
A data analytics course is an educational program designed to teach individuals the skills and techniques necessary for analyzing and interpreting data to extract meaningful insights.
For more details visit: https://datamites.com/data-analytics-certification-course-training-chennai/
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
5 Steps To Measure ROI On Your Data Science Initiatives - WebinarGramener
Gramener's Chief Decision Scientist & Co-Founder Ganes Kesari conducted an exciting webinar on how to measure ROI on your data science initiatives.
In this webinar people from the C-suite level CEO, COO, Directors, Managers across various industries joined.
Ganes Kesari covered the following points with industry examples:
-Identifying business use cases with a high impact
-Choosing effective success indicators
-Ascertaining that the consequences may be traced back to your data project
The attendees had a good time. Learnings from the webinar:
-Why do businesses struggle to get a return on their data investments?
-A straightforward framework for calculating the return on investment from your data projects
-Benchmarking of typical payback from data initiatives in the industry
To check out the complete recording of the webinar please visit:
https://info.gramener.com/5-steps-to-measure-roi-on-your-data-science-initiatives
To know more about data advisory check out:
https://gramener.com/advisory-consulting/
Data Analytics Certification in Pune-JanuaryDataMites
A data analytics course is an educational program designed to teach individuals the skills and techniques necessary for analyzing and interpreting data to extract meaningful insights.
For more details visit: https://datamites.com/data-analytics-certification-course-training-pune/
how to successfully implement a data analytics solution.pdfbasilmph
The adoption of data analytics in business has demonstrated a transformative power in modern entrepreneurship. By analyzing vast reservoirs of data, businesses can make informed decisions, optimize operations and predict trends, thus fueling growth.
Similar to Self-service Analytic for Business Users-19july2017-final (20)
IT Operation Analytic for security- MiSSconf(sp1)stelligence
IT Operation Analytic: Using Anomaly Detection , Unsupervised Machine Learning, to distinct normal and abnormal behavior and enhance efficiency of SIEM detection and alert capability.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
4. Strategic Planning:
where should we put
our next store?
CEO: how do we
mitigate the risk of
M&A
Merchandizing: are we
pushing the right
products at each store?
Customers: how can I
ensure my profitable
customers don’t churn
Product Strategy: do
we need to acquire or
partner?
CMO: how do I ensure
my social media
investment = sales
Driving more revenue from your
existing business
Expanding your business with
confidence
What are the most important decisions?
5. Organization target and KPI
Organization
Business
Functional
Target Outcomes
• Company Vision
• Key Markets
• Org structure
• Competitive
differentiation
• Sustainability
• Operational planning
• Business execution
• Resource management
• Departmental
management
7. Common Type of Business Analytics
• Reporting : summarize historical data
• Trending : identify pattern in time series data
• Segmentation : identify similarities within data
• Predictive Modeling : prediction future of events
Source: The value of business analytics,Evan Stubbs,Wiley and SAS.
9. Top five barriers facing organizations today
Foundational Information Challenges
Multiple versions of
the truth
64%
Data spread across
too many apps and
systems
67%
Data not timely
enough
60%
Data not clean
enough to use
58%
Technology not able
to meet needs 57%
Source
10. TOP FIVE BENEFITS OF PREDICTIVE ANALYTICS
Achieve competitive advantage 68%
55%
52%
45%
44%
How has your benefited from predictive analytics:
Related Research Points:
•Management (76%) has no
doubts that predictive
analytics is a top priority.
•Almost two thirds (65%)
of marketing use
today and another
fifth (19%) by end
of 2015.
New revenue opportunities
Increased profitability
Increased customer service
Operational efficiencies
Source
11. Difficult integrating into our
information architecture
Cannot access the
necessary source data
Results not
accurate
No
challenges
Too hard
to use
55%
35%
22%
20%
18%
What technical challenges have been
encountered in its use of predictive analytics:
Related Research Points:
• Midsize (73%) and Very Large
(65%) businesses especially
have difficulty integrating
predictive analytics into their
information architecture.
• Largest barrier to making
changes to predictive analytics
technology is lack of resources
(59%).
Technical Challenges In Predictive Analytics
Source
15. Business
Knowledge
Skill Required for Data Scientist
Business
Knowledge
Predictive
&
Modeling
Hacking
data Skill
What if Self-service Technology
drive process for People.
DataBlend
&Predictive
17. A big data foundation must meet the
following roles & responsibilities:
Information Consumers
• Digest information and perform basic
interactions
Knowledge Workers
• Utilize and interact analytics to drive
actions and decisions.
Designers
• Enable the design and use of information across
roles.
Analysts
• Mash-up data and design analytics to provide
foundational insights for business.
Data Geek
• Enable big data to be exploited in an
immature world through Data Scientists.
Enabling The Five Analytic Personas
18. Business Analysts Can Help Close the Gap
Data Artisan
Capabilities of Data Scientist
that Drive Largest Value Today
Business or Data Analyst
19. • Line of business focused
• Understands business requirements
• Analytic thinker
• Accesses data, blends
and analyzes
• Drives business change
• Consumes reports, analytic apps,
and
analyst insight
• Shares insights with colleagues,
management, etc.
Data Analyst
Business
Decision Maker
Analytics Must Deliver Business Insight
Through Those Who Know the Business
22. Business Understanding
Determine business objectives
Assess situation
Resources (data!), risks, costs & benefits
Determine data mining goals
Ideally with quantitative success criteria
Develop project plan
Estimate time line, budget, but also tools and
techniques
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Understanding
Data
Understanding
Data
Preparation
Modelling
Evaluation
Deployment
23. Collect initial data
Describe data
Persist results
Explore data
Persist results
Verify data quality
Carefully document problems
and issues found!
Data Understanding
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Understanding
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Understanding
Data
Preparation
Modelling
Evaluation
Deployment
24. Select data
Clean data
Construct data
Generate derived
attributes
Integrate data
Merge information from
different sources
Format data
Convert to format convenient for
modelling
Data Preparation
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Data
Preparation
Modelling
Evaluation
Deployment
25. 20% 20%
14%
10% 10%10%
Data understanding and preparation will usually consume half or more
of your project time!
What % of time in your data mining project(s) is
spent on data cleaning and preparation?
8%
4%
25%
25%
39%
Percentage of responses
Percentageoftime
Source : M.A.Munson, A Study on the Importance of and
Time Spent Different Modeling Steps, ACM SIGKDD
Explorations Newsletter
13, 65-71 (2011)
Source: KDNuggets Poll 2003
Data Preparation
26. 2-4 Sources
31%
5-10 Sources
40%
11-15 Sources
9% 13%
Over 15 Sources
Only 6%
of organizations
have all their
data in
one place
SOURCE: “Lack of Data Blending Capability is Costing Time and Money” survey of data
analysts
How Many Data Sources do Organizations Use ?
27. Organizations are Stuck in Excel
Limited Functionality
8% ofWorkforce
26 Hours PerWorker Week
NotAutomated; Not Controlled
80% of Data Input is
Manual Copy / Paste
26 hours
80%
-$60B
5M Advanced Spreadsheet users in US x 8 hours / week
on repetitive manual tasks
Wastes $12,000 per user per year. 1.3B hours/year
Source: IDC
28. Generate test design
Build model
Feature eng.,
optimize model parameters
Assess model
Iterate the
above
Select modelling technique
Assumptions, measure of
accuracy
Modelling
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Data
Understanding
Data
Preparation
Modelling
Evaluation
Deployment
29. Where your model will be deployed ?
Do you need to distribute your
computations? (avoid!)
C++
Java
C#
R
Matlab
Mathematica
Python
Scala
F# Clojure
Breadth
(quality of general purpose tooling)
Depth
(qualityofdataanalysistooling)
Should I use general purpose language?
Breadth = performance, lots of general
purpose libraries and tooling, easy
creation of web services
Should I use data analysis language?
Depth = easy data manipulation, latest
models and statistical techniques available
Can I afford a prototype?
Modelling-Tooling Selection
31. Review process
Determine next steps
To deploy or not to deploy?
Evaluate results
Business success criteria fulfilled?
Evaluation
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Understanding
Data
Understanding
Data
Preparation
Modelling
Evaluationn
Deployment
32. Plan monitoring
and maintenance
Produce final report
Plan deployment
Review project
Collect lessons learned!
Deployment
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Preparation
Modelling
Evaluation
Deployment
35. Fastest data
blending in the
hands of the
business analyst
Sophisticated
analytics that are
easier to use: no
coding required
Automatically
share the insight
and foresight of
analytics with
decision makers
Solutions empowers the Business Analyst Answering
the important questions faster & simper
36. Fastest data
blending in the
hands of the
business analyst
Sophisticated
analytics that are
easier to use: no
coding required
Automatically
share the insight
and foresight of
analytics with
decision makers
Solutions empowers the Business Analyst Answering
the important questions faster & simper
37. Analytic Consumption is Fragmented
12%
…..of users leverage Alteryx to push to
VisualAnalytics platforms
38. 38
1. Complexity of Analysis
Data Analysts need more power tools
to drive the growing size and number
of data sources in their daily analytic
work.
2. Ease of Use
Alteryx sophisticated data blending
and analytics is easier to use than
most of today’s dashboarding and
reporting tools.
3. Business Benefits
Today’s BI/Analytics tools have failed
to drive business benefits. We are in
rarified air.
Complexity of Analysis
vs. Ease of Use
39. Single, easy to use workflow for analysts
=
Efficient creation of predictive analysis
and visualization on BI
=
Saves time for analysts & delivers a
more agile business
Build Analytic Workflows With No Coding, No Specialist Tools
40. It is often said Analysts spend 80% of their time prepping and cleaning data
According to users of Alteryx, Designer cuts data prep by 30% leaving more time for testing hypothesis
and evaluating models
Data Preparation
The Data Preparation palette offers 20 standard tools with a range of capabilities
41. Many of us face the reality that our data is not stored in just one system. Accessing and
blending that data is just one step in the analytic workflow
With Alteryx Designer, blending data from multiple sources is easily accomplished, regardless
of data structure and format
Data Blending
42. Predictive analysis is about forecasting events using a range of statistical and machine learning
techniques
By highlighting and extracting certain traits from our data we can identify trends and use these to predict
behaviours which may occur / have occurred in the past, present or future
Predictive Analysis
Alteryx Designer offers over 30 predictive tools based on the R statistical programming language.
Each tool is highly customisable to meet your specific needs
44. No Coding
Repeatable Workflow
Enterprise scalability
Scale analytics to service users in the
systems/technologies they depend on
Ensure data governance
Ensure data quality by providing transparent
data management and auditability to data
sources, authors and transformation
Eliminate data & analytic silos
Bridge the gap between disparate teams and
departments by collaborating in a secure,
centralized analytic platform
Unlock all your data
Securely connect business users to all data regardless
of source or data type
Automate time-consuming, manual data
tasks, and adjust analytic queries easily
Drag & drop tools using an intuitive user
interface to prep, blend, and analyze data
Platform Differentiation
45. Platform for Self-Service Data Analytics
Enrich
Prep & Blend Analyze
Input All Relevant Data
Share
Output All Popular
Formats
Descriptive->Predictive
46. Fastest data
blending in the
hands of the
business analyst
Sophisticated
analytics that are
easier to use: no
coding required
Automatically
share the insight
and foresight of
analytics with
decision makers
Solutions empowers the Business Analyst Answering
the important questions faster & simper
47. Predictive modeling is slow
because...
Solution
Modelers have to research the right
approaches for solving new problems
DataRobot knows hundreds of
modeling approaches and evaluates
them in parallel
Model validation and model tuning
take a long time to get right
DataRobot automates model validation
in a safe way and tunes models
automatically
Traditional integration approaches are
labor intensive and error prone
An API-based implementation reduces
implementation time from months to
hours
Modelers can only work on a couple of
problems at a time
The modeling API enables modelers to
work on hundreds of problems at the
same time
Can data scientists have more
productivity and business outcome?
48. $111M+
120+
IN FUNDING
250,000,000+
MODELS BUILT ON
DATAROBOT CLOUD
I N S U R A N C E B A N K I N G H E A L T H C A R E F I N T E C H OIL & GAS
#1 RANKED
DATA SCIENTISTS
4
50+
TOP 3 FINISHES
The world’s most advanced Enterprise Machine Learning platform
DATA SCIENTISTS &
ENGINEERS (OF
200+)
2012FOUNDED
HQ in Boston, MA
52. Fastest data
blending in the
hands of the
business analyst
Sophisticated
analytics that are
easier to use: no
coding required
Automatically
share the insight
and foresight of
analytics with
decision makers
Solutions empowers the Business Analyst Answering
the important questions faster & simper
56. Visual Analytic
What questions we are going to
ask?
Data Visualization
What chart to visualize
our data?
How to create ABC
reports?
World of wizard
Let’s gather business
requirement
How can I use data to introduce
measurable business benefit?
Transition from insight to action
Let’s brainstorm
62. Unilever:
• Successfully cleansed & blended 40+ product formats from 28
countries & numerous languages for a universal view of laundry
demand. Providing insights to 10,000 marketers globally.
CROSSMARK:
• Reduced data prep time from 9+ months to 14 days. The
company now expects to deliver insights to 9x its customer
base, going to 90% from earlier 10%.
“We need Alteryx to take what has happened before and blend it with real-time data from anywhere we can possibly
find it, andTableau to visualize that, so we make better decisions at the right time.”
Ryan Howarth,Global Market Insight & Analytics Manager, Unilever
Move from spending 90% of
your time in data
preparation to 90% in data
analysis and discovery.
Data Discovery
Data Preparation
Without
Alteryx
With
Alteryx
Customers Love Alteryx &TableauTogether
70. #inspire15
Our Businesses Moving Faster
Innovation
Zone
Various data
sources
Rapid Prototyping
and iterative changes
Rapid
Analytics
Delivery
Facilitate 360 views
Data Driven Decisions
Business Owned Innovation: Alteryx and Tableau Platform
71. #inspire15
Solving Problems Faster
• Resource Optimization
• Ad hoc Reporting
• Data blending and transformation
• Deprecation of custom code
• Geo Coding
• Prototyping
• Logic Consistency
• Data Validation
• What If Testing
Our Businesses Moving Faster
72. #inspire15
Our Businesses Moving Faster
• Custom code maintenance eliminated
• Shadow IT tasks no longer needed
Right technology, right fit, right solution: Alteryx + Tableau Platform
73. #inspire15
• Sample – disparate
data sources
• Deprecate old code
• TDE generates in
minutes
Our Businesses Moving Faster
Right technology, right fit, right solution: Alteryx + Tableau Platform
74. #inspire15
Feedback from our platform users
• “With Alteryx…I feel like I can do anything and then visualize it in Tableau”
• “On average, I am able to get back at least a full day of each week”
• “…the tool is easier, more intuitive and scalable to use (for immediate and
future needs)”
• “I am able to see my results in 30 minutes instead of the 6 hours it used to
take…”
• “Alteryx is allowing me to create prototypes of data sets quickly that allow me
to create a Tableau output for visualizing…”
Our Business Moving Faster
75. #inspire15
Spread the Word…Collaborate and Share
• Humble beginnings
• Teams curious enough to try
• Field Operations
• Finance
• R&D
• GHE
• New teams reaching out
• RSA
• Presales
Keeping the Momentum Going
76. #inspire15
Spread the Word…Community
• Meet Ups and
User Forum
• Data Visualization
and Analytics
Conference
• Analytics
Enablement
Center
Keeping the Momentum Going