Automated decision making using Predictive Applications – Big Data ParisLars Trieloff
Predictive Applications enable automated data-driven decisions using big data, machine learning, artificial intelligence and optimization algorithms. With this, they are able to scale decision making, improve the quality of decisions and circumvent cognitive biases that cloud human decision making.
Automated Decision making with Predictive Applications – Big Data HamburgLars Trieloff
Most businesses are making most decisions the way Lizards do: based on very simple reflex-response patterns and let cognitive biases taint their decision making. Instead of letting gut feel and biases take over, predictive applications make decisions fast, cheap and fact-based.
Automated Decision Making with Predictive Applications – Big Data DüsseldorfLars Trieloff
Another installment and iteration of my talk on predictive applications, automated decision making and why cognitive biases prevent us from making the best decisions at scale
Automated decision making with predictive applications – Big Data BrusselsLars Trieloff
My slides from Dataconomy's Big Data, Brussels event in March 2015. Key topics: what are predictive applications and how can they help companies make better decisions, faster and cheaper.
Automated decision making using Predictive Applications – Big Data ParisLars Trieloff
Predictive Applications enable automated data-driven decisions using big data, machine learning, artificial intelligence and optimization algorithms. With this, they are able to scale decision making, improve the quality of decisions and circumvent cognitive biases that cloud human decision making.
Automated Decision making with Predictive Applications – Big Data HamburgLars Trieloff
Most businesses are making most decisions the way Lizards do: based on very simple reflex-response patterns and let cognitive biases taint their decision making. Instead of letting gut feel and biases take over, predictive applications make decisions fast, cheap and fact-based.
Automated Decision Making with Predictive Applications – Big Data DüsseldorfLars Trieloff
Another installment and iteration of my talk on predictive applications, automated decision making and why cognitive biases prevent us from making the best decisions at scale
Automated decision making with predictive applications – Big Data BrusselsLars Trieloff
My slides from Dataconomy's Big Data, Brussels event in March 2015. Key topics: what are predictive applications and how can they help companies make better decisions, faster and cheaper.
Data Natives 2015: Predictive Applications are Going to Steal Your Job: this ...Lars Trieloff
Fears of robots taking away blue collar jobs have been coming and going over the last decade. But this time it’s different: a new breed of predictive applications, or white-collar robots are going after knowledge-worker and managerial jobs. Using automated data-driven decisions, they speed up and improve critical business processes and leave employers and employee’s scratching their heads what is coming next. Lars Trieloff, who is building predictive applications for a living at Blue Yonder explains what happens, why it happens and what it means for you (and your boss).
Big Data Berlin – Automating Decisions is the Next Frontier for Big DataLars Trieloff
Just collecting, storing and analyzing data is not enough. In order to benefit from it, you have to overcome organizational and human inertia and establish automated processes that use insights gained from your data.
This presentation has been presented at http://dataconomy.com/28-august-2014-big-data-berlin/
An agile development process is designed to allow us to respond to change, but this process depends on the people using it. As participants in an agile process, do we think and behave in a way that helps or harms our process? As individuals, are we actually as tolerant of change and randomness as our manifesto says we are, or do we subscribe to our methodology of choice in hopes of a smooth, predictable project? One can easily fall into the trap of being a tourist in his or her professional life--someone whose day gets worse when things don't go as planned. This talk will illustrate what it means to do the opposite and wander through our development process by fighting our bias toward stability and predictability. We'll see how "wandering" through some of our typical activities--like testing, planning, and organizing teams--can help us take full advantage of changing requirements and volatility, improve our agile process, and make our days get better with randomness.
Business model innovation by experimentationEnergized Work
How to maximize learning and minimize risk.
All new products start as a series of unvalidated assumptions. The most critical assumptions are usually implicit and relate to the purpose of the product and the value it is intended to deliver. The more key assumptions involved, the greater the risk. It is enough to have 7 key assumptions about which you are 90% certain for the combined odds of success to be below 50%.
Contrary to popular belief, when we know very little about a situation, it only takes a small amount of new data to realise significant insights.
Unfortunately, people often underestimate the value of information and misunderstand risk. As Product Owners we are often afraid to test our assumptions. We routinely pile on additional risk without a second thought.
Do we have a death wish or are we simply masochists? Risk management is the bread and butter of the finance and insurance industries. Isn’t it time we evolved?
In this fast paced and practical session we will explore answers to the following questions:
- What is risk and how do we quantify and manage it?
- How do we assess the value of information?
- How can experimentation reduce risk and where does it fit in the product development cycle?
- What makes a good experiment?
- How to run experiments in a cost effective manner?
- What are good metrics?
- How to obtain Zen like focus and prioritisation?
New concepts will be introduced, examples will be given and we will then point out where to seek further information. Hold onto your hats.
Stop Getting Crushed By Business PressureArty Starr
This is my story of lessons learned on how to stop the crushing effects of business pressure... I was team lead with full control of our green-field project. After a year, we had continuous delivery, a beautiful clean code base, and worked directly with our customers to design the features. Then our company split in two, we were moved under different management, and I watched my project get crushed.
As a consultant, I saw the same pattern of relentless business pressure everywhere, driving one project after another into the ground. I made it my mission to help the development teams solve this problem. This is my story of lessons learned on how to transform an organization from the bottom up. I'll show you how to lead the way.
**Warning:** This strategy won't work in all organizations. In some cases, management doesn't want to know the truth. However, in most organizations I've worked with, management wants to improve, but doesn't know how to fix the system.
The crushing business pressure is caused by a broken feedback loop that's baked into the organization's design. In this presentation, I'll show you how to fix the broken feedback loop. Learn how to:
* Gather evidence of developer productivity loss
* Identify the key organizational changes required for success
* Make the case to management for improvement
* Partner with your manager for long-term success
If the system is broken, we need to fix the system. You can *change* the system by making the decision to lead.
**Note:** *This talk is not strictly dependent on attending, "Top 5 Reasons Why Improvement Efforts Fail", but you'll get way more out of the session, if you attend both.*
Root cause analysis is a combination of the art and science used to find the underlying reasons for a given effect. One of the most widely used root cause analysis tools is the 5 Whys. It is a simple, effective method of problem solving that can help teams identify and eliminate the root cause of a problem
Big Data Munich – Decision Automation and Big DataLars Trieloff
My presentation from Big Data Munich: How decision automation based on big data and machine learning can help you run a better business and avoid common cognitive biases.
Data Natives 2015: Predictive Applications are Going to Steal Your Job: this ...Lars Trieloff
Fears of robots taking away blue collar jobs have been coming and going over the last decade. But this time it’s different: a new breed of predictive applications, or white-collar robots are going after knowledge-worker and managerial jobs. Using automated data-driven decisions, they speed up and improve critical business processes and leave employers and employee’s scratching their heads what is coming next. Lars Trieloff, who is building predictive applications for a living at Blue Yonder explains what happens, why it happens and what it means for you (and your boss).
Big Data Berlin – Automating Decisions is the Next Frontier for Big DataLars Trieloff
Just collecting, storing and analyzing data is not enough. In order to benefit from it, you have to overcome organizational and human inertia and establish automated processes that use insights gained from your data.
This presentation has been presented at http://dataconomy.com/28-august-2014-big-data-berlin/
An agile development process is designed to allow us to respond to change, but this process depends on the people using it. As participants in an agile process, do we think and behave in a way that helps or harms our process? As individuals, are we actually as tolerant of change and randomness as our manifesto says we are, or do we subscribe to our methodology of choice in hopes of a smooth, predictable project? One can easily fall into the trap of being a tourist in his or her professional life--someone whose day gets worse when things don't go as planned. This talk will illustrate what it means to do the opposite and wander through our development process by fighting our bias toward stability and predictability. We'll see how "wandering" through some of our typical activities--like testing, planning, and organizing teams--can help us take full advantage of changing requirements and volatility, improve our agile process, and make our days get better with randomness.
Business model innovation by experimentationEnergized Work
How to maximize learning and minimize risk.
All new products start as a series of unvalidated assumptions. The most critical assumptions are usually implicit and relate to the purpose of the product and the value it is intended to deliver. The more key assumptions involved, the greater the risk. It is enough to have 7 key assumptions about which you are 90% certain for the combined odds of success to be below 50%.
Contrary to popular belief, when we know very little about a situation, it only takes a small amount of new data to realise significant insights.
Unfortunately, people often underestimate the value of information and misunderstand risk. As Product Owners we are often afraid to test our assumptions. We routinely pile on additional risk without a second thought.
Do we have a death wish or are we simply masochists? Risk management is the bread and butter of the finance and insurance industries. Isn’t it time we evolved?
In this fast paced and practical session we will explore answers to the following questions:
- What is risk and how do we quantify and manage it?
- How do we assess the value of information?
- How can experimentation reduce risk and where does it fit in the product development cycle?
- What makes a good experiment?
- How to run experiments in a cost effective manner?
- What are good metrics?
- How to obtain Zen like focus and prioritisation?
New concepts will be introduced, examples will be given and we will then point out where to seek further information. Hold onto your hats.
Stop Getting Crushed By Business PressureArty Starr
This is my story of lessons learned on how to stop the crushing effects of business pressure... I was team lead with full control of our green-field project. After a year, we had continuous delivery, a beautiful clean code base, and worked directly with our customers to design the features. Then our company split in two, we were moved under different management, and I watched my project get crushed.
As a consultant, I saw the same pattern of relentless business pressure everywhere, driving one project after another into the ground. I made it my mission to help the development teams solve this problem. This is my story of lessons learned on how to transform an organization from the bottom up. I'll show you how to lead the way.
**Warning:** This strategy won't work in all organizations. In some cases, management doesn't want to know the truth. However, in most organizations I've worked with, management wants to improve, but doesn't know how to fix the system.
The crushing business pressure is caused by a broken feedback loop that's baked into the organization's design. In this presentation, I'll show you how to fix the broken feedback loop. Learn how to:
* Gather evidence of developer productivity loss
* Identify the key organizational changes required for success
* Make the case to management for improvement
* Partner with your manager for long-term success
If the system is broken, we need to fix the system. You can *change* the system by making the decision to lead.
**Note:** *This talk is not strictly dependent on attending, "Top 5 Reasons Why Improvement Efforts Fail", but you'll get way more out of the session, if you attend both.*
Root cause analysis is a combination of the art and science used to find the underlying reasons for a given effect. One of the most widely used root cause analysis tools is the 5 Whys. It is a simple, effective method of problem solving that can help teams identify and eliminate the root cause of a problem
Big Data Munich – Decision Automation and Big DataLars Trieloff
My presentation from Big Data Munich: How decision automation based on big data and machine learning can help you run a better business and avoid common cognitive biases.
Automated decision making with predictive applications – Big Data AmsterdamLars Trieloff
My slides from tonight's talk at Impact HUB in Amsterdam on big data, machine learning, cognitive biases and how to overcome them with predictive applications.
Mobile technology adoption continues to grow, and recent consumer trends include rise in wearable tech and VR / AR, as well as growing use of chatting apps, mobile commerce, and consumption of mobile video content. Marketers are responding to these trends with location based marketing, video content and ramification, among other tactics, in order to offer a more relevant and instant shopping experience.
Basic java important interview questions and answers to secure a jobGaruda Trainings
P2Cinfotech is one of the leading, Online IT Training facilities and Job Consultant, spread all over the world. We have successfully conducted online classes on various Software Technologies that are currently in Demand. To name a few, we provide quality online training for QA, QTP, Manual Testing, HP LoadRunner, BA, Java Technologies, SEO, Web Technologies, .NET, Oracle DBA etc.
26 Disruptive & Technology Trends 2016 - 2018Brian Solis
Introducing the “26 Disruptive Technology Trends for 2016 – 2018.” In this report, we’ll explore some of the disruptive trends that are affecting pretty much everything over the next few years at least those that I’m following. It’s not just tech, though. The report is organized by socioeconomic and technological impact.
Obviously, this is not an exhaustive list of every technology and societal trend bringing about disruption on planet Earth. What follows thought definitely affects the evolution of digital Darwinism, the evolution of society and technology and its impact on behavior, expectations and customs.
Bad AI showing sexist or racist correlations makes headlines. Nobody sets out to make a bad system, so why does this happen. I take a look at all the ways bias creeps into AI and where you should put effort to avoid it.
Slides annotated from a talk given at ImpactfulAI meetup 19th June 2019 London
You are the ultimate data wrangler. The polyglot master of python and R. You know all about the differences of linear versus logistic regression. You know when to use a dimensionality reduction algorithm and when to use a neural net. You have petabytes of data taking structural-form at your command, and you have the R-squared score to prove it!
But all of your data wrangling and number crunching won't matter if the decision makers ignore your data.
The tools to communicate the message in your data are simple, yet they can be a hard to learn. So, let’s talk about the five critical communication tools you need to master "The Art of Speaking Data."
The Best & Worst Uses of AI in Software TestingEficode
Ingo Philipp
Distinguished Evangelist – Tricentis
Ingo Philipp champions the methodologies and technologies at the core of the company’s continuous testing solution. In his previous position as a senior product manager, he orchestrated product development and product marketing.
This is a presentation I gave at the Fluoro Safety Conference 2015
The talk explores where data can help detect human behavior that may help identify early interventions before mental health issues become a risk factor
EMI & Traceability – Maintaining Quality, Safety and ComplianceNorthwest Analytics
Keeping the recall from the door is the task that never ends. It depends on a suspenders and belt strategy that prevents noncompliant production with systems in place to reconstruct events if something goes wrong.
While the most dramatic headlines often come from the FDA regulated industries of pharmaceutical and food, recalls are not good for anyone. All manufacturers face recall challenges from regulators, supply agreements and class action lawsuits.
The value chain of raw materials-to-process-to-finished goods-to-customer needs the combined attention of Enterprise Manufacturing Intelligence (EMI) and traceability systems to maintain quality, safety and compliance. During recalls both quality and genealogy systems are critical to characterizing the problem and untangling the mess.
What is at stake?
• In 2012 the FDA had 4,075 recall events (life sciences and food combined).
• In an Ernst & Young study, 77% of respondents estimated an average impact $30,000,000 per incident. 23% of respondents cited even higher costs.
• The cost of poor quality (COPQ) is estimated at 30% of gross pharmaceutical sales.
An integrated IT strategy is critical to combat these challenges. This coordinates existing systems including ERP, WMS, MES, quality management, and traceability. The traceability and process performance data collected directly impact:
• Supplier management
• Logistics & warehousing
• Manufacturing
• Product recall management
The complimentary roles of EMI and traceability in regulated industry production and supply chains will be the topic of a web conversation with David Miller, President of Mobia Solutions and one of the industry’s leading experts in technology, inventory management and traceability. The complimentary webinar EMI & Traceability –Maintaining quality, safety and compliance is available at:
- http://www.nwasoft.com/resources/webinars/emi-traceability-maintaining-quality-safety-and-compliance
Demystifying AI: From Technology to Business ValueMarlon Dumas
Talk on Artificial Intelligence for business applications, delivered by Marlon Dumas at the Pärnu Finance Conference on 12 April 2019 https://pood.aripaev.ee/finantskonverents-2019
Data Storytelling - Game changer for Analytics Gramener
50 Percent of Data Science Projects Fail at
Consumption: Can Storytelling Be Your Game
Changer
Growth of Self Service BI has generated a lot of
dashboards, but “lots” does not always mean “good” or
“useful”.
• While advances in AI/ML lead to deeper insights,
business teams struggle with the adoption of
algorithms and consumption
• How can data officers and analytics leaders
get better business ROI from their data science
investments?
• This session will show you how to unleash the
power of data storytelling for business decision-
making, using industry examples
Partner Alliance Webinar - Sales Tax | Fixed Assets Solutions - An OverviewNet at Work
You Will Learn:
• How you can benefit from selling these compliance products
• The pitfalls your customers should be looking out for
• The benefits to having solid compliance practices in house
• The ROI your customers can expect
Role of Data Science in ERM @ Nashville Analytics Summit Sep 2014John Liu
An overview of how organizations can leverage data science and predictive analytics to improve enterprise risk management. Applications for risk identification, mitigation and management will be discussed, as well as methods to facilitate strategic integration across an organization.
I developed this presentation to discuss the framework for automation and autonomic operations in particular in the Finance domain. It is high level introductory but includes guidance of how to best select AI and RPA projects with higher implementation success rates. If you are interested in a copy dont be shy! Reach out!
Startup Metrics: The Data That Will Make or Break Your Business by Alistair C...Lean Startup Co.
If you’re being methodical about growth, analytics matters. For startups, analytics is about measuring the right metric, in the right way, to produce the change the business needs most at that point in time. That’s harder than it sounds: you need a solid understanding of your business model; an awareness of what’s most at risk; and a clear idea of where to draw the line between success and failure. Metrics measure not only the health of your business, but also your journey to product/market fit; the value of your company; and the reliability of your underlying infrastructure. Join Lean Analytics co-author Alistair Croll for an all-day, in-depth look at analytics, measurement, and working with data. We’ll cover:
The five stages of growth every company goes through, and how they guide your choice of metrics
Six business-model archetypes and their unique measurement challenges
What “good enough” looks like for fundamental metrics
How to think about cohorts, segments, percentiles, and histograms
Measuring and aggregating infrastructure KPIs such as latency and availability
Using the Lean Analytics cycle to improve through experimentation
This workshop is relevant for people working in standalone startups and for corporate entrepreneurs. It will combine presentations, case studies, and interactive discussion of the audience’s specific measurement challenges. Attendees need not be technical but should come armed with a basic understanding of web analytics, business metrics, and their current business model, plus a willingness to share with one another.
Maintenance: How not to hate it - v.13Brian Gongol
A three-fold approach to (1) understanding maintenance from a theoretical and practical standpoint, (2) making maintenance more pleasant to do through practical tips, and (3) communicating more effectively the need to take maintenance seriously.
Putting the F in FaaS: Functional Compositional Patterns in a Serverless WorldLars Trieloff
Presented at #ServerlessConf 2017 in New York City. Don't go looking for serverless patterns in strange places, take existing functional programming patterns instead.
Digital marketing rapidly introduces new channels, concepts and context into marketing. This can lead to confusion and cognitive dissonance between traditional right-brain marketers and digital left-brain marketers. By going beyond the surface of what is visible in terms of vendors and products and concentrating on the fundamental building blocks of marketing, "The DNA of Marketing" offers a new look at marketing and a way to make sense of digital marketing innovation.
Combine Social Media with Social Communities in CQ5 to open additional channels for your marketing campaigns and increase targeting accuracy, maximize conversion and drive profitability.
A short look on Day's Advanced Collaboration for Communiqué 4 and an outlook what Social Collab for Communiqué 5 will offer. Held by Lars Trieloff at Day's Tech Summit 2008 in Basel.
Lars Trieloff's presentation "The Zero Bullshit Architecture" on how not to design your enterprise content centric application. (And tips on how to do it better)
Presentation held at Web Montag Stockholm, March 2008. Introduction of JCR, Sling and the µjax AJAX-based access layer to a content repository. The presentation includes the demo of the µjax Dojo integration.
µjax is a lightweight AJAX library for accessing content in a Java Content Repository (JCR) over the web. This presentation was given at Web Montag in Berlin, 01-21-2008.
Living in a multiligual world: Internationalization for Web 2.0 ApplicationsLars Trieloff
Lars Trieloff's presentation at Web 2.0 Expo Berlin covers the why and how-to of internationalization for web 2.0, consolidating i18n technology and enabling user-contributed translations.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Show drafts
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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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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.
4. — Holger Kisker, Forrester Research
“Even after more than 20 years of
using BI, they still base nearly 45%
of business decisions on
qualitative decision factors
instead of quantitative, fact-based
evidence.“
5. If data is not used for decision
making, what is used then?
23. • Drill-down analysis … misunderstood or
distorted
• Metrics dashboards … contradictory and
confusing
• Monthly reports … ignored after two
iterations
• In-house analyst teams … overworked
and powerless
How Data-Driven Decisions
REALLY work
CO M M U N I C AT I O N
B R E A K D O W N
34. • Business rules are like programs – written by
non-programmers
• Business rules can be contradictory,
incomplete, and complex beyond
comprehension
• Business rules have no built-in feedback
mechanism:“It is the rule, because it is the rule”
Business rules are Programs,
just not very good ones.
35. — Mark Twain
“It ain’t what we don’t know
that causes trouble, it’s what we
know for sure that just ain’t so”
48. — Daniel Kahneman
“All of us would be better
investors if we just made fewer
decisions.”
49.
50. How we are making decisions
(Like the big apes we are)
Anchoring effect
IKEA effect
Confirmation bias
Bandwagon effect
Substitution
Availability heuristic
Texas Sharpshooter Fallacy
Rhyme as reason effect
Over-justification effect
Zero-risk bias
Framing effect
Illusory correlation
Sunk cost fallacy
Overconfidence
Outcome bias
Inattentional Blindness
Benjamin Franklin effect
Hindsight bias
Gambler’s fallacy
Anecdotal evidence
Negativity bias
Loss aversion
Backfire effect
51.
52. K-Means Clustering
Naive Bayes
Support Vector Machines
Affinity Propagation
Least Angle Regression
Nearest Neighbors
Decision Trees
Markov Chain Monte Carlo
Spectral clustering
Restricted Bolzmann Machines
Logistic Regression
Computers making decisions
(cold, fast, cheap, rational)
53. • A machine learning algorithm is a system that
derives a set of rules based on a set of data
• It is based on systematic observation, double-
checking and cross-validation
• There is no magic, just data – and without data
there is no magic either
Machine Learning means
Programs that write Programs
77. If you ordered 8,5 cases, you
would waste a lot of meat,
the ideal order amount is 8
cases.
78. Predictive Apps in a Nutshell
Batch and streaming data ingestion, batch
and streaming delivery (with real-time option)
Reduce risk and cost » increase revenue and profit
Trend Estimation Classification Event Prediction
Optimize Returns
Collect Data Predict Results Drive Decisions
79. — John Maynard Keynes
“When my information
changes, I alter my conclusions.
What do you do, sir?”
80. One Common Platform for
Predictive Applications
Your own and third-
party data, easily
integrated via API
Link
Build Machine
Learning and
application code
Build
Automatically run
and scale ML models
and applications
Run
Monitor and inspect
resource usage and
model quality
View
Your data stored in
high-performance
database as a service
Store
81. — Kevin Kelly
“The business plans of the next
10,000 startups are easy to
forecast: Take X and add AI”