This document discusses Accenture's approach to data modernization. It outlines key trends in data-driven organizations, including democratizing data, incorporating new data sources, focusing on advanced analytics, adopting big data and hybrid architectures, and changing skills requirements. The document then presents a high-level 9-step approach to agile analytics that engages stakeholders, identifies value opportunities, formulates hypotheses, understands data sources, defines models, prepares data, prototypes and iterates, pilots and executes projects, and delivers actionable insights. It also notes some common challenges organizations face in data transformation, such as unrealistic technology expectations, inadequate delivery approaches, skills gaps, and poor data governance. Finally, it poses questions to help organizations assess their readiness
"Planning Your Analytics Implementation" by Bachtiar Rifai (Kofera Technology)Tech in Asia ID
Bachtiar is a tech startup & science enthusiast with more than 7 years experience in digital marketing, ecommerce, analytics and product development. Bachtiar has spend his career life as marketing leader at top ecommerce such as Lazada & Blanja.com. Currently Bachtiar develop a startup called Kofera, a technology company who provides Software as a Service (SaaS) marketing automation platform powered by Artificial Intelligence (AI) and machine learning. Established in 2016, Kofera helps companies build & optimize PPC campaign using machine learning algorithm to maximize business ROI. Kofera has helped many clients from various industries. Recently, Kofera received pra-series A funding lead by MDI Ventures and followed by Indosterling, DNC & Gunung Sewu.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
Data Science Salon: Quit Wasting Time – Case Studies in Production Machine Le...Formulatedby
Presented by Yashas Vaidya, Sr Data Scientist at DataIku
Next DSS MIA Event - https://datascience.salon/miami/
The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning.
Panelists from a large company, a small company and a software consulting firm will share insights on how their companies are tackling the arena of Big Data and how to leverage a variety of data sources for strategic decision-making.
Operationalizing Data Science: The Right Architecture and ToolsVMware Tanzu
In part one of this two-part series, you learned some of the common reasons enterprises struggle to turn insights into actions as well as a strategy for overcoming these challenges to successfully operationalize data science. In part two, it’s time to fill in the architectural and technological details of that strategy.
Pivotal Data Scientist Megha Agarwal will share the key ingredients to successfully put data science models in production and use them to drive actions in real-time. In this webinar, you will learn:
- Adopting extreme programming practices for data science
- Importance of working in a balanced team
- How to put and maintain machine learning models in production
- End-to-end pipeline design
Presenter: Megha Agarwal, Data Scientist
Five Pitfalls when Operationalizing Data Science and a Strategy for SuccessVMware Tanzu
Enterprise executives and IT teams alike know that data science is not optional, but struggle to benefit from it because the process takes too long and operationalizing models in applications can be hairy.
Join guest speaker, Forrester Research’s Mike Gualtieri and Pivotal’s Jeff Kelly and Dormain Drewitz for an interactive discussion about operationalizing data science in your business. In this webinar, the first of a two-part series, you will learn:
- The essential value of data science and the concept of perishable insights.
- Five common pitfalls of data science teams.
- How to dramatically increase the productivity of data scientists.
- The smooth hand-off steps required to operationalize data science models in enterprise applications.
Presenter : Guest Speakers Mike Gualtieri, Forrester, Dormain Drewitz and Jeff Kelly, Pivotal
Presentation by Michiel De Keyzer (PwC) at the Data Vault Modelling and Data ...Patrick Van Renterghem
This presentation explains why data is an asset as any other asset, and what this means in practice, followed by some tips and tricks on how to get management buy-in, support and stakeholder engagement for your data governance programme and how to do this from the start. Additionally, Michiel zooms in on how to implement data governance elements pragmatically and gradually, delivering tangible results, approaching it through well scoped use cases and solving data quality issues.
Throughout the presentation, different real-life cases are used as an example, as well as "tools" that are used in practice throughout data governance projects.
Dat de snelle ontwikkelingen op het gebied van digitale technologie de samenleving veranderen is overduidelijk. We beseffen alleen nog niet altijd hoe ingrijpend die veranderingen zijn. Het gaat niet enkel over automatisering, of over het efficiënter maken van wat we al deden door het inzetten van nieuwe technologie. Steeds meer merk je dat de digitale revolutie de regels van het spel zelf verandert. Neem Uber als voorbeeld: het grootste taxibedrijf ter wereld bezit zelf geen enkele auto en zet zo het klassieke model van zaken doen helemaal op zijn kop.
Ook in de sfeer van educatie, gemeenschapsvorming, maatschappelijke en culturele actie zie je gelijkaardige ontwikkelingen.
In een dergelijke context verandert de manier waarop we aan sociaal-cultureel werk doen fundamenteel. De digitale transformatie dwingt je om de rol die je als organisatie speelt en de manier waarop je je doelen nastreeft grondig te bevragen en voor een stuk te herdefiniëren.
Hoe kunnen we digitale transformatie echt begrijpen? Wat betekenen deze ontwikkelingen voor het sociaal-cultureel werk? En hoe kunnen we ons ertoe verhouden? Jo Caudron, co-auteur van het boek 'Digital transformation', legt aan de hand van 7 metaforen uit wat digitale transformatie precies inhoudt.
Presentatie op studiedag "Sociaal-cultureel werk in digitale transformatie" (9 juni 2016).
"Planning Your Analytics Implementation" by Bachtiar Rifai (Kofera Technology)Tech in Asia ID
Bachtiar is a tech startup & science enthusiast with more than 7 years experience in digital marketing, ecommerce, analytics and product development. Bachtiar has spend his career life as marketing leader at top ecommerce such as Lazada & Blanja.com. Currently Bachtiar develop a startup called Kofera, a technology company who provides Software as a Service (SaaS) marketing automation platform powered by Artificial Intelligence (AI) and machine learning. Established in 2016, Kofera helps companies build & optimize PPC campaign using machine learning algorithm to maximize business ROI. Kofera has helped many clients from various industries. Recently, Kofera received pra-series A funding lead by MDI Ventures and followed by Indosterling, DNC & Gunung Sewu.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
Data Science Salon: Quit Wasting Time – Case Studies in Production Machine Le...Formulatedby
Presented by Yashas Vaidya, Sr Data Scientist at DataIku
Next DSS MIA Event - https://datascience.salon/miami/
The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning.
Panelists from a large company, a small company and a software consulting firm will share insights on how their companies are tackling the arena of Big Data and how to leverage a variety of data sources for strategic decision-making.
Operationalizing Data Science: The Right Architecture and ToolsVMware Tanzu
In part one of this two-part series, you learned some of the common reasons enterprises struggle to turn insights into actions as well as a strategy for overcoming these challenges to successfully operationalize data science. In part two, it’s time to fill in the architectural and technological details of that strategy.
Pivotal Data Scientist Megha Agarwal will share the key ingredients to successfully put data science models in production and use them to drive actions in real-time. In this webinar, you will learn:
- Adopting extreme programming practices for data science
- Importance of working in a balanced team
- How to put and maintain machine learning models in production
- End-to-end pipeline design
Presenter: Megha Agarwal, Data Scientist
Five Pitfalls when Operationalizing Data Science and a Strategy for SuccessVMware Tanzu
Enterprise executives and IT teams alike know that data science is not optional, but struggle to benefit from it because the process takes too long and operationalizing models in applications can be hairy.
Join guest speaker, Forrester Research’s Mike Gualtieri and Pivotal’s Jeff Kelly and Dormain Drewitz for an interactive discussion about operationalizing data science in your business. In this webinar, the first of a two-part series, you will learn:
- The essential value of data science and the concept of perishable insights.
- Five common pitfalls of data science teams.
- How to dramatically increase the productivity of data scientists.
- The smooth hand-off steps required to operationalize data science models in enterprise applications.
Presenter : Guest Speakers Mike Gualtieri, Forrester, Dormain Drewitz and Jeff Kelly, Pivotal
Presentation by Michiel De Keyzer (PwC) at the Data Vault Modelling and Data ...Patrick Van Renterghem
This presentation explains why data is an asset as any other asset, and what this means in practice, followed by some tips and tricks on how to get management buy-in, support and stakeholder engagement for your data governance programme and how to do this from the start. Additionally, Michiel zooms in on how to implement data governance elements pragmatically and gradually, delivering tangible results, approaching it through well scoped use cases and solving data quality issues.
Throughout the presentation, different real-life cases are used as an example, as well as "tools" that are used in practice throughout data governance projects.
Dat de snelle ontwikkelingen op het gebied van digitale technologie de samenleving veranderen is overduidelijk. We beseffen alleen nog niet altijd hoe ingrijpend die veranderingen zijn. Het gaat niet enkel over automatisering, of over het efficiënter maken van wat we al deden door het inzetten van nieuwe technologie. Steeds meer merk je dat de digitale revolutie de regels van het spel zelf verandert. Neem Uber als voorbeeld: het grootste taxibedrijf ter wereld bezit zelf geen enkele auto en zet zo het klassieke model van zaken doen helemaal op zijn kop.
Ook in de sfeer van educatie, gemeenschapsvorming, maatschappelijke en culturele actie zie je gelijkaardige ontwikkelingen.
In een dergelijke context verandert de manier waarop we aan sociaal-cultureel werk doen fundamenteel. De digitale transformatie dwingt je om de rol die je als organisatie speelt en de manier waarop je je doelen nastreeft grondig te bevragen en voor een stuk te herdefiniëren.
Hoe kunnen we digitale transformatie echt begrijpen? Wat betekenen deze ontwikkelingen voor het sociaal-cultureel werk? En hoe kunnen we ons ertoe verhouden? Jo Caudron, co-auteur van het boek 'Digital transformation', legt aan de hand van 7 metaforen uit wat digitale transformatie precies inhoudt.
Presentatie op studiedag "Sociaal-cultureel werk in digitale transformatie" (9 juni 2016).
Mainframe Customer Education Webcast: New Ironstream Facilities for Enhanced ...Precisely
Check out our latest Mainframe Customer Education Webcast, featuring new Ironstream facilities for enhanced z/OS Analytics. Product Management Directors Ed Wrazen and Ed Hallock spoke about what’s new in the Ironstream z/OS data forwarder, as well as new features and facilities including:
• Data Loss Protection
• Advanced Filtering for SMF data
• Splunk Applications for Ironstream
You'll also learn about integration with Splunk’s IT Service Intelligence for monitoring the availability of critical business services running on z/OS platforms.
Big Data Analytics to Enhance Security
Predictive Analtycis and Data Science Conference May 27-28
Anapat Pipatkitibodee
Technical Manager
anapat.p@Stelligence.com
Anomaly Detection in Time-Series Data using the Elastic Stack by Henry PakData Con LA
Abstract:- The Elastic has released a commercial machine learning plugin that allows you to create a model of your time series data using an unsupervised machine learning approach. Walk through a few common use cases to see how this plugin may help with finding anomalies in your data.
NUS-ISS Learning Day 2016 - Big Data AnalyticsNUS-ISS
A real-time descriptive data analytics of your data seating inside of your NoSQL database. A time series data will be index to the lucene-based search server called ElasticSearch. This indexed data will then be visualised through the visualisation tool called Kibana. This tool can show charts, trends, maps and graphs based on your data. You can customise the filters to really get what you want from your data. Learn how you can quickly understand and get insights from their data.
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.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
Bersin by Deloitte - Demystifying Big DataNetDimensions
- How to start with the data you already have
- How data integration is essential to analytics
- How to move from transactional metrics to business metrics
Data-Analytics-Essentials-Building-a-Foundation-for-Informed-Business-Choices...Attitude Tally Academy
Unlock the power of informed decision-making with our guide, "From Data to Decisions: Building a Solid Foundation for Business Success" Explore the essentials of data analytics, empowering your business to thrive in a data-driven era. Discover strategic insights, navigate through information overload, and transform raw data into actionable intelligence.Whether you're a startup or an established enterprise, this resource is your roadmap to making sound business choices and charting a course toward success.Dive into the world of data-backed strategies and position your business for growth in today's competitive landscape.
Useful Link:- https://www.attitudetallyacademy.com/class/pythonda
Part 2 - 20 Years in Healthcare Analytics & Data Warehousing: What did we lea...Health Catalyst
Lessons learned over 20 years. This time we focus on technology lessons learned from experience at Intermountain Healthcare, Northwestern Medicine and Cayman Islands Health Authority
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.
20 Years in Healthcare Analytics & Data Warehousing: What did we learn? What'...Health Catalyst
The enterprise data warehouse (EDW) at Intermountain Healthcare went live in 1998. The EDW at Northwestern Medicine went live in 2006. Dale Sanders was the chief architect and strategist for both. The business inspiration behind Health Catalyst was, in essence, to create the commercial availability of the technology, analytics, and data utilization skills associated with these systems at Intermountain and Northwestern. Lee Pierce assumed leadership of the Intermountain EDW in 2008. Andrew Winter assumed leadership of the Northwestern EDW in 2009, and transitioned leadership of the EDW to Shakeeb Akhter in 2016. This webinar is a fireside chat among friends and colleagues as they look back across their healthcare IT decisions to answer these questions:
What did we do right and what did we do wrong?
What advice do we have for others in this emerging era of Big Data?
What does the future of analytics and Big Data look like in healthcare?
Data Integrity: From speed dating to lifelong partnershipPrecisely
Governance has little to do with governance…it’s about delivering and demonstrating value. It’s one thing for your colleagues to intellectually believe in the value of data, good data, and governed data, but it’s another thing entirely to have them emotionally engaged and excited to be involved. In this presentation from the CDO Sit-Down series, Shaun Connolly, Vice President of International Strategic Services, shares his thoughts and experience on approaches to win over reluctant leaders and business teams and describe the key components of successful programs.
How organizations can become data-driven: three main rulesAndrea Gigli
The presentation shows how organization can successfully become data driven and avoid wasting time and money. It explain how to prioritize business questtions, how to combine properly people, tech&data and processes, and how to structure a transforamtional journey for becoming a data driven.
Agile BI: How to Deliver More Value in Less TimePerficient, Inc.
Learn how to:
Construct a BI and analytical environment that provides the critical functionality that enables your customers to provide timely answers, supporting modern agile business
Leverage agile delivery concepts to deliver value in days rather than in months
Build a support organization that enables your users to create increased value from your company’s information assets
Business Intelligence (BI) and Data Management Basics amorshed
A one-day training course on the Concepts of Data Management and Business Intelligence (BI) in the DX age
A Basic Review of BI and DM
How to Implement BI
A review of BI Tools and 2022 Gartner Quadrant Magic
Basics of Data warehouse (DWH)
An introductions to Power BI
Components of Power BI
Steps for BI Implementation
Data Culture
Intro to ETL and ELT
OLAP files and Architecture
Digital transformation or DX review
A glance at DMBOK2.0 framework
BI Challenges
Data Governance
Data Integration
Data Security and Privacy in DMBOK2.0
Data-Driven Organization
Data and BI Maturity Model
Traditional BI
Self-service BI
who is DMP
who is BI developer
what is Metadata
what is Master data
Data Quality
Data Literacy
Benefits of BI
BI features
How does BI Works?
Modern BI
Data Analytics
BI Architecture
Data Types
Data Lake
Data Mart
Data Silo
Data Visualization
Power BI Architecture and components
The Business Value of Metadata for Data GovernanceRoland Bullivant
In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change.
Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Centric - Jaap huisprijzen, GTST, The Bold, IKEA en IENS. Zomaar wat toepassi...BigDataExpo
Tijdens deze presentatie wordt duidelijk hoe je machine learning kunt toepassen in het dagelijks leven. Denk aan het kopen van een huis, het kijken van Goede Tijden Slechte Tijden, shoppen bij IKEA en het bezoeken van restaurants.
In this session we'll dive into the journey that Google chooses to take in order focus on AI: what was the mindset, what were the challenges and what is the direction for the future.
Pacmed - Machine Learning in health care: opportunities and challanges in pra...BigDataExpo
The potential of personalized medicine based on machine learning is huge, but big challenges must be overcome to implement this technology in practice. Hidde will discuss both sides of the story, including a case study on the intensive care.
De Toekomst Verkenner is een ‘award winning’ innovatie van PGGM, die in een rap temp doorontwikkeling naar een platform maakt.
In zijn presentatie zal Mladen Sančanin vertellen hoe PGGM real time data en algoritmes heeft ingezet om dit platform te bouwen en hoe PGGM innovaties vanuit haar ‘Big Data Lab’ ondersteunt?
In een half uur worden veel ervaringen gedeeld over het opzetten van innovatieprojecten gebruik makend van data en het inrichten van data lab in een corporate omgeving.
Universiteit Utrecht & gghdc - Wat zijn de gezondheidseffecten van omgeving e...BigDataExpo
Het GGHDC onderzoekt wat de gezondheidseffecten zijn van omgeving en leefstijl in relatie tot het dagelijks leven van mensen. Het onderzoekscentrum is opgebouwd rond een gedeelde data- infrastructuur van de Universiteit Utrecht en het Universitair Medisch Centrum Utrecht (UMCU).
Rob van Kranenburg - Kunnen we ons een sociaal krediet systeem zoals in het o...BigDataExpo
IoT, Big Data, AI creëren een nieuwe situatie met betrekking tot het nemen van beslissingen door beleidsmakers. Toch verschuift er weinig in ons democratisch bestel, terwijl onze data in handen zijn van GAFA, China en andere nieuwe vormen van bestuur die nog ontstaan in de digitale transitie. Wij, in Europa, staan stil.
OrangeNXT - High accuracy mapping from videos for efficient fiber optic cable...BigDataExpo
Construction companies such as BAM Infra Telecom rely on accurate, up-to-date maps. Google Maps isn’t enough, but doing on-site surveys is expensive and time-consuming. However, driving through and recording 360° video from a car is cheap and easy. Using machine learning, we turn videos into highly accurate maps.
Dynniq & GoDataDriven - Shaping the future of traffic with IoT and AIBigDataExpo
Dynniq is a high-tech, innovative company offering smart mobility solutions and services internationally. We will present advanced IoT use cases Dynniq is working on, and share how GoDataDriven helps set up an AI capability. We will share our learnings, and show what makes data science in the mobility domain unique.
Teleperformance - Smart personalized service door het gebruik van Data Science BigDataExpo
Bij Teleperformance helpen we klanten waarde toe te voegen aan het klanttraject. We gebruiken Data Science voor onze Omnichannel-klantinteracties om de behoeften van de klant te voorspellen, zodat we het beste antwoord kunnen geven.
FunXtion - Interactive Digital Fitness with Data AnalyticsBigDataExpo
Digital is the new Personal. FunXtion Interactive is een interactieve trainingservaring voor zowel binnen als buiten de sportschool. FunXtion is revolutionair in de fitness branche en volledig data driven, by design. FunXtion laat zien hoe zij real-time data gebruiken voor ondersteuning van beslissingen, proces automatisering, personalisatie en product innovatie.
fashionTrade - Vroeger noemde we dat Big DataBigDataExpo
Big Data was de verzamelnaam voor alles wat je nog niet deed, maar al wel door Google of Amazon was uitgevonden. Inmiddels doen we al die dingen wel dus heet productaanbevelingen weer gewoon productaanbevelingen, fraudebestrijding weer fraudebestrijding, en spraakherkenning nog steeds spraakherkenning; geen Big Data. Geeft niet, want nu is er AI. Deze keynote legt uit of dat anders is, en waarom.
BigData Republic - Industrializing data science: a view from the trenchesBigDataExpo
What does it take to bring machine learning algorithms to production and start delivering business value? How can teams of data scientists and engineers effectively collaborate on a single product, integrate with existing IT systems and keep business stakeholders involved? Using real-life examples, we discuss the challenges and best practices.
Bicos - Hear how a top sportswear company produced cutting-edge data infrastr...BigDataExpo
Industry expert Dave Vanhoudt will set out his vision for the future of data infrastructure. Dave will highlight the key role automation must play in any data infrastructure strategy today, drawing on his current role with Medtronic, and past experiences at AB Inbev, Baxter, BMW and Nike.
Endrse - Next level online samenwerkingen tussen personalities en merken met ...BigDataExpo
Digitaal is vrijwel alles meetbaar. Maar het is vaak een uitdaging om de impact van samenwerkingen tussen influencers (topsporters) en bedrijven te analyseren. Start-up Endrse gebruikt AI om socialmediacontent te analyseren om content van influencers en bedrijven beter op elkaar te laten aansluiten. Zo maak je impact bij het publiek!
Bovag - Refine-IT - Proces optimalisatie in de automotive sectorBigDataExpo
De ontwikkelingen in de automotive sector gaan snel: elektrisch rijdende auto’s, de snelle groei van private lease, over the air connectiviteit, services on the demand en advanced driver assistance is zo maar een greep uit deze ontwikkelingen. Voorbeelden van (big) data ontwikkelingen die van grote invloed zijn op de automotive retail. De transitie naar een nieuw verdienmodel daagt uit tot samenwerken en datagedreven procesoptimalisatie.
Wilco Schellevis, directeur van Refine-IT en Renate Weggemans, manager strategie en beleid, bij BOVAG Autodealers, nemen u mee in de case Dely-App. Een mooi staaltje samenwerken en datagedreven procesoptimalisatie in de automotive retail; gevangen in één app.
Schiphol - Optimale doorstroom van passagiers op Schiphol dankzij slimme data...BigDataExpo
Schiphol is Europa’s best connected airport en verwerkt op piekdagen tot 235.000 passagiers. Om deze soepel door de processen te leiden is een betrouwbare prognose van de drukte noodzakelijk. Schiphol laat zien hoe zij datatoepassingen ontwikkelt om het aantal reizigers zo accuraat mogelijk te voorspellen en hiermee processen in te richten.
Veco - Big Data in de Supply Chain: Hoe Process Mining kan helpen kosten te r...BigDataExpo
Veco is marktleider op het gebied van het ontwerpen en vervaardigen van precisie delen middels electroformeren. In deze presentatie zal uitgeleverd worden hoe Veco succesvol Process Mining heeft ingezet in de productie om doorlooptijd te reduceren en new business te creëren. Tevens wordt uitgelegd wat Process Mining is.
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
VU Amsterdam - Big data en datagedreven waardecreatie: valt er nog iets te ki...BigDataExpo
In zijn presentatie gaat Frans Feldberg in op het ‘Waarom, Wat, en Hoe’ van big data en datagedreven business model innovation. Hoe is de wereld, als het om data gaat, de laatste jaren veranderd? Waarom zijn big data, business analytics en kunstmatige intelligentie belangrijke digitale innovaties die hoog op menig managementagenda staat en waarom investeren organisaties aanzienlijk in big data en data science? Hoe kunnen organisaties waarde met data creëren door zowel het verbeteren van het bestaande business model als door nieuwe data-gedreven business modellen te ontwikkelen. Dit zijn vragen die in zijn presentatie beantwoord zullen worden.
Booking.com - Data science and experimentation at Booking.com: a data-driven ...BigDataExpo
At Booking.com we have experienced what a data driven organisation means for creating business impact. And what looks it like, when experimentation is part of your company culture.
During this session we will share our experiences and learnings on how data science and experimentation go hand in go.
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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.
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.
5. PREVENTIVE MAINTENANCE:
ANALYSIS OF CORRELATION
BETWEEN PREVENTIVE AND
CORRECTIVE MAINTENANCE
CUSTOMER
CHURN AND CLIENT
SATISFACTION
PREDICT ON AN INDIVIDUAL
CLIENT BASIS AND KNOW
EXACTLY WHAT TO PROPOSE AT
WHAT TIME TO PREVENT CHURN
AND TO IMPROVE CUSTOMER
SATISFACTION
SOME EXAMPLES
DECISION MAKING SPEED
AND ACCURACY
DYNAMIC STOCK SYSTEMS:
LOCATION BASED ON DEMAND:
ALGORITHMS BASED ON DATA
HELP YOU TO TAKE THE RIGHT
DECISION.
8. Democratization
of data and
data discovery
New
data sources
Focus on
advanced analytics
Big data and
hybrid
architectures
Changing skills
requirements
MARKET TRENDS IN A DATA DRIVEN
ORGANIZATION
9. Realign Resources
• Dedicated data leader
• Business + IT integration
• Skill scarcity
Create New Roles
• Data Scientists
• Big Data Engineers
Revise Processes
• Data governance
• Meta data
Organizational Changes
Develop “Data Lake”
• Central repository of data with
redundant nodes dedicated to
specific data usage cases
Procure new platforms
• Data storage
• Analysis and reporting
Integrate existing IT
• Architecture
• Infrastructure
• Tools
Technology Changes
Shift Mindsets
• Data as an asset
Introduce New Processes
• Training
• Proof of concepts business
as usual
Cultural Changes
Impacts
Implementing Big Data is not (only) an IT Initiative,
it is an organizational journey.
HOW DO WE MODERNIZE OUR DATA TO
UNLOCK THE VALUE BEHIND IT?
11. TRANSFORMATION TRACK RECORD: OBSERVATIONS
FROM THE FIELD
Unrealistic expectations about technology and data platforms (e.g. Hadoop will replace your EDW)
have hampered the success of data modernisation programmes
Technology has been regarded as a silver bullet to address delivery challenges attributable to
delivery approach and data architecture choices
• Delivery approaches have not been adapted at the pace or scale to meet business
expectations for prototyping and deployment speed
• Continued deployment of rigid, linear data architectures has diluted business engagement
and perceived value from investments in technology
Organisations have under-estimated the skills gap and organisational challenges to rotate to an
analytics and data value driven operating model
Existing data governance challenges have been exacerbated by the increased diversity in data
sources, data types and data platforms
12. 1. Democratization of data: Who are the decision makers in your organization to lead
data exploration and analytics themes to disrupt your market?
2. New data sources: How ready is your organization handle new data sources like
unstructured or external data?
3. Focus on advanced analytics: To which degree are you using your current and
planned analytics to make decisions based on hindsight or foresight?
4. Big data and hybrid Architectures: How many of your data sources can be unlocked
and made available in a data lake?
5. Changing skills requirements: What is your organization strategy to have the skills to
both connect, build, perform and consume analytics?
YOUR MODERNIZATION JOURNEY
QUESTIONS