Presentation given at the 2015 South African Data Management Association conference.
Check out our blog.masterdata.co.za for articles related to this pressie - coming over the next few weeks, or call us on +27114854856 for more information
The document summarizes the top 10 IT concerns in 2016 according to a survey of 785 organizations. The top concerns were alignment of IT with business, security and privacy, and speed of IT delivery. It also discusses how a managed services approach by Scalar Decisions can help address these concerns by removing day-to-day IT burdens, providing 24/7 security experts, and quickly implementing projects through an expert team.
We asked delegates at the recent Big Data Analytics and MapR Convergence events in London, about their progress with implementing Big Data in their organisations. Here is what they told us.
CAMMS is a business intelligence and data warehousing company that helps organizations gain insights from their data. It sets up centralized data warehouses that integrate various data sources and enables business intelligence capabilities. CAMMS follows best practices to design and implement customized enterprise data warehouses that meet clients' specific needs. The company has expertise using Microsoft technologies and works with clients across various industries to identify insights and help them make more informed decisions.
The most successful Enterprise SaaS companies know that growing revenue only through new customer acquisition is the less efficient way to scale. Rather, they understand that growing revenue within your existing customer base - through up-sells, cross-sells, and expanded use - is the most profitable way to scale.
In fact, Enterprise SaaS companies that grow revenue - and company valuation - by expanding revenue within their existing customer base also know the key to making this work is to focus on - and operationalize - Customer Success.
This presentation - Customer Success for Security Software - is from Pulse 2014, the biggest Customer Success industry event ever and included panelists from ProofPoint, Rapid7, WhiteHat
The Trusted Path That Driven Big Data to Successankitbhandari32
The four D.A.T.A. questions formulated by Carsten Lund Pedersen & Thomas Ritter for big data are the following: Data, Autonomy, Technology & Accountability.
The document summarizes the top 10 IT concerns in 2016 according to a survey of 785 organizations. The top concerns were alignment of IT with business, security and privacy, and speed of IT delivery. It also discusses how a managed services approach by Scalar Decisions can help address these concerns by removing day-to-day IT burdens, providing 24/7 security experts, and quickly implementing projects through an expert team.
We asked delegates at the recent Big Data Analytics and MapR Convergence events in London, about their progress with implementing Big Data in their organisations. Here is what they told us.
CAMMS is a business intelligence and data warehousing company that helps organizations gain insights from their data. It sets up centralized data warehouses that integrate various data sources and enables business intelligence capabilities. CAMMS follows best practices to design and implement customized enterprise data warehouses that meet clients' specific needs. The company has expertise using Microsoft technologies and works with clients across various industries to identify insights and help them make more informed decisions.
The most successful Enterprise SaaS companies know that growing revenue only through new customer acquisition is the less efficient way to scale. Rather, they understand that growing revenue within your existing customer base - through up-sells, cross-sells, and expanded use - is the most profitable way to scale.
In fact, Enterprise SaaS companies that grow revenue - and company valuation - by expanding revenue within their existing customer base also know the key to making this work is to focus on - and operationalize - Customer Success.
This presentation - Customer Success for Security Software - is from Pulse 2014, the biggest Customer Success industry event ever and included panelists from ProofPoint, Rapid7, WhiteHat
The Trusted Path That Driven Big Data to Successankitbhandari32
The four D.A.T.A. questions formulated by Carsten Lund Pedersen & Thomas Ritter for big data are the following: Data, Autonomy, Technology & Accountability.
Cyber fraud and Security - What risks does family office's face intoday's wo...Kannan Subbiah
Presented at the Private Wealth Management Summit 2017 held at Mumbai, India.
Security has to be considered as the foundation on which one can build a business. Gone are the days when we can build a perimeter, sit back and feel secure. In today’s digital environment we partner with others, we outsource, we have alliances, we let our customers into our systems and as we extend our networks.
In the digital economy, effective cyber security can mean the difference between a business’s success and its failure.
Highlights of IBM Analytics Research ReportPaul Gillin
These highlights come from the IBM report, Analytics: the real-world use of big data
(http://www.slideshare.net/pgillin/big-data-analytics-study-4-13annotated). This document is used in a blog post that shows how to write a summary of a complex research report quickly.
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
Big data analytics can provide valuable insights for small and medium enterprises (SMEs). While SMEs have less data than larger corporations, analytics can determine relevance within available data and help SMEs make strategic decisions, improve operations, enable faster analysis, and gain competitive advantages. Cloud computing provides a cost-effective way for SMEs to access big data tools without large upfront investments. Choosing the right analytics framework and solutions tailored for SMEs' needs, along with proper resources and organizational alignment, are keys to success with big data projects.
Big data and analytics have become top priorities for companies. To fully leverage data, companies need the ability to collect and manage multiple data sources, build advanced analytics models, and transform their organization to make better decisions using data and models. While skepticism exists, companies that learn core big data skills may gain a competitive advantage, as these capabilities become more important for competition.
7 critical elements of a data strategy.Objectivity
Smart data management is the responsibility of everyone in the C-suite.
A clear, balanced data strategy will provide insights that drive decision
making and maintain a competitive advantage.
Please see our “The need for a data strategy” free white paper
for more details: https://www.objectivity.co.uk/the-need-for-a-data-strategy-white-paper/
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Karin Patenge "DIGITAL TRANSFORMATION DATA DRIVEN BUSINESS Bedeutung und Nutz...GEOkomm e.V.
1) Digital transformation and data-driven business models are becoming increasingly important as information and data have become critical business assets.
2) Data has huge emergent potential value when combined with other data and consumed by multiple parties, but most organizations currently understand and leverage only a small percentage of their data's value.
3) To truly be data-driven, organizations must move beyond simply collecting data to integrating data acquisition, management, sharing, and analytics across business functions and ecosystems.
Making the Most of Big Data Through Technology and Organizational DesignJason Wilson
This document discusses how increased technology and data availability has led to the rise of "Big Data". It presents findings from interviews and surveys with business leaders on their relationship with data, investment in technology, and organizational empowerment. The key findings are that a minimum investment in technology is needed to make data-driven decisions, increased investment leads to more data insights, and organizational design has a strong impact on the ability to make data-driven decisions. The major barrier to fully utilizing data is organizational structure, not lack of technology.
UltramaxIT is an analytics training and consulting company that offers various analytics courses and services to help businesses utilize data and gain insights. It has over 20 years of experience working with over 300 corporate clients and training over 20,000 students. UltramaxIT partners with leading analytics companies and has a global presence and advisory board to bring the best analytics solutions and training to clients worldwide.
InfoMINING is a Colorado LLC established in 2009 that focuses on providing financial analysis, benchmarking, and executive software reporting solutions for healthcare and blood banking companies. It uses data analytics to provide business insights and key performance indicators (KPIs) to help executives make faster, more precise decisions. InfoMINING offers services such as financial gap analysis, balanced scorecard consulting, and executive dashboard software to measure KPIs and monitor systems and processes. Its donorMINING software is tailored for the blood banking industry.
The document discusses making advanced analytics work for companies. It provides guidance on choosing the right data, building models that predict and optimize business outcomes, and transforming a company's capabilities. It emphasizes starting with identifying a business opportunity and determining how analytics can improve performance. While big data can solve companies' problems, organizational change is needed to fully exploit analytics capabilities. Research shows companies using big data and analytics have 5-6% higher productivity and profitability than their peers.
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.
The, What, Why, & How of MDM in Digital Business Transformation SlideshareProfisee
MDM not only makes digital transformation possible, it optimizes the results of these efforts while reducing the risk of tactical and strategic failures. This webinar dives into the critical role that MDM plays in digital business transformation initiatives
Executives are still waiting on our “Big Data Deep Insights”. Many of us are down the path of collecting, extracting, and analyzing our ever-growing data in Hadoop environments. We are building our data science expertise and expanding data governance. Yet still we are not getting what we are waiting for.This talk is about:
1. Getting to the right questions
2. Setting expectations with the executive team
3. The unintentional consequence of suddenly having lots of data
4. Framing the boundaries of our data science
5. Pragmatic data governance
6. Looking outside your data to 3rd party data
While companies are investing heavily in data analytics technologies, many are not seeing significant returns because they lack the capabilities to properly analyze data and implement changes based on insights. For businesses to truly benefit from big data, managers must focus first on using data to guide operational decisions, establish processes for cleaning and analyzing data, and drive cultural changes to support evidence-based decision making. Only after achieving these foundations can companies hope to leverage more advanced big data technologies and analytics to gain competitive advantages.
Data governance is a data strategy that incorporates disciplines like data quality, data management, data standards, policies, and business process management to support the business strategy. It applies these disciplines enterprise-wide with everyone involved to proactively govern data and maximize its value in supporting business goals.
Rob Karel - Ensuring The Value Of Your Trusted Data - Data Quality Summit 2008DataValueTalk
- The document discusses building a business case for trusted data and master data management (MDM) initiatives through a bottom-up valuation approach. It recommends starting with an individual line-of-business process to identify and address data quality issues to quickly realize value.
- Examples of target processes include reducing call center inefficiencies through better customer data, decreasing wasted marketing costs from improved targeting, and lowering supply chain breakdowns by ensuring data integrity. Metrics like data freshness, accuracy, and completeness should be used to ensure initiatives are on track.
- A multi-phase, long-term view of data governance as a "trusted data program" is advocated over viewing MDM as the goal in itself. Buy-
Cyber fraud and Security - What risks does family office's face intoday's wo...Kannan Subbiah
Presented at the Private Wealth Management Summit 2017 held at Mumbai, India.
Security has to be considered as the foundation on which one can build a business. Gone are the days when we can build a perimeter, sit back and feel secure. In today’s digital environment we partner with others, we outsource, we have alliances, we let our customers into our systems and as we extend our networks.
In the digital economy, effective cyber security can mean the difference between a business’s success and its failure.
Highlights of IBM Analytics Research ReportPaul Gillin
These highlights come from the IBM report, Analytics: the real-world use of big data
(http://www.slideshare.net/pgillin/big-data-analytics-study-4-13annotated). This document is used in a blog post that shows how to write a summary of a complex research report quickly.
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
Big data analytics can provide valuable insights for small and medium enterprises (SMEs). While SMEs have less data than larger corporations, analytics can determine relevance within available data and help SMEs make strategic decisions, improve operations, enable faster analysis, and gain competitive advantages. Cloud computing provides a cost-effective way for SMEs to access big data tools without large upfront investments. Choosing the right analytics framework and solutions tailored for SMEs' needs, along with proper resources and organizational alignment, are keys to success with big data projects.
Big data and analytics have become top priorities for companies. To fully leverage data, companies need the ability to collect and manage multiple data sources, build advanced analytics models, and transform their organization to make better decisions using data and models. While skepticism exists, companies that learn core big data skills may gain a competitive advantage, as these capabilities become more important for competition.
7 critical elements of a data strategy.Objectivity
Smart data management is the responsibility of everyone in the C-suite.
A clear, balanced data strategy will provide insights that drive decision
making and maintain a competitive advantage.
Please see our “The need for a data strategy” free white paper
for more details: https://www.objectivity.co.uk/the-need-for-a-data-strategy-white-paper/
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Karin Patenge "DIGITAL TRANSFORMATION DATA DRIVEN BUSINESS Bedeutung und Nutz...GEOkomm e.V.
1) Digital transformation and data-driven business models are becoming increasingly important as information and data have become critical business assets.
2) Data has huge emergent potential value when combined with other data and consumed by multiple parties, but most organizations currently understand and leverage only a small percentage of their data's value.
3) To truly be data-driven, organizations must move beyond simply collecting data to integrating data acquisition, management, sharing, and analytics across business functions and ecosystems.
Making the Most of Big Data Through Technology and Organizational DesignJason Wilson
This document discusses how increased technology and data availability has led to the rise of "Big Data". It presents findings from interviews and surveys with business leaders on their relationship with data, investment in technology, and organizational empowerment. The key findings are that a minimum investment in technology is needed to make data-driven decisions, increased investment leads to more data insights, and organizational design has a strong impact on the ability to make data-driven decisions. The major barrier to fully utilizing data is organizational structure, not lack of technology.
UltramaxIT is an analytics training and consulting company that offers various analytics courses and services to help businesses utilize data and gain insights. It has over 20 years of experience working with over 300 corporate clients and training over 20,000 students. UltramaxIT partners with leading analytics companies and has a global presence and advisory board to bring the best analytics solutions and training to clients worldwide.
InfoMINING is a Colorado LLC established in 2009 that focuses on providing financial analysis, benchmarking, and executive software reporting solutions for healthcare and blood banking companies. It uses data analytics to provide business insights and key performance indicators (KPIs) to help executives make faster, more precise decisions. InfoMINING offers services such as financial gap analysis, balanced scorecard consulting, and executive dashboard software to measure KPIs and monitor systems and processes. Its donorMINING software is tailored for the blood banking industry.
The document discusses making advanced analytics work for companies. It provides guidance on choosing the right data, building models that predict and optimize business outcomes, and transforming a company's capabilities. It emphasizes starting with identifying a business opportunity and determining how analytics can improve performance. While big data can solve companies' problems, organizational change is needed to fully exploit analytics capabilities. Research shows companies using big data and analytics have 5-6% higher productivity and profitability than their peers.
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.
The, What, Why, & How of MDM in Digital Business Transformation SlideshareProfisee
MDM not only makes digital transformation possible, it optimizes the results of these efforts while reducing the risk of tactical and strategic failures. This webinar dives into the critical role that MDM plays in digital business transformation initiatives
Executives are still waiting on our “Big Data Deep Insights”. Many of us are down the path of collecting, extracting, and analyzing our ever-growing data in Hadoop environments. We are building our data science expertise and expanding data governance. Yet still we are not getting what we are waiting for.This talk is about:
1. Getting to the right questions
2. Setting expectations with the executive team
3. The unintentional consequence of suddenly having lots of data
4. Framing the boundaries of our data science
5. Pragmatic data governance
6. Looking outside your data to 3rd party data
While companies are investing heavily in data analytics technologies, many are not seeing significant returns because they lack the capabilities to properly analyze data and implement changes based on insights. For businesses to truly benefit from big data, managers must focus first on using data to guide operational decisions, establish processes for cleaning and analyzing data, and drive cultural changes to support evidence-based decision making. Only after achieving these foundations can companies hope to leverage more advanced big data technologies and analytics to gain competitive advantages.
Data governance is a data strategy that incorporates disciplines like data quality, data management, data standards, policies, and business process management to support the business strategy. It applies these disciplines enterprise-wide with everyone involved to proactively govern data and maximize its value in supporting business goals.
Rob Karel - Ensuring The Value Of Your Trusted Data - Data Quality Summit 2008DataValueTalk
- The document discusses building a business case for trusted data and master data management (MDM) initiatives through a bottom-up valuation approach. It recommends starting with an individual line-of-business process to identify and address data quality issues to quickly realize value.
- Examples of target processes include reducing call center inefficiencies through better customer data, decreasing wasted marketing costs from improved targeting, and lowering supply chain breakdowns by ensuring data integrity. Metrics like data freshness, accuracy, and completeness should be used to ensure initiatives are on track.
- A multi-phase, long-term view of data governance as a "trusted data program" is advocated over viewing MDM as the goal in itself. Buy-
This document provides information on data governance and discusses several challenges and approaches to data governance. It discusses that 80% of enterprise data is unstructured and spread across many sources like web data, enterprise applications, emails, and social media. Governing such diverse data assets is a complex long-term journey. It also discusses why data governance is needed, challenges of data governance, and different routes and frameworks to conduct data governance assessments and develop solutions. These include using cases studies, lean six sigma methodology, enterprise data architecture approaches, and linking data governance with machine learning. The document concludes by emphasizing structure of data, experimenting with different assessment and solutioning methods, and leveraging machine learning as a new capability.
value and implications of master data management.pptxMuhammad Khalid
A consistent and uniform set of identifiers and attributes that describe the core entities of the enterprise, and are used across multiple business processes.
An effective data management solution can help businesses achieve best business practices and quality customer service responses. It helps make the process easier and faster.
The data management procedure employed by your firm is capable of building your brand or breaking it all over. So, be wise in choosing the right strategy.
Big Data_Analytics - Stick Man PresentationAlan Taylor
This document discusses how companies can leverage big data to gain competitive advantages. It emphasizes the importance of first understanding the opportunities and threats of big data before developing strategies. It also stresses the need to extract value from existing stored data and to manage data security and flows from multiple sources and across the entire enterprise. Finally, it asserts that proper data governance can help companies achieve greater efficiencies, lower risks, and increased revenues through better use of big data.
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
The document discusses why businesses should not be intimidated by data governance and provides a framework for implementing an effective data governance program. It notes that proper data governance starts with strong policies and oversight to ensure consistent and proper handling of data. The framework outlines a top-down approach with three phases - plan, design, and execute. Businesses are advised to start small by focusing on a single department or initiative and then expand data governance practices over time for maximum buy-in and success. Effective data governance provides many benefits including improved decision making, risk management, and the ability to treat information as a valuable business asset.
As businesses generate and manage vast amounts of data, companies have more opportunities to gather data, incorporate insights into business strategy and continuously expand access to data across the organisation. Doing so effectively—leveraging data for strategic objectives—is often easier said
than done, however. This report, Transforming data into action: the business outlook for data governance, explores the business contributions of data governance at organisations globally and across industries, the challenges faced in creating useful data governance policies and the opportunities to improve such programmes.
Data-Driven is Passé: Transform Into An Insights-Driven EnterpriseDenodo
This document summarizes a presentation on transforming companies into insights-driven enterprises. It discusses how most companies are currently data-driven but struggle to consistently turn data into effective actions. An insights-driven approach involves building multidisciplinary insights teams, establishing good data governance foundations, and combining the right tools and processes into systems of insight. Data virtualization is highlighted as a key technology enabler for systems of insight by providing agile data access and logical abstraction across structured and unstructured data sources. Examples are provided of how data virtualization has helped customers achieve single customer views and build logical data warehouses.
Data governance, Information security strategyvasanthi4ever
Data governance refers to decision making and authority over organizational data. It requires cross-functional teams to identify data issues and communication between business and IT. As data volumes double every 1-2 years and data breaches increase, data governance is necessary to prevent potential disasters like data loss. Initial attempts at data governance in the 1970s failed due to lack of data stewards and executive involvement. Reasons for implementing a data governance program include when an organization grows large or complex, and to meet regulatory requirements. The goals of a data governance program are to ensure transparency, reduce costs, and enable better decision making.
The document discusses a survey of 300 enterprise organizations about data ownership and big data initiatives. It finds that marketing and sales are most involved in purchase decisions, but sales, business development, and insights/analytics have the most influence. Most functions see their involvement peaking late in the purchase process. Organizations need strategies to align functional areas and determine influence. Data initiatives are being driven by needs for better analytics, marketing intelligence, and predictive capabilities rather than just data quality issues.
IT and business leaders must increase their efforts to evolve from traditional BI tools, that focus on descriptive analysis (what happened), to advanced analytical technologies, that can answer questions like “why did it happen”, “what will happen” and “what should I do”.
"While the basic analytical technologies provide a general summary of the data, advanced analytical technologies deliver deeper knowledge of information data and granular data.” - Alexander Linden, Gartner Research Director
The reward of a smarter decision making process, based on Data Intelligence, is a powerful driver to improve overall business performance.
Wiseminer is the only and most efficient end-to-end Data Intelligence software to help you make smarter decisions and drive business results.
Contact us: info@wiseminer.com
In this ppt document we have mentioned 7 best data management tips and practices for small business owner so that you can manage your data carefully. Odyssey provides best data management service at best price.
Balancing Business Value and Business Values with Big DataSAP Analytics
As companies accelerate their use of big data in the pursuit of business value, they must address the moral and ethical implications or risk alienating the very people they seek to serve.
Présentation Forrester - Forum MDM Micropole 2014Micropole Group
Présentation du Cabinet Forrester lors du 3eme Forum MDM Micropole le 19 novembre 2014 à Paris.
Forrester présente les tendances du marché du Master Data Management et de la gouvernance des données.
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
Drive Your Business with Democratized Data
Data democratization is the concept of creating a self-serve data layer where business users can access and manipulate data efficiently and effectively. This makes data handling more agile and flexible based on company needs rather than being forced to compromise user experience and data integrity to meet rigid compliance mandates. Democratizing data is essential for businesses as it transforms operations by providing employees with information to take action and make decisions. The key is to involve people across the organization, ensure data is accessible, adopt the right technology and governance practices, and set success metrics to maximize the value of shared data.
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
Similar to Moving from passive to active data governance (20)
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
The document discusses avoiding compliance pitfalls related to anti-money laundering (AML) regulations. It recommends establishing a data management system, conducting organizational screening against legislations, and reporting suspicious activity. It also warns of primary failings seen in identifying customer details, conducting sanctions screening, and failing to report qualifying transactions, which can damage an organization's reputation. Effective information management that supports AML and fraud prevention includes having data governance, quality, and master data management practices.
Studies show that poor data quality has a negaitve impact on customer experience, analytics and marketing.
This presentation discusses solutions to the problem of poor customer data quality
Get the survey results http://www.masterdata.co.za/index.php/whitepapers/file/77-whitepaper-extracting-marketing-value-from-big-data
Using gis to enhance customer experienceGary Allemann
The document discusses how geographic information systems (GIS) can enhance the customer experience. It provides examples of how telecommunications, insurance, and government organizations have used big data and GIS analytics to gain insights into customer interactions and preferences. This allows them to better meet customer needs, detect fraud, optimize networks in high usage areas, and improve delivery of social services. Location data, transaction records, and demographics are analyzed to understand customers and identify discrepancies or coverage gaps to target for improvement.
Data is becoming increasingly important for powering operations, nurturing decisions, and sustaining competitive advantage. As data becomes more central to business success, organizations require a chief data officer to ensure data meets business needs and is properly governed, moving from a paradigm of "data first" to encourage data discovery while maintaining business control over analytics. A chief data officer may become responsible for overseeing big data strategy and management as the role of data grows in importance.
Big data myths are busted in this document which outlines common misconceptions about big data and provides guidance on where to start with big data initiatives. Some myths that are dispelled are that big data is only about external data, size, specific technologies like Hadoop, and that it will solve all data quality problems. The document recommends taking an agile analytics approach starting with identifying use cases then integrating, preparing, analyzing and visualizing data to deploy solutions in under 4 weeks.
What is the value of data? Data governance must look beyond master data to deliver real value.
Visit www,masterdata.co.za/index.php/data-governance-solutions
Bridging the gap between relational and spatial data
How data quality links customer to spatial data sets see http://www.masterdata.co.za/index.php/geocoding-cres
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
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17. Data Assets shared with all
Need for greater agility and
utility of business data
Requires appropriate
ownership and control
Avoid reactive practise that
limit scope and purpose
Need to identify and trust
the right data
Understand the nuances of
data
Provide context for data
20. Data Governance Wave
Key Take Aways
Vendors are emerging to
support the strategic side
of data governance
Data Governance must
support planning,
operations and
transparency
Source: Forrester Research, Inc.