Data strategy
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Startup and Growth companies that have unique and compelling product ideas still need to find a strategic pathway towards building that vision into a final product. Designing and building features is just part of the puzzle and fast iterations are only helpful if you are gaining real and useful learning from those releases. Data strategy ensures that each product feature released is backed by data to measure its impact and effectiveness.
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Our Advice
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Have the business drive your Data Management project.
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Impact and Result
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<p>Becoming a data-driven organization is something many companies aspire to, but few are able to obtain. Let’s face it: Data is confusing. It is complicated, dirty, and spread out all over a business. While companies are making big investments in Data Management projects, only a few are seeing the payoff. </p>
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<p>New research from Experian shows that despite many ongoing data initiatives, 69 percent of organizations struggle to be data-driven. The struggles are real. Companies face a large data debt, look at data projects through a siloed lens, and still have a large volume of inaccurate data. In fact, 65 percent report inaccurate data is undermining key initiatives. <br></p>
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<!-- wp:paragraph -->
<p>However, the tide is turning. Businesses are starting to adopt data enablement, or a practice of empowering a larger group of individuals within the business to understand and harness the power of data and analytics. Companies that empower wider data usage are better able to comply with regulations, improve decision-making, and, of course, deliver a superior customer experience. Are these the results you’re striving for? </p>
<!-- /wp:paragraph -->
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<p>Join us to uncover new research from more than 500 Data Management practitioners as we take a deep dive into:</p>
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<ul><li>The top challenges in becoming a data-driven organization </li><li>Trends and the rise of data enablement </li><li>The profile of a mature organization </li><li>Tips for how you can adopt data enablement practices</li></ul>
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Data Leadership - Stop Talking About Data and Start Making an Impact!
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<p>For any organization to be successful, whatever we do with data must connect to meaningful business improvements—and those must be measured. If current data efforts lack results or accountability, then Data Leadership is our answer.</p>
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<p>But Data Leadership isn’t really about the data at all. What makes Data Leadership so powerful is its ability to completely transform organizations. Going beyond traditional data management and governance, Data Leadership builds momentum and delivers the change we’ve long known our businesses need. Data Leadership helps us overcome the lingering data challenges our legacy approaches never will.</p>
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<p>This webinar will cover the key concepts of Data Leadership, and what anybody can do to start making a bigger impact for their teams and businesses. Whether your role today is large or small, Data Leadership will be essential to your future data success! </p>
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<p>Key Learnings Include:</p>
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<ul><li>What Data Value really is, and why creating it is the goal of everything we do with data</li><li>Introduction to the Data Leadership Framework</li><li>Why Data Leadership is fundamentally about balance</li><li>How to immediately start making a Data Leadership impact in your organization</li></ul>
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How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
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Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
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Unit 2.pptx
1. The document discusses traits that are important for effective data analysis and visualization. It outlines traits like curiosity, critical thinking, understanding data, attention to detail, learning new technologies, and communicating results clearly.
2. Key traits of meaningful data that enable useful analysis are discussed, such as high volume, being historical, consistent, multivariate, atomic, clean, and dimensionally structured.
3. Visual perception and how the human brain interprets visuals is also covered. For effective data visualization, visuals must be designed based on principles of visual perception so that insights can be easily understood.
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The document discusses the importance of developing a data strategy before building a data warehouse. It defines a data strategy as a unified, organization-wide plan for using corporate data as a vital asset. The data strategy should address critical data issues like quality, metadata, performance, distribution, ownership, security and privacy. Developing a data strategy requires identifying strategic and operational decisions, aligning the strategy with business goals, and answering many questions across various data-related topics.
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The enterprise marketer's playbook: Building an integrated data strategy.
An integrated data strategy can help any business see customer journeys more clearly ― and then give customers more relevant ads and experiences that get results. So why doesn't everyone have such a strategy? We look at what sets the marketing leaders apart.
Let marketing data be your guide
If you've ever felt too swamped by data to find the customer insights you need, you're not alone. But there's a new and better approach to gaining deeper audience insights: building an integrated data strategy.
Read this report to learn how:
86% of senior executives agree that eliminating organizational silos is critical to expanding the use of data and analytics in decision-making.
75% of marketers agree that lack of education and training on data and analytics is the biggest barrier to more business decisions being made based on data insights.
Leading marketers are 59% more likely to use digital analytics to optimize the user experience in real time.
Develop and Implement an Effective Data Management Strategy and Roadmap
Treat data as an asset and gain a competitive advantage.
Your Challenge
Despite the growing focus on data, many organizations struggle to develop an effective strategy for their data assets. This is due to their intangible nature and varying use across the business.
Data Management is a business process managed by IT. This creates a challenge for IT as it is required to create and manage complex systems of operations that link closely to integral business operations.
Our Advice
Critical Insight
Data Management is not one size fits all. Cut through the noise related to Data Management and create a strategy and process that is right for your organization.
Have the business drive your Data Management project.
It all starts and ends with Data Governance. At a minimum, invest in Data Governance initiatives.
Impact and Result
Coordination between IT and the business will create a Data Management strategy that understands and satisfies the data requirements of the business.
Data Management requirements and initiatives will be derived from the following: business goals and strategic plans, current capability assessments, business drivers for data, understanding of market and technology opportunities, and a clear understanding of the business’s drivers regarding data.
Creating a clear Data Management Strategy and developing a roadmap of initiatives will allow IT to create a plan for how to bridge the gap between IT and the business and create a Data Management framework that supports the business’s immediate and long-term data requirements.
Data-Ed Webinar: Your Data Strategy
In many organizations and functional areas, data has pulled even with money in terms of what makes the proverbial world go round. As businesses struggle to cope with the 21st century’s newfound data flood, it is more important than ever before to prioritize data as an asset that directly supports business imperatives. However, while organizations across most industries make some attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. Data Quality), the results of these efforts frequently fall far below expectations. At the root of many of these failures is poor organizational Data Management—which fortunately is a remediable problem.
This webinar will cover three lessons, each illustrated with examples, that will help you establish realistic goals and benchmarks for Data Management processes and communicate their value to both internal and external decision-makers:
How organizational thinking must change to include value-added Data Management practices
The importance of walking before you run with data-focused initiatives
Prioritizing specification and Data Governance over “silver bullet” analytical tools
Discuss foundational data-centric concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Developing a Data Strategy
This document discusses the development of a data strategy for an organization. It begins by introducing the presenter and organization. It then covers why a data strategy is needed to address common data issues. The strategy should define what the data team will and will not do. Developing the strategy requires gathering information, consulting other teams, and linking it to the organization's mission. Key aspects of the strategy include objectives, principles, delivery areas, and ensuring it is concise enough to be accessible and remembered.
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<!-- wp:paragraph -->
<p>Becoming a data-driven organization is something many companies aspire to, but few are able to obtain. Let’s face it: Data is confusing. It is complicated, dirty, and spread out all over a business. While companies are making big investments in Data Management projects, only a few are seeing the payoff. </p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>New research from Experian shows that despite many ongoing data initiatives, 69 percent of organizations struggle to be data-driven. The struggles are real. Companies face a large data debt, look at data projects through a siloed lens, and still have a large volume of inaccurate data. In fact, 65 percent report inaccurate data is undermining key initiatives. <br></p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>However, the tide is turning. Businesses are starting to adopt data enablement, or a practice of empowering a larger group of individuals within the business to understand and harness the power of data and analytics. Companies that empower wider data usage are better able to comply with regulations, improve decision-making, and, of course, deliver a superior customer experience. Are these the results you’re striving for? </p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>Join us to uncover new research from more than 500 Data Management practitioners as we take a deep dive into:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>The top challenges in becoming a data-driven organization </li><li>Trends and the rise of data enablement </li><li>The profile of a mature organization </li><li>Tips for how you can adopt data enablement practices</li></ul>
<!-- /wp:list -->
Data Insights and Analytics: The Importance of Effective Communications in An...
This document discusses effective communication of analytics insights. It emphasizes customizing communication based on audience and using various channels like visualization. Growing an organization's data literacy is compared to learning to fly, moving from passive understanding to active use of data to make decisions. Aligning an organization around data-driven decisions involves setting a vision, purpose and future state. Data storytelling and compelling visuals are encouraged to engage audiences beyond simply presenting data.
Data Leadership - Stop Talking About Data and Start Making an Impact!
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<p>For any organization to be successful, whatever we do with data must connect to meaningful business improvements—and those must be measured. If current data efforts lack results or accountability, then Data Leadership is our answer.</p>
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<p>But Data Leadership isn’t really about the data at all. What makes Data Leadership so powerful is its ability to completely transform organizations. Going beyond traditional data management and governance, Data Leadership builds momentum and delivers the change we’ve long known our businesses need. Data Leadership helps us overcome the lingering data challenges our legacy approaches never will.</p>
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<p>This webinar will cover the key concepts of Data Leadership, and what anybody can do to start making a bigger impact for their teams and businesses. Whether your role today is large or small, Data Leadership will be essential to your future data success! </p>
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<p>Key Learnings Include:</p>
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<ul><li>What Data Value really is, and why creating it is the goal of everything we do with data</li><li>Introduction to the Data Leadership Framework</li><li>Why Data Leadership is fundamentally about balance</li><li>How to immediately start making a Data Leadership impact in your organization</li></ul>
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Data Quality for Non-Data People
Most companies do not think of data when they start out, let alone the quality of that data. With the proliferation of data and the usages of that data, organizations are compelled to focus more and more on data and their quality.
Join Kasu Sista of The Wisdom Chain to understand how to think about, implement, and maintain data quality.
You will learn about:
What do data people think about?
How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
How to talk to your data people
Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
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Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control data quality issues in your organization.
LDM Slides: How Data Modeling Fits into an Overall Enterprise Architecture
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
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Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
Data-Ed: Data-centric Strategy & Roadmap
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
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Organizations continually fail to generate ROI on their governance initiatives because they are too narrow in scope. To be effective, Business Intelligence (BI) governance must cover both data and visualizations.
This presentation will provide a practical step-by-step guide for implementing effective BI governance and a toolkit for addressing the three critical aspects of any program:
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Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...
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Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
DAS Webinar: Emerging Trends in Data Architecture – What’s the Next Big Thing?
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY® survey on Emerging Trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Strategy - Executive MBA Class, IE Business School
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
Master Data Management's Place in the Data Governance Landscape
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
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Develop and Implement an Effective Data Management Strategy and Roadmap
Develop and Implement an Effective Data Management Strategy and Roadmap
Smart Data - The Foundation for Better Business Outcomes
Smart Data - The Foundation for Better Business Outcomes
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Data Insights and Analytics: The Importance of Effective Communications in An...
Data Insights and Analytics: The Importance of Effective Communications in An...
Data Leadership - Stop Talking About Data and Start Making an Impact!
Data Leadership - Stop Talking About Data and Start Making an Impact!
LDM Slides: How Data Modeling Fits into an Overall Enterprise Architecture
LDM Slides: How Data Modeling Fits into an Overall Enterprise Architecture
Master Data Management - Aligning Data, Process, and Governance
Master Data Management - Aligning Data, Process, and Governance
A Practical Guide to Implementing Effective BI Governance
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Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...
When and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data Architecture
DAS Webinar: Emerging Trends in Data Architecture – What’s the Next Big Thing?
DAS Webinar: Emerging Trends in Data Architecture – What’s the Next Big Thing?
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Data Strategy - Executive MBA Class, IE Business School
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Master Data Management's Place in the Data Governance Landscape
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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.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
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/
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Data strategy
- 2. Projects & Pain • 2/3 of my projects required a data strategy • Spoke with Developers and Designers about their pains • “Clients want a magic dashboard with correct answers”
- 3. Solution • Formalize the framework and a deliverable • Framework = “measure, learn and build” • Deliverable = strategic/ tactical dashboard
- 4. Create ideas / make suggestions Ask questions / create hypotheses BuildCollect data Analyze data
- 5. Analytics The discovery and communication of meaningful patterns in data.
- 6. Analytics The discovery and communication of meaningful patterns in data. Analytics starts with collecting and visualizing data.
- 7. Analytics The discovery and communication of meaningful patterns in data. Patterns start to emerge after slicing, dicing and comparing.
- 8. Analytics The discovery and communication of meaningful patterns in data. Assignment of meaning to trends requires an understanding of the business goals.
- 9. Analytics The discovery and communication of meaningful patterns in data. Once you’ve discovered the meaning of trends in your data, you have information that you can act on.
- 10. Why data? There are things we know we know There are things we know we don’t know There are things we don’t know we know There are things we don’t know we don’t know Thanks, Donald Rumsfeld and Lean Analytics
- 11. Why data? There are things we know we know Easy Data There are things we know we don’t know There are things we don’t know we know There are things we don’t know we don’t know Thanks, Donald Rumsfeld and Lean Analytics
- 12. Why data? There are things we know we know Easy Data There are things we know we don’t know Easy Questions There are things we don’t know we know There are things we don’t know we don’t know Thanks, Donald Rumsfeld and Lean Analytics
- 13. Why data? There are things we know we know Easy Data There are things we know we don’t know Easy Questions There are things we don’t know we know Valuable Intuitions, and Difficult Questions There are things we don’t know we don’t know Thanks, Donald Rumsfeld and Lean Analytics
- 14. Why data? There are things we know we know Easy Data There are things we know we don’t know Easy Questions There are things we don’t know we know Valuable Intuitions, and Difficult Questions There are things we don’t know we don’t know Potential Opportunities Thanks, Donald Rumsfeld and Lean Analytics
- 15. Why data? Data validates what we know, underscores what we don’t know, surfaces our intuitions and exposes potential opportunities.
- 16. Types of data • Quantitative and Objective - Information collected in sufficient quantity that significance can be determined numerically • Qualitative and Subjective - Information collected where significance is determined experientially
- 17. Types of data • Quantitative and Objective - Measured by instrumenting an app with tracking code, querying the database or log files • Qualitative and Subjective - Measured by interacting one-on-one with users
- 18. How we work with data Design & BuildCollect & Analyze Define goals & Craft hypotheses
- 19. How we communicate data
- 20. Business Model Canvas, Personas, User Journeys, User Interviews Learn Design & Build Take steps based on things we’ve learned. Might start with a design sprint. Measure Collect qualitative reports from user interviews. Collect quantitative data from app. A likely iteration for stickies
- 21. Iteration Planning 1. Project Kick Off and Learning Activities. 1 week. 2. Design Sprint - design and build a low fidelity version with one or more proposed features and use to interview users. We might do more than one of these. 1 week. 3. Product Development - incorporate what we learned into the actual product and release for real world feedback. 1 week. 4. Repeat #2 or #3 with new knowledge.
- 22. Questions?