DataScienceConnect Atlanta 2019 - Building Data & Analytics Teams

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Creating an Effective and Diverse Data and Analytics Team Building data and analytics teams is a competitive endeavor yet one executive has found success in rounding out his data team by developing a conglomerate of data specialists. Once core functions are identified, learn how to equip your team for deeper skills and greater growth.

Data & Analytics

Maturity Very low
Executive sponsorship Medium - at a very high level
Impact of data Unclear
Focus Internal and External

Systems of record
(source systems)
Enterprise data and
analytics environment
(system of intelligence)
Real time
Data virtualization
Enterprise data management framework
(Data governance, data quality, etc)
Open Integration (API)

https://news.greylock.com/the-new-moats-53f61aeac2d9
Systems of Intelligence

From... ...to
https://news.greylock.com/the-new-moats-53f61aeac2d9

ImpactBehaviors
Data teams Operational teamsPartnership
Insights

ImpactBehaviors
Data
Algorithm Actions Impact
● Change in
behaviors and
culture
● Process change
● Adoption of
solutions
● Business value:
measure and
communicate
● Keep it simple
● Quality
● Variety
Data teams Operational teamsPartnership
Repeat, over and over and over
Insights
Insight views
● Flexible
● Business aligned

To be published soon by IsCDO (https://www.iscdo.org/)
Partnership
By Andrew Sohn
https://www.linkedin.com/in/andrewsohn/
https://twitter.com/Molsonix

Guiding
principles to
build teams
Talent density (Netflix)
Maturity awareness
Business value is top priority
Tap into experience

Talent density (Netflix)
https://hbr.org/2014/01/how-netflix-reinvented-hr

Maturity awareness
Data and analytics journey is just starting
Stakeholders are not fully aware of the impact of data and
analytics
Low maturity will require significant foundation building from
the top and the bottom

Business value is top priority
Business value (impact) of data and analytics solutions is the
top priority → low hanging fruit!
Flexible generalists will thrive in low maturity environments
As maturity increases, focus on specialization

Tap into experience
(Very) long average tenures
Legacy companies have undocumented, ungoverned
ecosystems
Data and analytics innovation spurs interest in reinventing

Functional organization for success
Information
Enablement (aka,
Governance)
● Data quality
● Engage data owners
and stewards
● Transparency and
traceability
● Analytics governance
Design &
Architecture
● Lead technology
roadmap
● In collaboration with
IT, manage
infrastructure
● Alignment across
technology activities
Business Insights &
Data Science
● Traditional BI
delivery, including
self service
● Advanced analytics /
data science
● Rapid prototyping
and data product
delivery
● Business insights
Product and Change
Management
● Manage and
enhance data
products
● Bring and keep the
team closer to the
field - active
partnership

Thank you!
Juan F. Gorricho
@jgorricho
https://www.linkedin.com/in/juangorricho
juan@juangorricho.com

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.

Presumption of Abundance: Architecting the Future of Success

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Hot Technologies with Dr. Claudia Imhoff, Dr. Robin Bloor and SAS Live Webcast on Jan. 14, 2015 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9431631f43a8c7561f2ba996750a4612 When resources are scarce, organizations focus heavily on keeping processes intact and costs down. The result is often a cycle of decisions that hinders development and ultimately leads to zero innovation. But these days, the market is teeming with game-changing solutions with more attractive price points, paving the way toward a new mindset and an era of abundance. Register for this episode of Hot Technologies to learn from veteran Analysts Claudia Imhoff and Robin Bloor as they discuss how the proliferation of data and analytics is forcing the enterprise to rethink and redesign its architecture. They’ll be briefed by Gary Spakes of SAS, who will explain his company’s approach to Big Data analytics. He will show how disruptive technologies like Hadoop can give organizations the scalability and reliability they need, and at the same time boost data discovery, analytic innovation and time-to-value. Visit InsideAnalysis.com for more information.

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Big Data has moved beyond being just a buzzword. Organizations are operationalizing various Big Data technologies to answer critical business questions and power sophisticated workloads. Building on the success of their 2012 “Big Data Comes of Age” research report, EMA VP of Research, Shawn Rogers, EMA Senior Analyst, John Myers, and 9sight Consulting Founder and Principal, Dr. Barry Devlin, will reveal their latest big data research findings during this informative Webinar. Attendees will learn not just the what's of Big Data technologies but also the why’s of use cases, implementation strategies and technology choices, as well as discover: >>Most popular use cases for big data based on nearly 600 projects reviewed in this research >>Which Hadoop distributions are gaining traction >>The technical and business-driven-challenges for Big Data >>Most popular data sources for Big Data >>How organizations are continuing the trend of implementing the EMA Hybrid Data Ecosystem (HDE) in association with their Big Data initiatives

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Our ability to produce, ingest and store data has grown exponentially, but our ability to parse out insights from data has not. In the 90s, an organization’s data would live in a data warehouse with an ETL pipeline and one reporting layer on top. Information was well controlled if not somewhat limited in breadth and slow to trickle down. Now with the onset of self-service analytics, anyone can create a report and an insight and there are many different sources of “truth.” For example, a seemingly straightforward question like "how many customers do we have?" will likely return difference answers from sales, finance and customer success, depending on their definitions and the data at hand. There is simply too much data (and duplicate data), too many tools, and too many systems storing data -- leading to time consuming searches, confusion and a lack of trust. Hear Stephanie discuss how a data catalog can help solve the noise to signal problem - making information easier to find, easier to understand and more trustworthy. She will describe how organizations like Safeway, Albertsons, Munich Re and Pfizer leverage a data catalog to find data and collaborate on data, gain a fuller understanding of its meaning and ultimately, solve important problems.

Chief Data Officer: Customer Analytics Innovation

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一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单

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Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Subhajit Sahu

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.

一比一原版(BU毕业证)波士顿大学毕业证成绩单

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BU毕业证【微信95270640】购买（波士顿大学毕业证成绩单硕士学历）Q微信95270640代办BU学历认证留信网伪造波士顿大学学位证书精仿波士顿大学本科/硕士文凭证书补办波士顿大学 diplomaoffer,Transcript购买波士顿大学毕业证成绩单购买BU假毕业证学位证书购买伪造波士顿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。专业为留学生办理波士顿大学波士顿大学毕业证offer【100%存档可查】留学全套申请材料办理。本公司承诺所有毕业证成绩单成品全部按照学校原版工艺对照一比一制作和学校一样的羊皮纸张保证您证书的质量！如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈 1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】 2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】 3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。国外波士顿大学波士顿大学毕业证offer办理方法： 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：我们有专业老师帮你查询波士顿大学波士顿大学毕业证offer）； 2开始安排制作波士顿大学毕业证成绩单电子图； 3波士顿大学毕业证成绩单电子版做好以后发送给您确认； 4波士顿大学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波士顿大学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）。二条巴掌般大的裤衩衩走出泳池山娃感觉透身粘粘乎乎散发着药水味有点痒山娃顿时留恋起家乡的小河潺潺活水清凉无比日子就这样孤寂而快乐地过着寂寞之余山娃最神往最开心就是晚上无论多晚多累父亲总要携山娃出去兜风逛夜市流光溢彩人潮涌动的都市夜生活总让山娃目不暇接惊叹不已父亲老问山娃想买什么想吃什么山娃知道父亲赚钱很辛苦除了书籍和文具山娃啥也不要能牵着父亲的手满城闲逛他已心满意足了父亲连挑了三套童装叫山娃试穿山伸

Empowering Data Analytics Ecosystem.pptx

benishzehra469

Show drafts volume_up 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.

Adjusting primitives for graph : SHORT REPORT / NOTES

Subhajit Sahu

Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is Multiply with different modes (map) 1. Performance of sequential execution based vs OpenMP based vector multiply. 2. Comparing various launch configs for CUDA based vector multiply. Sum with different storage types (reduce) 1. Performance of vector element sum using float vs bfloat16 as the storage type. Sum with different modes (reduce) 1. Performance of sequential execution based vs OpenMP based vector element sum. 2. Performance of memcpy vs in-place based CUDA based vector element sum. 3. Comparing various launch configs for CUDA based vector element sum (memcpy). 4. Comparing various launch configs for CUDA based vector element sum (in-place). Sum with in-place strategies of CUDA mode (reduce) 1. Comparing various launch configs for CUDA based vector element sum (in-place).

一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单

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一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单

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一比一原版(YU毕业证)约克大学毕业证成绩单

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一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理

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原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样

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一比一原版(CBU毕业证)卡普顿大学毕业证成绩单

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一比一原版(UVic毕业证)维多利亚大学毕业证成绩单

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做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样

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原版定制【Q微信:741003700】《(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书》【Q微信:741003700】成绩单、雅思、外壳、留信学历认证永久存档查询，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。【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【Q微信741003700】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741003700】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【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。

一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理

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原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】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41543339】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41543339】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！

Criminal IP - Threat Hunting Webinar.pdf

Criminal IP

一比一原版(TWU毕业证)西三一大学毕业证成绩单

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TWU毕业证【微信95270640】西三一大学没毕业>办理西三一大学毕业证成绩单【微信TWU】TWU毕业证成绩单TWU学历证书TWU文凭《TWU毕业套号文凭网认证西三一大学毕业证成绩单》《哪里买西三一大学毕业证文凭TWU成绩学校快递邮寄信封》《开版西三一大学文凭》TWU留信认证本科硕士学历认证 [留学文凭学历认证(留信认证使馆认证)西三一大学毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。国外毕业证学位证成绩单办理方法： 1客户提供办理西三一大学西三一大学本科学位证成绩单信息：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：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） — — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — - 一真实留信认证的作用(私企外企荣誉的见证): 1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。 2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。 3：凡是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部网内查询个人身份证信息后同步读取人才网入库信息。 4：个人职称评审加20分个人信誉贷款加10分。 5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。广州火车东站的那一刻山娃感受到了一种从未有过的激动和震撼太美了可爱的广州父亲的城山娃惊喜得几乎叫出声来山娃觉得父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到拉

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DataScienceConnect Atlanta 2019 - Building Data & Analytics Teams

1. Building Data & Analytics Teams Juan F. Gorricho @jgorricho https://www.linkedin.com/in/juangorricho juan@juangorricho.com

2. Chief Data & Analytics Officer SVP, data & analytics Several data and analytics leadership roles

3. Maturity Very low Executive sponsorship Medium - at a very high level Impact of data Unclear Focus Internal and External

4. Data Actions & Business Impact

6. Data Actions & Business Impact

10. Systems of record (source systems) Enterprise data and analytics environment (system of intelligence) Real time Data virtualization Enterprise data management framework (Data governance, data quality, etc) Open Integration (API)

11. https://news.greylock.com/the-new-moats-53f61aeac2d9 Systems of Intelligence

12. From... ...to https://news.greylock.com/the-new-moats-53f61aeac2d9

13. ImpactBehaviorsInsights

14. ImpactBehaviors Data teams Operational teamsPartnership Insights

15. ImpactBehaviors Data Algorithm Actions Impact ● Change in behaviors and culture ● Process change ● Adoption of solutions ● Business value: measure and communicate ● Keep it simple ● Quality ● Variety Data teams Operational teamsPartnership Repeat, over and over and over Insights Insight views ● Flexible ● Business aligned

16. To be published soon by IsCDO (https://www.iscdo.org/) Partnership By Andrew Sohn https://www.linkedin.com/in/andrewsohn/ https://twitter.com/Molsonix

17. Guiding principles to build teams Talent density (Netflix) Maturity awareness Business value is top priority Tap into experience

18. Talent density (Netflix) https://hbr.org/2014/01/how-netflix-reinvented-hr

19. Maturity awareness Data and analytics journey is just starting Stakeholders are not fully aware of the impact of data and analytics Low maturity will require significant foundation building from the top and the bottom

20. Business value is top priority Business value (impact) of data and analytics solutions is the top priority → low hanging fruit! Flexible generalists will thrive in low maturity environments As maturity increases, focus on specialization

21. Tap into experience (Very) long average tenures Legacy companies have undocumented, ungoverned ecosystems Data and analytics innovation spurs interest in reinventing

22. Functional organization for success Information Enablement (aka, Governance) ● Data quality ● Engage data owners and stewards ● Transparency and traceability ● Analytics governance Design & Architecture ● Lead technology roadmap ● In collaboration with IT, manage infrastructure ● Alignment across technology activities Business Insights & Data Science ● Traditional BI delivery, including self service ● Advanced analytics / data science ● Rapid prototyping and data product delivery ● Business insights Product and Change Management ● Manage and enhance data products ● Bring and keep the team closer to the field - active partnership

23.

24.

25. Guiding principles to build teams Talent density (Netflix) Maturity awareness Business value is top priority Tap into experience

26. Thank you! Juan F. Gorricho @jgorricho https://www.linkedin.com/in/juangorricho juan@juangorricho.com

Editor's Notes

Digitalizacion de los negocios Capacidad tecnologica
Digitalizacion de los negocios Capacidad tecnologica
Digitalizacion de los negocios Capacidad tecnologica

DataScienceConnect Atlanta 2019 - Building Data & Analytics Teams

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