JIRA 업무를 등록할 때의 기준 참고 (가이드라인)
Task, Sub-Task의 활용 : Task (Main), Sub-Task (시스템/업무/역할 구분)
필승@jjim
아이티랩에서 많은 정보공유 및 인맥 쌓으시길 바랍니다.
JIRA로 업무관리할때, 업무(Task)의 구분을 Epic으로 하니 편하네요.
잘 짜여진 스티브잡스의 WWDC 키노트!
스티브잡스의 WWDC에서의 키노트는 누구나 한 번쯤은 보거나 들었다.
하지만 많은 사람들은 문화적인 차이가 있기에 스티브잡스의 프리젠테이션과 같은 비주얼과 키워드만이 강조된 PT가 표현될 수 있다고 한다.
그렇다면 우리에 PT환경을 살펴보자! 먼저 보고형 PT가 많다.
형태로는 상사에게 현재 프로젝트의 진행경과나 투자자에게 신사업을 소개하는 PT 그리고 이미 내용을 알고 있는 청중에게 내용을 요약하여 보고하는 제안형 등이 있다. 여기에 유교사상이 더해져 "예와 격식를 갖춘 보고"가 우리의 PT를 말해준다.
이러한 정의는 우리의 PT를 잘못되었다고 얘기하는 것은 아니다. 하지만, 우리는 감성과 디자인이 중시되는 글로벌 경쟁환경에 와 있다.
글로벌 경쟁이 아니더라도 우리는 수많은 매체를 통해 감성에 호소하는 잘 짜여진 광고디자인을 보고 있다. 그렇기에 이젠 스크린에 수많은 글자들이 딱딱하게 정리되어진 보고형 PT는 특수한 경우를 제외하고는 식상하게 되었고 프리젠터에게 어떠한 차별된 경쟁력도 만들어주지 못한다.
그렇다면 우리는 어떻게 청중의 구미에 맞는 PT를 만들수 있을까!
가장 가까운 답을 애플 CEO인 스티브잡스가 보여주고 있다.
스티브잡스의 PT를 정의하자면 잘 짜여진 구조와 비주얼하면서 심플한 디자인 그리고, FUN이다.
스티브잡스 키노트의 구조
스티브잡스의 PT는 인트로를 멋진 이벤트로 장식한다. 이를 통해 청중의 관심을 유도하고 한시간 반이 넘는 그의 PT 내용을 큰 그림으로 제시해 궁금증을 유발시킨다.
큰 그림을 설명한 후에 카메라가 피사체를 줌인하듯이 매력있는 한부분을 소개시킨다. 이렇게 보통 3가지의 소주제를 처음 대주제를 소개한 기법을 사용해 궁금증을 유발시킨다.
드디어 청중이 기다리던 본론으로 들어간다. 비주얼한 디자인과 FUN 그리고, 스티브잡스의 화려하고 열정적인 PT가 어느새 소주제1에 대한 요약으로 넘어간다. 이렇게 스티브잡스는 궁금증 유발과 설명 그리고, 다시 한 번의 강조를 통해 자신이 표현하고자 하는 모든 것을 청중에게 전달하고 더 나아가 각인시킨다. 마지막 소주제3에 대한 요약이 끝나고 나면 결론에서 청중이 애플의 상품을 통해 누리게 될 멋진 미래를 느끼게 된다.
이렇게 스티브잡스의 PT는 미리 치밀하게 계산된 쇼다. 이글을 읽고 스티브잡스의 PT를 보기를 권유한다.... 더보기
최근 뉴스에 많이 등장하는 단어가 메타버스라는 단어입니다.
메타버스(Metaverse)는 가공, 추상을 의미하는 ‘메타(Meta)’와 현실세계를 의미하는 유니버스(Universe)’의 합성어로 3차원 가상 세계를 의미합니다.
기존의 가상현실(Virtual Reality, VR), 증강현실(Augmented Reality, AR)이라는 용어보다 확장된 개념으로 아바타를 통해 친구를 만나고 놀이, 업무, 소비, 소통 등을 하는 가상 세계를 말합니다.
글로벌 시장조사업체 스트래티지애널리틱스(SA)는 메타버스 시장이 현재 460억달러(약 52조원)에서 2025년 2800억달러(약 315조원)까지 성장할 것으로 전망했습니다.
특히 최근 코로나 19로 인해 비대면 문화가 본격화되면서 메타버스가 더욱 활성화되고 있습니다.
메타버스의 대표적인 사례는 네이버 자회사 제트에서 만든 SNS 플랫폼 ‘제페토(ZEPETO)’와 미국의 ‘로블록스’입니다.
제페토는 증강현실 아바타 앱 서비스입니다. 얼굴 인식을 통해 아바타를 만들어 가상의 공간에서 전 세계 이용자들과 소통하고 놀이, 쇼핑, 업무 등의 활동을 즐길 수 있습니다.
제페토의 인기가 많아지면서 가수나 브랜드를 홍보하기 위해 제페토를 활용하는 기업이 늘어나고 있습니다.
걸 그룹 블랙핑크는 제페토에서 블랙핑크 아바타를 선보였고 버추얼 팬사인회를 진행해 화제가 됐습니다. 이 행사에는 4,600만 명이 넘는 이용자가 다녀갔습니다.
현대차는 제페토에서 차량을 구현해 쏘나타 N 라인을 시승할 수 있게 했습니다. 아바타를 이용해 영상과 이미지를 제작할 수 있는 제페토의 비디오 및 포토 부스에서 쏘나타를 활용할 수 있게 하여 이용자들이 자동차로 콘텐츠를 생산할 수 있게 하였습니다. (보도자료 보기: 현대자동차, 메타버스 플랫폼 ‘제페토’에서 쏘나타 N 라인 시승 경험 제공)
코로나로 인해 대학 교육 환경과 문화가 변화하면서 대학교에서도 메타버스 활용이 늘어나고 있습니다.
2020년 3월 순천향대학교는 코로나로 인해 대면으로 진행하지 못하는 신입생 입학식을 가상 세계에서 열었습니다. SK텔레콤의 ‘점프VR’ 플랫폼을 통해 마련된 가상공간에서 학생들은 자신만의 아바타를 만들고, 학과 점퍼를 입고 참석할 수 있었습니다. 소속 학과에 따라 따로 마련된 방에서 학과별로 프로그램을 진행하기도 했습니다.
건국대학교는 축제 ‘Kon-Tact 예술제’를 가상 세계에서 열었습니다. 교내 공간을 모두 재현한 ‘건국 유니버스’에서 학생들은 자유롭게 만나고 축제를 즐길 수 있었습니다.
관광 업계에서도 여행과 메타버스를 결합하는 움직임이 활발합니다.
한국관광공사는 제페토에 한강공원을 구축하여 하루만에 전 세계에서 25만7000명이 방문하였습니다.
관광 데이터 유통 기업 트래볼루션이 석촌호수와 롯데월드 맵을 공개했습니다. 이용자는 실제 석촌호수 및 롯데월드와 유사한 가상 공간을 여행하며 맵 곳곳에 숨겨진 할인 쿠폰을 찾는 재미와 함께 오프라인 여행 시 할인 혜택까지 받을 수 있습니다. (보도자료 보기: 트래볼루션, 네이버의 메타버스 서비스 제페토 통해 한국의 여행지 맵 공개)
트렌드에 민감한 패션업체들도 메타버스 기술을 도입하고 있습니다.
구찌는 제페토에 이탈리아 피렌체를 배경으로 ‘구찌 빌라’에서 직접 상품을 둘러보고 구매할 수 있는 공간을 제작했습니다. 구찌 빌라에 방문하면 아바타를 통해 구찌 의상을 입어보고 포토존에서 사진을 촬영할 수도 있습니다.
메타버스 관련 교육이나 연구 결과 등도 활발히 나오고 있습니다.
서초구 구립서초유스센터는 스마트기술과 메타버스를 활용한 스마트유스센터를 구축했습니다. 가상 유스센터를 통해 청소년들이 시·공간의 제약 없이 자유롭게 자치활동, 동아리 활동, 청소년지도사와의 온라인 교류 등을 실현할 수 있도록 하였습니다. (보도자료 보기: 구립서초유스센터, 스마트유스센터로 포스트 코로나 시대의 미래인재 키운다)
이러닝 전문 기업 오픈컴즈는 에듀테크, 퀘스트러닝 전문 연구 기관 오픈러닝랩 박형주 박사와 공동으로 ‘메타버스 신사업 아이디어 노트’ 이러닝 교육 과정을 선보였습니다. (보도자료 보기: 오픈컴즈, 오픈러닝랩과 국내 첫 메타버스 비즈니스 실무 교육 과정 선보여)
산업조사 전문 기관인 씨에치오 얼라이언스(CHO Alliance)가 ‘5G로 부상하는 메타버스(Metaverse) 비즈니스와 XR(VR/AR/MR) 기술, 시장전망’ 보고서를 발간했다. 새롭게 급부상하는 메타버스 비즈니스에 대응해 관련 산업인 XR 기술과 시장 환경에 주목함으로써 XR산업과 메타버스 시장의 주요 이슈와 시장동향, 나아가 유망기술과 선진국의 육성정책, 국내외 관련 선도기업의 사업 동향과 전략을 조사 분석해 본서를 출판했습니다. (보도자료 보기: 씨에치오 얼라이언스, ‘5G로 부상하는 메타버스 비즈니스와 XR 기술, 시장전망’ 보고서 발간)
메타버스 보도자료를 더 보려면 아래 링크를 참고하세요.
https://www.newswire.co.kr/?md=A11&no=531... 더보기
이 링크는 내용을 가져올 수 없습니다.여기를 클릭해서 내용을 입력해 주세요. |
ETL(Extraction, Transformation, Loading)
ETL이란 데이터 웨어하우스(DW, Data Warehouse) 구축 시 데이터를 운영 시스템에서 추출하여 가공(변환, 정제)한 후 데이터 웨어하우스에 적재하는 모든 과정을 말한다.
일반적으로 발생하는 데이터 변환에는 필터링, 정렬, 집계, 데이터 조인, 데이터 정리, 중복 제거 및 데이터 유효성 검사 등의 다양한 작업이 포함된다.
데이터웨어하우스(DW, Data Warehouse)
Raw Data를 통한 분석자료를 제공하여 조직내 의사결정을 지원하는 정보관리 시스템
DW 4가지 특성
- 주제지향(Subject Oriented): data를 categorizing하여 End User에게 이해하기 쉬운 형태 제공
- 통합(Integrated): raw data를 일관적인 포맷으로 변환하여 저장
- 시계열(Time Variant): DW내의 data는 일정기간동안 정확성을 나타낸다.
- 비휘발성(Nonvolatile): DW에 적재 후 일괄처리(batch) 작업에 의한 갱신 이외에는 삽입, 삭제 등의 변경이 수행되지 않는다.
AWS는 DW를 아래의 3가지 티어로 나누고 있다.
- 하단티어: DB Server
- 중간티어: data를 액세스하고 분석하는 데 사용되는 분석 엔진으로 구성
- 상단티어: 통계, 분석, 데이터 마이닝 및 AI를 통해 결과를 제시하는 프론트엔드
DW의 이점
- 더 나은 의사 결정
- 여러 소스로부터의 데이터 통합
- 데이터 품질, 일관성 및 정확성
- 인텔리전스 기록
- 분석 처리프로세스를 트랜잭션 데이터베이스로부터 분리하여 두 시스템의 성능을 모두 향상시킴
카카오의 DW 운영 예시(Slide p5, OLAP-OnLine Analysis Processing)
- A 기사를 본 사용자들의 연령대 비율 - 30대 90%
- IT 카테고리 기사 中 가장 인기있는 기사 - A기사
- A기사를 본 User가 같이 많이 보는 기사 - B, C기사
- 아이폰7에 관심있는 User는? N명, Ryan
참고
https://aws.amazon.com/ko/data-warehouse/
https://ko.wikipedia.org/wiki/%EB%8D%B0%EC%9D%B4%ED%84%B0_%EC%9B%A8%EC%96%B4%ED%95%98%EC%9A%B0%EC%8A%A4
https://www.slideshare.net/deview/236-67609108
https://docs.microsoft.com/ko-kr/azure/architecture/data-guide/relational-data/etl
http://21flowers.tistory.com/entry/ETL%EC%9D%B4%EB%9E%80
출처: https://pizzaplanet.tistory.com/entry/Data-Warehouse-ETL-간략-개념-정리 [pizzaplanet]... 더보기
HOW AI-DRIVEN E-COMMERCE CAN BENEFIT TELECOMS
https://emeldi.com/2018/07/03/ai-driven-e-commerce-can-benefit-telecoms/
With the general status of the marketplace, e-commerce within the CSP space has become more saturated and competitive; thus for a digitally enabled operator to succeed, it needs to have accurate, comprehensive customer data in order to deliver smarter and quicker services to the ever increasing demands of its end-users.
As a business leader, CSPs need to know that every aspect of their business, from product selection, and inventory management to the outlay of your online and physical store, depends on its understanding of its clientele. However, due to the increased use of e-commerce sites and apps that leverage artificial intelligence, customer expectations have become quite dynamic and satisfying them, almost elusive. Predicting a client’s next move in such an environment is a stressful and daunting prospect.
Operators are taking great pains to ensure they are not left behind; and are quickly leveling the playground with their competitors using Artificial Intelligence to enhance their digital-commerce landscapes ushering in to their subscribers the future of telecommunication end-user services and customer support. This will eliminate the over-reliance on the assumption based demographic data that provides limited intelligence on clients and enable the use of data-based decision-making algorithms that grow smarter with time. The latter, can collect real-time interactive data on users’ needs at a deep level and enable precise predictions of the products that they will most likely buy, or services which they may find of interest.
With AI-driven e-commerce, operators will have the power to reduce manual retailing operations and customer support, while tailoring personalized experiences for their subscribers.
CORE AI TECHNOLOGIES CURRENTLY IN APPLICATION
2017 saw the introduction of many landmark artificial intelligence technology developments that have transformed the digital-commerce marketplace significantly. For instance, companies such as Google, Pinterest and Bing launched visual search capabilities; Facebook leveraged AI to detect mental illness among its users and many others.
Customer Service Chatbots
Thanks to the emergence of intelligent chatbots, telecommunication firms, and e-commerce sites have been able to offer 24 hr. service all week round to their clients. These smart bots automate customer service inquiries, route clients to the right agents, and prospects with purchasing intents to sales personnel. Thus, by providing a seamless brand experience, these intelligent beings can enhance communication efficiency in your enterprise significantly.
Retailers have leveraged the technology on their websites and through chat enabled applications such as Messenger and Facebook. Prospect customers can use either text or speech, at times both, to communicate with the chatbots. The bots can assess, respond to customer queries, assist with the selection procedure, and perform simple tasks. Check our article about How Conversational Commerce (Chatbots) is Transforming the Telecom Industry
Customer Relationship Management (CRM)
Historically, CRM mainly relied on people to gather and assess data so they can best attend to their customers. This was a very labour-intensive, error-prone, time-consuming, and most importantly, costly approach. Currently, with AI, you can quickly examine bulks of data and precisely identify a customer who is likely to make a purchase and how you can best facilitate him or her.
Courtesy of Machine learning, advanced cloud CRM can now learn with time to predict and score agreements more accurately. This frees up time for the sales team to focus on creating and fostering relations that improve business value.
Product Content Management
From inventory management to product cataloging, Artificial Intelligence enables firms to offer unified customer experiences by organizing and tracking crucial data and materials. Product content management is vital for a business to provide distinctive, yet consistent user experiences across all platforms.
In order to deliver unique and consistent customer experiences across all touchpoints, operators don’t have an option but to take into consideration product content management (PCM). Product Managers need to manage the complexity of a CSP product or service that’s available across different segments in a centralized manner and be able to adapt according to changing customer account configurations, allowing customers to have a consistent experience regardless of the channel they use to engage; online, over the phone, or in your store.
Customer Service Enhancement Systems
With prevailing competitive trends, outstanding customer service is vital. AI allows operators to focus on what is most crucial; satisfying subscribers’ needs and replying to their demands irrespective of the hour or day.
A hybrid customer service environment should be able to balance between man and machine. It leverages the best of AI while still utilizing agent knowledge and preserving customer context. While chatbots and machine learning techniques excel with Tier 1 service engagements by answering those common customer questions, they may not always have the answer to the more complex, situation or customer specific questions.
After becoming the first TV service to collaborate with Amazon, DISH Network was looking to enhance their customer service by leveraging the AI technology. Thus, it designed a DVR protocol that was compatible with Alexa products of Amazon. This move enabled its users to access voice aided TV navigation at no extra fee.
Later in 2016, the company announced the launch of another accessibility device, the Voice Remote that it had developed. Due to its size and design, the Company’s VP, compared the Voice Remote to a smartphone since it boosted ease of access, the speed of functions and convenience.
Artificial Intelligence Automation Software
Unlike what it may seem in the movies, integrating AI and machine learning doesn’t mean that the machines are taking over. These artificial technologies are not aggressive or frightening; instead, they avail the opportunity for CSPs to offer precisely what the end-user needs, exactly when they need it.
For instance, in 2016, CenturyLink applied an automated sales application into its model. Popularly known as Angie, this automated sales assistant was the product of Coversica, a company famed for providing AI based lead generating software. According to a report written by Harvard Business Review, Angie handles an average of 30,000 emails every month and assesses the responses offered to identify likely leads. A review of the period indicated that sales reps saved the time they used on follow-up and outreach. By the end of the period, each sales rep had an outstanding number of 300 accounts, far out-performing baseline expectations of un-assisted lead-generation.
That same year, Epson America, the printer and imaging giant, also piloted Conversica AI. Chris Nickel, Epson’s senior manager of commercial marketing, said, “Before, if we gave 100 leads to the reps, we might get a couple of responses from customers. Now, if we give 100 leads to the AI assistant, we get 50 responses.” Epson reports that the official response rate with the AI assistant is 51%, representing a 240% increase from the baseline established at the beginning of the pilot, and a 75% increase in qualified leads. According to Nickel, that has produced $2 million in incremental revenue in just 90 days.
Internet of Things (IoT)
Cohesiveness facilitates the smooth running of things, and IoT provides connectivity for all aspects of your daily life. From lights programming to syncing several devices, car, appliances and washing machine, while also monitoring air quality and traffic lights, the Internet is enhancing life globally. IoT innovations have so much to offer the telecommunication industry.
AI Driven Sales
Irrespective of the degree of change that the world undergoes, commerce can only depend on sales. Artificial intelligence assists with the whole customer experience from prospecting to purchase and even the after-sales service.
HOW CSPS CAN LEVERAGE THE ARTIFICIAL INTELLIGENCE CORE TECHNOLOGIES
DEVELOP INTELLIGENT SHOPPING JOURNEYS
When customers engage with their current (or prospective) operator, the product recommendations that they are shown should be based on their actual clicks collected by the intelligent search engines. The site should be able to use AI to develop a predictive model for every customer, therefore creating unique customer experiences.
On the other end, the live click system should have the capability to provide smarter actionable and merchandising insights for the commerce team. Thus, making it easier for a team member to approach a client with the right information and engage with them in a way that will help in closing the sale.
PROVIDE TAILORED PRODUCT RECOMMENDATIONS
Customers want to spend the least possible time finding the products and service which best suit their needs. Therefore, by providing custom product recommendations, CSPs shorten search durations significantly and increase sales frequently.
Gradual Implementation of Custom Recommendations
Operators are advised to find that implementing AI driven recommendations gradually will be most beneficial to their business in terms of customer retention and increased NPS numbers. For instance, instead of updating the entire order capture engine with custom recommendations, they will achieve better results in identifying and optimizing a smaller subset of key product lines across all channels. This method will enable product managers to perfect a technique which works best for their specific market, and also understand their customers better.
CUSTOMIZE SEARCH RESULTS
Digitally-enabled subscribers are not patient. They will not scroll through a multitude of pages of product images or service information to find their ideal bundle. Instead, if they cannot see the desired item on the first page, they are very likely to abandon without converting. CSPs now use predictive behavior systems to show customers the products or services they are most likely to buy from each category. This way, you reduce the time used searching and channel it towards increased conversions.
Strategic Positioning of the Search Bar
For customized search results to work effectively, the end-user needs to search for a commodity, that is, engage with it. CSPs employ techniques to encourage them to do so by tactically placing the search bar on targeted pages and sections, and displaying sticky search components rendering it visible even as one scrolls through the products.
Quality of Data
Lastly, the accuracy of customized search outcomes relies on the quality of the data which is kept within the PCM. Through the use of fit-to-purpose telecom-specific product catalogues such as Emeldi Commerce Omni-channel Platform for CSPs, product managers are able to observe Commerce Cloud best-practices to ensure that their product data is well-maintained across all channels enabling machine-learning algorithms to accurately sift and optimally categorize product offerings to provide customers with high-converting and personalized experiences.
CONVERT YOUR DATA INTO ACTIONABLE INTUITIONS
Sorting through bulks of data manually is not only time-consuming but also challenging to keep track of it all. Deciding what information will be useful in driving revenue and conversions will be the hardest part of the task. AI data analysis protocols, however, can quickly turn data into actionable intuitions. CSPs can harness this functionality further by developing a change roadmap for their omni-channel digital–commerce environments.
Predict Purchase Patterns
Omni-channel platforms use collected data to reveal purchase patterns in personalizing and designing unique experiences for end-users. AI commerce applications automatically assess the data and provide information in order to optimize customer experiences and drive further interactions. This feature will also allow analysts to quickly determine co-purchase patterns and create product combinations, bundles, and deals that users desire most.
Allow AI to Redesign Your Script
While merchandising is an art; merging it with data science of customer behavior makes it even better. However, instead of focusing on the aesthetics, empower your employees with the customer behavior algorithm so they can accomplish unimaginable fits. In addition to enabling employees, this approach also allows merchandisers the liberty to be innovative and formulate experiences that will improve your brand and bottom-line.
HARNESSING AI FOR PHYSICAL STORES
While the purpose of this article has been to emphasize how CSPs can use AI to improve their omni-channel digital-commerce platforms, consumer behavior systems and data analysis tools can also be used to benefit traditional brick and mortar stores.
Layout Modification
Once accurate information has been gleaned on specific customers and their purchasing patterns, CSPs with physical point of sales locations can use this information to guide them into laying out key sections of their stores. For instance, since many people love purchasing accessories for hand-held devices , store managers can display some eye-catching accessories at the front of the store. Once people are lured into the store, they get to see so many other attractive items that have been strategically arranged according to the collected behavioral statistics.
Things Can Only Get Better
Integrating AI engines with digital-commerce platforms will improve operations significantly as operators will no longer be forced to second-guess decisions since the data provides the solutions. Better still, AI systems get smarter with time since their prediction algorithms generate more precise personalization and insights as they collect and analyze more data.
Consequently, CSP business decisions will be more precise, manual merchandising tasks will be reduced, and product managers will have more time to focus on business growth and development.
While the idea of utilizing AI for e-commerce can appear intimidatingly complex, several simple applications can come ready-to-deploy out-of-the-box requiring no data scientists or specialized expertise to implement and ensure improved returns within a short time.
One example that cannot go unmentioned is Amazon’s new initiative; Amazon Go which is using technology found in self driving cars, computer vision, sensor fusion, and deep learning to ensure customers have a hassle free experience in a unique no-checkout store.
CONCLUDING INSIGHTS
It’s not shocking to see voice interfaces and chatbots being the most commonly used application of Artificial Intelligence in the telecommunication industry. Firms with large B2C transactions are most appropriate to merit from both voice and text applications, for several reasons:
Customer service comprises the most significant expense for operators with large subscriber bases. Luckily, chatbots provide a potential for considerably enhanced efficacies. Rogers Communications for example managed to drop customer service call volumes by about 13% in 2015 and internal customer satisfaction scores improved by 65%.
Additionally, chatbots can also be trained in speech recognition protocols and used to reduce the data analysis task for companies that receive large volumes of customer care requests.
A few years from now, many Fortune 500 telecommunication firms will be leveraging predictive maintenance applications and will be collecting massive amounts of data and tailoring custom maintenance efforts consequently.
Due to their extensive and distributed infrastructure, it is likely that telecom companies will host a network of firms leveraging data for operations and uptime. Also, it will be a while before Small and Medium sized businesses adopt such technological innovations.
AI & EMELDI COMMERCE OMNI-CHANNEL PLATFORM
As part of its strategic approach towards true Headless Commerce, and enhancing digital-commerce capabilities by integrating best-in-breed digital-commerce technologies, the Emeldi Commerce microservice API continues to grow to allow for further interoperability with conversational commerce vendors.
The emergence of AI-powered personalization engines will remain as a driving force supporting personalized shopping experience and product recommendations, and over the next 5 years we will see a significant degree of variation of players being introduced into the market.
Emeldi has invested heavily into the development and deployment of intelligent predictive care/sales, and customer journey personalization, while ensuring that Emeldi Commerce Headless Commerce API has been designed to seamlessly allow the introduction of leading 3rd party analytics solutions into its omni-channel integrated landscape, both deployable on premises or in cloud.... 더보기
2019년 비즈니스 트렌드입니다. 참고하세요.
https://www.tableau.com/reports/business-intelligence-trends
01
The rise of explainable AI
As organizations rely more on artificial intelligence and machine learning models, how can they ensure they’re trustworthy?
02
Natural language humanizes your data
Advancements in NLP systems enable all people to unlock natural conversations with data.
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Actionable analytics put data in context
BI platforms evolve to put data where people want to take action.
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Data collaboratives amplify social good impact
Focused efforts from public and private-sector organizations strengthen ‘data for good’ movement.
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Codes of ethics catch up to data
In light of regulations like GDPR, leaders assess the future of ethical data practices.
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Data management converges with modern BI platforms
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Data storytelling is the new language of corporations
Finding and communicating data insights is now a team sport.
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Enterprises get smarter about analytics adoption
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Data democracy elevates the data scientist
Data scientists develop soft skills to drive organizational change.
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Accelerated cloud data migration fuels modern BI adoption
Data is moving to the cloud faster than ever, driving organizations to rethink their data strategy.
Read more at https://www.tableau.com/reports/business-intelligence-trends#xAcwxM4oDPpCzwsj.99... 더보기
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