29. Passion for the
business
(ビジネスへの情熱)
Trustworthiness
(信頼感)
Teamwork and Collaboration
(チームワークとコラボレーション)
Communication
(コミュニケーション力)
Adaptability
(適応力)
Creative problem
Solving
(創造的問題解決力)
Taking ownership
(責任感)
Client focus
(お客様中心)
Drive to achieve
(目標達成への推進力)
IBMers Value
お客様の成功
イノベーション
信頼と責任
IBM ファウンデーショナル・コンピテンシー
そして、業務プロセスに人の相乗効果をもたらす環境を実現するのに欠かせない要素技術がこの前のセッションでご紹介した分析技術だと思っています。
ソーシャルウェアは業務をしていると自然に
情報がたまる
自分のためにしていることが人のためになる
という特徴を持っています。
この人々のはつながりや働き日々の活動から生まれる膨大な非構造化データを分析し意味あるものに変えていく。
これが分析の技術の役割です。
この技術は、ソーシャルを発展させる上で必須となることでしょう。
<Original Speaker Note>
We are at a tipping point in the convergence of social software and analytic technologies.
Businesses require a tight coupling of the two, in what we call “socially synergistic solutions.”
We are at a tipping point in the convergence of social software and analytic technologies, and business will increasingly require a tight coupling of the two to maximize their success. Customers will want to combine data from sensors and streams, their enterprise data, and social data -- that's data from or about people -- and use computation and analytics to bring the information together, aggregate, filter, and correlate it, and analyze it together to generate new insights. (as consumer products companies, for example, want to do to understand how their brands are perceived and how that influences sales in different demographics. And they will want to use analytics to more effectively take advantage of emerging techniques in social software to involve people, get their participation and improve their performance, and influence their behavior (as the city of Dubuque wants to do, by combining sensor data, analytics, and techniques taken from digital games, to reduce the use of resources like water.)These capabilities have traditionally existed in two different silos, with one set of technologies used to analyze a companies formal data like transactions, metrics, and KPIs, and another set of tools used to support the often tacit and unstructured human and collaborative work that goes on. And we've seen some blending of the two, initially with people emailing a spreadsheet, or setting up a discussion space to discuss data from analytic systems. And increasingly, systems like Cognos 10 and Manyeyes integrate collaboration tools with the reporting and visualization ones. What we are describing here goes beyond that, taking the deep analytic capabilities and applying them to the social data.
