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application of
business intelligence (bi) in
fashion retailing -- bossini
The U.S. sub-prime mortgage crisis in 2007 had lifted the
curtains to the financial tsunami which had sparked panic and
great depression all around the globe. On top of this, the
national debt issues in several European countries have set
off chain reactions, triggering the Euro zone debt crisis and
hitting the global economy severely.
The purchasing power and willingness-to-buy of customers
were adversely affected due to the economic downturn, which
had a direct impact on the performance in the retail industry.
Under the impact of globalization, how can enterprises
maintain their competitiveness when faced with challenges
and opportunities along with intensified market competition?
Bossini International Holdings Limited, headquartered in Hong
Kong, offers a range of casual wear apparel products, with a
total of over a thousand retail stores covering 36 countries and
regions around the world. For the last 25 years, Bossini has
successfully expanded their business into the international
marketplace by establishing an extensive global operating
platform and distribution network. In recent years, in order to
keep abreast of the internal operation and strengthen its
competitive advantage, Bossini has been paying more attention
in the development and application of Business Intelligence (BI),
hoping to improve operating efficiency as well as speed up
decision-making. We are glad to have invited Mr. Francis Wong,
Associate Director of Information Technology and Warehouse,
to share with us insights about demand, usage and prospects of
Business Intelligence from the retail industry perspective.
Although Bossini’s Information System (I.T.) is still at a
preliminary stage, it has taken up an important role as a
‘business enabler’ in the company, collecting all the scattered
data and providing significant information for decision-makers
instantly with the use of data, statistics and quantitative
analysis, according to Mr. Wong. The technology
system was mainly applied in three operating
categories: sales, products and inventory, and
Bossini is now trying to extend this I.T. system to
other components of the supply chain so as to
enhance the quality and efficiency of
decision-making. Since 2011, Bossini has been
performing data transferring via the transitional
system, transforming Excel files into
merchandising planning. Besides, the data
warehouse has started for two years.
The Importance of Business Intelligence
In the past two decades, Bossini has large
amounts of data stored in different forms. The
management team noticed the importance of
these data, and would like to organize these
operating data systematically into meaningful
information, to serve as reference for decision
makers. The data warehouse has facilitated the
update, storage, acquisition and archival of
information, as well as reduced the chance of
duplicated data, suitable for different application.
Feature Story
Risk Management and Business Intelligence Program The Hong Kong University of Science and Technology風險薈訊
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In the preliminary stage, the
enterprise has to set up the basic
layer of information system by
collecting all the data needed and
establishing the core
system—transitional system. This
system is mainly used for
operational management by helping
to sketch the management branch
plot and special analysis of key
planning. Thus, Bossini can
response to the fast-changing
market environment in a very short
period of time by extracting timely
and accurate information from the
system at any situation, avoiding the
circumstances from worsening. In
the past, we might need to spend a
long time to collect and organize
information; now decision making
has become easier with the help of
Business Intelligence tools. With just
a few clicks, we can check a variety
of different scenarios.
As a leading apparel brand in the
region, Bossini emphasized on the
importance of Business Intelligence,
and had applied BI in Business
Analytics and Advanced Planning,
so as to create market value for the
enterprise.
Business Analytic
Business analytic refers to
understanding the past business
performance based on historical
data, statistics and quantitative
analysis, followed by building
predictive modeling and computing
business planning. Bossini highly
values inventory data, and they have
developed key performance
indicators (KPI) to quantify
performance, so that effective
management can be achieved. Mr.
Wong pointed out that in order to
develop a set of measurable and
representative KPI, they have to first
define the rules, collect the historical
data for calculation and transform
inventory data into stock out rate.
Advanced Planning
Budgeting enables business goals to
be more specific, thus helping the
management team to allocate
resources more effectively. However,
the accuracy of budgeting depends
on how frequently the data is
updated. To avoid significant losses
resulting from large differences
between budget assumptions and
actual environmental changes,
Bossini uses rolling forecasts to
make quarterly adjustments to the
original budget and prepare detailed
monthly budget. By compiling rolling
forecast, we can keep a finger on
the pulse of changing market
conditions and adjust the business
focus accordingly, keeping the
budget close to reality, thus greatly
improve the accuracy of budgeting
and fully utilize the indicating
function of the budget.
Bossini has taken steps ahead of
most other retail apparel brands to
make use of technology to improve
its profitability, for the purposes of
meeting its market demand more
accurately and better arrange its
merchandising planning, discount
promoting events and sales network
distribution, etc. In order to design
budget and development plans
systematically, Bossini makes use of
software available in the market,
such as PowerPivot to analyze
information related to inventory or
the merchandising plan, and take
advantage of TM1 to edit, merge
and view extensive
multi-dimensional data. Mr. Wong
mentioned that Bossini is going to
put more resources on I.T. for
developing a unique software for
new projects to facilitate long term
analysis.
Supporting Measures
When Bossini first introduced
Business Intelligence (BI) into its
operations, it had made moderate
adjustments in different areas to
ensure full utilization of the
advantages of BI tools with work
coordination achieved.
Since the set-up cost for BI tools is
always high, the company has to
budget a certain amount of capital
for purchasing the BI tools. Apart
from that, BI tools are new to most
Then, based on the results of
carrying out apple-to-apple
comparisons between
different retail stores,
follow-up action is provided.
If several retail stores
continue to show poor
performance, Bossini can
spot the problem at an earlier
stage and find out the
causes to determine whether
it is due to difficulties at the
supply chain or the
replenishment process. The
KPI enables Bossini to
analyze the performance of
each retail store effectively,
easing budgeting and
generating better return rate
by reducing logistics costs.
Mr. Francis Wong, Associate Director of
Information Technology and Warehouse
of Bossini International Holdings
Limited, headquartered in Hong Kong
堡獅龍企業有限公司信息技術及倉儲部副總 監黃志斌先生Feature Story
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of the employees. They might not
understand the usage of these tools
and thus misinterpret them as a
simple reporting system, and
therefore underestimate its
contribution to the company.
Hence, in order to gain support from
employees on these new tools, the
company should provide more
information to explain the ability of BI
tools to provide instant analysis for
enterprise decision-making.
Moreover, training should be
provided to employees to ensure
effective application of BI tools as
the software tends to be relatively
recent.
Conclusion
As the competition of pursuit for
efficiency in society grows
increasingly keen, the retail industry
has been taking an active role in
enhancing its business automation,
modernization, as well as promoting
the integration of information
technology and business operations.
Moreover, historical data and
operating information are considered
to be important assets of a company.
Thus, the enterprise should have an
all-inclusive data warehouse in order
to improve efficiency, and to provide
a comprehensive planning and risk
assessment. Bossini has gained an
insight into the importance of
Business Intelligence earlier than its
competitors and thus captured this
golden business opportunity. By
using the new intelligence analysis
system, which provides them with
updated information, performance
indicators, related data and real-time
forecasts, management level
decision-making and actions are
supported. Furthermore, Bossini is
going to improve their customer
relationship by investing in the
Customer Relationship Management
(CRM) system in the near future,
creating another competitive
business advantage.
堡獅龍國際集團（Bossini）以銷售休閒服裝為主要業務，擁有過千間連鎖店，遍 佈全球三十六個國家，其總部則設於香港。在過去二十五年，堡獅龍（Bossini） 品牌不斷邁向國際，並成功建立了龐大的全球營運平台及分銷網絡。近年，為了 能夠隨時掌控公司的營運資料和強化企業競爭優勢，堡獅龍愈來愈重視商業智能 的發展和應用，希望能借助各種資訊技術去提昇營運效率及決策速度。我們有幸 邀請到堡獅龍企業有限公司信息技術及倉儲部副總監黃志斌先生，分享有關零售 業對商業智能的需求、應用及展望。 據黃副總監表示，雖然堡獅龍的信息技術 (I.T.) 系統尚處於起步階段，但卻擔當 了一個「商業啟動器」的重要角色，完整地蒐集散落於各處的資料，並利用數據、 統計和定量分析為決策者快速提供重要的相關資訊。過去品牌一直主要發展三個 營運範疇的技術系統：銷售、產品及庫存，然而他們將陸續嘗試擴展至供應鏈上 的其他組件，從而提高決策品質和效率。由二零一一年起，堡獅龍已開展資料傳 輸 / 轉 換 系 統 的 工 作，將 一 個 個 Excel 檔 案 轉 換 成 商 品 銷 售 計 劃 (merchandising planning)，至今資料庫已啟動了約兩年。 商業智能的重要性 堡獅龍在營運的二十五年間，以各種形式儲存了大量數據資料。管理層洞悉此等 數據的重要性及可塑性，因而欲將近年的營運數據加以分類整理，繼而轉化成具 意義的資訊，以供決策者參考。此資料庫的可貴之處在於能便利資料的更新、儲 存、取得和歸檔，並能減少資料重複，適合各種不同的應用。 在起步階段，企業需要做好資訊系統的基本層，收集所需數據，建立核心系 統——轉換系統(Transition System)。此系統主要用於營運管理，有助於企業擬 出重點規劃 (Key Planning)的管理分支圖(Management Branch Plot)及特別分析 (Special Analysis)。因此，堡獅龍能因應市場環境的急速變化，在不同的情況 下從資訊系統提取準確的即時資訊，快速回應事件，避免情況惡化。以往可能需 零七年美國次貸危機引發金融海 嘯，令全球陷入一片經濟「大蕭 條」的恐慌，加上近年歐洲國家 逐一出現國債問題，形成連鎖效 應，令歐元區的債務危機爆發， 衝擊全球經濟。公眾消費意欲及 購買力受經濟拖累，直接影響零 售業的表現。其次，面對全球化 令市場競爭加劇的難題，企業應 如何應付當前的挑戰與機遇，同 時保持其市場優勢呢？Feature Story
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商業智能於服裝零售業的應用
—堡獅龍國際集團
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要花上一段時間來蒐集和整理資訊；現 今在商業智能工具的幫助下，只需點擊 幾下滑鼠，便能查看不同的情景，方便 作出決定。 作為本地主要的零售服裝品牌，堡獅龍 著重商業智能，並應用於商業分析技術 (Business Analytic) 及 先 進 規 劃 (Advanced Planning)，從 而 提 昇 企 業 市場價值。 商業分析技術 (Business Analytic) 商業分析是指以過往數據、統計和定量 分析作為基礎，了解品牌經營業績，建 立預測模型和制定商業規劃。堡獅龍非 常著重庫存的數據，並設定了關鍵績效 指標(KPI)，量化績效，譬如倉儲缺貨 率，從而達致有效的管理。黃副總監表 示，要制定一套可衡量和具代表性的關 鍵績效指標，首先需要給標準下定義， 並使用過去收集的數據進行精密計算， 將庫存數據轉為倉儲缺貨率。接著，根 據指標對各間零售店的銷售表現進行比 較，作出相應的跟進工作。若個別商店 表現欠佳，堡獅龍便能更快發現問題， 及早找出原因所在，以釐定是供應鏈還 是補貨工序上出現問題。關鍵績效指標 不但能有效分析各零售店鋪的表現，降 低物流成本，為企業帶來更好的回報 率，同時亦方便企業作整體預算。 首先，商業智能工具本身成本較高，公 司在預算上要分配一定分量的資金用作 購買商業智能工具。另外，商業智能工 具對於大部分員工是一項較新的事物， 員工或許未能完全了解商業智能工具的 作用，誤以為它是單純的報表，因而低 估了它對公司的貢獻，因此，公司需要 向員工提供有關商業智能工具的資訊， 讓員工知道商業智能工具不僅是報表， 也可支援即時分析及企業商業決策，從 而增加員工對這項工具的信任；除此之 外，商業智能工具的運作及應用都較一 般軟件嶄新，公司需要尋找合適的人選 並為員工提供適當的培訓，讓員工能有 效地運用商業智能工具。 結語 面對現今求新求快的社會潮流，零售企 業近年積極推動商業自動化和現代化， 促進了資訊科技與企業營運的融合。然 而，歷史數據及營運資料是一間公司的 重要資產，企業必需具備完善的資訊數 據庫，從而提高效率、作更全面的規劃 和風險評估。堡獅龍比同儕更早洞悉商 業智能的重要性，成功地把握這商業契 機，以新型的資訊分析系統提供資訊、 績效指標、相關數據及即時預測，為制 定管理決策和行動提供支援。堡獅龍更 會在來年投放資源發展客戶關係管理系 統(CRM)，以進一步提升企與和客戶的 關係，締造新的商業優勢。 先進規劃 (Advanced Planning) 企業預算有效地將各項目標具體化，有 助管理層正確地分配資源。然而，預算 的準確性取決於資料數據更新的速度。 為了避免因預算假設與實際環境變化差 異而造成重大損失，堡獅龍採用了滾動 預測，按季度將原定的預算結果不斷的 進行修改，編制出每月的詳細預算。由 於能因應時間和巿場環境變化而不斷加 以調整和修改預算，使之貼近實際情 況，因而能大大提高預算的準確性，充 分發揮預算的指引作用。 此外，堡獅龍比大多數零售服裝品牌更 早以技術提高自身的盈利能力，務求更 準確應付巿場需求及更妥善安排採購計 劃、折扣推廣活動和銷售網絡分配等 等。為了更有規劃地進行預算和發展計 劃，企業採用了市場上現有的分析軟件 PowerPivot來進行與庫存或採購計劃相 關 的 分 析 ， 並 在 規 劃 時 利 用 T M 1 去 編 輯、合併和檢視大量多方面的資料。黃 副總監亦表示，公司將投放更多資源研 製獨有軟件為新項目作長遠分析。 配套措施 為了推行商業智能這項新技術，堡獅龍 在不同方面也有適當的調整，以配合這 項新技術的運用並充分發揮其作用。Feature Story
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Text 撰文WAN Siu Wah Diana 尹紹樺 TSE Sui Sang Jason 謝瑞生 Year 2 students of
Risk Management & Business Intelligence Program 風險管理及商業智能學課程二年級學生
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In recent years, firms and governmental organizations have been pushing for better decision making processes. In this essay, we shall seek to explore interesting trends so as to enhance our current risk analytics.
Intention Awareness and
Behavioral Economics
Having had its roots in a military setting, intention awareness stresses on the need to be keenly aware of one another’s intent in a strategic sense. Our physical reality is constructed from the knowledge of intent, after having crossed the boundaries of our human cognitive realities. It is therefore crucial to formalize and even quantify these intangible intentions using system software. Predictive capabilities can be developed to interpret whether individual actors’ intentions were friendly or foul (IRAHSS 2011 Report). Intention awareness can be applied to complement risk analytics in its strategic foresight. This includes technologies to model and forecast the behavior of consumers, one’s
competitors and even those of extremist organizations (IRAHSS 2011 Report).
Moreover, behavioral economics reveals how humans might act in seemingly unpredictable ways. We need to revolutionize our conventional models which currently do not account for the psychology of economic agents.
Prediction Markets
Not too long ago, the potentiality of prediction markets has spurred interest of many large corporations and
governments to experiment and invest in this technology. Prediction markets are similar to that of stock markets whereby traders bet on the probability of a given event outcome.
This platform has been producing consistent results that are just as accurate as that of expert opinion (Dye, 2008). Prediction markets are more than surveys to get the average opinion. Instead, the market identifies wisdom in crowds by attracting participants who feel confident enough to put the money on the line. As such, there provides a financial incentive that improves market efficiency significantly (Lee, 2009). The prospects of prediction markets lie in its ability to produce reliable sources of information. Through what is known as a predictive cost-benefit analysis, the prices are interpreted as
market-aggregated forecasts. Risk analysts obtain glimpses of public sentiment of risks in several policies.
We can assess feasibility of long term investment projects with people betting on whether the firm will say that the money is well spent in ten years’ time. Prediction markets are promising in its integration as part of risk analytics to add value to decision making.
Revisiting Our Mindset of
Handling Risk
Lastly, we need an entirely novel way to dealing with risks and uncertainty. Dave Snowden describes risks as not arising from linear causes and effects. Complex adaptive systems are often not causal but dispositional (IRAHSS 2011 Report). This implies that risks evolve in random, unexpected ways. Our current models (like the Bayesian network) are insufficient in its predictive capacity in complex spaces (Mackness, 2012).
Dave Snowden proposes the use of distributed cognition, which is in line with that of prediction markets. Through information technology and engaging whole populations in distributed cognition, we can easily detect underlying problems within the
population itself (IRAHSS 2011 Report). Risk analysts gain useful information and substantially reduce the risk of ivory tower decision making.
In addition, we should know that there are ultimately no generally agreed upon solutions for strategic foresight. Overly formal and structured approaches are essentially inappropriate as they focus on reducing uncertainty rather than absorbing it. In complex systems, new patterns can only be discovered if we attempt to absorb uncertainty. Hence, we need versatility in our risk
management. Rigidity will suppress creativity and hinder us from making the right decisions (IRAHSS 2011 Report).
The Future
Development
of Risk
Analytics
This future trend is about how we aim to move towards utilizing behavioral networks (such as that of an
agent-based model) to identify and monitor potential risks in the economy, as well as that in the society.
Digest
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近年來，企業和政府組織一直致力於推 動更完善的決策過程。本文將探索風險 分析的一些有趣趨勢，以提升現時風險 分析的成效。 意向意識(Intention Awareness) 與行 為經濟學(Behavioral Economics) 意向意識源於軍事應用，強調敏銳觀察 他人意向對於策略制定的重要性。當我 們 的 思 維 超 越 了 人 類 認 知 現 實 (cognitive realities)的界限，認知現 實 的 知 識 便 可 建 構 成 為 物 理 現 實 (physical reality)。因此，以系統軟 件把這些無形意向系統化甚至量化是相 當重要的。預測能力將發展成能判斷出 別人的行動是出於善意還是惡意(IRAHSS 2011 Report)。 意向意識可用以補足風險分析，使策略 性預測更為準確，當中的技術將可用於 模擬及預測消費者、競爭者甚至是激進 組織的行為 (IRAHSS 2011 Report)。 傳統的行為經濟學往往認為人類的行為 似乎都是不可預測的，這是因為傳統的 模型並沒有考慮到經濟主體(economic agents)的心理因素。我們需要革新現有 的模型，未來這個發展趨勢的重點，將 會 是 如 何 利 用 行 為 網 路 ( b e h a v i o r a l networks)（例如以個體為本的模型）來 識別和監控經濟以至社會的潛在風險。 預測市場 (Prediction Markets) 在不久前，「預測市場」的發展潛力開 始吸引到許多大型公司和政府嘗試並投 資於這項技術。「預測市場」與股票市 場的運作原理相似，交易者都是對事件 結果發生的可能性下注。 「預測市場」能得出與專家意見一樣準 確的預測結果(Dye, 2008)。與統計調 查不同，「預測市場」不僅是為了得出 一般的意見，更希望藉著吸引大批充滿 信心的投資者進行投資，從而得出結合 了「市場智慧」的預測意見。如是者， 「預測市場」為大幅提高市場效率提供 了經濟誘因(Lee, 2009)。 「預測市場」的未來發展取決於其預測 資訊的可靠性。通過「預測成本效益分 析」，價格可解讀為市場的綜合預測結 果。風險分析師可採用多種方法來預測 公眾對風險的情緒反應。例如我們可以 根據人們對於某公司過往十年的資金運 用是否得宜所作的投資決定，來評估長 期投資計劃是否可行。「預測市場」有 很大潛力成為風險分析的重要一環，有 助於提升決策效能。 重新審視我們處理風險的思維方式 我們需要以一種全新的方式來處理風險 和不確定性。Dave Snowden指出，風險 並不是由單一的因果關係所產生。在 複 雜 適 應 系 統 (Complex adaptive system) 中， 單純的因果關係並不常 見，但 卻 存 在 傾 向 性 (IRAHSS 2011 Report)。這意味著風險的演變往往 是隨機和難以預料。現有的模型（如 貝 氏 網 絡）對 於 複 合 空 間 (complex spaces) 的 預 測 能 力 顯 然 是 不 足 的 (Mackness, 2012)。 Dave Snowden 提 出「分 佈 式 認 知」 (distributed cognition)，其 原 理 與「預測市場」不謀而合。利用資訊 科 技 以 及「分 佈 式 認 知」對 總 體 (population) 進行分析，我們便可輕 易地發現總體中潛藏的問題 (IRAHSS 2011 Report)。風險分析師可藉此獲 取有用的資訊，以大大減少因「象牙 塔式」脫離現實的決策而產生的風險。 此外，我們須知道策略性預測最終不 會有廣泛認同的解決方案。但可以肯 定的是，過於系統化及結構化的方案 並不合適，因為這些方案往往集中於 減少不確定因素，而非把其納入決策 的考慮範圍。在複雜的系統中，只有 嘗 試 在 決 策 時 把 不 確 定 因 素 納 入 考 慮，才能發現新的分析方法。這也是 風險管理需要循多方向發展的原因。 一成不變只會抑壓創意，並妨礙我們 作 出 適 當 的 決 定 (IRAHSS 2011 Report)。風險分析的未來發展
References
Mackness, J. (2012, January 19). Dave Snowden on The 21st Century University [Web log post]. Retrieved 31 Jan, 2013, from
http://jennymackness.wordpress.com.
Lee, D. (2009, June 8). Can You Run a Government With Prediction Markets? [Web log post]. Retrieved 31 Jan, 2013, from
http://www.psmag.com.
Dye, R. (2008). The promise of prediction markets: A roundtable. The McKinsey Quarterly 2008 Number 2. Retrieved 31 Jan, 2013, from http://faculty.haas.berkeley.edu.
International Risk Assessment and Horizon Scanning Symposium (IRAHSS) 2011 Report. (2011). Risk Assessment and Horizon Scanning. Retrieved 31 Jan, 2013, from http://app.rahs.gov.sg.
Text 撰文
CHAN Chin Hung Zephyr 陳展鴻
Chinese version 中文譯本
CHEUNG Hoo Cheung Godric 鄭皓翔 Year 2 students of
Risk Management & Business Intelligence Program 風險管理及商業智能學課程二年級學生
Digest
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Data Visualization
Data visualization is the process of presenting data in a visible form. It usually refers to information that has been abstracted into some graphical forms, such as attributes or variables for units of information. The software commonly used to visualize data includes Microsoft Office, Apple iWork, Many Eyes and InstantAtlas etc.
Data visualization is very important in the field of business intelligence as it can visualize abstract and
hard-to-understand data gathered from data mining. Visualized data like charts and mind maps can increase the readability of the data patterns and lower the background knowledge requirement of the users.
ETL (Extract, Transform and Load)
ETL is the process commonly used in handling database system, especially in data warehouse. It is the short for three database functions: extract, transform and load. These three functions are used together to extract data from one database and present them in another form. To be more specific, Extract is the process of extracting data from a database. Transform is the process of
converting data from its previous form to a designated form. It is usually done by using rules or lookup tables or by combining the data with other data. Lastly, Load is the process of writing the data into the target database, which may or may not be blank.
ETL is an important tool when combining heterogeneous sources into one cohesive central repository, which can greatly enhance the business intelligence of a company.
Risk Appetite
Risk appetite is the level of risk that an investor is prepared to accept. In general, investor risk appetite changes in response to financial market condition. As stock market rises, investors will find returns on stock market investment higher than deposit. Naturally, they are willing to take on more risks, i.e. having a greater risk appetite. If the stock market continues to perform well, investors' risk appetite will keep on increasing, and investors will be more willing to invest in high-risk
derivatives. In contrast, when stock market falls, investors' risk appetite will shrink. They may abandon higher risk investment and turn to products with more stable but lower returns.
Data Warehouse
Data warehouse is a data storage theory in the information system. It makes target data easier to analyze and produces useful information by applying specific data storage methods. The information produced can serve as the basis for decision making. The data stored in the data warehouse are arranged in time sequence. Data
warehouse contains a lot of historical data. Users can search for the required data through specific analysis tools. Data warehouse is first proposed by 'the father of the data warehouse', W.H. Inmon, in 1990. The main purpose is to facilitate systematic analysis of data through the data storage structure put forward in the data warehouse theory. It enables analysis methods (such as online analytical processing, data mining) to be applied more smoothly, and assists in the establishment of decision support systems and executive information systems. It also help decision makers extract useful information from large amounts of data more efficiently in order to facilitate decision making and response to rapid changes in the external environment. Overall, it fosters the development of business intelligence.
Online Analytical Processing (OLAP)
Online Analytical Processing is an approach that allows users to quickly analyze data multi-dimensionally. Users can access integrated information by using analytical operations, such as consolidation, drill-down, slicing and dicing, drill across, drill through and pivot. The information thus gathered is commonly used in decision support system (DSS) or data warehouse. The main functions of OLAP include assisting large-scale data analysis and providing a basis for decision-making.
The concept of online analytical processing was first introduced by E.F. Codd in 1993. The purpose is to enable analysts to analyze multidimensional data swiftly, consistently and interactively. When being applied to business intelligence, OLAP can be used together with data mining technology and data warehouse, allowing users to view and analyze data in different perspectives, so as to make decision and achieve business value.
Glossary
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風險胃納
風險胃納指投資者承受風險的程度。在一 般情況下，投資者的風險胃納會隨著金融 市場而改變。當股市持續上升，股票投資 的回報比存款高，投資者的冒險意欲自然 隨之上升，即所謂風險胃納増加。若股市 繼續屢創佳績，投資者的風險胃納便會不 斷增加，此時，投資者會更願意投資於風 險較高的衍生工具。相反，當股市下跌， 投資者或會因風險胃納變小而放棄風險較 高的投資產品，轉投較穩定但回報較低之 存款或定息投資工具。資料倉儲
資料倉儲是一種資訊系統上的資料儲存 理論，該理論透過利用某些特殊的資料 儲存方式，使資料更易於分析和處理， 以產生有用的資訊作為決策的基礎。利 用資料倉儲方式儲存的資料一般按時間 排序，通常一個資料倉儲會包含大量的 歷史資料，用戶可運用特定的分析工具， 搜尋所需的資訊。 資料倉儲是由資料倉儲之父 W.H. Inmon 於 1990 年首先提出，主要的功用是按照 資料倉儲理論的資料儲存架構，有系統 地分析和整理資訊系統所累積的大量資 料，從而使各種分析方法（如線上分析 處理、資料挖掘）的運行更加順暢，並 協助建立決策支援系統及主管資訊系統， 幫助決策者能有效率地從大量資料中分 析出有用的資訊，方便制定決策和迅速 回應外在環境的變化，促進商業智能的 發展。ETL（提取、轉換、加載）
ETL 是處理資料庫系統中一個常用的程序， 尤其常見於資料倉儲中。ETL 的三個資料 庫 功 能 分 別 為 提 取（Extract）、轉 換 （Transform）和加載（Load）。ETL 的用途 是從一個資料庫中提取資料，並以另一種 方式表現出來。 就這三個功能的實際用途而言，「提取」 是從資料庫讀取資料的程序；「轉換」是 將資料從原來的格式變換成特定格式的程 序，轉換的方法包括使用查找表或與其他 資料合併等；而「加載」則是指將資料複 寫到目標資料庫的程序。 ETL 可以將不同格式和來源的資料整合成 一個中央資料庫，對商業智能的發展有莫 大的幫助。 Text 撰文CHOI Wai Man Niki 蔡慧敏 LAI Chun Wing Raymond 賴俊榮 LAU Ching Yin Chris 劉正賢 Year 1 students of
Risk Management & Business Intelligence Program 風險管理及商業智能學課程一年級學生
數據可視化
數據可視化是以視覺形式表示數據的過 程。這種數據的視覺表現形式通常是指一 種以某種圖象形式擷取得來的訊息，例如 相應訊息單位的各種屬性和變數。常用於 數 據 可 視 化 的 軟 件 包 括 Microsoft Office、Apple iWork、Many Eyes 和 InstantAtlas 等。 數據可視化對商業智能界極為重要，並經 常用於處理從數據挖掘所得來的一些抽象 而難明的訊息。經處理的訊息通常以圖表 或心智圖等形式表達，目的是增加訊息的 可讀性，從而降低資料使用者的背景知識 要求。線上分析處理
線上分析處理是一套用以快速地從多維度 分析資料的方法。利用積存、下鑽、切片 與切塊、橫向鑽取、穿透鑽取和樞紐分析 等操作，用戶可以獲得經整合後的資訊。 這些資訊常用於決策支援系統或資料倉 儲。線上分析處理主要用於大規模資料分 析及統計，以及為決策提供基礎和參考。RMBI Newsletter ISSUE 5 風險薈訊第五期
December, 2012 二零一二年十二月
RMBI newsletter aims to provide a channel to share up-to-date interesting risk management or business intelligence issues. We welcome your valuable comments about the contents and your articles sharing relevant information in future issues.
《風險薈訊》旨在提供渠道，分享最新風 險管理及商業智能的資訊。歡迎 閣下就 本刊內容提出寶貴意見及投稿分享相關資 訊。 Advisory Committee: Prof Mike K P SO 蘇家培教授 Program Director Dr. Adela S M LAU 劉秀梅博士 Senior Instructor
Miss Sandra M K WONG 黃銘君小姐 Executive Officer
Risk Management and Business Intelligence Program
The Hong Kong University of Science and Technology 香港科技大學 風險管理及商業智能學課程 Phone: 2358 6955 Fax: 2335 9317 Email: [email protected] Website: http://www.rmbi.ust.hk Acknowledgement 鳴謝
Some illustrations in this newsletter are from Morgue File.
本刊部份插圖選自 Morgue File. (http://www.morguefile.com/) All contents and information in this newsletter are proprietary to Risk Management and Business Intelligence Program, The Hong Kong University of Science and Technology and are subjected to copyright protection. Republication, redistribution or unauthorized use of any content or information contained in this newsletter is expressly prohibited without the prior written consent from the RMBI Program, HKUST. 本刊所載的內容及資料，均屬香港科技大 學風險管理及商業智能學課程所有，且受 版權保護。任何人士如未獲本課程事先給 予書面許可，一律禁止轉載、發放或擅用 本刊的任何內容或資料。 線上分析處理這個概念是由 E.F. Codd 在 1993 年所提出，其目的是 協助分析人員從不同角度，迅速、 一致及互動地分析資訊。當應用於 商業智能上，線上分析處理能配合 資料倉儲、數據挖掘等技術，讓用 戶可快速地從不同角度檢視和分析 資訊，制定策略，以實現商業價值。
