原創譯文 |「數據貨幣化」戰略的五大要素

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本文為燈塔大數據原創內容，歡迎個人轉載至朋友圈，其他機構轉載請在文章開頭標註：

「轉自：燈塔大數據；」

當今，在認知計算時代下的數字化商業模型中，數據帶來了新的收入流。如果一個公司能夠高效地利用數據，那麼認知計算學就能為其帶來額外的收入流。

在大數據中，我們稱之為「數據貨幣化」。數據貨幣化已經在全行業中掀起了改革的浪潮，提高了用戶體驗，使更精準的個性化市場和銷售策略成為可能，還有效地防止了詐騙的發生。

大數據的興起推動了各行各業的改革，大數據在成本優化和用戶體驗提高方面已經顯出了巨大的作用，越來越多的公司發現大數據能夠為他們帶來新的收入流。

從銀行業到電信業，從能源業到零售業，只要手握數據，這些公司就能創造出新的盈利點。這些行業都正在經歷著數據價值「貨幣化」的過程，通過優化數據收集和儲存過程獲得了更大的盈利空間。

麥肯錫全球研究所的《大數據研究報告》顯示，在創新、競爭和生產效率的發展前線上，大數據可以為客戶端用戶和企業端用戶創造7000億美元的價值。想要獲得這一價值，就必須要在技術、基礎設施、人力方面有足夠的投入，政府也需要給予一定的支持

在利用數據挖掘利潤之前，你必須先找准目標客戶，並列出行業競爭對手，分析他們成功的原因。

以樂購（Tesco）公司為例，他們需要關注零售商和購物商場的運營情況，獲取人們的購物活動信息，從而進行物流及庫存管理和客戶來源地區分析，因為這些分析需要基於真實的客戶行為數據

2

發現數據集特點——原始數據or修正數據？內部數據or外部數據？

數據貨幣化並非僅僅是儲存和出售數據。數據貨幣化對數據分析過程、分析結果和合作夥伴都有一定要求。我們不妨組建一支集中化管理的數據科學隊伍，與公司企業合作，分析不同數據集特徵，探索應用案例，引進新的業務團隊。

目前IBM與很多零售公司都建立了合作關係，這些零售公司用Hadoop和Spark整理數據，形成供給鏈實時報告，然後將報告賣給批發商。值得注意的是，這些數據不僅包括銷售點的購買數據，還有從銀行處獲得的交易數據。

在Apache Spark和Kafka的幫助下，形成報告只需要數小時的時間，正確使用可擴模型可以將整體收入提高25%。分析這些報告可以為公司在客戶區分和交叉銷售分析方面提供很大幫助。

3

技術的合規性和合法性問題

分享數據時，人們通常會遇到數據被盜用的問題。因此我們應該建立明確的問責機制和准入門檻，遵守國家關於數據安全、隱私和自留責任等方面的政策，以確保客戶不會對我們喪失信任，也不會觸發任何法律法規的禁區。公司隱私政策必須言簡意賅、通俗易懂

4

數據服務與商業模型

要落實數據貨幣化戰略就必須選擇合適的商業模型，建立有力的戰略聯盟，找到靠譜的合作夥伴。

很多公司專門做高級大數據服務。如果這些數據公司能夠為客戶提供大量有價值的數據，那麼就可以達到雙贏的結果

5

確立技術戰略——Hadoop、Spark和IBM Watson數據平台

開源技術為公司企業在數據貨幣化的發展中提供了有力支持。越新的數據，價值越高。Apache Spark和Kafka等技術都能為企業提供快速的實時數據分析，這種數據處理方式和管理方式是前所未有的。

簡單來說，這些改變都是為了一個結果——提高數據的靈活性。

理想的大數據環境是由開放標準驅動的，並且是鼓勵合作的。Hadoop、Spark和IBM Watson等大數據平台可以為數據貨幣化戰略奠定堅實的基礎，幫助企業迅速地實現數據的貨幣化

英文原文

5 key attributes of effective data monetization strategy

In cognitive computing era, new revenue generation stream has emerged with data at center of the modern digital business model. One of the key capabilities cognitive computing enables for an organization is the ability to generate additional revenue streams by using data effectively.

In the big data world we call it data monetization. The internal data monetization has already done amazing job at transforming business in all verticals by improving customer experience, enabling more personalized marketing and sales, deterring fraud and so on.

The emergence of big data has shown to transform professions and industries. We are seeing big data doing wonders with cost optimization and enhancing customer experience. We are increasingly seeing a growing trend among our customers to create new revenue streams with big data.

Customers ranging from banks, telecommunication providers, energy and utilities companies and retailers have potential to earn new revenues from the vast amount of data they hold. Each of these businesses are experimenting with different ways to monetize the value of the data they gather during their normal operations. Each are expecting to make considerable revenues based upon the difference between the cost of collecting and storing the data, and what the insights and outcomes can be sold for.

As per the McKinsey Global Institute report on 「Big data: The next frontier for innovation, competition, and productivity,」 big data can create as much as $700 billion in value to consumer and business end users. Capturing this value will require the right enablers, including sufficient investment in technology, infrastructure and personnel as well as appropriate government action.

1. Identifying your target customers' needs, requirements and aspirations

Before you embark on journey to make money out of your data. It is important you profile your target customers, verticals and their parameters for success.

Case in point is telcos targeting retailers and mall operators with insights about anonymous movement of people throughout the property and surrounding. Delivering store or business catchment analysis based on real behavior, not just proximity to your location.

2. Identifying data assets—raw and refined, internal and external

Data monetization is much more than just storing and selling the data. Data monetization is about making revenue out of data enablers like insights, outcomes and partnerships. Companies can benefit from a centralized Data Science team that partners with the business and potential customers by identifying data that differentiates, exploring use cases to solve, and helping to jumpstart business teams.

One of the customer engaged with us is a retail company who is selling real time supply chain report to merchant wholesalers. The company is using the data from their Hadoop and Spark cluster to generate revenue-driving reports for wholesalers. The key parameter here is blending of purchase data from POS with transaction data from banks. With Apache Spark and Kafka, they run these reports in just hours, and with the scalability models in place they expect to grow this business to 25% of overall revenue. The analytics from these reports help merchants with customer segmentation, cross-sell analytics, and more.

3. Addressing regulatory and legal issues with technology.

How you share your data is about balancing needs to innovate against the risk of using your data. Strike that balance with clear responsibilities and pragmatic access, enforce compliance to data security, privacy and retention policies and processes to ensure continued trust by consumers and meet regulatory and legal requirements. Company privacy policies must be clear and well-understood by overall business and technical team. Access should be determined by the use case requirements and priorities.

4. Data as a service and business model

Operationalizing your data monetization strategy calls for having the right business model, the right strategic alliances and the right partner.

The companies are working on driving sophisticated big data as a service business models based on both volumes and values. The win-win business model will be highly influenced by the number of insights business can provide to customers and value those insights can generate for their customers.

5. Defining the technology strategy—Hadoop, Spark and IBM Watson Data Platform

The emergence of open source technologies gives tremendous power to organization in this new emerging data monetization space to break even more swiftly. Data provides maximum value when it is fresh. Technologies like Apache Spark and Kafka give real time analysis capabilities to business at lightning speed. This technology has a wholly different approach to data and data management than what we had before. It is the key enabler to the far reaching transformation that is really 「big data.」

In short, these changes all lead back to the simplest of facts in the underlying technology—the agility of data.

A big data environment that supports collaboration powered by open standards is ideal. IBM Watson Data Platform provides the power of machine learning and cognitive computing based on open source 「Apache Spark」 to enterprises. Data platforms such as this will form solid foundation for a data monetization strategy and will enable organizations to quickly and easily monetize data.

翻譯：燈塔大數據

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