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Big Data Mining In Email Receipts Pays Off With Human Analysis Added

14/9/2017

The search for big data knows no limits. As Apple was releasing its latest iPhone, alternative big data mining providers were scraping social media reaction to feed into trading algorithms (the stock sold off after the product announcement). Now new analysis from Macquarie examines the value of email receipt big data mining, pointing to the ability to predict Amazon’s retail sales to various degrees of success before the quarterly earnings announcement. But the key is not only technical, but applying the human touch makes the difference.

Big Data Mining learns a lot by studying consumer emails
For Macquarie, diving into the big data pool was an eye opening experience. What they ultimately found was that quantitative investment methods with alternative data are best used when combined with fundamental analysis, not in replacing human analysis.
For their study, Macquarie worked with email receipt data provided by Quandl, a platform for financial data. The company scans numerous niche information sources including millions of email inboxes where receipts of consumers can be found. This provides insight into transaction level information such as product description, taxes paid and shipping accounts all from artificial intelligence scans of their email accounts. The data is updated weekly and covers a wide variety of e-commerce platforms.
From Macquarie’s standpoint, transforming the raw transaction data into tradable information was no small task, but the exercise proved meaningful.
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How big data helps transform Intime Retail

13/9/2017

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Department stores in China have been having a hard time. More than half experienced a sales decline from 2011 to 2016, according to an industry survey.
Intime Retail (Group) Company, while still making money, started to smell the danger.
Noticing the shrinking market size, falling industry employee numbers and growing competition from e-commerce, Intime decided it had to change.
That’s good, but to go about that exactly?
Well, how about a partnership with the biggest rival, Alibaba!
Two years ago, Intime and Alibaba began working closely together. In January this year, Alibaba spent HK$29.6 billion to take Intime private, which was then delisted from the Hong Kong bourse.
Chen Xiaodong, chief executive of Intime, says Alibaba’s big data technology has helped transform the department store operator since the tie-up.
So, what is the first step of change?
Asked this question, Chen offered this response: “A retailer should go back to being what it is supposed to be — a retailer.”
What most department stores are doing now is not retailing, but property leasing, Chen noted.
They only care about getting the rents, and are not paying enough attention to whether the shops serve the needs of the neighborhood.
“It may have worked 20 years ago but not today,” said Chen.
Using big data to get a grip on consumers
Employing Alibaba’s big data skills, and data collected by Alibaba’s numerous units including Tmall, Alipay and Alibaba Cloud, Intime can now accurately analyze consumers’ habits in a 10-kilometer periphery of its department stores.

Such data can map out the family structure, average age, and consumption power of the shoppers within the periphery.
Chen said the analysis can help the company determine the right type and size of shops in a department store, the kind of products and brands a shop should offer, as well as the number of items from each brand.
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It used to be enough for wealth advisers to be fun on the golf course — now it's all about big data

12/9/2017

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Wealth advisors used to succeed by making insightful lunch conversation, being fun on the golf course and winning referrals. These days, effectively leveraging big data is a better bet for successful marketing.
The growing hoard of wealth invested with robo-advisor platforms is forcing advisers to change how they operate. By the end of 2017, $160 billion of assets will be invested in digital advice platforms, up from $65 billion a year ago, according to research by Aite Group and Broadridge. That number will swell to $400 billion by the end of 2018. Competition from these low-priced services and the trend of replacing mutual funds with lower-priced ETFs, is compressing fees to such an extent that advisers will need to add more clients in order to generate the same fees.
Using data will help advisers get there. Just as Amazon can harness data to predict what you will shop for and data helps Netflix predict which TV series you’ll want to watch, advisers today can leverage a huge reservoir of data to reveal everything from what’s going on in a client’s personal and professional life to how they want to communicate about their money decisions.

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Overcoming the Hurdles of Big Data in Marketing

5/9/2017

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The marketing communications industry has always worked with a high level of data. But over the last decade, it’s been transformed by a new degree of granularity — the number of data streams available, new insights, and the ability to take action on those insights. We’re now working with up to 10 to 50 times the amount of data we had access to before, and it has the potential to unleash a new level of understanding, creativity, consumer value, and monetization.

Of course, there are a few pitfalls, which fall primarily into three categories.
The first has to do with understanding privacy rights: making sure everyone knows how their personal and consumer data will be used. Ideally, marketing creates a value exchange — providing benefit to consumers in exchange for collecting their information for the purposes of targeting media or custom messages.

This is what online services like Google and Facebook would argue they already do — free services in exchange for data collection and targeting. There is no true corollary for offline media at present, but when you look at the mergers of Time Warner and ATT, and the future direction of Comcast, both companies plan to inject as much data as they can into their advertising delivery and distribution vehicles.

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Turning big data into business insights: The state of play

4/9/2017

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We live in an increasingly data-driven society, in which information is becoming as much of a currency as money. Many consumers use free services from internet giants like Google, Facebook, Amazon, Microsoft and Apple, for example, and in return allow these corporations to track and monetise their online behaviour.
One of the biggest questions of the day is the openness of such transactions, and the level of control that individuals have over the fate of the personal information they -- sometimes unwittingly -- divulge to organisations with which they interact online. Recent votes on both sides of the Atlantic have highlighted the capacity for data-savvy organisations to hoover up and profile large amounts of user data -- including demographics, consumer behaviour and internet activity -- in order to micro-target adverts, news stories and services in support of particular goals or causes.
Clearly, the data floodgates are now opening for businesses of all sizes and descriptions, bringing myriad opportunities for timely analysis in pursuit of competitive advantage. Although the focus is currently slanted towards customer behaviour, data is available at multiple points in the product or service supply chain, and comes in many forms -- traditional (structured), ad hoc (unstructured), real time, and IoT- or M2M-generated, to name but a few.
Companies that implement big data analytics successfully can reap rich rewards from cost-saving efficiencies and revenue-generating innovations. This can help businesses achieve a digital transformation, allowing them to maintain competitiveness in the face of any disruptive startups -- which are data-driven almost by definition -- that spring up in their markets.
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The Predicament of Extracting Big Data Patterns

30/8/2017

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Like any other person out there in the world that creates data or has something to do with data understands the predicament of deciphering data in general to gather a meaning insight of things from it. But as we all know data is tactfully divided into two genres—structured data that is organized in a format or repository, and unstructured data that comprises of data without format such as emails, as well as social media generated data. Data scientists spend most of their time cleaning these data to gain a market or consumer pattern that would help companies steer themselves towards more profitability.

Formatting and merging the unstructured data with the structured data is a tedious process, but on the other hand it’s a crucial one as well. Data scientists look forward to easier processes that would help them efficiently organize the unstructured data and in turn upload the relevant data within the right placement of the structured data format. The integration of structured and unstructured data is necessary as most of the industry draw market patterns from the uncategorized unstructured data. Nevertheless, the challenge of obtaining a measurable and accurate form of big data often surpasses the human capacity to produce intricate error free information.
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HOW BIG DATA ENHANCES GOOD HOSPITALITY

29/8/2017

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When applied correctly, technology streamlines processes and helps restaurants run more efficiently. It also helps staff manage the flow of guests and understand more about the people who are dining at a restaurant. The New York Times has a quick look at a few companies tackling the collection and application of restaurant data, from table turns to customer spend and loyalty.
Erik Oberholtzer, founder and chief executive at the expanding Tender Greens, sums up the big data challenge as it applies to restaurants: “The goal is to leverage the technology to do what we would do if we had one little restaurant and we were there all the time and knew every customer by name.”
Thanks to the systems restaurants use, collecting data on everything from popular menu items to customer loyalty is reasonably easy. What’s more challenging, though, is applying that data in fast, easy-to-understand, actionable ways that help time- and resource-crunched restaurateurs do better business. Currently, many restaurants use disparate systems to manage different parts of the business. For example, according to the article, Tender Greens uses three: one for in-store sales; one for online orders and delivery; and a third to track customer loyalty and purchase history. Using many different systems often means having to apply information from one to another — potentially losing some useful data in the process. Other, newer systems — like NYC-based Salido — are working to streamline this process and create a one-stop shop for all data and analytics from server performance to menu trends and ingredient sourcing to customer preferences and order history.
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Companies Love Big Data but Lack the Strategy to Use It Effectively

23/8/2017

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Big data has shifted the ground under every business, enough so that many managers are waking up to the fact that they are already behind in developing a smart data strategy.
Data has always been important in business, of course. But with the arrival of digital data—its volume, depth, and accessibility—it has become clear it is key to helping companies develop sustainable competitive advantage.
“The new attention being given to data today is because suddenly, everywhere, it’s become much cheaper to measure,” says John A. Deighton, the Baker Foundation Professor of Business Administration at Harvard Business School. “Used well, it changes the basis of competition in industry after industry.”
The problem is that, in many cases, big data is not used well. Companies are better at collecting data–about their customers, about their products, about competitors– than analyzing that data and designing strategy around it.
That’s one reason eight HBS professors pooled resources in June to launch the Competing on Business Analytics and Big Data Executive Education program. “It was unprecedented to engage eight faculty in a single program,” says Deighton, “and it reflects the fact that data questions touch every part of the enterprise.”
The program drew C-suite executives and senior managers to look at how big data affects the supply chain, marketing, human resources, and other key business functions. Attendees studied how market-leading companies are harnessing data to reshape their companies, and explored how they can put data to work for them in ways that create value for their own businesses.
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STEERING BIG DATA PROJECTS IN THE MODERN ENTERPRISE

16/8/2017

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Just a few years ago, enterprise organizations had to be convinced that Big Data was a real-world opportunity worth investing in. By 2016, 63% of those enterprise leaders were saying they considered Big Data and advanced analytics initiatives a necessity in order to remain competitive. This year started with even bigger spike in number of new Big Data projects announced by top players like Hortonworks and MapR as well as a set of new technologies coming out to market, including Cloudera Kudu – a new tool designed to offer enterprise-level reliability while keeping NoSQL-like performance.
Big Data has come a long way in a short amount of time, and it shows no signs of slowing down, so it’s up to today’s business leaders to determine how they can leverage Big Data trends in an enterprise environment. Though this is not an easy undertaking, the start of every new project can be broken into three parts: an initial assessment, choosing a plan of attack, and determining specific and achievable goals.
Assessing Your Big Data Standing
When you start going to the gym, a personal trainer can estimate your fitness level in order to suggest the right program for your needs. The same holds true in the world of Big Data; it makes sense to evaluate, on a high-level, the current data and decision-making maturity level of your organization before investing more resources into new initiatives.
The Big Data Maturity Model presented by my colleagues Serge Haziyev and Iurii Milovanov explores five levels of data, starting with a company that is already paperless but still suffering from a huge amount of manual data processing, including ad-hoc ETL jobs and Excel reports, to a “wisdom level” where decisions are being made automatically with limited supervision. It’s essential to understand where you are to determine where you’re going.
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Behind the hype: Machine learning in investment management

15/8/2017

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Big data: Costly, but is it useful?Although big data is usually directly associated with machine learning, there is still a debate whether new data sources, such as web crawling through news or social media, credit card data, geolocation data, and so on, is helpful in the investment process. Some specific examples of trading strategies based on such data include using Twitter sentiment to make bets on the equity market as a whole or individual stocks in particular or using geolocation data to estimate retail activity relevant to individual stocks (e.g., footfall at retail stores).
The Barclays report states that 54% of surveyed investment managers use alternative data, such as web crawling social media data, satellite data, or credit card data. This finding suggests it is less prevalent than tick data (100% usage) or fundamental data (62% usage), such as balance sheet or income statement data, but more prevalent than economic data (38% usage), such as employment or inflation figures, or sell-side data (31% usage), such as analyst reports or broker recommendations.
Despite the widespread use of alternative data, 80% of surveyed investment managers in the Barclays report said that their biggest challenge was in assessing the usefulness of the data.
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<<Previous

Big Data Mining In Email Receipts Pays Off With Human Analysis Added

How big data helps transform Intime Retail

It used to be enough for wealth advisers to be fun on the golf course — now it's all about big data

Overcoming the Hurdles of Big Data in Marketing

Turning big data into business insights: The state of play

The Predicament of Extracting Big Data Patterns

HOW BIG DATA ENHANCES GOOD HOSPITALITY

Companies Love Big Data but Lack the Strategy to Use It Effectively

STEERING BIG DATA PROJECTS IN THE MODERN ENTERPRISE

Behind the hype: Machine learning in investment management

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The leading "Management Consultants in Big Data"
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Categories

Archives

The leading ​"Management Consultants in Big Data"Business-Savvy, Vendor-Independent Expertsto help you thrive in the Big Data JungleUK - 0207 164 6260USA - 415-500-9874Or click here to contact us

The leading "Management Consultants in Big Data"
Business-Savvy, Vendor-Independent Experts
to help you thrive in the Big Data Jungle

UK - 0207 164 6260
USA - 415-500-9874
Or click here to contact us