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Verbeke, Baesens and Bravo have written a data science book focusing on profit.

After a quick overview of data preparation, each classical machine learning algorithm is discussed (high level).

Profit Driven Business Analytics is an excellent reading, should you need to justify data science projects and focus on moving from accuracy percentage to benefits in money.

Data Science Book: Profit Driven Business Analytics

Verbeke, Baesens and Bravo have written a data science book focusing on profit.

After a quick overview of data preparation, each classical machine learning algorithm is discussed (high level).

Profit Driven Business Analytics is an excellent reading, should you need to justify data science projects and focus on moving from accuracy percentage to benefits in money.

Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data into Profitable Insight Hardcover – Special Edition, November 5, 2014

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Critical analysis of Big Data challenges and analytical methods

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners.

The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals.

This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns.

Big Data: The Management Revolution

“You can’t manage what you don’t measure.” There’s much wisdom in that saying, which has been attributed to both W.

Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.

Before long, they developed algorithms to predict what books individual customers would like to read next—algorithms that performed better every time the customer responded to or ignored a recommendation.

As the tools and philosophies of big data spread, they will change long-standing ideas about the value of experience, the nature of expertise, and the practice of management.

Business executives sometimes ask us, “Isn’t ‘big data’ just another way of saying ‘analytics’?” It’s true that they’re related: The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage.

For instance, our colleague Alex “Sandy” Pentland and his group at the MIT Media Lab used location data from mobile phones to infer how many people were in Macy’s parking lots on Black Friday—the start of the Christmas shopping season in the United States.

At the same time, the steadily declining costs of all the elements of computing—storage, memory, processing, bandwidth, and so on—mean that previously expensive data-intensive approaches are quickly becoming economical.

As more and more business activity is digitized, new sources of information and ever-cheaper equipment combine to bring us into a new era: one in which large amounts of digital information exist on virtually any topic of interest to a business.

Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations.

The data available are often unstructured—not organized in a database—and unwieldy, but there’s a huge amount of signal in the noise, simply waiting to be released.

We just have more data.” The second question skeptics might pose is this: “Where’s the evidence that using big data intelligently will improve business performance?” The business press is rife with anecdotes and case studies that supposedly demonstrate the value of being data-driven.

We conducted structured interviews with executives at 330 public North American companies about their organizational and technology management practices, and gathered performance data from their annual reports and independent sources.

But across all the analyses we conducted, one relationship stood out: The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results.

So does accurate information about flight arrival times: If a plane lands before the ground staff is ready for it, the passengers and crew are effectively trapped, and if it shows up later than expected, the staff sits idle, driving up costs.

So when a major U.S. airline learned from an internal study that about 10% of the flights into its major hub had at least a 10-minute gap between the estimated time of arrival and the actual arrival time—and 30% had a gap of at least five minutes—it decided to take action.

It calculated these times by combining publicly available data about weather, flight schedules, and other factors with proprietary data the company itself collected, including feeds from a network of passive radar stations it had installed near airports to gather data about every plane in the local sky.

Every 4.6 seconds it collects a wide range of information about every plane that it “sees.” This yields a huge and constant flood of digital data.

PASSUR believes that enabling an airline to know when its planes are going to land and plan accordingly is worth several million dollars a year at each airport.

couple of years ago, Sears Holdings came to the conclusion that it needed to generate greater value from the huge amounts of customer, product, and promotion data it collected from its Sears, Craftsman, and Lands’ End brands.

It took so long mainly because the data required for these large-scale analyses were both voluminous and highly fragmented—housed in many databases and “data warehouses” maintained by the various brands.

This is simply a group of inexpensive commodity servers whose activities are coordinated by an emerging software framework called Hadoop (named after a toy elephant in the household of Doug Cutting, one of its developers).

The PASSUR and Sears Holding examples illustrate the power of big data, which allows more-accurate predictions, better decisions, and precise interventions, and can enable these things at seemingly limitless scale.

We’ve seen big data used in supply chain management to understand why a carmaker’s defect rates in the field suddenly increased, in customer service to continually scan and intervene in the health care practices of millions of people, in planning and forecasting to better anticipate online sales on the basis of a data set of product characteristics, and so on.

When data are scarce, expensive to obtain, or not available in digital form, it makes sense to let well-placed people make decisions, which they do on the basis of experience they’ve built up and patterns and relationships they’ve observed and internalized.

Mauboussin, in this issue.) For particularly important decisions, these people are typically high up in the organization, or they’re expensive outsiders brought in because of their expertise and track records.

First, they can get in the habit of asking “What do the data say?” when faced with an important decision and following up with more-specific questions such as “Where did the data come from?,” “What kinds of analyses were conducted?,” and “How confident are we in the results?” (People will get the message quickly if executives develop this discipline.) Second, they can allow themselves to be overruled by the data;

They can only give you answers.” Companies won’t reap the full benefits of a transition to using big data unless they’re able to manage change effectively.

Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions.

On the contrary, we still must have business leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with customers, employees, stockholders, and other stakeholders.

The best data scientists are also comfortable speaking the language of business and helping leaders reformulate their challenges in ways that big data can tackle.

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