Does intuition make for infallible results? They simply cannot do it. At least not yet. A later chapter will look at fledgling artificial intelligence approaches and software heading us toward a place where at least some intuition will be possible on the part of machines. A minority of observers especially those from the traditional scientific community as opposed to those from the business, social research, or marketing research communities are deeply skeptical of Data Science as a discipline.
Furthermore, they are skeptical especially because of the human factor. Chris Anderson … wrote in that the sheer volume of data would obviate the need for theory, and even the scientific method. These views are badly mistaken. The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning [and] we may construe them in self-serving ways that are detached from their objective reality.
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Not all bias is bad. Bias informed by experience and knowledge of the topic area being explored is our friend. Bias based on practical, pragmatic analysis of real world situations and real world information needs is our friend. Fact is, there's a subjective aspect to Data Science which does not exist in fields involving the practice pure-science — fields such as chemistry where one is looking for strictly defined and provable empirical results.
The informed and rational bias of of the Data Scientist in the process of unearthing, combining, and imposing questions on data is not only a valid aspect of the overall equation, but a fundamentally necessary element of the overall equation. Bottom line: Data Science is essentially social science, and social science although based on empirical, statistical research is largely subjective.
This performance difference remained robust after accounting for the contributions of labor, capital, purchased services, and traditional IT investment.
It was statistically significant and economically important and was reflected in measurable increases in stock market valuations. Leaders will either embrace this fact or be replaced by others who do. In sector after sector, companies that figure out how to combine domain expertise with Data Science will pull away from their rivals. We can't say that all the winners will be harnessing Big Data to transform decision making. But the data tell us that's the surest bet.
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It is a use-case-driven, iterative, and agile exploration of granular data, with the intent to derive insights and operationalize these insights into down-stream applications. I must run and follow them. For I am their leader. Given highly limited amounts of data as compared to what we have available today , the top executives of a firm were indeed the people best positioned to digest that relatively small core of data and make informed assumptions.
Introducing this fundamental idea into the culture of an enterprise is often the biggest hurdle in creating a Data Science initiative that is not only efficient but also has a significant voice in the decisions derived from fresh- breaking BI. Top management of course still remains at the helm of the enterprise. They chart the course, steer the vessel, and thus define in what waters the enterprise shall be making its way.
Thereby they also define in what areas of inquiry Data Scientists must focus their attention. There is no substitute for this domain expertise, this knowledge of where the biggest profit opportunities and often the most treacherous shoals and currents lie. Thus the value of the HiPPO kingpins will morph from being seat-of-the-pants dictators to simply knowing what questions to ask. They can only give you answers.
Clear goals must be set. And they must be set by domain experts who understand how a market, discipline, or environment is developing, who can think in new ways and ponder novel solutions, who can embrace compelling new ideas — and can do all these things while at the same time balancing the needs of all stakeholders, including customers, stockholders, and employees.
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Data Science demands practitioners with skill-sets not native to most enterprises — not even most IT departments. In particular we are talking about integrating statisticians most of all statisticians skilled in the social sciences rather than business applications , programmers skilled in new non- traditional software such as Hadoop for cleaning and modeling unstructured data, and professionals skilled in predictive analysis and data visualization.
These professionals — whose techniques along with their data are largely unstructured — occupy a previously undefined place on the map of the enterprise, one which lies in a one-time no-man's-land somewhere midway between the IT and marketing departments. With these new professionals must come new chains of command, work-flow-practices, and procedures. Some toes will be stepped on. IT professionals and marketing professionals will lose some measure of autonomy.
A new and expanded atmosphere of collaboration — both in practice and attitude — must be made to prevail. Let's face it. The introduction of a Data Science initiative to the enterprise is a tacit acknowledgment that existing methods of analytics, research, and innovation have fallen short. Any professionals associated with these legacy methods are going to feel upheaved and threatened. If management does not astutely handle the introduction of the new paradigm, giving all a sense of ownership, it is very easy for organizational infighting over data ownership, decision authority, and other issues to arise, costing time and money.
On the other hand, when shared ownership and collaboration is made a top priority, the near-term result is far more likely to be a flowering of data- driven innovation, a rich Data Science culture, and clearly defined progress into new, previously unexplored and highly productive terrains of BI. At the outset, the vision for a Data Science-driven model of decision making must be clearly enunciated and shared with all relevant staff, making them all stakeholders in the process.
Although tasks can be commanded and responsibilities assigned, enthusiasm and passion cannot be. And these last two attributes are essential for success in Data Science, just as they are in most other things. At the same time however, it is critical to devise and implement training programs for any and all legacy staff whom the enterprise sees as becoming part of the new equation.
The guinea pigs in the study were some , Facebook members who received News Feeds front-weighted with either positive or negative posts and images, this to see the extent to which their emotions could be impacted.
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Problem: The guinea pigs were never informed of their participation in the test. And for that communication we apologize. We never meant to upset you. Many Data Scientists see it as their job to create a rubric of ethical behavior in which their profession should place itself.
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One group which has taken on this job is a nonprofit founded in called the Data Science Association. The code of ethics promulgated by this organization covers a range of areas, from protecting customer privacy to protecting practitioners from erroneous claims made by software innovators as to the efficacy of various Data Science tools.
The Data Scientist shall take reasonable measures to persuade the client to use Data Science appropriately. As the widely critical reaction to the Facebook study suggests, one of the chief ethical dilemmas confronting the Data Scientist is balancing transparency and a basic respect for privacy with the need to legitimately accumulate and use data for research purposes. On the up-side, most users of the Internet rather enjoy having Amazon of Netflix suggest books, music, and films based on their prior purchases and clear preferences.
Most also enjoy when Facebook makes solid recommendations for likely new friends, and when LinkedIn proposes a business connection which makes sense. But all of these conveniences — all of these results of Data Science — come at a cost. And that cost is the sacrifice of some measure of privacy.
Research shows that at the same time as users enjoy the perks enumerated above, they also resent the loss of privacy.
A recent Pew Research Internet and American Life study shows that 86 percent of Internet users have taken at least some steps towards removing or masking their identities while online. Some data gathering firms, attempting to address this, have been quite pro-active and open concerning their business models. Acxiom Corporation, for example, is a firm solely focused on the acquisition and sale of data on individuals and corporations. They've recently launched aboutthedata. Overall, however, the ethics concerning the gathering, use, and analysis of customer information remains very fuzzy, to say the least.
Let's say I have you in one of my Facebook studies, and you're coming to my lab and we are analyzing the strength of the connections between you and your friends. I'm getting information about your friends and their friends without their consent. It's a very, very ethically sensitive area. But these guidelines — adherence to which are a prerequisite to any Federal funding — chiefly address subjects engaged in voluntary direct research after having given informed consent to the research being transacted. Users are particularly exposed and at risk when they participate in open social networks such as Facebook or Instagram, or maintain a Google identity including gmail or use a free e-mail service such as Yahoo.
The currency paid is personal data — buying habits, etc.
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Confusion shall prevail for the forseeable future. We've been looking to companies like Google or Facebook to do the right thing and to set the standard but to the extent these are enforced or that other companies have to follow, a lot of this stuff isn't in place … It's the sort of thing where … [many] people don't object and it doesn't seem that bad, but it really opens the door up to worse things. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.
Hubbard, Much of our best analytical thinking is graphically-oriented.
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