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Digest...

Is your data scientist producing analytics for machines or humans?

Analytics for machines: In this case, the ultimate decision maker and consumer of the analysis is a computer. Online ad or content targeting, algorithmic trading, product recommendations are just a few examples. These data scientists are building very complex models ingesting enormous data sets and trying to extract subtle signals with machine learning and sophisticated algorithms. These digital models act on their own, choosing which ads to display, making recommendations to users, or automatically trading in the stock market, often in less than the blink of an eye.

Analytics for humans: The ultimate decision maker and consumer of the analysis here is another human. Analyzing the effectiveness of products, understanding user growth and retention, producing reports for clients are just a few examples of the work these data scientists do. They might be sifting through the same large data sets as their analytics-for-machines counterparts, but the results of their models and predictions are delivered to a human decision maker (often a non data-scientist) who has to make product or business decisions based on these recommendations.

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Digest...

The ROI of this data-driven style of mar­ket­ing is clear: increased engage­ment, more ad impres­sions, bet­ter cus­tomer reten­tion, increased sales, etc. I believe three inno­va­tions in ana­lyt­ics are enabling mar­keters to rein­vent themselves.

1. Visual Data with Context

Tra­di­tional reports dis­play black-and-white data. It’s just about the num­bers. Adding con­text and con­tent enhances your reporting—the data is col­or­ful, Hi-Def. More impor­tantly, it con­nects peo­ple to their cam­paigns in a more visual way. With con­text and con­tent, your report­ing has the abil­ity to show how the num­bers are con­nected to the cus­tomer experience.

2. Action­able Data

As ana­lyt­ics soft­ware con­tin­ues to improve, data is becom­ing sharper and more insightful—the data is smarter now. This smarter data lets mar­keters quickly iso­late cus­tomer insights, which is a key to greater data rel­e­vancy, and it’s enabling mar­keters to make bet­ter real-time decisions.

3. Smarter Analysis

With smarter data, analy­sis is sim­pli­fied. You get bet­ter, visu­al­ized data that make insights eas­ier to iden­tify and respond to. This shift has also made it so any­one can now mine data for insights because the tools are there for mar­keters and ana­lysts of all types to dig into the data. Now, busi­nesses big and small can take the data they’ve been col­lect­ing and uncover use­ful cus­tomer infor­ma­tion, which is dri­ving change in the way mar­keters work.

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Digest...

So how do you find and hire these newly essential employees? Not easily, according to data scientists Max Song and Carl Shan, who are working on a Data Science Handbook, and have published 120 Data Science Interview Questions.

Roughly 140,000 people are employed as data scientists right now, but by 2018, we’ll need 440,000 to 490,000, according to that report. The shortfall is due to the fact that this job is so new there’s not a direct educational equivalent, or an obvious career path. There’s no data science major in college, with the closest equivalent being perhaps a rare statistics major. But data scientists also need a firm grounding in computer science.

Employers and recruiters need to broaden their search to people who may not have the title of data scientist, and may have followed diverse educational and career paths.

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Intermediate/ Excerpt...

One of the top takeaways: “Marketers recognize the importance of data, but aren’t widely using it to make informed decisions.” Here are the supporting facts and figures:

• 76% of marketers agree they need to be more data-focused to succeed
• 49% of marketers report “trusting my gut” to guide decisions on where to invest their marketing budgets
• 72% of marketers agree that long-term success is tied to proving marketing return on investment

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Digest...

It's about gaining the benefit of true analytics without requiring a team of technologists, mathematicians and data analysts hanging over the marketer's shoulder to interpret the numbers. Perhaps most importantly, it's about finding the right balance between the art and science of marketing, combining creativity and the insights from marketing analytics tools to support business decisions.

There's a lot of lip service given today to using data to drive more strategic and successful marketing campaigns and other activities. But it's one thing to deliver "descriptive analytics" on the same tired data time and time again, and another to use the data for insights that actively drive more revenue and higher profitability. That's why the distinction between data scientist and a data artist is so important.

"A data scientist is responsible for understanding and advancing the nature of data, its collection methods, and the algorithms for processing it .... A data artist is responsible for delivering fresh insights from data to help an organization meet its goals. This is the person who takes the output from decision-support systems and turns it into consumable theories, postulates and hypotheses that can be tested and applied to the business."

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