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Data Engineers Will Be More Important Than Data Scientists - Forrester

Data Engineers Will Be More Important Than Data Scientists - Forrester | The MarTech Digest | Scoop.it

"So, let’s look at how insight driven businesses are overcoming these issues. They take back ownership of data engineering and the computer science side of data architecture, management and governance. Data Engineers instrument data and analytics. They harness the strategy and investment plans of Data Architects. They enable analytics and data science. They adopt and activate data governance policies. They ensure data and analytic investment is getting its full return vertically and horizontally.

 

  • Want to accelerate data science – create a data engineering workbench.
  • Want to ensure data lake adoption – create a data engineering workbench.
  • Want to activate data and analytics in systems and processes – create a data engineering workbench.
  • Want to create consistency and reduce data risk – create a data engineering workbench."
CYDigital/marteq.io's insight:

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This news comes to you compliments of marketingIO.com. #MarTech #DigitalMarketing

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Who Does What in the Data Science Industry [Infographic] : MarketingProfs

Who Does What in the Data Science Industry [Infographic] : MarketingProfs | The MarTech Digest | Scoop.it

marketingIO: One Source for All Marketing Technology Challenges. See our solutions.

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Two Types of Data Scientists: Which is Right for Your Needs? - DataInformed

Two Types of Data Scientists: Which is Right for Your Needs? - DataInformed | The MarTech Digest | Scoop.it

iNeoMarketing executes your revenue-generating efforts with the right metrics and the appropriate reports. Contact us to learn more.

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Oracle’s Datalogix Deal Driven By Marketers’ Need To Mix First- And Third-Party Data - AdExchanger

Oracle’s Datalogix Deal Driven By Marketers’ Need To Mix First- And Third-Party Data - AdExchanger | The MarTech Digest | Scoop.it

From MarTech 101 to Advanced Techniques, iNeoMarketing has you covered. Contact us.

CYDigital/marteq.io's insight:

Note the last paragraph: that's how you get it done!

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Marketing-Tech Shopper, Meet The Data Scientist - CMO.com

Marketing-Tech Shopper, Meet The Data Scientist - CMO.com | The MarTech Digest | Scoop.it

iNeoMarketing.com bridges the gap between your MarTech and your in-house experience. Contact us.

CYDigital/marteq.io's insight:

Just be careful of wonk-overload. Once the wonks get together, the wonkfest begins and everyone else is held hostage.

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Today's Data Scientists: More Marketer, Less Scientist - CMO.com

Today's Data Scientists: More Marketer, Less Scientist - CMO.com | The MarTech Digest | Scoop.it

► About Us: iNeoMarketing provides Marketing Technology services, applications and support to B2B companies who do not have the required resources, knowledge or expertise. Visit us at ineomarketing.com. ◄

CYDigital/marteq.io's insight:

With business, communication and flexibility skills embedded into the Data Scientist, and the Marketer taking on more of an analytical role, which would you hire?

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Stop Searching for That Elusive Data Scientist - HBR

Stop Searching for That Elusive Data Scientist - HBR | The MarTech Digest | Scoop.it
Instead, develop the skills on your team.


Digest...


Based purely on the data, a really good data scientist will probably tell you the odds are poor that you’ll be able to find and hire really good data scientists. Surveys say there simply aren’t enough people with the unusual blend of software skills and statistical savvy to go around. Arguably even more important, high-impact data scientists bring collaborative temperaments and business acumen to data-driven initiatives. Unfortunately, there’s no shortage of individuals with just enough statistical and software knowledge to be data-dangerous. For many organizations, a mediocre data scientist may be worse than none at all.

 

The smartest thing I’ve seen organizations start doing is seed-fund and empower small cross-functional data-oriented teams explicitly charged with delivering tangible and measurable data-driven benefits in relatively short periods of time. The accent is on the word team; the emphasis is on building greater data capability than better digital infrastructures. The goal is to make all of the organization — not just the geeks and quants — more conversant in how to align probability, statistics, technology and business value creation. No black boxes or centers of analytic excellence here; they want data science to be a cultural value, not just a functional expertise.

 

Without exception, every team I ran across or worked with hired outside expertise. They knew when a technical challenge and/or statistical technique was beyond the capability. But, unsurprisingly, the outside advisors — in one case, an academic, in others, quants from digital consultancies — were better able to collaborate with teams that had really tried to get their minds around a design challenge. The relationship was less of an RFP box-ticking exercise than a shared space for experimental design.

 

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CYDigital/marteq.io's insight:

Smart advice: build the cross functional team instead.


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The Question to Ask Before Hiring a Data Scientist - HBR

The Question to Ask Before Hiring a Data Scientist - HBR | The MarTech Digest | Scoop.it

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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CYDigital/marteq.io's insight:

If you need the latter, you've got to be careful. I have a friend, a statistical genius who holds 2 Ph.D degrees. Nice guy with whom to have a few cold ones, terrible when you ask him a stat question. Relates better to running code than a person. That's the kind of person you DON'T want on the team.

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3 Ways Analytics Is Helping Marketers Reinvent Themselves - Digital Marketing Blog

3 Ways Analytics Is Helping Marketers Reinvent Themselves - Digital Marketing Blog | The MarTech Digest | Scoop.it

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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CYDigital/marteq.io's insight:

An analytics professional has to be a part of the Marketing Operations staff. Call it a pseudo data scientist.

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Carolyn M Todd's curator insight, August 5, 2014 7:22 PM

Marketing analytics...new trend or something that has always been a mainstay of business?  What do you think?  This article provides some good insights into how data has changed.

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How to find -- and hire -- data scientists - VentureBeat

How to find -- and hire -- data scientists - VentureBeat | The MarTech Digest | Scoop.it

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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CYDigital/marteq.io's insight:

It'll be more than 500K data scientists needed, as more technology will drive the need beyond what we can see today. Tough to find and hire today: the skills are there but spread across multiple individuals.

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Digital Marketing: Stop ignoring data and start learning | MarketingExperiments | #TheMarketingTechAlert

Digital Marketing: Stop ignoring data and start learning | MarketingExperiments | #TheMarketingTechAlert | The MarTech Digest | Scoop.it

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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CYDigital/marteq.io's insight:

You hire data jockeys, or you outsource to a 3rd party data management provider. You can't sit on this stuff: doing nothing is not an option.

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Chuck Taylor's curator insight, September 22, 2014 10:33 AM

The biggest challenge marketers face today is leveraging the multitude of data sources and buying channels to maximize their marketing insights.

Elise Perkins's curator insight, December 9, 2014 11:29 PM

This article talks about how data is not only changing the rules of journalism but other fields, like marketing as well. There is power in the numbers, and its about how you can use those numbers that will insure success in the future, but the old stand by no longer applies. 

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The Data Artist Balances the Art and Science of Marketing - ClickZ | #TheMarketingAutomationAlert

The Data Artist Balances the Art and Science of Marketing - ClickZ | #TheMarketingAutomationAlert | The MarTech Digest | Scoop.it

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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CYDigital/marteq.io's insight:

Theory, theory, theory. The notion is spot-on, but try to find someone who fits this description amongst the qualified populace. I'm not disparaging the Data Jockeys: all I'm saying is that it is difficult to find these qualities. And if you do, you'll need to ask why they're not creating a start-up.

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Denis Marsili's curator insight, December 28, 2014 11:57 AM

There's a lot of lip service given to using data to drive more strategic and successful marketing campaigns, but it's one thing to deliver "descriptive analytics" on the same tired data and another to use the data for insights that actively drive...

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Infographic: Anatomy of a Data Scientist - FICO Labs Blog | #TheMarketingAutomationAlert

Infographic: Anatomy of a Data Scientist - FICO Labs Blog | #TheMarketingAutomationAlert | The MarTech Digest | Scoop.it
Called the sexiest job of the 21st century by Harvard Business Review, the rise of Big Data has fueled demand for data scientists.
CYDigital/marteq.io's insight:

Depending on the size of your organization, you're going to need one of these.


  • See the article at from ficolabsblog.fico.com
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