About 41 results
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Data science management and the post, lever, and balance method
Scope, time, and people are the interwoven dimensions that comprise any project, program, or strategic outcome.
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Leadership requires that you don't get caught in thoughtless execution
There's more value in defining a problem that there is in solving it. Make sure your big data team is asking the right question before it starts developing an answer.
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Let maturity and evolution determine where the data science team reports
The size and maturity of your organization has a lot to do with how your data science team should be structured.
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Call in a Black Belt to manage your big data analytics team
Even though it's tempting to embrace the latest and greatest theories on management, sometimes a leader needs to go old school to get the job done.
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Don't draw strategic conclusions from the wrong marketing data
If you're not careful, the data you're collecting for your marketing analysis may be telling you the wrong story about your target market.
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The best big data strategy is the strategy that keeps adapting
If human behavior undergirds your big data strategy, you must constantly be on alert for a shift in how your target is behaving.
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Why samples sizes are key to predictive data analytics
In order to use big data for predictive analytics, you must take sample sizes seriously and understand the risks about sampling assumptions.
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Measure actual customer behavior using big data analytics
Discover how measuring actual customer behavior instead of behavioral intent can dramatically increase your marketing effectiveness.
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Use big data analytics to identify and retain your best customers
A big data strategy should include using analytics to identify your best customers and then offering them a free service to engender loyalty.
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Use Big Data for marketing accountability
Astute marketing executives can leverage the power of big data analytics to fortify their value in the organization. Take some time today to see how your executives feel about your...
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Double-down on your Big Data resources
Take some time today to re-evaluate the value on your Big Data strategy--you might want to double down on your resources.
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The Big Data magic act
The organization that wants to achieve causal ambiguity must focus on big data innovation, retaining key analytic talent, and protecting the organization's information prowess.
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With Big Data, what's better--qualities or quantities?
When selecting the data scientists that will fuel for your Big Data innovation and/or strategy, you must make sure you understand how they feel about research.
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Get a woman's touch on your Big Data team
If there's a gender imbalance on your Big Data strategy team, you're doing yourself a great disservice.
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To get Big Data buy-in, IT should let go of proof of concept
If your company is currently experimenting with Big Data in an attempt to prove some sort of concept--interrupt this right away. If you're going to prove something--it better be va...
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Goldilocks and the three business analysts
John Weathington has some suggestions for you when you're surveying the landscape of business analysts to augment your team of information specialists.
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Big Data is not just about the customers
Even if you have great ideas for how to use your Big Data once you have it in place, have you considered where this data will come from?
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Life after your Big Data strategy
Where will your Big Data resources go after your strategy successfully completes?
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Framing your Big Data strategy for success
As an information strategist, John Weathington frequently sees organizations who are unsure of their Big Data strategy six months into the effort. Here are his tips for framing Big...
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Recouping your Big Data investment in one year
Although the horizon for your Big Data strategy may be three to five years, there's no reason why you can't recoup your investment within the first year.