8 Ways To Monetize Data - InformationWeek

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Data Management // Big Data Analytics
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1/22/2016
09:05 AM
Lisa Morgan
Lisa Morgan
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8 Ways To Monetize Data

Data may be a company's most valuable asset, but few are maximizing its economic benefit. Here eight ways that organizations are deriving real, bottom-line value from their data.
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Stop Revenue Leaks 

Busy hospitals, clinics, and healthcare providers can easily lose track of the services they've rendered. Each procedure has a description and an assigned code, both of which may include errors. Using analytics, those organizations can identify patterns associated with the codes and the procedures, so that patient invoices can be flagged for potential errors or missing charges. Intelligent data use also helps those same organizations improve the ROI of collections. Specifically, they are able to identify the right person to contact, the channel that is most likely to elicit a response, and the time of contact that is most likely to yield a positive result. 
'Who is likely to pay and how much they will actually pay are common predictive models,' said Alex Guazzelli, chief scientist at advanced analytics software company Opera Solutions, in an interview. 'Companies are segmenting their collections to see who's likely to pay, and who's likely to pay more so they can maximize their collections. A lot of people don't pick up their phones anymore, so it's important to understand which channels you should use to contact a person and the right time to make that contact.' 
(Image: Yourschanz via Pixabay)

Stop Revenue Leaks

Busy hospitals, clinics, and healthcare providers can easily lose track of the services they've rendered. Each procedure has a description and an assigned code, both of which may include errors. Using analytics, those organizations can identify patterns associated with the codes and the procedures, so that patient invoices can be flagged for potential errors or missing charges. Intelligent data use also helps those same organizations improve the ROI of collections. Specifically, they are able to identify the right person to contact, the channel that is most likely to elicit a response, and the time of contact that is most likely to yield a positive result.

"Who is likely to pay and how much they will actually pay are common predictive models," said Alex Guazzelli, chief scientist at advanced analytics software company Opera Solutions, in an interview. "Companies are segmenting their collections to see who's likely to pay, and who's likely to pay more so they can maximize their collections. A lot of people don't pick up their phones anymore, so it's important to understand which channels you should use to contact a person and the right time to make that contact."

(Image: Yourschanz via Pixabay)

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CuseOrange87
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CuseOrange87,
User Rank: Apprentice
4/6/2016 | 11:40:01 AM
Re: Sysiphean work
There are some concrete examples of practical big data monetization/industry use in the marketing technology world, where companies like Adobe and Circulate monetize data for publishers and platforms very effectively
soozyg
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soozyg,
User Rank: Ninja
2/6/2016 | 4:46:25 PM
Re: Sysiphean work
Yes, I can totally see that. Data has no purpose unless it can be applied to a human issue, unless it can answer a real question. I wonder how often analysts are surprised by the results even after the data is accurate and is applied to an algorithm or designed program.
jagibbons
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jagibbons,
User Rank: Ninja
2/6/2016 | 4:15:28 PM
Re: Sysiphean work
The tools help. Then, the human looks to see if what the computer identified makes sense. The tool is important, but not the part of the equation. A good analyst with a good tool and good criteria can make far better decisions than with any of those key pieces missing.
soozyg
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50%
soozyg,
User Rank: Ninja
2/6/2016 | 4:12:52 PM
Re: Sysiphean work
Ah, I see.

And do you have to review the data in context to get the effect? Does the initial plan help?
jagibbons
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50%
jagibbons,
User Rank: Ninja
2/6/2016 | 4:09:19 PM
Re: Sysiphean work
That's the problem with analyzing only raw data points using algorithms. You can only see patterns, not what those patterns might mean. Causation requires experience and logic to think about whether one action really can reliably affect another. That's the human part of the process. We ask, "Does this really make sense?" Or are both correlated items really the effects of something completely different. This is partly why the role is called a data "scientist."
soozyg
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50%
soozyg,
User Rank: Ninja
2/6/2016 | 3:22:58 PM
Re: Sysiphean work
Even if a pattern is somewhat consistent but the variables don't have a cause and effect relationship, you can't use them to make business decisions.

And you can see the cause and effect in the data?
jagibbons
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50%
jagibbons,
User Rank: Ninja
2/6/2016 | 1:39:00 PM
Re: Sysiphean work
When looking at large data sets, there will be patterns that appear to be there but don't mean anything. Even if a pattern is somewhat consistent but the variables don't have a cause and effect relationship, you can't use them to make business decisions. That is a trap of big data and algorithm analysis. Look up the Parable of Google Flu to see how computer date analysis can lead to misleading results.
soozyg
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50%
soozyg,
User Rank: Ninja
2/6/2016 | 1:20:38 PM
Re: Sysiphean work
@jagibbons what do you mean by this?

There are countless naturally correlations that have no causal relationship.
soozyg
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50%
soozyg,
User Rank: Ninja
2/6/2016 | 1:18:54 PM
Re: Pricing
For example, my daughter recently bought a dress for a school dance. On the designer's website it was something like $179. On Amazon, it was something like $30. Crazy!

Also, I did some research for my mother's business, which is homemade luxury faux fur quilts. The prices ranged from $15 to thousands.
shamika
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50%
shamika,
User Rank: Ninja
1/30/2016 | 8:54:13 AM
Re: Sysiphean work
I agree with you. Without a proper plan it is not worth of having data. It is important to see what you need to do with the data.
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