Onnowpurbo at 02:41, 7 April 2021

2021-04-07T02:41:20Z

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	ensure that you account for these types of constraints in your current and evolving		ensure that you account for these types of constraints in your current and evolving
	data science environment.		data science environment.
			Managing data transfer and computation
			across-country borders
			If your company has divisions in a number of different countries or does business
			(and therefore has many customers) in many countries, one major challenge you
			might face is how to manage data that needs to cross international borders.
			You need to carefully consider a number of different aspects of the data puzzle if
			your company has an international component. Here’s a list of the major
			concerns:
			» » Legality: Legal constraints to moving data across borders is a consideration
			that a company must stay on top of. Laws and regulations differ from country
			to country, so different solutions may be possible, depending on which
			country you’re doing business in. The restrictions are also different depending
			on which type of data you’re moving out of a country. Data with personal
			information is usually much more difficult to move than non-sensitive data.
			Breaking laws related to data transfer can be quite costly and can severely
			impact the company brand if it is determined that you violated cus-
			tomer trust.
			» » Data transfer approach: This refers to how you actually execute the data
			transfer. It’s typically quite costly and also differs from country to country.
			Depending on the volume of data you want transferred, and the data transfer
			frequency, you can either rent space in existing connectivity infrastructures
			and data links or — if you cannot get your requirements met regarding
			aspects such as capacity, security, or exclusivity — invest in your own links.
			CHAPTER 3 Dealing with Difficult Challenges
			43» » Possibilities for local computation and storage: If you can store the data
			and carry out the analysis in the country where the data has been captured,
			you might be able to lower the cost and increase the speed of delivery.
			However, to get this setup to work efficiently, you need to properly think
			through what your distributed computational architecture will look like. What
			will be done where? and where will the source data be kept, for example? Will
			there be a central point of data storage and global analysis, or will there be
			only distributed setups? How you answer these questions depends a lot on
			what type of business is being conducted and what the setup looks like in
			different countries.

Onnowpurbo: Created page with "Getting Data from There to Here When a company decides to embark on a journey to become data driven, the focus is naturally on the data itself, which inevitably leads to a gre..."

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Created page with "Getting Data from There to Here When a company decides to embark on a journey to become data driven, the focus is naturally on the data itself, which inevitably leads to a gre..."

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Getting Data from There to Here
When a company decides to embark on a journey to become data driven, the focus
is naturally on the data itself, which inevitably leads to a greater awareness of the
actual variety of data needed to gain full proactive and data-driven control of their
current business. On top of that, companies soon realize that in order to expand
CHAPTER 3 Dealing with Difficult Challenges
41beyond what is possible today, the data sets need to become even more varied. At
this point, many companies start to realize that the data which is fundamental to
becoming truly data driven might actually belong to someone else or is located in
another country, with other data regulations. This section explains how to strate-
gically approach such practical challenges as part of your data acquisition.
Handling dependencies on data
owned by others
Dealing with proprietary data is an unavoidable yet manageable challenge faced
by any company striving toward becoming fully data-driven. Typically, what hap-
pens is that you have identified and carefully specified all the data you need in
your data strategy and when you then start looking into how to strategically
approach capturing the data, you realize that you have a data ownership problem.
If you use only data generated from your internal IT environment, you have, of
course, less of a problem. If that’s the case, however, then your company probably
isn’t truly data-driven in the proper sense. A data-driven business accounts for
how its products and/or services are used and how it performs in real-life
settings, not merely in the lab environment. And anytime you start using data
generated by life in the real world, you run into the data ownership problem.
What kind of data am I talking about? First and foremost, this involves data owned
by your customers, but it can also include data owned by your customers’ custom-
ers, depending on which business you’re in. You have to take the time to truly
understand the detailed context of the data you need. It can relate to issues of data
privacy, but it doesn’t have to. It can simply be the case that the data you need in
order to better understand your business performance or potential belongs to
someone else.
Don’t get discouraged when it comes to ownership issues. Most situations can be
solved from a legal perspective if you’re willing to address them openly with the
data owners, explaining why you need the data and how you will treat the data
after it’s in your possession. It’s all about gaining trust with regard to how, and
for what purpose, the data will be used. (It wouldn’t hurt to also spell out how
your work may, if possible, contribute back to the owners of the data.)
At the end of the day, you need to be absolutely certain that you understand (and
are complying with) the legal constraints that apply for each different type of data
you intend to use. Your use of the data must also be regulated by way of a contrac-
tual setup with the party owning the data, including what rights your company
has related to data access, storage, and usage over time.
42
PART 1 Optimizing Your Data Science InvestmentLaws and regulations have a habit of changing over time. Lately, the trend is to
increase restrictions even further in order to protect an individual’s right to their
own data. One recent example is the quite restrictive General Data Protection and
Regulation (GDPR) enacted by the European Union (EU) that went into effect in
May 2018. Given recent news of the misuse of data by entities such as Cambridge
Analytica and Facebook, the U.S. and Canada are definitely looking into legislation
similar to the EU’s GDPR.
Anything that helps to protect an individual’s right to privacy is all for the best,
but just remember that the way you deal with privacy legislation today will most
probably be quite different in the near future. Therefore, you should strategically
and proactively think through your infrastructure setup and your data needs to
ensure that you account for these types of constraints in your current and evolving
data science environment.

Data Science Strategy: Memperoleh Data - Revision history

Onnowpurbo at 02:41, 7 April 2021

Onnowpurbo: Created page with "Getting Data from There to Here When a company decides to embark on a journey to become data driven, the focus is naturally on the data itself, which inevitably leads to a gre..."