Five Steps to Master Big Data and Predictive Analytics in 2014 - Forbes

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Five Steps To Master Big Data

and Predictive Analytics in

2014

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Source: predictiveanalytics.org

As recently as the past two

years, one of the seminal

Five Steps To Master Big Data and Predictive Analytics in 2014 - Forbes http://www.forbes.com/sites/benkerschberg/2014/01/03/five-steps-to-mas...

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issues regarding Big Data was

storage, especially with respect

to the exponential growth and

size of unstructured data that

did not fit into databases (e.g.,

video feeds, PowerPoint

presentations). Indeed

petabytes and exabytes of data

exist in science, technology,

commerce, national defense,

telecommunications, and

other fields. Today, however,

the competitive landscape is

very different. Proper storage

is merely a pre-condition to

finding the real jewels in Big

Data—turning data from

massive streams into

knowledge, and thereby

actionable intelligence in real

time as events unfold.

The following five steps are

imperative to master Big Data

and drive business growth.

1. Infer, Infer, Infer.

Understand that not all Big

Data is useful data. According

to the renowned AT&T Bell

Telephone Laboratories

statistician John Tukey, “data

may not contain the answer.

The coordination of some data

and an aching desire for an

answer will not ensure that a

reasonable answer can be

extracted from a given body of

data.”


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In an era of data-centric

science, we now have

advanced analytics that permit

inferences from granular data.

Inferences transform data into

knowledge, which results in

greater process transparency

and improvements. When

evaluating the need to institute

analytics as part of your data

strategy, it is important to

remember that actionable

knowledge is not inherent in

data per se; rather, it must be

extracted based upon

established rules and

algorithms. While extraction

and inference require the type

of core platform described

below, this should not be

overly complicated. As the

National Research Council of

the National Academies states,

even “naive users” should be

able to “carry out massive data

analysis without a full

understanding of systems and

statistical uses.” Data

scientists play an

indispensable role in today’s

corporation, but business line

executives should not have to

rely on them to run analytics

and make the inferences that

are the basis for decisions. See

Frontiers in Massive Data

Analysis (National Academy of

Sciences 2013). As McKinsey


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puts it, “sophisticated

analytics solutions . . . must be

embedded in frontline tools so

simple and engaging that

managers and frontline

employees will be eager to use

them daily.” Mobilizing Your

C-Suite For Big Data

Analytics (McKinsey &

Company 2013).

2. Empower a C-Level

Data and Predictive

Analytics Champion. With

big data analytics changing

rapidly and straining

information structures,

corporations and governments

need what McKinsey calls

“executive horsepower” or

“top-management muscle”

behind its data initiatives. Id.

Accordingly, a C-level officer

(e.g., Chief Data Officer, CTO,

or Chief Analytics Officer) who

comes from both a supply

chain and analytics

background must have the

mandate to lead model

analytic centers. In order to

succeed, analysts with deep

data experience must have a

clear strategy with defined

initiatives to achieve business

results. A forward-thinking

analytics strategy thus needs

to take place at the business

unit level. Why? First,

priorities will differ by


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business unit; the treatment of data in one business unit may have

little utility in another. Second, management priorities have to

reinforce functional level goals with targets and metrics. A C-level

executive who can work with business line managers and still

champion analytics in the C-suite is a must.

3. Assess And Modify Your Supply Chain In A

Multidimensional Global Context. Do not examine your supply

chain without first considering logistics at a macro level. According to

the World Economic Forum’s Outlook on the Logistics & Supply

Chain Industry (2013), “ratios of trade to GDP for the world as a

whole have increased from 39% in 1990 to 59% in 2012.” This change

is in large part the result of a “targeted and concerted effort by

industry and governments to increase economic growth and jobs.”

How does your supply chain fit into this larger context? Consider this:

a corporation’s failure to maximize the knowledge in its data and

thereby to contribute to unnecessary logistics costs imposes upon

itself and others (as well as international trade) what amounts to an

inefficiency-based tax. How are corporations faring on other fronts?

They spend an astonishing average of eight percent of net sales on

transportation, warehousing, customer service, administration, and

inventory carrying costs. Yet many do not have a comprehensive view

of their data, let alone their upstream or downstream logistics

functions. See Supply Chain Logistics As A Driver of Business

Strategy and Profitability (C.H. Robinson 2013).

Being in the blind has transformative impacts—and not the kind you

want. What effect is your logistics framework having on your

company? On customer loyalty? How can you use most effectively

leverage your data both past and present, and what technology do you

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need to do so? And while an analysis of your supply chain will

ultimately include your relationships with parties such as customers,

manufacturers, providers and retailers, it should begin with an

inward-facing assessment of key assets.

4. Give Your Data Time-Critical Situational Awareness.

In most organizations, data must be pulled from disparate and

distributed sources and then processed to yield actionable

intelligence. Analytics help a business line identify potential points of

improvement. Corporations need to make changes not only in real

time as events unfold, but also within the constraints posed by the

increasingly distributed nature of modern data sets.

Current supply chain management must be concerned with multi-

dimensional data that includes temporal and geospatial elements.

Examples of temporal data are the acquisition of data from sources

such as the Internet, speech and video data, real-time imaging from

satellites, and ground-based sensors. Such inputs can be difficult to

analyze because the different sources that comprise the data stream

have different latencies. Moreover, the amount of temporal data is

growing exponentially. Geospatial data, on the other hand, tracks

location, whether that of a storm, a car, or a tornado that may render

impassible to your trucks certain highways, thus demanding quick

redirection to avoid time lost and equipment damage. For shippers,

for example, both elements come into play: it is useful to know the

location of ships, containers, and even packages in real time and/or

two days prior in order to see if interim movement in is unusual and

requires action. Coupled with temporal data, a logistics analyst can

make informed decisions as events in his supply chain unfold.

5. Rely On A Core Platform That Creates Derivative

Intelligence And Knowledge In Real Time

Building a robust supply chain management platform from scratch or

by combining point solutions is nearly impossible. From the

perspective of cost alone, it is much more effective to partner with a

third-party cloud-based solution provider. Your criteria when you

choose a platform should be stringent. According to Tim Fleischer,

CEO of TransVoyant, a technology and services company that enables

sub-second operational decisions and support, criteria should


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include:

Supply chain management should take place on a platform that

resides in a cloud such as Amazon Web Services (AWS), which

recently was predicted to be worth $50 billion by 2015. See Tiernan

Ray, Amazon’s AWS A $50 Billion Value (Barron’s Nov. 18, 2003).

Cloud computing is not going anywhere, and the security of such

massive vendors is appropriately robust. The power of the cloud

allows for the extraordinary processing power made possible by

distributed computing. Now, statistical inferences can turn data into

actionable intelligence that supports reasoned decisions. Moreover,

companies can scale as they wish and absorb only the marginal costs

of their expansion.

First, no data latency—you need to see your assets in real time, in

motion, as they unfold. This is critical to actionable intelligence.

Second, the platform must be multi-dimensional. Failure to capture

temporal and geospatial data will leave even the savviest company

flat-footed. Third, your core platform must allow you to visualize your

data assets in multi-dimensions so that you can see what is happening

in real time. Fourth, it must be flexible enough to accommodate

different supply chains in your organization. Cargo shipping may bear

little resemblance from auto parts to Apple computers.

Finally, the platform must yield derivative intelligence that will

become your company’s intellectual property and thus comparative

advantage. This also applies to risk, such as controlling your DSOs,

another nugget to be gleaned from your knowledge-filled data.

Big-Data Analytics

Data Mining Tools

Real-Time Data

Data Infrastructure

Data Management Platform

Web Data Visualization

Outsource Data Center

Data Management Consultants

See Also:


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Netflix is no stranger

to successful shows, and many

of them have had their

moment in the sun at one

point or another the past few

years. House of Cards, Orange

is the New Black, The

Unbreakable Kimmy Schmidt,

Bojack Horseman, Master of

None, Daredevil, Jessica

Jones. All of these shows have

been released, binged and

entered into the pop culture

lexicon, the subject of

discussion for at least a week

or two after release, given the

binge-watch nature of the

service.

But Making a Murderer is

different.

Not just because the genre has

switched from scripted drama

and comedy to a true crime

documentary. But because this


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is the first Netflix show that

seems to have completely

consumed its viewerbase from

top to bottom. Though many of

the shows mentioned above

are popular, they have pretty

specific audiences. But

Making a Murderer? The

story of Steve Avery has

seemed to hook pretty much

everyone who has laid eyes on

it.

The question is not if you’ve

seen Making a Murderer yet.

It’s how long did it take you to

watch it? A week? That’s

insane. Three days? That’s

more like it. One day? There

you go.

I have never seen a show

consume the pop culture

conversation like this outside

of when programs like The

Walking Dead and Game of

Thrones kill off a major

character. Everyone on my

Facebook and Twitter

is talking about it

(two very, very different

groups), and all my friends

have seen it as well. My wife

and I spent about two hours of

a three hour dinner party with

our friends last night talking

not about their newly

announced baby, but about

Making a Murderer, and just


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how enraged the show has

made us.

And we’re not alone.

This is, of course, the

fundamental draw of Making a

Murderer. Unlike all Netflix’s

other shows, this is a real

story, with real stakes, and real

people who are still suffering

from the injustices we see

onscreen.

To briefly recap, Making a

Murderer is the culmination of

a decade of work by the

project’s creators, Laura

Ricciardi and Moira Demos,

who exhaustively chronicled

the horrifying story of Steven

Avery and his nephew


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Brendan Dassey. The two

Wisconsin natives were

accused of raping and killing

Teresa Halbach, a young

woman who had come to take

pictures of Avery’s car for

AutoTrader magazine. Steven

Avery was supposedly the last

person to see her alive, and his

nephew eventually confesses

that he and his uncle raped,

tortured, and killed her,

burning her body in the fire pit

directly outside the house.

If that sounds relatively

clear-cut, you’re in for quite a

ride. As it happens, Avery just

finished 18 years of a prison

sentence for a crime he was

exonerated from by DNA

evidence. Now, as his $36M

lawsuit begins against the

county, he’s suddenly being

accused of a brutal murder,

and the same people who

railroaded him the first time

are deeply involved with the

new case.

I won’t go through the entire

story here, you’re better off

watching the show for yourself,

if you’re one of the few people

who haven’t yet. There are

hooks after episodes one, three

and four, and if you make it

that far, I can’t imagine you

won’t be compelled to finish


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the story. I don’t think I’ve

ever heard of anyone stopping

midway through.

Once you’re done, the question

becomes…which part of it

makes you the most furious?

Which thing that occurs is the

most ludicrous miscarriage of

justice in your view?

Is it that the State’s case

against Steven and Brendan

rely on a story that Teresa was

stabbed and killed in either his

bedroom or his garage, yet

literally zero blood was ever

found in either place?

Is it that the despite swearing

to stay away from the case,

Mantiowoc County police are

heavily involved, and key

evidence like a literal key (to

Teresa’s car) and a flattened

bullet with her DNA on it are

found only after their

detectives are left alone to

“search” his property

themselves?


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Is it that a Manitowoc County

officer is recorded calling in

the missing car days before it

was supposed to be found? Is it

that this car is found parked in

Avery’s salvage yard just a few

hundred feet from a car

crusher that could have easily

reduced it to scrap?

Is it that Brendan Massey’s

statements are dragged out of

him by investigators who

promise they’ll send him home

if he says whatever they want

to hear? Is it hearing a boy

with a very low IQ wonder

about if he’ll manage to make

it home in time to watch

Wrestlemania if he simply

admits to raping and

murdering a girl? Is it that

later he recants all of this and

says he got it directly from

James Patterson’s book, Kiss

the Girls?

Is it the fact that despite what

seems to be a pretty clear

frame job by police,

incompetence by investigators

and outright psychological

warfare employed against an

intellectually challenged

minor, the jury still doesn’t

seem to understand the

concept of “reasonable doubt”

and both are convicted?


14 of 19 1/4/2016 4:29 PM

In a way, Making a Murder

channels Serial, the recent

true crime podcast that took

the nation by storm. But this

time, the answer seems much

more obvious, the injustice of

it all truly harrowing. It’s hard

to imagine someone will watch

all ten episodes of Making a

Murderer and genuinely think

Steve and Brendan are guilty,

while Serial left its own case

much more up in the air.

It’s hard to watch. You will yell

at your TV. You will grow to

loathe characters like “creepy

mustache prosecutor” and

“stupid smiley useless defense

attorney.” You will develop

your own suspicions about

what happened (“What about

those two neighbors who were

each other’s alibis? Whose

calls was Teresa ignoring? Why

does her brother weird me out

so much?”). But most of all you

will be profoundly sad about

how on so many levels, the

justice system failed here.


15 of 19 1/4/2016 4:29 PM

In a way, Making a Murderer is justice for Steven Avery and Brendan

Dassey. Obviously, they’re still in prison, a situation which needs to

be rectified, but a problem always was that even if they were found

not guilty, at least in Wisconsin with the facts the police and media

presented, everyone still would have believed they did it. What

Making a Murderer does is restore their reputations, and tell their

horrible story to those who have never heard it. The show itself is its

own form of justice, showing the real heroes and villains of the case

for the world to see.

This feels like the first truly national conversation Netflix has started

about one of its shows. Yes, many are discussing the latest schemes of

Frank Underwood or heroics of Jessica Jones for a few days after

those shows air, but this feels like something much more profound. A

genuine phenomenon, possibly the service’s first.

It remains to be seen if true justice will be served for Avery and

Dassey, if somehow, some way they’ll be proven innocent after all this

time. But if I had to guess, this is not the last time Netflix will visit this

case, judging by the response of their audience.

Follow me on Twitter, on Facebook, and on Tumblr. Pick

up my sci-fi novels, The Last Exodus and The Exiled

Earthborn, which are now in print and online.

Why do people still love The Walking Dead? Watch below:


16 of 19 1/4/2016 4:29 PM

6

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Sneaky Method

An Extremely

Brilliant Way to

Pay Off Your


18 of 19 1/4/2016 4:29 PM


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Five Steps to Master Big Data and Predictive Analytics in 2014 - Forbes