deliverable 5 powerpoint

Tools and InformationSome useful links from today’s call are the following:
– Tooltip: https://help.netronic.com/en/visual-advanced-production-scheduler/setting-up-thevaps/how-to-configure-tooltips
– Define alternative routings: https://help.netronic.com/en/visual-advanced-productionscheduler/setting-up-the-vaps/alternative-routings-alternative-machine-centers
– Apply alternative routings: https://help.netronic.com/en/visual-advanced-productionscheduler/finite-capacity-scheduling-instruments/apply-alternative-routing
If you have any questions let me know,
Paulina Soto
Business Development Manager
Microsoft Dynamics 365 Business Central | NAV
LinkedIn: https://www.linkedin.com/in/paulinasotog/
VAPS: VISUAL ADVANCED PRODUCTION SCHEDULER
Highlights:
•
•
4 Training sessions have been completed with Pauline in Germany and Stoneridge of the
overview of what VAPS can achieve with the correct information. Machine Capacity, Labor
Capacity and Materials capacity for the production department.
Findings from our training currently that MWR needs to investigate and change for
implementation. (Routings, alternate Routings, Machine centers and Work Centers).
April 29th, ISM6200 Module 5, Objective 1 and 2 Recording.mp4 (sharepoint.com)
https://rasmussenedu.sharepoint.com/sites/ISM6200CBESection01CBEBusin_df5499df-e55c-11ee-8d7c91ac0a440c12/_layouts/15/download.aspx?UniqueId=92173826%2D9c84%2D4959%2Db761%2D1222d
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https://rasmussenedu.sharepoint.com/sites/ISM6200CBESection01CBEBusin_df5499df-e55c-11ee-8d7c91ac0a440c12/_layouts/15/download.aspx?UniqueId=92173826%2D9c84%2D4959%2Db761%2D1222d
292fbb6
username is jenny.efta@smail.rasmussen.edu
Password is Hunter55@ (capital H)
May 2nd, ISM6200 Module 5 Objective 3 and
Deliverable 5 Recording
May 2, 2024, 12:03AM
30m 44s
Jan Hoffman 0:10
Good evening.
Welcome to business intelligence and analytics.
Tonight is module 5.
I’m gonna talk about the third objective in module 5.
I did modules one and two.
I believe it was on Monday night for you.
And then I’m also going to cover deliverable 5.
So in the first part of module 5, we talked about structured and unstructured big
data and we said that about 20% of organizations have structured data data.
Structured data is easier to work with.
Typically consist of files or or tables or data sets, and unstructured data could be
audio files, video files, Excel spreadsheets, and so forth, and so unstructured data
constitutes about 80% of of enterprises needed that they have so makes it more
challenging to work with unstructured data.
There is a movement afoot to try to corral unstructured data.
Or is there is an episode on 60 minutes this past week that talked about just about
that.
And so if you get a chance to take a look at it, please do so.
That would have been April.
28, Alright, so tonight we’re going to be talking about it’s still big data, but we’re
going to recommend business decisions based upon analytical methods.
So I wanna ask you a question and I don’t have any participants.
I’m just gonna let you ponder about this for a second, but you know, I mean, you
have a lot of expertise behind you from your work experience and from your Masters
program.
Your bachelor’s program, and if you know so much, why do you need to turn to
analytics to help you with decision making?
So think about that for a second.
I mean, you know, maybe we have a Harvard educated individual with a doctorate
degree and attorney degree and maybe an MBA on top of that.
And you know, are they the most brilliant person in the world?
Probably not, but you know, could they make decisions without using BI and
analytics?
They could.
Would they be accurate?
Maybe, maybe not.
And so it helps to be able to use data driven information to make your decisions.
If the data has been cleaned, the data hasn’t been manipulated, the date is not going
to lie to you and therefore it should be give you truthful information that you should
be able to make data driven decisions based upon it.
If the data has been mishandled, then you know clearly it would be dubious for you
to use it.
But if you’re procedures in your organization are such that it doesn’t get messed
with, then you know then it’s are good.
It’s something that would be very reliable for you.
I personally believe though you should have a core team of people that surround
you that have expertise in areas that may may not be your strong suit.
So for example, if you’ve got great business administration skills, but yet you deal in
situations where you know a statistician might be helpful.
Uh.
A mathematician might be helpful.
Umm.
And you know, other individuals may be a big help to you to make decisions.
There’s no harm in consulting with them quickly to see what their thoughts are and
see if they agree with the data driven information, and if so then you know, go with
that and hopefully you’re on the right path.
Sometimes your core team may have some differences of opinion based upon
experiences they’ve had or based upon some element that may be big data didn’t
consider, and so you know, you’d have to maybe take that into consideration and
alter your decision a bit.
So I believe personally this is me.
Your text will tell you data driven decisions.
This is the way to go, but I believe I still want to rely somewhat on the people around
me and you know I want to make sure that the people around me know what they’re
doing.
And have a lot of expertise in the their field so that I can depend upon them.
Don’t.
That’s a personal comment I’m making there.
So we know that leaders are challenged every single day with tough decisions.
You know, they have to try to their visionaries.
They have to try to move the ball forward.
They have to try to.
You know, determine where do we want our company to grow?
How do we want it to grow?
Where do we want to be five years from now?
10 years from now and then take steps to make that happen, and in the meantime,
they’ve got clients who want a piece of their time.
Maybe clients are upset.
Maybe clients need some handholding.
Maybe there’s international clients that you have to go visit, but now it takes time to
get there and so forth.
And so sometimes it takes away from your ability to focus on those long term
objectives.
So we always have short term objectives as well, but if you can use your Analytics, it
can help you to make decisions quicker, faster and hopefully better.
So there are some tools available to you when you’re trying to make some decisions
now, one of which is a decision tree, and it’s used for predicting outcomes.
Which ones are possible and describing which outcomes are probable?
Anna can be used to represent all kinds of categorical areas as well as continuous
scenarios.
No computation is needed.
Makes it easy to work with and it allows you to visualize all of the potential outcomes
of a decision.
So I’m sure that many of you have used a decision tree in some point in time and
you probably had training on decision trees as well.
So let me continue.
They’re used for should we sell this part of the business?
Should we expand our R&D effort, invest more money in it?
Should we launch this new product?
Or maybe you know Marcus not ready for it.
Should we change your business model?
Should we change our strategy?
I mean, there’s all kinds of things that we can use and decision trees for.
You can use them in your personal life as well.
Should I buy this make and model car?
This make model car should we move you know just it’s used for so many different
things.
So business and personal life, this is a decision tree that I created.
It’s extremely simplistic, but I just wanted an example to share with you, so in my
decision tree this is for my husband and myself.
Should we move, you know, right now, housing prices are still high and we can make
some money off of our House.
So should we move well if the answer to that question is yes, we decide, yes, where
should we go?
Should we go to the suburbs or should we go to the city?
And if I say we’ll go to the suburbs, then which suburb should we go to?
One that’s close by one that’s a little bit further out, you know, and I could list the
suburbs and make a decision there.
And if I choose one of the suburbs, should we move into a single family home?
Should we get an apartment?
Should we get a condo?
You what should we do if we move into the city?
Do we want to be in the heart of the city or on the outskirts of the city?
Do we want to be in a condo or an apartment or home?
You know, so you can hang a lot of things off of each one of these circles that you’re
seeing here to further your decision making to help you get to your end goal.
So let’s say I say no, we’re not going to move.
So then should we repair our home in?
Should we repair a roof?
Umm.
And if we repair the roof, do we repair all of it, or do we completely replace the roof?
We know we’ve got some broken shingles.
Should we just replace them or should we just, you know, redo the entire roof?
It’s going to need it in another year or so.
So why spend money on it now if we’re going to replace it, we might as well do it all
on time.
And if we want to change what’s what color do we want to go get?
Do we wanna go with tile?
Do we wanna go with some other type of material?
Maybe we want to repair our deck.
Maybe we want to put a hot tub in our deck.
Maybe we want to add more and you know and footprint to our deck.
And so you could hang a bunch of things off of your repairs to, you know, other
things that might need to be repaired around the house.
Or should we move?
No, not going to move, but maybe we should renovate and should we renovate our
bathrooms?
Our our kitchen and if we say kitchen, are you going to do the floors?
Are you going to be the cabinets?
Are you going to do the lighting same thing in the bathroom?
We’re going to do the cabinets.
You’re going to do the floors again and maybe replace all of the, umm, bathroom
facilities in there.
You know, so again, you have many options that you can hang up.
Any one of these.
So that’s what I like about decision trees.
Is that you can take them to the enth degree and hopefully you will get to a point
where you are comfortable with one of these options and then that’s the one that
you would pursue.
Here is a decision tree for a project manager umm, for a project manager and this is
decision helper.
And so here we have an experienced project manager and they’ve been given a new
project and they have the option of doing this project or not managing the project
or not.
And so should I start this project?
Umm, no.
Don’t want to do it, so then you say no.
I don’t want to do this project.
Please don’t involve me or maybe I say yeah, maybe I do want to do this project and
my next question is, is it important for the company?
And if the answer to that question is no, it’s not important to the company.
Well, is it important to my career?
Will it help my career if I do this project?
And if the answer is no, then don’t do it.
Not important to the company, not important to your career.
And don’t do it.
Or if it isn’t important to the company, but it is something that will help your career,
then you have.
You may want to go ahead and do it if you say it is important for the company.
Yes, you want to do the project and you may ask, is it too risky to do?
And if it’s too risky, then you’re going to say umm, I don’t think I want to do this
project.
And if you don’t think it’s risky, then yeah.
OK.
Then I’ll do the projects.
That’s not risky, so I’ll I’ll start the project.
So this is a different type of decision tree.
And so you can see how it can be used in this instance.
I have another example.
This was really hard to read, but this one has to do with actions that a company can
take an action.
One is whether they make some kind of investment action tours and different kind of
investment.
And actually three is do nothing at all, and then each of the lines that you see tells
you how much incremental revenue that you will either gain or lose by implementing
this particular umm component of the decision tree.
And so all the way down and then it will, you know, help you guide your decision as
to what you feel you’d be comfortable with.
Another tool that can be used is the M analytical hierarchical process, and this
process compares multiple alternatives, each with several criteria to help select the
best option, and each criterion carries its own weight and the higher the weight.
More important criterion is and so it’s used in complex environments in which there’s
many variables to consider and you have to prioritize them and select them, and it
typically provides you with the best choice response.
It is a tool that compares the multiple choices, which I’ve already said, and each
choice has several criteria and you define the criteria, act to choose from, and then
it’s carried out in two phases, which includes the hierarchical design, and then the
evaluation itself.
And it was developed in the 70s and he said that you have to, Thomas say, to you
said that you have to define your ultimate goal and all of the possible solutions.
And then you also have to define your criteria that you’re going to use.
It’s compared to samples at a time in your sub criteria and so I have an example to
share with you.
This one is for the Jones family and the Jones family wants to get a new car and so
they have lots of decisions to make.
They have to decide between cost, safety, style or capacity.
Those are their four main criteria that they’ve identified.
So the way they would approach this is they would say is the cost of the vehicle more
important to us than the safety of the vehicle.
You know, and that will take you down a certain path if is the cost of the vehicle
more important than the style of the vehicle.
And if we say no styles more important and that’s going to take you down a different
path, if we say the cost of the vehicle is more important than the capacity of the
vehicle, then that takes us could take us.
You know, takes us down a different path.
So let’s say our family, we have the two adults and we have three children.
So we need five people right to fit into this vehicle that we’re getting.
Obviously safety is a big concern.
Style is somewhat of a concern and you know cross is obviously concern.
So then we would compare is safety more important than the style?
Your safety more important than the capacity, this setting more important than the
cost.
It’s style more important than the capacity or more important than the cost.
And so we would compare each one of them to one another to at a time, and you
will eventually come up with the criteria.
That’s the most important to you, and it could be anyone of any one of them.
For whatever reasons.
So if you look at the capacity, if we chose capacity as our #1 issue, then we’re gonna
look at, do we want cargo capacity or do we want passenger capacity?
Well, if we’ve got five people in our family, then we probably want passenger
capacity.
So then that’s going to take us to automobiles that can carry five people.
Yeah, there’s lots of options there, right.
If we send safety was our number one concern, then we’re going to take a look at the
cars that have been rated in the most safe and you know, there’s all kinds of
consumer magazines that will rank the cars based on different things.
And so that one of the rankings will be on safety.
So you would probably take a look at those cars that have a seating capacity of five.
The style of the car, if that’s the most important to you, then maybe you’re looking at
an SUV.
Maybe you’re looking at umm, I, umm.
A Sudan luxury sedan or regular sedan.
Umm.
Probably not.
A devil might be a little bit too difficult to get everybody in the back seat and car
seats and all that, so they probably rule that one out.
You probably rule out a roadster.
Umm, so you might like that, but it’s probably not the best car for your family.
And then if you decide cost is the most important factor, what cost is it the purchase
price that you have to outlay for the car?
Is it the fuel cost?
You know how much gasoline or or is this carbon, your tank, or how much electricity
is it going to use?
The maintenance cost is is a car that is known for high maintenance cost or resale
value.
The most cars don’t carry much resale value in since Copeland.
A lot of cars have carried resale value because when cars were so much more difficult
to get, so there’s still some good pricing on resales right now, but that probably will
phase out on time.
So you have to decide which one of those costs would be the most important to you,
and you would compare them to at a time as purchase price more important than
the fuel price is purchase price.
More important than the maintenance cost is purchase price.
More important than the resale cost?
And then as the fuel cost more important than maintenance or or resale or purchase
price and you’ll eventually come up with your for your number one criteria, which is
going to steer you towards a handful of cars to take a look at.
So this is an example of the analytical hierarchical design where you’re comparing 2
things at a time and you’re really taking lots of things into consideration based upon
your criteria that you find unified.
This is another example of.
This was a little bit harder to read.
It starts off with supplier selection and do you want your supplier?
Do you have product development capability?
Manufacturing capability?
Umm, all of the capability or costing time capability and so you would rank product
development against manufacturing product development against quality product
development against cost and time and the manufacturing against quality
manufacturing against costing time and so on and so forth.
And assuming that you said the product design was the most important, then there
are whole bunch of other options for you to consider.
And as you can see, this thing goes on and the whole intent is to give you the very
best option for you to to choose not requirement that you choose it, but it’s going to
hopefully get you to the best option based upon the criteria that you’ve described.
So that concludes Module 5 and now let me start you in on your deliverable 5.
So big data is a hot topic.
Today has been for a while now and it says that the majority of the world’s data has
been created in the last few years or an average of 2.5 Quinn trillion bytes of data
generated daily.
And that’s the future of business decision making.
So in deliverable 5, you are the Chief Information Officer for a large publicly traded
company and on the executive team you have the CEO of the company, you have the
Chief Financial Officer, you have the Chief Marketing Officer, the Chief Operations
Officer, the Chief Human Relations Officer and yourself, the Chief Information Officer
and the executive leadership team has asked you to prepare a presentation about
big data and how it will be useful to the company and to the team members, right.
So you are to research any publicly traded company and you’re going to create a
PowerPoint presentation now, sometimes you’re going to find information that’s
solid as a rock that you can use in deliverable 5.
Sometimes you may not be able to find all the information that you’re looking for.
Continue to use that company, but then you can make up information if you need to
do so, but try to find one that has most of this information in it so you’ll have a title
slide and then you’ll have a definition of being data’s impact on analytical decision
making, and then you’re going to type up a summary of how big data can impact
each of these departments.
So perhaps you would have a page slide for each one of these.
So let’s think about finance for a moment.
How could the finance, the Chief Financial officer of this company, use big data to
their advantage?
What kind of information?
Or do we have in our cloud computing about finance that would be beneficial for the
finance manager to have access to it, to know umm, well for the sample they could
very easily pull up umm prior financial statements to see how the company is
tracking in a particular category or product or something of that nature.
And they can get a couple years worth of data.
They could potentially pull information about their top 20% of their clients because
usually the top 20 is going to yield 80% of your business.
So you want to take a look at who are those top 20 customers who have they been
and who are they today and are you retaining them year to year?
Yeah.
And you know what is it that they acquire and maybe that feeds into our strategic
planning to help them grow their business by coming up with solutions for them.
Maybe it’s to track our revenue and profitability over the past 10 years to see, you
know, what proportion are we growing at and is that proportion going to continue or
is it going to excel the marketing department of Chief Marketing Officer, what could
they possibly get out of big data?
And so I think about that they can get all kinds of information regarding how much
they’ve spent on marketing, what type of advertisements yields them the best results.
So lots of good information from marketing the operations department, what type of
information does big data hold?
That’s the operations officer could retrieve and make use of the Information officer,
which is yourself.
What big data information is there that would help you make decisions for your
department and the HR department?
What?
What’s available to them, and definitely things like how many people did we?
Offer a job to over the past five years.
Accepted.
Yeah, that might be an interesting fact to know, because maybe there’s something
about your recruiting that needs to be changed or updated or what’s our retention
rate been for the last, you know, ten years or we have an uphill climb or or decline
and that sort of thing.
So you want to think about you need 4 examples in each one of these categories.
SO4 for finance, for, for marketing, for for the others as well, and I probably would
put these on individual slides.
Next, you’re going to choose one of those departments so you can choose any one
of these departments, and you’re going to then determine the the KPI’s for that
department.
So if you chose.
Human resources that maybe one of your KPI is you want your retention, you know
you want it to be as low as possible so that you know you’re keeping most of your
people on board and not losing them.
That might be a key performance indicator, perhaps?
How many people are accepting your jobs compared to how many you offer?
That might be A KPI.
And so think about some KPI and again you need at least three for each one of these
and then discuss the planning, the variables and the measurements that you would
put in place for that for that department.
In this instance, the Human resources Department, what measurements would you
put in place for them?
Discuss operations or implementation of the method.
So how would you go about putting these key performance indicators in place and
the measurements in place about that?
And then what kind of data visualization could you use?
So maybe you could you know if you can’t find anything on a particular company
that will help you, then maybe you think this one up and you say OK, I can say that
umm I can do a bar chart that shows how well I did against each of my KPIs.
So maybe it’s a bar chart that shows the objective and then how we came in the
second objective and how we did third objective and how we did.
And of course you can make those numbers up if you’d like, so that would be umm
what you could do for that one.
Next, you’re going to do a decision tree for implementing or not implementing big
data.
So this needs to be a realistic decision tree, doesn’t have to be very long, but it has to
be realistic and at the end of the decision tree you want to come to the decision.
Yes, we should implement big data for our company.
OK, that’s the whole premise of deliverable 5.
Should we be implementing big data and that’s where we’re finding the benefits to
all these departments and how we might go about it.
And then we have to make a decision or we going to implement it or not.
So a word to the wise, make your answer come out to be.
Yes, we’re going to implement big data.
OK, but you need to decision tree that you is going to get you to that decision.
Next, you’re going to have your final recommendation, so this is going to say, I
recommend that we’re going to implement big data in the following DEPARTMENTS.
I would list those departments and maybe we want to make a recommendation that
it be expanded to some of the other departments in the organization as well.
And you could certainly do that.
Or maybe you put that expansion in your conclusion that you think this is, you know,
a great idea.
Best thing since sliced bread and you want to try to?
That was the pilot it in these first couple of departments.
And then like to see it expand throughout the rest of the company in the year ahead
or something like that, you need the three peer reviewed references for this
particular paper.
So make sure you do your in text citations and and put them on the reference page
as well.
So I don’t.
I don’t think you’ll have too much trouble with this one, but it has a lot of peace parts
to it, so feel free to reach out if you have questions or if you don’t perform well on it.
You need some assistance.
Let me know that I am receiving a lot of requests from students to review their
papers and I’m teaching 5 courses, so I take the request in on a first in first out basis
and so I am also getting requests for people who got sees on their paper.
We want to try to improve upon a sea grade and I’m happy to do that, but I’m
putting them at the bottom of the list because I want to get through.
I want everybody to pass, you know?
So that’s my priority and once I get through those papers and I’ll focus on some of
these others to help people improve their scores, not that I don’t want to do it. I do.
I just want to try to get help.
Everybody pass the course first, so anyway, next week we’re gonna focus on
deliverable 6 and there’s a real.
I I I think you’ll find it an interesting discussion.
So I hope to see you next week sessions.
So thank you very much.
And this weekend, we have the Kentucky Derby and we have Cinco de Mayo, so we
have lots of activities going on this weekend and I hope that you have fun and that
you stay safe.
So thank you.
Have a great weekend.
Bye bye.
Jan Hoffman stopped transcription
April 29th, ISM6200 Module 5, Objective 1 and 2
Recording
April 29, 2024, 12:01AM
30m 15s
Jan Hoffman 0:09
Good evening.
Welcome to business intelligence and analytics.
Tonight I’m going to be talking about module 5 and the 1st 2 objectives and so on.
Come Thursday evening, same time I’m going to be doing module 5, the third
objective and lookable 5, so I thought I’d kind of split this one up because there’s a
lot of information to digest.
Alright.
Umm, some of you have come forward with questions on deliverables and by all
means keep doing that.
I’m happy to try to help you.
I am teaching 5 courses and so sometimes the 48 hour turn around is a little bit
challenging based upon other courses that I teach as well, but I’m happy to try to
help you in a reasonable time frame and I will do just that.
Sure.
OK.
Competency for module 5 is to analyze big data for business decision making.
What is big data?
Big data is the fact that we can umm, store voluminous amounts of data, whereas
before many years ago, when all we had were people copies of things, we were in
out of storage space, now we have cloud computing and that gives us the ability to
store a great deal of information and retrieve it and are very, you know, easy and
quick at time frame.
So our objectives tonight are defined big data and it’s importance on analytics and
then evaluate big data platform services and technology.
So these are the first two objectives.
We have another Objective with that will cover Thursday night.
So big data is a term that describes very large amounts of hard to manage data and
the data can be structured or unstructured and no define those in a moment in an
effort to make effective business decisions, you have to do data mining on your big
data and analyze it, which is what you did in deliverable 4 you had.
You you were asked to data mine and assuming that you had all this data that you
could data mine from which you did for the gas grills, trying to determine, you know,
what types of information could you review and analyze that might give you some
insights into some predictive analysis that you can make that would help your
business?
And so that’s what big data and data mining you know are we use the two terms
together typically because the data is the actual, you know documentation that we’ve
got stored and it could be files that could be pictures, it could be videos, it could be
anything.
But anyway, so it’s all of that information and to be able to analyze that we have to
think of mine, we can decide what it is that we want that we want to explore.
What to look at when examine closely to see if it will help us make some predictive
analysis.
Some of the benefits of big data we can get better perspective of our customers, we
can get more, learn more information about them, we can improve our operations,
we can develop some market Intelligence about how our customers buy what they
buy, when they buy from us, we can get data driven innovations because we can
come up with ideas based upon the the data mining that we’ve done that will give us
some predictions on people’s behavior and buying behaviors and that hopefully will
allow us to come up with some innovative, maybe disruptive innovations or
incremental innovations to be.
Able to generate more business for our company.
I mentioned this that in years past we used to store anything in file cabinets and you
know one cabinet wasn’t enough.
You had to get 2, then you’d get three and you’ll get 10 and you know it just
required way too much.
We also stored data on UMM in data centers with real real tapes that were, you
know, this big round and heavy and you had to load them onto a system whenever
you wanted to record information on if you wanted to retrieve information, you had
to find the right tape, load it, find the right location on the tape which you know to
go out to be able to do that.
And then you would be able to work with that program and then when you’re done
and you’ve got to take it off the tape drive and put it back in the rack and you know,
put up another tape drive.
If you do something else and these systems require raised floors because we had air
condition, the rooms have to be freezing almost and so we had to put in cables
underneath the flooring to keep these things cool.
And it was just, you know, expensive, time-consuming.
Not very productive or efficient, but it’s what we had at the time.
We evolved to just management, which clearly required a smaller footprint and
require all of the air conditioning, which meant the cables and the rice flour wasn’t
necessary.
So it was a big improvement, but we really started to see an improvement where we
saw when cloud computing came into being.
And as I mentioned, you can have a public cloud with which you share with other
organizations in that space that you acquire on that cloud.
It can be this much or this much, or that much you know, and supposedly it can
expand as you need for it to expand.
You can also buy a private cloud where you’re the only company that’s on that cloud.
Now the cloud computing the companies that sell cloud computing.
Amazon, Microsoft, IBM, you know host of others.
Umm, they will if you buy a public invite into a public cloud, obviously they’re going
to partition it such that you can’t get into other companies data.
Your privacy is protected and so forth, so there’s nothing wrong with the public
cloud.
It’s just not going to be as expensive as private cloud.
It’s just like with our.
Yeah, umm, if you have a smartphone and you know I’ve so often I’ll get a message
that I need, you know, I need to.
I need more storage and charge me for more storage.
Or are you going to do?
You’re going to start deleting everything, or are you gonna?
You know, you don’t have time at that moment to do that.
So you just buy more storage space and that’s the way it is with cloud computing
too.
Today, companies can accumulate large, large amounts of data.
Voluminous amounts of data, and like I said, the data can be structured more
unstructured and that you have to come through it trying to identify information
that’s going to help you make good sound business decisions.
You stored a boatload of information.
Not all of it’s going to be helpful to you.
So you have to ascertain what is it that you want to data mine?
What fields do you want to data mine to be able to really closely examine it to see if
you can make some predictive analysis out of it.
So in Deliverable 4 I had told you that you were the manager of a Home Depot store,
and if you think about Home Depot with every transaction they make, they’ve they’ve
captured what you purchased, how you paid for it when you purchased it.
So they know seasonality, they know day the week, they know the week, they know,
you know whether you used to Home Depot credit card or another type of credit
card.
If you paid cash and so they know what the weather was like on that particular day.
I mean so that there’s lots of information that they can mine to try to ascertain what
can they do to drive their sales.
We looked at the price of gasoline and we said, you know, if the price of gas gets
way too expensive, people aren’t gonna want to drive to the store.
So what does that tell you?
That tells you that maybe you need to look at having an online presence, and maybe
you needed an livery service.
You know, so these people don’t have to use their gas to come in or are there
electric vehicle to come in?
They don’t need to use their charge to come in anyway, those are just some
comments on what you just went through and there are some big data
characteristics and we’re going to talk about three of them.
One is volume and it represents the amount of data collected.
Makes sense.
Volume represents an amount.
It’s sometimes called a data set and it can include petabytes of information.
That’s a lot.
I saw has bridy and bridy means to process structure and structure data processing
both structured data.
As the name implies, is highly organized and makes it much easier to search for
information, and it would be, you know, you could use straightforward search on
algorithms to find it.
Velocity is the speed at which the data arrives in the data center.
I think it would draw water versus a massive flow of water that’s going to flood your
house in the street and the neighbors homes, etcetera.
So a big data solution would be to slow the flow of information down as well as
having the ability to manage high levels of data flow.
So we have a volume and we have velocity and we have variety.
Those are the three big data characteristics.
So organizations can help position to improve their decision making by analyzing the
volume of the big data, the velocity of their big data, and the variety of their big
data.
You don’t have any participants thus far tonight, so I don’t have any questions, so I’ll
move on right here is the picture of structured and unstructured data.
If you look at the left side of your screen structured data and it says it’s structured
data can be displayed columns, rows, relational databases.
So you can think of, you know, Excel files can be, you know, databases and so forth,
numbers, dates, strings of information.
According to Gartner Consulting Group, 20% of enterprise data is structured, so 20%
is structured.
Structured data requires less storage because you can put it one right after another
and it’s easier to manage as a result of that and protect it.
Unstructured data is on your right side of your screen and it says it can’t be displayed
in rows, columns or databases.
It includes images, videos, audio where processing files, emails, spreadsheets and so.
It’s very unstructured and very hard to work with.
Gartner Group says 80% of enterprises have unstructured data.
It requires a lot more storage and it’s much more difficult to manage and protect.
Now there are efforts underway.
I have to think of an article I read where I heard that they were really they.
What did they ask?
Working on managing unstructured data.
Somehow being able to manage it better so that it makes it easier to find it, retrieve
it, work with it.
I don’t think he quite there yet, but that’s something I heard in.
I know what it was.
It was a gentleman from let me see.
I’m sorry, I I think he was from.
I think it’s from University of Colorado.
He was a speaker I heard recently.
He was talking about artificial intelligence and machine language, and he did
mention that they were trying to get their hands, arms around unstructured data and
make it, you know, something a little bit easier to work with.
I can find.
Well, he didn’t have any handouts for if I can find a note that I took, pass it along to
you.
Right.
Structured data.
Umm, so you know, I lived in Washington DC for 14 years.
So we had these beautiful cherry blossom trees.
And so when I found this template, it reminded me of cherry blossom time and
Washington.
So that’s why I’m using this template because it’s April cherry blossom time, so
there’s a method to my madness here.
So when you’re working with structured data, you’re gonna find that data is
contained in fields and it could be, you know, a first name and last name.
Your street address, your city, your state, your zip code.
So the data is typically pretty easy to data mine and it’s pretty easy.
You know to retrieve and and to work with unstructured data.
Not so organized and not so easy to search or work with.
Umm, so the unstructured data has two approaches, a supervised approach and an
unsupervised approach.
Supervised means that there’s a set of rules that are used to draw inferences from
the input data.
So for example, if I wanted to rent an apartment, I would go and fill out an
application at the apartment complex.
They then would take my application along with my application fee and they send it
or fax it or whatever to an agency who does these credit searches for missing.
It’s not seconds, but a lot of companies and this apartment complex gave them some
parameters, and so the this company is using AI.
They’re going through my information and they’re they’re trying to determine.
Did I hit the mark?
Hit the mark, hit the mark, hit the mark.
Miss the mark reject automatic reject.
OK, so for example, they’re going to look at.
They’re going to look at my driver’s license numbers or anything.
Do I have any outstanding warrants or anything like that?
Nope.
OK.
Go ahead.
Umm.
If finances do I have the?
Do I have the appropriate amount of income to be able to rent this apartment?
Yes.
OK, great.
Continue on do I have a references?
Yes.
Continue on Umm David.
Criminal record?
No.
OK, that’s good.
Continue on and so on.
So it keeps on going and it will ask have I ever been?
Because have ever been evicted from any place before.
Yes, but rejected.
And so that’s part of the criteria that they put in there.
Now, not all apartment complexes would put that criteria in there, but if they did it
once, they hit something that didn’t meet the criteria, it would kick out my
application.
So the next day I go back to the apartment complex and they told me my I’ve been
rejected and they don’t know why.
They don’t know why, and you can, you know, holler and yell and and beg them to
tell you.
But they don’t know why.
And if you call the agency that doesn’t, they don’t know why either it’s that’s their
system.
That’s their algorithm that they use and it either passes you or rejection one or the
other.
What?
Umm.
OK, so in a supervised data modeling model, the attributes and the target are
selected.
In the unsupervised machine learning can develop algorithms to analyze your data
sets and from it they can uncover hidden patterns.
So machine learning is where systems can learn from themselves.
They pretty much they can teach themselves and they learn from other systems.
And algorithms are generated and they you know if it.
If it kicks out a pattern, then the algorithm is going to continue and continue and
continue, and so the machine learning premise is that it it uses these patterns that it
finds to be able to help us be able to make predictive type analysis from it.
So that’s what the unstructured data looks like.
So, as you might suspect, it is a bit more challenging to work with.
Machine language is a bit more challenging to work with than, say, linear regression
and residual plots and so forth.
Evaluating big data.
There are.
Couple of approaches.
One of them is theory driven and the others data driven, and so the theory driven is
used.
Guide the conception of the data being collected and then the analytical techniques
are used to develop reliable information based on predictive models.
So when we’re guiding the conception of the data being collected and then we’re
trying to analyze it to be be able to make our predictive models, umm, the effective
data mining is what you did in Deliverable 4 is where you identified the different
ways to conduct data mining and then the type of information you would want fields
that you may want to look at if you wanted to make a data.
Different decisions or predictive analysis on your big data of grill sold at Home
Depot.
A big data benchmarks are developed to evaluate and compare the performance of
systems and architectures.
When we think of benchmarks, sometimes we’re looking at how do we compare to
someone else?
How does this program compare to another program?
How does this product compared to another product?
And so Benchmarking is used in the lots of different ways.
And so in this instance, big data benchmarks are developed to evaluate and compare
the performance of systems and architectures.
And organizations will measure their performance with benchmarking because it
uses the velocity, the volume, and the variety and so additional assessments can be
qualitative or quantitative.
And so, for example, if I wanted to know how does my company, how does my
company S throughput measure up to my top three competitors and so my
throughput might be how many words do I produce in an hour and I want that
compared I want it benchmarked against my top three competitors.
And so benchmarking can help me do that using velocity, volume and variety.
We also can get viewpoints from our stakeholders via surveys or interviews, and so
they can tell us, you know what their thoughts are on our products or offerings or
our pricing or whatever it is that we want to know.
And once that data is collected, then we can use a set of principles to try to analyze.
You know the results that they’ve given to us.
Did they like our product?
Did they like this functionality?
What?
What fields would they like to see on our product, or what enhancements would they
like to see on it?
What is one thing that they would change about our product?
I mean, those are all kinds of things that you can use when you’re doing surveys or
interviewing to be able to conduct benchmarks on your product compared to others.
The principles of Benchmarking include the operational framework, which is big data
relationships and dependencies, fluid information, the data models that flow of the
data, how it’s stored, how it’s handled and platform functionality, the architectural
interfaces and interactions of each of our systems need to be evaluated.
After the key principles have been addressed, then you have to create a
methodology to further evaluate and validate your big data platforms and so that
includes the scope of what you’re trying to do.
Here’s where you’re going to define your key performance indicators.
What is it that’s going to tell you whether it’s successful or not?
What are your values and what are your objectives?
And so that’s the scope of your benchmark, and you’re going to plug in where you’re
going to define your variables, benchmarks and the measurements that you want to
put in place your operations when you actually prepare your input data.
And then you’re going to run your experiment, and then you’re going to analyze
your results and and try to interpret the, interpret them, and then hopefully visualize
them where you, you know, created chart or graph, something that makes it easy to
observe.
Ohh.
Observe and comprehensive.
Comprehensive. Excuse me.
Don’t talk to me.
I select concludes Module 5, deliverables one and two, and.
Again, I don’t have any purchase my so I didn’t get any questions whatsoever.
And if you watch this when you watch this, if you have questions, let me know when
Thursday night, I will complete this session.
You going over Objective 3 and deliverable 3.
So for right now, I’m going to stay on the line for a couple more minutes to see if
anybody calls in and and so I’ll address any questions they have.
And also if you have any topics that you would like for me to explore related to
business intelligence and analytics, please share them with me and I’ll be happy to
research them and share them with you.
So just let me know.
Well, I didn’t get anymore.
Participants.
So I’m going to say goodnight to thank you very much.
Jan Hoffman stopped transcription
ISM6200CBE Section 01CBE Business Intelligence and Analytics (11 Weeks) – CBE Online
Course – 2024 Spring Quarter
Deliverable 5 – Big Data Presentation
Deliverable 5 – Big Data Presentation
Assignment Content
1.
Competency
Analyze big data for business decision-making.
Student Success Criteria
View the grading rubric for this deliverable by selecting the “This item is graded
with a rubric” link, which is located in the Details & Information pane.
Scenario
Big data is the hot topic of the company you work for. With the majority of the
world’s data being created in the last few years or an average of 2.5 quintillion bytes
of data generated daily, it is the future of business decision-making. You are the
Chief Information Officer for a large publicly-traded company and part of the
executive leadership team. The executive leadership team consists of the Chief
Executive Officer (CEO), Chief Finance Officer (CFO), Chief Marketing Officer (CMO),
Chief Operations Officer (COO), Chief Information Officer (CIO), and Chief Human
Resources Officer (CHRO). The Executive leadership team has asked you to prepare
a presentation about big data and how it will be useful to the company and to each
of the team members in their individual roles.
Instructions
Research any publicly traded company and create a PowerPoint presentation.
Remember that each department officer wants to know how the decision to use big
data will help him or her specifically. In your presentation, include:
1. Title slide. Make sure to include the name of the publicly-traded
company you are writing about.
2. Definition of big data’s impact on analytical decision-making
3. Summary of how big data could impact each department
4. Finance
5. Marketing
6. Operations
7. Information
8. Human Resources
9. Analysis of one of the departments using the big data evaluation
10. Determine the scope (KPIs).
11. Discuss the planning (variables and measurements).
12. Discuss operations or implementation of the method.
13. What data visualization method could be used for results?
14. A decision tree for implementing or not implementing big data for the
company
15. Final recommendation
16. Conclusion
17. Provide attribution for credible sources needed in completing your report
Resources
18. Rasmussen College Writing
Guide: https://guides.rasmussen.edu/writing/professional
19. Grammar Checking – How do I create a Grammarly
account? https://rasmussen.libanswers.com/faq/32707
20. Discovery: https://guides.rasmussen.edu/discovery
21. ProQuest (PQ Central): https://guides.rasmussen.edu/pqcentral
22. Companies & Industries page within the School of Business
Guide: https://guides.rasmussen.edu/business/companyindustryinfo
23. PowerPoint tab of the Business Writing
Guide: https://guides.rasmussen.edu/business/businesswriting
Submission
Details & Information
•
•
•
Assessment due date6/14/24, 11:59 PM (CDT)
o You can’t submit work after the due date.
o You can’t make a new submission attempt after due date.
Grading rubricThis item is graded with a rubric
Attempts3 attempts left
Grading
Maximum points
4 points
DescriptionI strongly encourage you to participate in the live session or watch the archive of the
live session before you begin Deliverable 5. I add tips and suggestions on how to approach the
deliverable. At times, I may add specific instructions. If you have any questions on the
deliverable, please don’t hesitate to contact me. Dr. Jan Hoffman
Deliverable 5 – Big Data Presentation
Rubric Details
Maximum Score
4 points
•
Grade for Deliverable 5
100% of total grade
A – 4 – Mastery
4
B – 3 – Proficiency
3
C – 2 – Competence
2
F – 1 – No Pass
1
I – 0 – Not Submitted
0
•
Criterion 1
0% of total grade
A – 4 – Mastery
Included an appropriate title and a strong conclusion.
0
B – 3 – Proficiency
Included an appropriate title and a mostly clear conclusion.
0
C – 2 – Competence
Included a somewhat relevant title and a somewhat clear conclusion.
0
F – 1 – No Pass
Title is unclear or missing, or conclusion is limited or missing.
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 2
0% of total grade
A – 4 – Mastery
Clearly and strongly defined big data’s impact on analytical decision-making using clear
examples in a well-designed presentation.
0
B – 3 – Proficiency
Defined big data’s impact on analytical decision-making using some examples.
0
C – 2 – Competence
Defined big data’s impact on analytical decision-making; no examples provided.
0
F – 1 – No Pass
Unclear verbiage used to define big data’s impact on analytical decision-making.
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 3
0% of total grade
A – 4 – Mastery
Clearly and strongly summarized how big data could impact each department (Finance,
Marketing, Operations, Information, Human Resources) in a well-designed presentation.
0
B – 3 – Proficiency
Adequately summarized how big data could impact each department (Finance, Marketing,
Operations, Information, Human Resources). Missing no more than one department.
0
C – 2 – Competence
Summarized how big data could impact each department (Finance, Marketing, Operations,
Information, Human Resources), but some summaries need development. Missing no more than
one department.
0
F – 1 – No Pass
Unclear summary of how big data could impact each department or missing a summary for
more than one department.
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 4
0% of total grade
A – 4 – Mastery
Clear and strong analysis of one of the departments using the big data evaluation (KPIs,
variables and measurements, operations, and visualization method), using examples in a welldesigned presentation.
0
B – 3 – Proficiency
Sufficient analysis of one of the departments using the big data evaluation (KPIs, variables and
measurements, operations, and visualization method); some examples provided.
0
C – 2 – Competence
Reasonable analysis of one of the departments using the big data evaluation (KPIs, variables and
measurements, operations, and visualization method); no examples provided.
0
F – 1 – No Pass
Unclear analysis of one of the departments using the big data evaluation (KPIs, variables and
measurements, operations, and visualization method).
0
I – 0 – Not Submitted
0
•
Criterion 5
0% of total grade
A – 4 – Mastery
Completed a clear and robust decision tree for the final recommendation for implementation of
Big data, in a well-designed presentation.
0
B – 3 – Proficiency
Completed an effective decision tree for the final recommendation for implementation of Big
data.
0
C – 2 – Competence
Completed an acceptable decision tree for the final recommendation for implementation of Big
data.
0
F – 1 – No Pass
Created a rudimentary decision tree for big data implementation recommendation.
0
I – 0 – Not Submitted
0
•
Criterion 6
0% of total grade
A – 4 – Mastery
Thorough and detailed final recommendation. Used and relied on all credible sources in a wellcrafted presentation.
0
B – 3 – Proficiency
Strong final recommendation. Used and relied on mostly credible sources in the presentation.
0
C – 2 – Competence
Reasonable final recommendation. Used and identified some credible sources in the
presentation.
0
F – 1 – No Pass
Recommendation is missing or not based on data. Failed to use or identify credible sources in
the presentation.
0
I – 0 – Not Submitted
0
eliverable 5 – Big Data Presentation
Rubric Details
Maximum Score
4 points
•
Grade for Deliverable 5
100% of total grade
A – 4 – Mastery
4
B – 3 – Proficiency
3
C – 2 – Competence
2
F – 1 – No Pass
1
I – 0 – Not Submitted
0
•
Criterion 1
0% of total grade
A – 4 – Mastery
Included an appropriate title and a strong conclusion.
0
B – 3 – Proficiency
Included an appropriate title and a mostly clear conclusion.
0
C – 2 – Competence
Included a somewhat relevant title and a somewhat clear conclusion.
0
F – 1 – No Pass
Title is unclear or missing, or conclusion is limited or missing.
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 2
0% of total grade
A – 4 – Mastery
Clearly and strongly defined big data’s impact on analytical decision-making using clear
examples in a well-designed presentation.
0
B – 3 – Proficiency
Defined big data’s impact on analytical decision-making using some examples.
0
C – 2 – Competence
Defined big data’s impact on analytical decision-making; no examples provided.
0
F – 1 – No Pass
Unclear verbiage used to define big data’s impact on analytical decision-making.
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 3
0% of total grade
A – 4 – Mastery
Clearly and strongly summarized how big data could impact each department (Finance,
Marketing, Operations, Information, Human Resources) in a well-designed presentation.
0
B – 3 – Proficiency
Adequately summarized how big data could impact each department (Finance, Marketing,
Operations, Information, Human Resources). Missing no more than one department.
0
C – 2 – Competence
Summarized how big data could impact each department (Finance, Marketing, Operations,
Information, Human Resources), but some summaries need development. Missing no more than
one department.
0
F – 1 – No Pass
Unclear summary of how big data could impact each department or missing a summary for
more than one department.
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 4
0% of total grade
A – 4 – Mastery
Clear and strong analysis of one of the departments using the big data evaluation (KPIs,
variables and measurements, operations, and visualization method), using examples in a welldesigned presentation.
0
B – 3 – Proficiency
Sufficient analysis of one of the departments using the big data evaluation (KPIs, variables and
measurements, operations, and visualization method); some examples provided.
0
C – 2 – Competence
Reasonable analysis of one of the departments using the big data evaluation (KPIs, variables and
measurements, operations, and visualization method); no examples provided.
0
F – 1 – No Pass
Unclear analysis of one of the departments using the big data evaluation (KPIs, variables and
measurements, operations, and visualization method).
0
I – 0 – Not Submitted
0
•
Criterion 5
0% of total grade
A – 4 – Mastery
Completed a clear and robust decision tree for the final recommendation for implementation of
Big data, in a well-designed presentation.
0
B – 3 – Proficiency
Completed an effective decision tree for the final recommendation for implementation of Big
data.
0
C – 2 – Competence
Completed an acceptable decision tree for the final recommendation for implementation of Big
data.
0
F – 1 – No Pass
Created a rudimentary decision tree for big data implementation recommendation.
0
I – 0 – Not Submitted
0
•
Criterion 6
0% of total grade
A – 4 – Mastery
Thorough and detailed final recommendation. Used and relied on all credible sources in a wellcrafted presentation.
0
B – 3 – Proficiency
Strong final recommendation. Used and relied on mostly credible sources in the presentation.
0
C – 2 – Competence
Reasonable final recommendation. Used and identified some credible sources in the
presentation.
0
F – 1 – No Pass
Recommendation is missing or not based on data. Failed to use or identify credible sources in
the presentation.
0
I – 0 – Not Submitted
0