visualization vs. analytics - jpk group · 11/28/2019 · visualization tools like domo, power bi,...

Finance Analytics Institute Page | 1

NOVEMBER 2019

Finance Analytics Institute

Authors: Robert J Zwerling, P.E. & Jesper H Sorensen

www.fainstitute.com

Visualization

vs. Analytics

what each tool is, how they are

different & where they apply


Overview

The difference between what we do and what we are capable of doing would suffice to

solve most of the world's problems – Mahatma Gandhi1

Data visualization and analytics tools are often confused by Finance, IT, and the business

in that each sees these providing analytics. However, this is not the case. Data

visualization does, well, visualization of data, and analytics does analytics calculations

on data. The former displays the past and the latter predicts the future.

The difference between these tools is analogous to Microsoft Word vs. Excel. Word is for word-

processing and Excel for calculations, yet how often do we do word processing in Excel . . . too often!

This confusion runs with visualization and analytics tools, and it doesn’t help when visualization

vendors extend their claims either.

Data visualization is about charting and dash-

boarding, a most valuable capability to bring

information to what has happened; i.e. hindsight

information. Analytics uses mathematics, statistics

and Artificial Intelligence (AI) to reveal unbiased

insight about the current environment and

predictions about what has a probability to happen.

As such, visualization looks at the past, and analytics

yields insights of the current and foresight about the

future. These tools are valuable individually and

symbiotically together. It’s not one or the other, or

one before the other. Each has a value to bring in

the business landscape.

This research paper defines the next generation Finance technology toolbox and how it is applied. It

provides guidance on the fit of Data Visualization and Analytics tools in the stair-steps of the Evolution

of Business Partnering, moving from a Finance Business Partner to an Analytics Business Partner.

We’ll clarify the differences between arithmetic vs. mathematics, dashboards vs. predictions, and

charting vs. AI. The value of having both tool sets is to answer the five key questions to making data

driven decisions for business growth and optimization of: What Happened, Where did it Happen, Why

did it Happen, What Will Happen, and How To make it Happen?

By knowing the difference

between data visualization

and analytics tools, we’ll

better be able to solve issues

and achieve accelerated

growth through insights and

foresight


Background

The role of Finance has evolved over the past 30 years with labels, such as, the Modern CFO, Next Generation

Finance, and Agile Finance. With new titles come new requirements for Finance in the 2020s and beyond.

Finance need adopt new people skillsets and software tools to master the new requirements.

In the 21st Century, Finance wants to be a partner who adds value to the business, and not just a scorekeeper

reporting center. But how much value does Finance really add today?

Most Finance organizations still operate as a supporting partner, using a strong financial backbone combined

with business acumen and collaboration. Fundamental, but does this earn Finance a seat-at-the-table? Not

really, as Finance uses little more than financial data, and fewer use advanced analytics to generate insight and

foresight to impact the strategic direction.

This must change, with the introduction of the 3rd Generation Partner . . . the Analytics Business Partner (ABP).

This concept is depicted in the Evolution of Business Partnering1 chart below, which defines the journey to the

ABP through the incorporation of new technologies and broader skillsets.

The Mindset is the role toward decision making. To support decisions Excel was the tool of the 90s, then BI in

the late 90s to early 2000s to collaborate, followed by Visualization tools to challenge people’s “feelings”

towards decisions. Today, we have the rise of modern enterprise analytics tools applying advanced mathematics

and AI for true quantitative unbiased insights that influence and impact strategic decisions.

Of the five key questions to making data driven decisions for business growth and optimization of What

Happened, Where it Happened, Why it Happened, What Will Happen, and How To Make It Happen, data

visualization answers the first three and analytics the last three. Both tools overlap the Why it Happened.

Excel BI Visualization AI & Analytics


Most Finance (and Operations) groups have not yet crawled from the primordial cloud of the 1st Generation

Partner. Our research2 shows about two thirds of Finance is largely consumed in Excel and BI, with about 22%

involved with visualization tools and moving to the 2nd Generation Partner. Less than 4% are using analytics,

though over 60% are considering or planning analytics projects over the next few years.

Further, look at the image to the right that asks

the approach to “analytics”. Spreadsheets and

dashboards are included, and as we shall see

later, these tools are a misnomer to the

definition of analytics.

The confusion throughout business of the term

“analytics” incorrectly frames where companies

are in their analytics journey and leads to

misdirection of where they need to go.

Case and point to this misdirection is found by

the Finance Analytics Institute Benchmark

Survey3 that assesses the Finance toolbox, the

Analytics Intelligence provided by Finance, and

its Partnering capabilities. Of interest, is that

many surveyed say they are already doing

analytics yet have nothing more in their toolbox

than Excel. What is merely variance analysis or

data visualization is often confused as being

analytics.

With this background, the pages that follow will assess and clarify the difference in tools and applications

between data visualization and analytics.


The Workings of Visualization Tools

Visualization tools like Domo, Power BI, Qlik, and Tableau to name a very few, are extensions of Business

Intelligence (BI) reporting. These tools use historical data to visualize trends in a multi-dimensional manner.

Dashboards are of prime value to combine visual charts with tabular data of KPIs and key values for comparisons.

The image to the right is of Microsoft Power BI1, where data

and images of trends can work together to offer a view to

the past and present. Like a car’s dashboard, the numerical

readings at the top tell key performance data needed to be

known; e.g. if we’re running low on gas. A properly

designed dashboard will have the charts coordinated with

the top data so that information can be gained of how the

key performance data came to be.

But dashboards are not predictive, and views of past data can lead to false negatives or positives of the future.

Look at the image on the left below from the visualization tool Datahero2. The historical trend is essentially up.

So, what’s the next bar to follow? Up? Down? What decision would you make if you predicted up? What would

happen if you guessed wrong? As seen on the chart to the right below, the next bar was substantially down.

Interestingly, as we shall learn in the next chapter, an analytics tool would have predicted this downturn through

the Statistical Process Control Index formula.

Note, that dashboards can and often do contain forecasts, but these are most often data loaded from other

sources and not analytically calculated; e.g. the sales forecast downloaded from the Salesforce.com system is

the salesman’s biased view of what will close and when. Like the prediction of the next bar on the trend chart

above, an unbiased statistical formula will often result in a better forecast compared to a biased forecast. With

no quota to meet or boss to please, mathematics calmly yields its quantitative analysis.

The good news about dashboards is the ability to bring numerous fields of data together

across several dimensions to gain an understanding about an area of performance.


The image below is a Sales & Marketing dashboard from Grow3. It graphically depicts many fields of data in

different forms; e.g. the large bar graph in the upper left corner is Revenue by Product, and the geographic chart

in the middle shows a “heat” map of US Site Visits. Gauges, charts, and tables disbursed through the screen add

data points about sales and marketing activities to date.

A highlight from this dashboard is that while revenue has

rapidly accelerated (Revenue by Product) the upper bar

chart Lead & Funnel Activity (with the purple bars) is rapidly

declining. Therefore, revenue growth can’t be sustained if

leads are falling.

This is the highest use of dashboards; i.e. to gain from

history what cannot be seen with Excel and BI reports. But

putting dashboards together takes technical and business

knowledge.

Too often dashboards are the purview of IT or consultants to design and program, where their focus is on “pretty”

rather than for “effective” decision making. The image below is a dashboard from Domo4 regarding Supply Chain.

It has many metrics but . . . What decisions can be made? Is inventory optimized? Are the channels aligned?

Are there any breakdowns in the supply chain? From this dashboard we simply cannot answer these questions.

So, while the dashboard is pretty it is not materially effective to support performance optimization and growth.

Visualization vendors try to make

their products more productive

to use. However, a large usability

gap exists for the business user.

For example, The Finance

Analytics Institute holds the

Analytics Academy semi-

annually, and over two days

provides attendees the “How To”

implement an analytics culture

for data driven decisions. Two of

the 19 sessions are dedicated to

visualization and storytelling with

visualization using Power BI. But,

as attendees have learned,

visualization even with Power BI

that they thought would be

intuitive, has a large learning

curve.


Applications of Visualization - Dashboards

As mentioned in the Overview, there are five questions to answer for data driven decisions of: What Happened,

Where it Happened, Why it Happened, What Will Happen, and How To Make It Happen? Visualization tools

can be used to answer the first two questions of What and Where, but only partially the third of Why.

Dashboards have good application in operational analysis (not analytics) and executive presentations. As seen

in the Sales & Marketing dashboard above, there is information that can be gleaned from the collection of charts,

especially, when trends can be visualized. Dashboards also have appeal to executives who have limited time

and attention. By crystallizing KPIs with a distillation of compelling charts, executives can quickly consume the

pulse of the business.

Another high value use of dashboards is in storytelling, as a key component of a good and compelling story is

visualization. The central tenants of a good story are clarity and brevity. The saying “a picture is worth a

thousand words” is so true, and graphics can drive a compelling epiphany to decision makers and reduce a ream

of reports to several figures that make the case.

This is often why visualization is used in conjunction with analytics, as the latter can be complex and hard to

comprehend. The story that can be woven with graphics can be used to demystify the intensity of analytics that

revealed the insights but could not be digested by executives.

Where Visualization Tools Fail

Visualization is not analytics, so for the Why, What Will, and How To, dashboards can fall short or are just not

capable. Note that visualization tools can answer the Why, so long as the Why doesn’t require analytics. As

more companies rely on making decisions with analytics as seen on the figure below from D&B/Forbes5, the

distinction between data visualization and analytics has growing importance. Below are two examples of where

the Why alludes visualization when analytics are needed:

• Marketing Intelligence: An auto manufacturer needed

to learn which incentives were driving retail sales, as

billions of dollars are spent on incentives; e.g. dealer

cash incentive, customer cash, 0% interest rates, etc.

This could not be answered by visualization tools as it

requires multi-dimensional correlative statistics over a

variable time horizon. Dashboards simply cannot

answer Why some incentives work and others not. The

solution was rendered by analytics that identified

which incentives and where dimensionally and when in

time these incentives are and are not effective. For

example, Why, does dealer cash incentives have

limited effectiveness. Analytics revealed that dealers

where pocketing the dealer incentives instead of

passing this along to customers in the first six months

of the year.


But, in the second half of the year, dealers use this incentive to incent customers to buy current year

model inventory to make room for new year model inventory. These insights enabled the productive

deployment of capital to generate retail sales.

• Sales Management: A Fortune 500 technology company needed to predict which individual sales

prospects would close in a quarter. The current CRM system merely recorded the salesmen’s biased

view as to what deals would book and when. Visualization (like the dashboard above) simply visualized

trends. The answer was the application of predictive analytics with AI that could use a collection of data

to find the patterns that were indicative of when a sale had a characteristic to close and when it did not.

This enabled the better allocation of sales resources to optimize sales bookings.

Companies seeking to incorporate analytics should develop a matrix for defining their requirements for analytics,

which will expose the more appropriate tools. For example, below is a matrix that PTC, Inc. (NASDAQ:PTC) a

$1.1 billion global software company used to specify the analytics capabilities desired to advance the

exploitation of their data. They already had visualization vendor Qlik in their Finance group and evaluated two

other data visualization vendors. None of the vendors could deliver the end-user driven, dynamic, and predictive

analytics capabilities they sought.

Even for Power BI,

predictive analytics and AI

was rated minimum to

none, and the ability to build

models and predictive

models is significantly high

(Note, Microsoft claims ML

capabilities but refers to

Azure Machine Learning

Services6 that is in the Azure

cloud platform). Power BI

does have DAX (Data

Analysis Expressions) that is

useful for computations but

it’s a programming language

you’ll need to learn.

As such, data visualization has its value when it’s used like a dashboard in a car; i.e. to tell key operational metrics

that need to be monitored. Value is also obtained from combining and visualizing trends and associated data

to give light on the mechanics behind the key operational data. However, when analytics is needed then

analytics tools should be applied, as use of data visualization for analytics would both fall short of the needs as

well as can mislead decisions.


The Workings of Analytics Tools

Analytics tools from Aurora Predictions, Alteryx, BigML, and Datameer to name a very few are a new class of

enterprise tools that apply mathematics and statistics on historical multi-dimensional and multi-source data to

make forecasts and predictions. These tools are also AI enabled to better drive strategy and decisions.

AI, as contained in most analytics tools, is in the form of

Machine Learning (ML) that has many powerful uses. The

figure to the right from BigML1, shows an application of the

ML algorithm of Decision Tree, which is used to target

individuals of interest with income over $50K.

Other examples of Decision Tree use include finding the

customers who will proceed to checkout, what are the likely

modes of failure in the supply chain, which drugs are most

likely to proceed to the next phase of trials, etc. However,

as seen on the figure, ML is not intuitive, in fact, it’s

technical, statistical, and requires data science expertise.

The table below from the Finance Analytics Institute2 highlights the complexity of ML through the progression

of its areas of ML Process, ML Division, ML Learning, and ML Algorithm. Each area sequentially combines to

deliver an ML result.

The ML Process starts with the

problem (Define Object) to be solved,

which is often done by a consultant.

Once defined, data is collected,

cleaned, and prepared. Then data

scientists determine the ML Division,

ML Learning and ML Algorithm to

program the model for the problem

to be solved.

The model is next “trained” and tested to determine if training was successful. If all goes well then, the model

is ready for deployment, but at any point in the ML Process, a failure can send the process back to any

intermediate step or the beginning.

With this complexity, it is no wonder ML projects often hit a wall3 as “eight out of ten organizations reported

their AI and ML efforts have stalled” and “96% of companies have experienced problems with data quality . . .

required to train AI and build model confidence”.


Applications of Analytics

To properly understand analytics tools, it is first necessary to make the distinction between Analytics, AI, and

ML. Analytics is about the application of mathematics and statistics. Mathematics includes the disciplines of

calculus, algebra, geometry, trigonometry; whereas mathematical statistics is about probability and outcomes.

Note, that Analysis, when used in the context of reporting and visualization, typically refers to the Arithmetic

functions to add, subtract, multiply, and divide.

Artificial Intelligence (AI) at the high level is a machine making a human decision. Any technology that can create

this action is considered a form of AI. ML is one such technology, and therefore a subset of AI. So too are

technologies like Systematic Intelligence™, neural networks, etc.

The wondrous image to the right from NASA4 of a

black hole is created by ML. We can’t be sure this

image is correct because we’ve never actually seen a

black hole, but by combining astronomer’s data and

algorithms from across the globe, the best unbiased

image was rendered.

Analytics tools derive their power from mathematics,

statistics, and AI; whereas, visualization is largely filled

with arithmetic. There are many very powerful down-

to-earth applications of analytics and AI in unbiased

forecasting and predictions.

For the applications of analytics tools, we return to the five questions for data driven decisions of: What

Happened, Where it Happened, Why it Happened, What Will Happen, and How To Make It Happen? The

wheelhouse for analytics tools is the last three questions of Why, What Will, and How To.

Decisions are all about the future! Consider, have you made a decision about the past? No, because the past

has happened. All that can be said about a decision made in the past is whether that decision had the desired

outcome of its future. However, we do use the past to make predictions about the future.

Analytics enables quantitative and unbiased forecasts and predictions about the future using

data of the past. This, as compared to the biased forecasts that are typically done; i.e.

forecasts made by humans.

While humans have a good intuition of the future and may have specific knowledge of it as well, predictions are

still at the mercy of human needs and emotions; e.g. I need to have this deal close this quarter, my boss wants

more revenue, if we don’t make budget someone will take the blame, etc. Alternatively, analytics have no

qualms with pride, prejudice, or politics so these calculations cater to objectivity.

Further, humans are not inherently quantitative creatures. We naturally learn language but require much effort

to learn mathematics. This inhibits what we do analytically to mostly small and high-level computations.


Conversely, analytics tools engage the mathematical and the detailed to enable us to do that which we cannot

through our limited tools of spreadsheets, BI, and visualization.

Applications of analytics and AI are vast. At the Analytics Academy there is a session on Applications of Advanced

Analytics with examples that include: “Fair Challenge” to assign stretch revenue goals that produce a similar

probability for all parties to achieve, AI forecasting for more accurate long-range planning, Monte Carlo

Simulation for inventory optimization, correlations to foot traffic in retail stores to know when and where to

spend promotion capital, and correlations of leading economic indicators to learn what affects demand.

So too, analytics are deployed

for trend prediction as seen on

the image to the right from

Aurora Predictions and as

reviewed at the Analytics

Academy. Here we have a

manufacturer of business and

consumer electronics that are

distributed by a variety of

retailer (city) stores through

the U.S. The box at the right-

hand side of the screen is the

Drill Path that systematically

drills through data.

The displayed Drill Path predictively finds those distributors, stores, and products that have good trends YTD

but predicted to go bad. The report in the middle of the image, automatically renders the results to display the

numerical YTD trend and an arrow to indicate the future prediction of the trend. Here we find the “horses that

will leave the barn” in advance so we can “close the barn door” before they leave.

The table to the right displays some of

the many and more varied applications

of analytics throughout a company.

Analytics tools provide the highest

value to business because it can deliver

unbiased predictions of the future, and

with that, the ability to make better and

strategic data driven decisions.

If spreadsheets and visualization are all that is in the Finance and Operations toolbox, then the best that can be

achieved is some influence on tactical decisions as a partial 2nd Generation Partner. To go the full 10 yards,

analytics are essential to become the strategic 3rd Generation Partner – The Analytics Business Partner.


Where Analytics Tools Fail

As seen in the D&B/Forbes5 figure below, setting budgets aside, the failure of analytics is about complexity, data

quality, culture, alignment, and skills gap (knowledge of mathematics). This means the adoption of analytics

tools is materially higher than data visualization. Further, the nature of many graphical outputs (e.g. Decision

Tree) make it difficult for executives to understand, and therefore they tend to discount what they can’t

comprehend.

Complexity requires specialized skills. For example, ML has numerous forms (e.g. R and Python) and many

different editors (even in the same form) – which to use? Further, the ML, statistical, and mathematical libraries

in analytics tools can be large, and you’ll need to know what the formulas do in order to properly apply them;

e.g. can I use the SPCI for fraud detection, when is Bayesian inferential modeling better than referential

correlations, can Decision Tree give a better prediction than Support Vector Machine, etc.

Enterprise analytics vendors take effort

to make their tools more usable, but

there remains an underlying design view

that users will be more like data scientists

than Excel analysts. Some vendors like

Alteryx position their product as “self-

serve analytics” or Aurora Predictions

that is “Snap-on analytics and AI for Excel

Users”. Both purports to be for the

business analyst, but only Aurora

Predictions has AI without ML and does

not require the user to know statistics,

thus enabling all users access to AI

forecasting and predictions.

Also, we distinguish enterprise analytics tools from desktop statistical tools and analytics cloud platforms.

Desktop stat tools (e.g. Crystal Ball, Mini Tab, etc.) are basically spreadsheet extensions. Used for small data

sets, these tools typically don’t have AI and require knowledge of the application of mathematics and statistics.

Analytics cloud platforms (e.g. Microsoft Azure, AWS, Oracle DataScience, etc.) are for use with large multi-

source data and are the purview of consultants, data scientists, programmers, and application developers.

As such, when engaging analytics tools, the user skills will be determining; i.e. the typical business analysts is an

Excel jockey but not a statistical/mathematical scholar. Also, analytics tools can require new workflows because

most need a specialized skill set.

Therefore, the less mathematical, data science, programming, and database knowledge needed by a business

analyst to use an analytics tool, the better the tool can be used, the faster it will be adopted, the less intrusion

into the existing workflow, and the quicker to deliver benefits from insights and foresight.


Conclusion

To reach the plateau of Analytics Business Partner, it’s about bringing the right tool to the right job. You can’t

build a house with only a hammer, and you can’t gain insights and foresights from only data visualization tools.

The value of having both tool sets is to

answer the five key questions to making data

driven decisions for business growth and

optimization of: What Happened, Where it

Happened, Why it Happened, What Will

Happen, and How To Make It Happen?

As seen on the image to the right from

D&B/Forbes1, organizations have a toolbox

to do ”analytics” that they lump together to

include spreadsheets, dashboards, and true

analytics.

It’s not one tool before the other, or one

instead of the other. They are both needed

and provide different values; data

visualization informs about the past, while

analytics reveals the current and future.

Note, while analytics tools overlap many visualization capabilities, data visualization is better suited for key

operations metrics and executive presentations.

Also, a note of caution. Beware IT or the CXO who “standardizes” on one tool. It’s like decreeing that all feet,

regardless of size, must fit into one shoe of one size. No organization can get past the limitations of one

technology and therefore a toolbox is needed.

If only data visualization is used then only the What and Where questions can be answered, but since decisions

are about the future, the critical What Will and How To questions will be subjected to merely biased guesses

and not the cool light of unbiased analytics.

As such, the conclusion is that business can only be optimized and strategy best derived by having the right tools that include both data visualization and analytics.

This applies to both revenue and expenses, capital projects and operating leases, and private industry and government.


To Learn More About This Topic

Finance Analytics Institute (www.fainstitute.com) offers books, articles, research, surveys, and forums to teach

how to implement analytics in Finance and Operations. FAI unique offerings include the Benchmark Survey, to

assess where you and your organization are on the journey towards Analytics Business Partnering and measures

you against your aspiration and peers (http://fainstitute.com/#benchmark); the Analytics Academy

(http://fainstitute.com/#academy) where executives, directors, managers, and analysts learn over two days the

“How To” implement an analytics culture, and; the Analytics Academy Comes To You where multi-departments

can gather at their company for a day and a half of a condensed Academy. The Academy’s syllabus is based on

the book, Implementing an Analytics Culture for Data Driven Decisions – A Manifesto for Next Generation Finance

available at http://fainstitute.com/book/ and Amazon (Kindle and paperback).

Ready to Be an Analytics Business Partner?

Contact Us: http://fainstitute.com/contact-us/

Phone: 650-678-6557

The Authors

Robert J Zwerling, P.E. and Jesper H Sorensen are the founders of the Finance Analytics Institute that fuses

Finance and Operations with analytics.

Mr. Zwerling is also Managing Director of Aurora Predictions (www.aurorapredictions.com) providing

LightZ™ analytics with AI software designed for Finance and Operation, which includes an intuitive

purpose-built user interface for the business analyst that removes the need for data scientists and

programming. Mr. Zwerling is a regular speaker on predictive analytics and AI, member of Stanford

University’s Hoover Institution, co-author of the book Vigilance The Price of Liberty, and has a

Bachelor of Engineering degree (Magna Cum Lauda) from Stony Brook University, Master of Science

in Mechanical Engineering (thermodynamics and fluid mechanics) from CSU Los Angeles, member

Tau Beta Pi engineering honor society, and a Registered Professional Engineer in California.

Mr. Sorensen is also a finance executive with a major technology company leading global finance for

a multi-billion-dollar business. He has a proven track record of advancing the analytics agenda, is a

regular speaker on Next Generation Finance, published numerous articles on Analytics Business

Partnering and holds several advisory positions including advisory board for Aurora Predictions and

an analytics expert for the International Institute of Analytics. He has a Master degree in Economics

and Management from the University of Aarhus, Denmark, certified Six Sigma Green Belt, and

certified in Strategic Direction and Risk Management from Stanford University.

©Copyright 2019 Finance Analytics Institute LLC, Robert J Zwerling & Jesper H Sorensen. All rights reserved. No

part of this document may be reproduced without this copyright notice.

http://www.fainstitute.com/

http://fainstitute.com/#benchmark

http://fainstitute.com/#academy

http://fainstitute.com/book/

http://fainstitute.com/contact-us/

http://www.aurorapredictions.com/


Bibliography

Overview

1. https://www.brainyquote.com/topics/difference-quotes

Background

1. Robert J Zwerling & Jesper H Sorensen, October 2019, Analytics Business PartneringTM – The Evolution

of Business Partnering

2. Finance Analytics Institute, Analytics Academy, September 2018

3. http://fainstitute.com/#benchmark

The Workings of Visualization Tools

1. https://powerbi.microsoft.com/en-us/what-is-power-bi/

2. https://datahero.com/solutions/sales/

3. https://www.grow.com/

4. https://www.domo.com/industries/manufacturing

5. Dun & Bradstreet / Forbes Insights 2017 Enterprise Analytics Study

6. https://powerbi.microsoft.com/en-us/what-is-power-bi/

The Workings of Analytics Tools

1. Blog BigML.com

2. Robert J Zwerling & Jesper H Sorensen, Finance Analytics Institute, Analytics Academy, AI & ML Basics

3. Caitlan Stanway-Williams, May 24, 2019, theinnovationenterprise

4. Ola Lutz, April 19, 2019, JPL-NASA, https://www.jpl.nasa.gov/edu/news/2019/4/19/how-scientists-

captured-the-first-image-of-a-black-hole/


Conclusion


https://www.brainyquote.com/topics/difference-quotes

http://fainstitute.com/#benchmark

https://powerbi.microsoft.com/en-us/what-is-power-bi/

https://datahero.com/solutions/sales/

https://www.grow.com/

https://www.domo.com/industries/manufacturing

https://powerbi.microsoft.com/en-us/what-is-power-bi/

https://www.jpl.nasa.gov/edu/news/2019/4/19/how-scientists-captured-the-first-image-of-a-black-hole/

https://www.jpl.nasa.gov/edu/news/2019/4/19/how-scientists-captured-the-first-image-of-a-black-hole/

visualization vs. analytics - jpk group · 11/28/2019 · visualization tools like domo, power bi,...

Documents