Intermezzo: storytelling

Yes, storytelling is hot. Does this mean that I have to tell you everything about all the hard labour, my setbacks and successes? I don’t think so, since you are a business reader and hopefully the best way to get your attention is to demonstrate the potential business value of AnRep3D. Yet, while reading a book about “storytelling” I realised that this blog doesn’t tell a lot about AnRep3D and its history. That’s why I will tell my story in a concise way. Just for now.

picture by Wikiimages on Pixabay

 

 

 

 

 

 

 

 

 

 

 

 

How it started

I remember visiting the “reality cube” of the University of Groningen in the year 2004 and being really impressed by what I saw. Then I asked people with the reality cube if it would be possible to visualise abstract data e.g. from measurements. Actually, this question was inspired by Michael Ridpath’s novel “Trading Reality” They told me they sometimes did visualise data, but it took the team a couple of weeks to prepare new software showing those data. This meant that such a 3D-graph would cost EUR 100 * 40 * 2 * 2 (tariff per hour * hours per week * number of team-members) = over EUR 15000

When I got back at the incubator where I was working back then, I told everybody about the potential of an automated conversion of data into 3D-graphs instead of this lengthy process they told me about. No human labour, just an input-file and an output-file within seconds.

Photo by pixel2013 on Pixabay

Nobody understood why 3D-graphs would add business value (think about the first comments on mobile phones: useless, no added value, no-one needed such a device) and actually they even didn’t understand what was meant by a 3D-graph (and until now, luckily even Excel still doesn’t understand).

Prototyping and crisis

The only way to educate my market was to come up with a prototype but I’m not a software engineer. Seeing no alternative, I learnt VRML (Virtual Reality Markup Language – later on called Virtual Reality Modelling Language).

After a while I was able to create a generator in QuickBasic that converted numbers into a 3D-graph in VRML. It worked, but then I discovered al kinds of flaws and started improving. At the same time I investigated all kinds of markets and in the end the financial markets seemed to be the most interesting ones (probably because I had limited knowledge of some other areas). VRBI (Virtual Reality Business Intelligence) was born!

My dream was to create a real VR environment, where one could walk literally through a graph. A more modest first step was to use stereoscopy. This time I failed completely as I really don’t understand hardware at all. I asked my oldest son for help – and actually hired him – but he didn’t like the subject although he is really good at hardware.

Recently I discovered my old blog-posts from over ten years ago about this period. It was very frustrating and I spent a lot of money buying devices, but things didn’t work out because of the crappy technology. Then the crisis hit us hard and for a couple of years I had to work as a consultant and travel a lot, just to let my family survive.

Entering the 3rd millennium

After a couple of years I decided to start again, taking smaller steps, just showing a 3D-graph on the screen just in the way the popular games did. That was what I was able to realise with some effort. While I was struggling with the technology, several browser plug-ins for VRML were terminated. Most standalone viewers were incomplete and VRML’s successor (X3D) never became popular, so I ended up in a vacuum. There was one good VRML stand-alone viewer left, but (at least back then) it was hard to work with and rather unstable. I didn’t expect my audience (primarily business people) to work with this kind of solution, so eventually I moved to HTML5, the modern Internet language. As a matter of fact, this new version uses JavaScript – something I don’t understand very well.

Long story short: fortunately a kind of cloud-service by Fraunhofer came up (called X3DOM), translating my X3D-like scripts. Finally I was able to jump into the future! My youngest son helped me by creating a Java-framework for input/output. I leant a bit of Java – just enough to re-write all the calculations within, needed for the X3D-like output and added the “engine” part of the generator.

 

A step aside

Meanwhile, my middle son decided to join our university of applied business sciences. I told him he could try and sell some spin-off products which were left unused after the focus on annual reports. About the same time I had a clash with the tax-department because my revenues were too low and I wasn’t allowed to go on as an entrepreneur. In the end, after 25 years, I handed over my company (SCIENTASSIST) to my son. For a couple of years I kept improving those spin-off products. Using them myself (and blogging about them – this old blog is partially in English) I was able to think like a user instead of being a visionary. In the end, VRBI was a nice product and I went back to the original idea, still inspired by Michael Ridpath.

 

AnRep3D was born (again)

After this experience, I went back to my original idea: visualising data from Annual Reports for a group of companies through several years. After modernising the old attempts, I knew I had to work with the AnRep3D generator myself to be able to come up with a mature product. Working on my blog-posts I discovered a lot of potential improvements. Finally I had a good, basic product and started my campaigns and here we are – two years and three months after the first post of this blog. After all it has been quite a journey!

Do you want to try the AnRep3D-generator yourself? Download a free demo-package, without registering!

For a better understanding of the generator we have a couple of short movies at our youtube-channel. Our email-address is info@anrep3d.com  and you can follow is on Twitter: @AnRep3D

Advertisements
Posted in Uncategorized | Tagged , , , , , , , , , , | Leave a comment

Agriculture in 3D – minerals, livestock and farmland – part II

It’s been a long time since part I of this post! Partially, because of other obligations but also because of an issue with our 3D-graphs not being accessible. Now they are available again and it’s time for the second part of the post on the issue of Nitrogen and Phosphorus in European agriculture.

manure spread over farmland

photo by Hans on Pixabay

For decades agriculture became more intensive in several European countries and as a result the amount of manure went up. Manure is a fertiliser, but when the amount of minerals exceeds the need of the farmland, it will be more of a poison. As more and more manure was deposited on the farmland, all kinds of side-effects came up as a result of this dumping. To understand the negative impact of the surplus this article can offer some insight.

Currently we have a climate crisis, but in the past we have dealt with acid rain and the ozone depletion. Acid rain was and is actually connected to the Nitrogen issue.

Dying forest

photo by alegria2014 on Pixabay

In the end it all comes down to the area of farmland related to the number of livestock held in this area. We don’t want to limit the number of animals – on the contrary with e.g. “megastables” coming up – but at the same time the surface of a country won’t increase (apart from legal tricks perhaps). How to get rid of the dung, holding these minerals? The easiest way is to dump it on the farmland, but this is not a sustainable solution. A long time ago, the EU came up with laws to reduce the surplus and here we are! To see whether these laws are successful, we put a couple of values in a table and generated a 3D-graph with the help of the AnRep3D generator. The data were mainly obtained from Eurostat.

The N- and P surplus-values were converted to gram per square meter of surplus (g/km2). The surface of farmland is in km2. LiveStock Units (LSU) are a way to translate different kinds of animals (geese, sheep, cattle, horses of even mooses) to a uniform value which can be used in calculations. Here we use thousands of them (kLSU = kilo-Livestock Units)

Lady with horsesPhoto by langll on Pixabay

The 3D-graph (showing the N and P surplus in relationship to the area of agricultural land and the number of LiveStock Units) is available. Below we present a couple of screenshots from different angles. Double-click a screenshot to see the live 3D-graph in your browser. Below the screenshots is an explanation of how to manipulate the 3D-graph. (Be aware: WebGL and Javascript have to be enabled, but that’s the common default setting).

3D-graph agriculture from different angles

 

Double-click the screenshot to see the live 3D-graph in your browser. For manipulation: Clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph – it’s relative of course). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, just refresh the graph.

What do we learn from the 3D-graph behind the screenshots? We can see that larger countries seem to have a smaller surplus than the smaller ones. (As the order is from large surface to small from left to right, it’s easy to recognise.) One of the reasons is that they also have a lower Livestock to Area ratio (kLSU/km2) However, it’s not a linear relationship. E.g. the UK has an issue with high suplus-value, despite the large surface of agricultural land. Spain, with a higher number of (k)LSU and a comparable surface has a lower surplus. Greece, have about the same surface for agriculture as Ireland, has a larger surplus despite the much lower LSU-count. For Spain the surplus went up from 2013 (front) to 2014 and 2015 (mid and rear), but Poland managed to reduce the surplus in 2014, but it went up again in 2015. If we can concentrate on the kLSU/km2 ratio (the shape of the “buildings” as seen from the top), we can see Denmark, the Netherlands and Belgium have a high ratio and their surplus is (very) high as well. Countries like Sweden and Austria have lower ratios (lower kLSU, larger area) and their surplus-values are much lower indeed. Yet, manure is not the only cause of the surplus and sources outside of agriculture are attributing as well.

GeesePhoto by Skitterphoto on Pixabay

Finally the green part is interesting. The surplus-value for Nitrogen (mainly ammonia and nitrate) is represented bu the yellow part of the building. The green part represents the P part (almost solely phosphate). Phosphate can endanger water quality by causing algae bloom. We can clearly see that Spain has a higher P-surplus than France with the N-ratio being the opposite. The same applies to Denmark in comparison with the Netherlands. Of course the surplus is also related to the amount of mineral needed in the soil. With a low level of certain minerals, the surplus could be lower – but not necessarily for both N and P. Then, the composition of the manure (coming from different types of animals, that is) can also cause differences between the surplus-values for N and P.

Well, that’s it for now. Next time we will revisit Energy once more, before turning back to Finance. Hopefully it is understood that AnRep3D has a lot of potential in different areas! For more information about the generator of 3D-graphs, please have a look at our website (https://anrep3d.com ) There a  free demo-package (zip) can be downloaded, unpacked in a folder and the .jar file can be used immediately. For a better understanding of the generator we have a couple of short movies at our youtube-channel. Our email-address is info@anrep3d.com and you can follow us on Twitter: @AnRep3D

Posted in Visualising Financial Information | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

Agriculture in 3D – minerals, livestock and farmland

To be honest, it was quite a struggle. In Europe we have very nice and publicly available statistics, so I thought it would be easy to show how AnRep3D is able to visualise agricultural data. Indeed it was quite easy to get the data for the Nitrogen (N) and Phosphate (P) surplus per hectare (is about 2.5 acres) of farmland. But then I noticed gaps, negative surplus-values (that’s possible of course – it means there is a lack of the mineral). And of course Europe is quite large, so I had to determine which countries to show and also which years to report. For clarity: nitrate and phosphate are useful compounds in agriculture, but a surplus will pollute land and water, causing damage to the environment. That’s why Europe collects those data.

Fertilising with manurePhoto by pascvii on Pixabay

It made sense to use the height of the AnRep3D “buildings” for nitrate (N – rather large values) with a green roof for phosphate (P – more modest values). To avoid confusion: N is the chemical symbol for Nitrogen and P for Phosphorus, but we use them to identify the derived compounds NO3 (-) and PO4 (3-).

We know the real issue with minerals on farmland is the number of animals in relation to the available amount of farmland. The manure has to go somewhere and putting it on the land is the traditional solution. With this in mind the width of the buildings could represent the number of animals and the depth the surface (square kilometers – km2 is about 247 acres) of farmland. So far so good, but then other issues arose. The agricultural land numbers includes wasteland and woods, but we all know the manure won’t get there. In the end it turned out the surface reported by the European statistics covers for permanent grassland, permanent crops and arable land. The others seem to be subcategories.

Farmland bird's view

Photo by Tom Fisk on Pexels

The years didn’t match quite well with those of the minerals, so in the end I took 2013, 2014 and 2015 applying interpolation for the minerals.

The next issue was about the animals. It’s nice to count them, but if one country has mainly cattle, the next one goats and sheep and another one mainly chickens? It’s not a fair comparison. Fortunately there is a standardised unit, correcting for the impact of the animal and this is the LiveStock Unit (LSU). Yet the numbers made no sense and didn’t match with my reference-values either. In the end I disovered the LSU-tables offer two units: numbers (just a headcount and not LSU at all) and real LSU. After this the input-file was ready in a couple of minutes.

Livestock

Photo by sasint on Pixabay

The only choice I had to make was about the size of the values. The N- and P-values were converted to gram of surplus per square kilometer (g/km2). The surface of farmland (arable, permanent crops and permanent grassland) was already converted from Hectare to km2. Only the LSU could either be in kLSU (kilo-LSU = thousands) or in hLSU (hecto-LSU = hundreds). The former offers a compact graph, the latter provides a better overview for smaller countries, but is very wide. I decided to generate two different 3D-graphs this time. The one with kLSU is compact enough to show all the countries in it in a glance (the other one is for part II in the next post). A couple of countries have very high values for the nitrate-surplus, with the Netherlands being the number one. Be aware that because of the parallax the names of the countries are not in front of their “buildings” in the screenshot.

3D-graph agriculture

Double-click the screenshot to see the live 3D-graph in your browser. For manipulation: Clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph – it’s relative of course). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, just refresh the graph.

For more detail a zoomed screenshot is shown below (also clickable).

Detailed 3D-graph agriculture

For now, this will do. In the next post we will discuss the LSU and surface of farmland – and show the other graph.

For more information about the generator of 3D-graphs, please have a look at our website (https://anrep3d.com) There a  free demo-package (zip) can be downloaded, unpacked in a folder and the .jar file can be used immediately. For a better understanding of the generator we have a couple of short movies at our youtube-channel. Our email-address is info@anrep3d.com  and you can follow is on Twitter: @AnRep3D

Posted in Uncategorized | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

AnRep3D in Telecommunications (TelCo)

AnRep3D could have different names for different markets and purpospes of course, like EnRep3D (meaning Energy Representation in 3D and this might be the best alternative as it sounds very similar). Or something like CuCaRep3D (Cure and Care Reporting in 3D) and maybe even TelCo3D. We would need a lot of different manuals. For now the full manual, provided with our free demo-package is still focusing at Annual Reports. Yet it is good to show some alternatives, despite the name of the generator. Today it’s TelCo’s turn.

Classic telephone pole

Photo by aitoff at Pixabay

 

 

 

Photo to the right by hpgruessen at Pixabay Modern telecom antenna

Let’s be honest: I don’t know much about Telecommunications (TelCo). I worked in this industry for about half a year, in a smaller company so I got a good overview of the complexity of the operations. Yet I’m not familiar with the strategy. To me Telco strategy is a strange mix of physics, economics and culture.

Usually I write about subjects I know, like finance (although a very broad area), energy and healthcare. Next time I hope to talk about the chemical part of agriculture, but first I would like to draw the attention of people in the world of TelCo. Don’t take this post too seriously, as I’ve just been collecting some numbers, to be able to show a 3D-graph.

From a consumer-perspective TelCo is about minutes, messages, megabytes and perhaps antennas, so those are the subjects I took.

Woman with phone

Photo by rawpixel at Pixabay

To get some data I took a Dutch marketing site, presenting minutes, texts and MB in thousands, which I converted by me to Mmin (millions of minutes of phone calls), Mtxt (millions of text messages) and GB (Gigabytes or billions of bytes). Then I went to the website of a Dutch regulator for the number of antennas active during a couple of years. It turns out there is a lot of them. Not millions, so I don’t present Mega-antennas but just their absolute number. The combination was available for four years, so it will be a rather simple graph this time.

 

First I show the input-file in Excel, just before saving it to a .csv-file (“MS-DOS format”, Excel still says after more than twenty years of Windows).

Inputfile AnRep3D in Excel

 

 

 

Then the output: the 3D graph in html (can be viewed in a webbrowser). The legend/key is put in the screenshot as a picture in picture, to provide some guidance.

Telco first 3D graph front-view

Double-click the screenshot to see the live 3D-graph in your browser. For manipulation: Clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph – it’s relative of course). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, just refresh the graph.

When I opened the graph is was surprised, as it is very different from the previous ones. Data-consumption has grown explosively, it seems. At the same time the number of text-messages went down, although not as impressive as one would expect. Most people replaced them by app-messages which will be in the data-segment. However, at the same time the Internet of Things (IoT) applications came up and sometimes a simple mobile phone sim-card is used to let those devices send their messages. This will put a halt to the decline of the text messages, I guess. The real IoT growth is in the data-segment of course.

Telco first 3D graph top-view

The number of antennas has increased slightly. So how do they keep up with the growth in data-usage? Were these antennas oversized from the beginning?

Although I thought this would be the end of this post, I was really curious about the background of this modest growth, so I dived a little deeper.

 

 

 

Photo (right) by republica at Pixabay

 

In the report of the regulator there is a classification in GSM (900 or 1800), UMTS and LTE. The latter is for real IoT-data. Counting the anntennas per category, an even more interesting graph could be created. A screenshot is shown below.

3D-graph antennas NL

Double-click the screenshot to see the live 3D-graph in your browser. For instructions on manipulation, see 3D-graph above

The LTE are coming from nowhere in 2013 to more or less the same number as all GSM-antennas (and nearly the same as UMTS) in 2016. All the growth in the number of antennas is coming from the LTE-group, but is there a relationship between data usage and LTE-antennas? Or was a lot of capacity available in the first place and has the percentage of occupation of the bandwidth gone up? I really wouldn’t know, but maybe some expert can elaborate us?

For more information, please have a look at our other posts at this blogsite, our website (https://anrep3d.com) or our youtube-channel. The free demo-package (zip) can be downloaded, unpacked in a folder and the .jar file can be started immediately. Our email-address is info@anrep3d.com  and on Twitter we are @AnRep3D

Posted in Uncategorized | Tagged , , , , , , , , , , , , , , , , , | Leave a comment

Non-financial use of AnRep3D: a healthcare example

In the final post on Amazon, Alphabet and Apple I already talked about the alternative use of AnRep3D. Although designed as a tool for the visualisation of data from Annual Reports (hence the name AnRep3D), the use it not limited to this purpose.

Woman pointing at graph

(Photo by geralt on Pixabay)

As a matter of fact, AnRep3D is a very generic and versatile instrument that is able to visualise a wide range of data sets on different subjects, in a 3D-graph.

 

 

Basically single graph-elements (“buildings”) are places in a chess- or checkerboard-like (whichever you like best) structure, also referred to as “Manhattan”. There is no need whatsoever to put years from front to back and neither do the labels in front need to refer to companies. In the end it’s all about a matrix, holding graph-elements.

Woman playing street-chess

(Photo by kevin laminto at Unsplash)

Getting to these elements, they only visualise three values in the input and set the height, width and depth of a “building”. The height is a bit special, as a part of it can be coloured green (behaving as a “roof”). A negative value is not a part of the height value, but added on top of it, providing a red “roof”.

This fourth option is nice and makes the graph more colourful and interesting, but there is no need to use it. The second value can simply be set to zero, creating a yellow building at one of the blocks between Manhattan’s Streets and Avenues.

For some it will be hard to look at AnRep3D this way, as about 50 posts have been dealing with the visualisation of financial data, taken from Annual Reports. On the other hand, this approach provides a lot of freedom to creative minds, thinking of alternative usage of the tool.

To be honest: we already hinted at those options when visualising energy in 3D, having years (yes, still years) and countries (not very different from companies) at the sides of the grid. The buildings themselves however, showed the energy-mixes for e.g. renewables (of which biofuels as a subsection), nuclear and fossil fuels as primary sources.

Electricity-transmission

(Photo by jplenio at Pixabay)

In this post I will give a very different example, taken from Healthcare in the Netherlands. This is because nearly everything in this country is measured and documented, so the result will be interesting.

To obtain two sets (still in time: annual values), I split the data between:

  • General Hospitals and
  • University Hospitals.

For both a set of characteristic values was obtained:

  • Total number of beds, of which beds in intensive care mentioned separately
  • Turnover (in millions of Euros. Hospitals do not have real “revenue” or “sale” values.)
  • Staff, measured in Full-Time Equivalents, rather than “bodies”.

Female doctor

(Photo by voltamax at Pixabay)

The data was obtained from a (Dutch) site on healthcare, with some small adjustments made.

Input-file for hospital 3D graph

The input-file will not be very different from the previous ones. It’s just numbers. Below a screenshot is shown. The additional semi-colons are irrelevant. The data were entered and ordered in Excel and saved as a .csv and doing so the additional semi-colons came in. Of course the first line is still the parameter-line!

The output is like always an html-file to be viewed in a webbrowser, where the 3D-graph will be shown and can be translated, rotated and zoomed in or out. Double-click the image (only a screenshot taken from one angle) to see the actual 3D-graph.3D-graph on hospitals

 

Double-click the screenshot to see the live 3D-graph in your browser. For manipulation: Clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph – it’s relative of course). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, just refresh the graph.

Interesting, but not surprising is that for General Hospitals and University Hospitals the ratio between turnover (width) and staff (depth) is not very different. The (large) university hospitals are together about half the size of the other hospitals for both the money and the people. Not a surprise, as the wages are the greater part of the total budget, despite the expensive devices.

On the other hand, the number of beds is much lower in University Hospitals. More like a quarter of the number of the General Hospitals. Apart from Outpatients and Daycare (the latter may or may not be reflected in the number of beds, but is certainly related to the staff) the patients in a University Hospital have usually more complicated health issues. Also interesting is that the changes over time are rather small. In a spreadsheet they look more impressive.

For more information, please have a look at our other posts, our website (https://anrep3d.com) or our youtube-channel. Again, the free demo-package (zip) can be downloaded, unpacked in a folder and the .jar file can be started immediately.

Our email-address is info@anrep3d.com and on Twitter we are @AnRep3D

Posted in Uncategorized | Tagged , , , , , , , , , , , , , , , , , | Leave a comment

fAAnG revisited – alternative financial ratios visualised IV

This will be the third and last post on this subject. As already announced, not all fAAnG companies will be in the graph – only the 3 A-companies: Amazon, Alphabet (although still represented by Google’s G in the acronym) and finally Apple.

Woman with iPad (Photo by cuncon on Pixabay)

Originally I expected the cash flow approach, together with current liabilities and current assets to be very interesting, but obviously it’s not. Only a fraction of the visitors of the previous series seems to be interested. Next time I will write about a very different subject, but of course still using the AnRep3D generator to create 3D-graphs. So let’s keep it short, because the subject is not as exciting as I thought.

Like before, I picked the numbers from both the form 10-K and the Annual Report. For numbers that were accumulative, I subtracted the previous totals. Although Apple has a broken bookyear, that’s irrelevant now because we are talking quarters. All companies are in USD, so that’s not an issue either.

The combination was a bit of a surprise, as Apple has a high cash flow, but relative to the total sales or revenue, Alphabet is in the lead. Then, Apple has more or less the same ratio of Current Assets to Current Liabilities, although the amounts are both twice as large.

Alphabet, on the other hand, has a much better ratio as the current assets are about the same as Apple’s, but its current liabilities are much lower! Neither Alphabet nor Apple show a negative cash flow during the period from the start of 2017. Yet Amazon did as the two red roofs show immediately.

Amazon Alphabet Apple 3D graph

Double-click the screenshot to see the live 3D-graph in your browser. For manipulation: Clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph – it’s relative of course). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, just refresh the graph.

For more information, please have a look at our other posts, our website (https://anrep3d.com) or our youtube-channel. Again, the free demo-package (zip) can be downloaded, unpacked in a folder and the .jar file can be started immediately. Our email-address is info@anrep3d.com On Twitter we are @AnRep3D and we have a page at LinkedIn.

That’s it for the 3A-companies! Let’s see what the next subject will be.

Posted in Visualising Financial Information | Tagged , , , , , , , , , , , , , , , , , , , , , | Leave a comment

FAANG revisited – alternative financial ratios visualised III

Two weeks ago I posted a rather heavy story, discussing a lot of financial theory. This time I will focus on the companies and the numbers again.

Before we go on, I have to share something. I really like MOOCs and I already attended a lot of courses at FutureLearn Some were about e.g. modern (online) marketing methods (as my education was about classical marketing), others were not related to AnRep3D at all.

Business Woman working

(Photo by Daniela_oliiver on Pixabay)

 

 

 

 

 

 

 

 

 

 

 

This time I took a course from edX about finding the right markets for ones technological innovations. I think AnRep3D is very innovative after all, but it was designed for the visualisation of financial information in the first place. Yet there are other opportunities out there, I think. This means that during the next period I will show examples of alternative subjects again, as I already did with energy. Of course energy will be an important target market as well and I will blog about it, but I am also thinking of new markets.

Autonomous light bulb

(Photo by ColiN00B on Pixabay)

A bit scary, because I know about Finance and Energy and the other subjects might be less familiar to me. Well, let’s see what comes up. I hope you will like it!

Back to the FAANG companies! We already saw Amazon in the 3D-graph and now we should combine it with Alphabet. Although the name of the holding changed from Google to Alphabet, the stock exchange still refers to it as GOOG, but I put in a label Alphabet.

Google SEO (Photo by Simon on Pixabay)

Of course I had to extract some numbers from the Forms 10-Q. The format is really nice and clear, but still companies have some room for their own structure. It turns out Alphabet only mentions cumulative “Net cash provided by operating activities”, so for Q2 – Q4 calculations were needed.  There is no Q4 of course as this will be covered by form 10-K, so the values for Q3 have to be subtracted for Revenue and Cash Flow. As the balance is a snapshot one has only to be careful to pick the right rows and columns – no calculations needed.

After generating the 3D-graph with the two companies in, I decided to reverse the order again. It seems so logical to enter the values chronologically, but the most recent values are the most interesting. By turning the 3D-graph around, we can see the reversed order, so no information gets lost. The picture below (clickable, as usual) is a composite. In the real 3D-graph the legend (key) is in front of the “buildings” and below ground-level, but to keep the picture more compact I inserted another screenshot.

Amazon Alphabet 3D

 

Double-click the screenshot to see the live 3D-graph in your browser. For manipulation: Clicking the right mouse-button, moving the mouse up and down will zoom the graph in and out. Clicking left and moving the mouse will tilt the graph in different directions (or move the observer’s viewpoint around a fixed graph – it’s relative of course). Double clicking in the graph translates it and moves the centre at the same time. As a result the way the graph tilts will change. Just try it. If you don’t know how to get the normal position back, just refresh the graph.

For more information, please have a look at our other posts, our website (https://anrep3d.com) or our youtube-channel. Again, the free demo-package (zip) can be downloaded, unpacked in a folder and the .jar file can be started immediately.

Our email-address is info@anrep3d.com  On Twitter we are @AnRep3D and we have a LinkedIn company-page.

That’s all for now. Next time I hope I will be able to add Apple and then we will probably move on, exploring other subjects. Yet Finance will be visited on a regular basis.

Mars rover (Photo by WikiImages on Pixabay)

Posted in Visualising Financial Information | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment