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6 year growth: Apple, Microsoft, Google, Amazon

6 year growth: Apple, Microsoft, Google, Amazon

Back in 2012 we did a side-by-side comparison of the four largest technology companies and their quarterly growth and other financial metrics. A year later in 2013 the four companies were again compared using Wolfram Alpha to generate lots of charts and tables by simply typing in “Google vs. Amazon vs. Apple vs. Microsoft” in the search bar.

Today, six years later, this same exercise reveals very strong growth. The big companies are getting (much) bigger. Here are some comparisons:

QuaerterlyRevenue20122018

With the underlying numbers:

NumberComparison

(Market cap as of market close on 2/3/2012 and 6/1/2018; sources the respective 10-Q filings; scales are the same for left and right charts. Google refers to its parent Alphabet, Inc.)

When taken together, over the last six years the four companies have grown as follows:

  • Revenue more than doubled (+112%, 13.3% annualized)
  • Income grew only moderately (+31%, 4.6% annualized)
  • Market cap tripled (+202%, 20.2% annualized)
  • Employees almost quadrupled (+276%, 24.7% annualized)

Of course, the $ numbers need to be inflation-adjusted, but US inflation rates were around 2% or less between 2012-2018, which amounts to about 10% over that period of time. Hence inflation is not qualitatively influencing this analysis or comparison.

Amazon grew the most, with its market cap growing more than 9 fold (+833%) and its employees more than 8-fold (+763%) to more than half a million people. Back in 2012, all four combined just exceeded $1 trillion in market cap; this has swollen to $3.3 trillion.

These are the biggest nominal market cap values in history. When comparing them to the GDP of countries, they would each rank in the Top-20. According to 2018 GDP projections by the International Monetary Fund, Apple would rank 18 behind the Netherlands (17th, $945,327 million), the other three companies would rank 19 behind Turkey (18th, $909,855 million). The market cap of these four companies combined would rank 5th behind Germany (4th, $4,211,635 million). In other words, only the top four countries by GDP (United States, China, Japan and Germany) are bigger than the market cap of Apple, Microsoft, Google and Amazon combined.

These corporations are transnational entities with a global customer base. Arguably, their size and economic power has grown so rapidly that the legal, tax and trade frameworks governing their operations can’t always keep up. Similarly, when companies get so large and rich, they can buy startups and entice talent to join them at a rate newer entrants or even governments cannot match. Apple’s cash position at the end of Q3’2017 was roughly $270 billion (source Asymco). It is not obvious that consumers always benefit from companies growing that large (see monopoly and anti-trust laws). Thankfully, the current technology oligopoly leads to healthy competition.

As before, there remain significant differences in the revenue segmentation across these four companies:

RevenueBySegment

Arguably, Microsoft has the broadest diversification and hence the most stability against disruptive innovation. Its three segments are not only roughly equal in size, but in turn contain a variety of different sub-segments. Productivity and Business Processes includes Office, Exchange, Skype, LinkedIn, Dynamics; Intelligent cloud includes Windows Server, SQL Server, Azure and Consulting Services; Personal Computing includes Windows, Devices, XBox and Search/Advertising. Microsoft’s Azure cloud services have closed the gap to Amazon’s AWS business and recently overtaken it by quarterly revenue.

If consumers were to search somewhere else than using Google, shop somewhere else than Amazon or buy no more iPhones, these companies would all shrink by an order of magnitude. Microsoft stands well positioned by comparison.

The following radar plot shows the above table numbers in a different perspective:

RadarPlot20122018

For each metric, 100% corresponds to the maximum of the four companies. Amazon has the most employees, Apple is the largest in quarterly revenue, profit and market cap. Some comments on the 2012 – 2018 changes:

  • Employees: Microsoft only added +35% of employees; Apple and Google more than doubled at about +160%; Amazon exploded by adding +763% to an almost 9-fold increase from 65,600 to 566,000.
    While Microsoft had more than twice as many employees as Apple in 2012, they are the same size now (~123,000).
  • Profits: While the green line (market cap) in the radar plot almost looks like an even-sized  rectangle, the red line (profit) is much tilted towards Apple and leaves comparatively little for Amazon.
  • Revenue per employee: Apple still takes the price in this rank (~$2million/year), with Google ($1.47million/year) and Microsoft ($0.87million/year). Amazon “only” earns $0.36million/year. In that metric, Amazon slipped from rank 2 to the bottom and Apple’s lead is not as strong as it was in 2012.

Much has been speculated about the future of the biggest technology companies and the nature of the next disruptions such as cloud, augmented reality (AR) and artificial intelligence (AI). Perhaps the biggest disruptor for this elite club of technology companies is Facebook, which only went public six years ago. FB currently has a $562 billion market cap. It is now bigger than these four were back in 2012, and about 70% of the size they are now. My own skepticism at the time of the Facebook IPO was proven wrong by its continued and strong growth. Their base of about 2 billion free accounts is by far the largest of any company ever. That said, I personally still have no Facebook account, while I’m using the products and services of each of the top four companies nearly every day! It will be interesting to see which one first breaks the $1 trillion market cap threshold.

 

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Posted by on June 3, 2018 in Financial, Industrial

 

World Inequality and the Elephant Curve

In December 2017 the World Inequality Lab (WIL) published its first World Inequality Report 2018. The lab consists of a five-member board and 20+ researchers, mostly from the Paris School of Economics (Thomas Piketty et al.) and the University of California at Berkeley (Emmanuel Saez et al.). Compared to previous work on economic inequality it is fair to say that research has significantly advanced over the last 5 years along several directions:

  • The free report itself is available both online as well as in various download formats and eight languages. It aims to become a data-driven foundation for societal and policy discussions about inequality.
  • All underlying data are openly published (via the World Wealth & Income Database WID) to support reproducibility and stimulate further research.
  • The methodology to aggregate data is encompassing more sources, more attributes (including age, gender, etc.) and better informed estimates, across a wider spectrum of countries and geographies (all important for policy discussions).
  • The visualizations have evolved beyond limited measures such as the Gini-Index and now typically include interactive charts (such as the for example at http://wid.world/country/usa/)

This report is quite detailed and holistic. Aside from the Executive Summary, Introduction, Conclusion and Appendices, it consists of the following five parts:

  1. AIM OF THE WORLD INEQUALITY REPORT 2018
  2. NEW FINDINGS ON GLOBAL INCOME INEQUALITY
  3. EVOLUTION OF PRIVATE AND PUBLIC CAPITAL OWNERSHIP
  4. NEW FINDINGS ON GLOBAL WEALTH INEQUALITY
  5. FUTURE OF GLOBAL INEQUALITY AND HOW IT SHOULD BE TACKLED

There are many interesting findings. Let me just provide three examples in this Blog, together with respective visualizations telling the “story in the data”.

Example 1: Inequality rising everywhere, but at different speeds

Here is a Figure E2a showing the Top 10% income shares across several large geographies over the period 1980-2016:

figure-e2a

From the report’s Executive Summary:

  • Since 1980, income inequality has increased rapidly in North America, China, India, and Russia. Inequality has grown moderately in Europe (Figure E2a). From a broad historical perspective, this increase in inequality marks the end of a postwar egalitarian regime which took different forms in these regions.

and further

  • The diversity of trends observed across countries since 1980 shows that income inequality dynamics are shaped by a variety of national, institutional and political contexts.

  • This is illustrated by the different trajectories followed by the former communist or highly regulated countries, China, India, and Russia (Figure E2a and b). The rise in inequality was particularly abrupt in Russia, moderate in China, and relatively gradual in India, reflecting different types of deregulation and opening-up policies pursued over the past decades in these countries.

  • The divergence in inequality levels has been particularly extreme between Western Europe and the United States, which had similar levels of inequality in 1980 but today are in radically different situations. While the top 1% income share was close to 10% in both regions in 1980, it rose only slightly to 12% in 2016 in Western Europe while it shot up to 20% in the United States. Meanwhile, in the United States, the bottom 50% income share decreased from more than 20% in 1980 to 13% in 2016 (Figure E3).

The latter is apparent from the supporting visualization in Figure E3, contrasting the Top 1% and Bottom 50% national income shares in the US with that of Western Europe:

figure-e3

figure-e3b

Although the y-axis does not start at 0% and is of different scale in both charts, the underlying story, i.e. the evolution of income shares of the rich (top 1%) and lower class (bottom 50%) over the last 35 years is apparent:

  • Income shares have changed significantly in the US:
    • The Top 1% nearly doubled their income share from 11% to 20%
    • The Bottom 50% saw their income share almost cut in half from 21% to 13%
  • Income shares have been fairly stable in Western Europe

 

Example 2: The elephant curve of global inequality

On this Blog we have written a lot about the Gini index. (See Gini posts) One of the limitations of the Gini index is that it reduces the entire inequality picture down to a single scalar value. Multiple distributions result in the same Gini index, which means that structural distribution changes may be masked out by a near constant Gini index.

For example, world inequality over the last 35 years has had both increasing effects (such as growth concentration at the top) as well as decreasing effects (raising hundreds of millions of people out of poverty in India and China). Visualizing the Gini index over time does not show this dynamic well.

Another chart to visualize this dynamic more clearly is the elephant curve – named after the shape of the animal. This curve lists all population groups in percentiles along the x-axis, sorted by increasing income from left to right. The first 99 % have the same x-axis spacing; the top 1% on the right is split into 10 subgroups of 0.1% each; the top 0.1% is again split into 10 subgroups of 0.01%, and finally the top 0.01% is again split into 10 subgroups of 0.001%. This gives a finer resolution near the top of the income distribution, highlighting the very disproportionate accrual of growth at the top. See Figure E4 for global inequality growth from 1980 – 2016:

figure-e4

The big bump on the left (head of the elephant) represents the large number of people lifted out of poverty (mostly in India and China). The steep rise on the right (trunk of the elephant) represents the disproportionate gains at the top of the economic income distribution. Again, from the Executive Summary:

How has inequality evolved in recent decades among global citizens? We provide the first estimates of how the growth in global income since 1980 has been distributed across the totality of the world population. The global top 1% earners has captured twice as much of that growth as the 50% poorest individuals. The bottom 50% has nevertheless enjoyed important growth rates. The global middle class (which contains all of the poorest 90% income groups in the EU and the United States) has been squeezed.

To underscore the last statement, here is the elephant curve of income growth from 1980-2016 for just the US-Canada and Western Europe (Figure 2.1.2):

figure-212

Note how in this chart, without China and India, the left side is flat, indicating that the lower economic classes have only had average or negligible income growth.

How did this translate into shares of growth captured by different groups? The top 1% of earners captured 28% of total growth—that is, as much growth as the bottom 81% of the population. The bottom 50% earners captured 9% of growth, which is less than the top 0.1%, which captured 14% of total growth over the 1980–2016 period. These values, however, hide large differences in the inequality trajectories followed by Europe and North America. In the former, the top 1% captured as much growth as the bottom 51% of the population, whereas in the latter, the top 1% captured as much growth as the bottom 88% of the population. (See chapter 2.3 for more details.)

It is noteworthy that the closer to the top, the higher the cumulative income growth, especially in the US. For example, Table 2.4.2 below shows that since 1980, US income has more than

  • doubled for the Top 10% (growth = 121%)
  • tripled for the Top 1% (204%)
  • quadrupled for the Top 0.1% (320%)
  • quintupled for the Top 0.01% (453%) and
  • septupled for the Top 0.001% (636%)

 

table-242

Another interesting finding from this is that pre-tax US income for the bottom 50% has essentially remained unchanged (growth = 1%) for an entire generation, with the bottom 20% even seeing their income shrink by 25%. Economic policies which exclude large portions of the population from growth for an entire generation are bound to increase tensions within that population, here primarily along the lines of economic class boundaries.

Example 3: Geographic breakdown of global income groups

In Part 2 the report looks at the share of Africans, Asians, Americans and Europeans in each of the global income groups and how this has changed over the last few decades. To illustrate, there are two snapshots in time, first at 1990 (Figure 2.1.5)

figure-215

and then at 2016 (Figure 2.1.6):

figure-216

Comparing these two area charts reveals a few interesting developments at the level of entire geographic regions:

In 1990, Asians were almost not represented within top global income groups. Indeed, the bulk of the population of India and China are found in the bottom half of the income distribution. At the other end of the global income ladder, US-Canada is the largest contributor to global top-income earners. Europe is largely represented in the upper half of the global distribution, but less so among the very top groups. The Middle East and Latin American elites are disproportionately represented among the very top global groups, as they both make up about 20% each of the population of the top 0.001% earners. It should be noted that this overrepresentation only holds within the top 1% global earners: in the next richest 1% group (percentile group p98p99), their share falls to 9% and 4%, respectively. This indeed reflects the extreme level of inequality of these regions, as discussed in chapters 2.10 and 2.11. Interestingly, Russia is concentrated between percentile 70 and percentile 90, and Russians did not make it into the very top groups. In 1990, the Soviet system compressed income distribution in Russia.

In 2016, the situation is notably different. The most striking evolution is perhaps the spread of Chinese income earners, which are now located throughout the entire global distribution. India remains largely represented at the bottom with only very few Indians among the top global earners.

The position of Russian earners was also stretched throughout from the poorest to the richest income groups. This illustrates the impact of the end of communism on the spread of Russian incomes. Africans, who were present throughout the first half of the distribution, are now even more concentrated in the bottom quarter, due to relatively low growth as compared to Asian countries. At the top of the distribution, while the shares of both North America and Europe decreased (leaving room for their Asian counterparts), the share of Europeans was reduced much more. This is because most large European countries followed a more equitable growth trajectory over the past decades than the United States and other countries, as will be discussed in chapter 2.3.

There are, of course, many more findings in this report. It is great to see that such rigorous data-driven analysis is made available free of charge and easy to consume (desktop, iPad, etc.). One can hope that such foundational work will lead to a more educated civic discussion about the current status of economic inequality, the impact of various policy tools as well as the geographic developments on these inequalities.

 
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Posted by on February 16, 2018 in Socioeconomic

 

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Data Visualizations in Healthcare

A couple of weeks ago I attended HIMSS 2017 in Orlando (Healthcare Information and Management Systems Society), the largest annual Healthcare IT event in the US. One of the big tenets of the show was system interoperability. There are lots of different vendors, few standards, and vast amounts of data being collected. There is an emerging set of APIs (such as HL7 FHIR) to ensure data can be shared among systems and follows a patient properly through the various providers she encounters during her episodes of care.

Somewhat serendipitously I came across a booth which had large prints of beautiful data visualizations on it. The booth was from Arcadia Healthcare Solutions. Given my background on data visualization and my being employed at Rennova Health Technology Solutions and responsible for our healthcare IT products and services, I was drawn to interpret these prints. They are also featured in an online data gallery, which I encourage you to explore.

One of these visualizations created by Jeff Solomon is called “The Health IT Space“. It displays highly aggregated data from various EHR (Electronic Health Record) systems. From the gallery:

The Electronic Health Record is a data gold mine. Each patient you see generates millions of detailed records in real time that can be extracted and analyzed for improved predictive algorithms, increased operational efficiency, better care quality, and so much more.

SmallAmbulatory

These graphs are stylized Entity Relationship diagrams from seven different EHRs. Nodes are data tables, and edges are relationships between these tables inferred from shared attributes.

MajorAmbulatory

The color-highlighted nodes are referring to patient data. The size of the node corresponds to the amount of records in the respective table.

LargeAmbulatory

Again, from the gallery description:

In each cluster, the core patient entity – the nucleus around which the rest of the data revolve – can be identified by its contrasting color. The tables containing the bulk of the clinically and operationally valuable data tend to form clusters of large, interconnected nodes, while a larger number of satellite tables house system configurations and other low-volume metadata that has very few relationships to the nucleus.

At the large end of entire health systems, the graph starts to look very busy:

LargeHealthSystem

It is pretty amazing how much data is being aggregated into these graphs. Nearly 5,000 database tables – hence the black areas where there are too many dots to separate them at this resolution. A combined number of 18 billion records! Nearly 300,000 relationships between these tables (again, the lines are too numerous to be distinguishable).

The Health IT SpaceI find it somewhat humbling to review these graphs. Our own MedicalMime EHR falls into the small category by these standards. Major and Large EHRs are at least one, maybe two orders of magnitude larger and more complex.

Interpreting the large amount of data contained in the EHR opens up many ways to improve healthcare, both medically for the patient as well as operationally for the providers. Visualizations can help us to better understand patterns and trends which otherwise would remain hidden.

The entire big picture of the above visualization is indicated on the right. For a Hi-Res version please contact the friendly folks from Arcadia Health Solutions directly from their website.

Another big trend at HIMSS’17 was Artificial Intelligence. Machine learning and predictive analytics received a lot of attention. Solutions like IBM’s Watson Health promise to bring world-class expertise into ordinary physician practices through subscription to hosted specialty knowledge in the cloud – whether curated by scientists or machine-learned using statistical techniques from big data. Health Catalysts healthcare.ai is an open platform to make machine learning techniques more accessible and bring them to small SW houses, not just the large companies with large R&D budgets. While certainly overhyped at the moment, acceptance for obtaining a “second medical opinion from the cloud / app” to improve clinical decisions is rising.

 
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Posted by on May 20, 2017 in Medical

 

Digital Wages in the Gig Economy

Digital Wages in the Gig Economy

A small research team from the Oxford Internet Institute has recently issued a report based on a three year investigation into the worldwide geographies of the so-called Gig-Economy, online work which allows many talented people in the low and middle income countries of the world to compete on a global stage. From the Executive Summary:

Online gig work is becoming increasingly important to workers living in low- and middle-income countries. Our multi-year and multi-method research project shows that online gig work brings about rewards such as potential higher incomes and increased worker autonomy, but also risks such as social isolation, lack of work–life balance, discrimination, and predatory intermediaries. We also note that online gig work platforms mostly operate outside regulatory and normative frameworks that could benefit workers.

One of the eye-catching and very information rich visualizations comes from a related Blog post by the “Connectivity, Inclusion, and Inequality Group” called “Uneven Geographies of Digital Wages“.

odesk_donuts_wages_v3-01

Dollar Inflow and Median Wage by Country

The cartogram depicts each country as a circle and sizes each country according to dollar inflow to each country during March 2013 (on the freelance work oDesk.com platform, rebranded in 2015 to Upwork). The shading of the inner circle indicates the median hourly rate published by digital workers in that country. The graphic broadly reveals that median wages are, perhaps unsurprisingly low in developing countries and are significantly higher in wealthier countries.

Another Blog post on the geographies of online work adds several more visualizations (based on 2013 data, so a bit dated by now). For instance, one world map highlights the relationship between supply and demand. It distinguishes between countries with a positive balance of payment (i.e. countries in which more work is sold than bought) and countries with a negative balance of payment (countries in which more work is bought than is sold). The figure more clearly delineates the geography of supply and demand: with much of the world’s demand coming from only a few places in the Global North.

online-contracting-paymant-balance

Balance of payments

Another very interesting and dense visualization is a connectogram (see our previous post on Connectograms and the Circos tool) demonstrating the highly international trade in the online Gig-Economy: 89% of the trade measured by value happened between a client and a contractor who are in different countries. The network therefore attempts to illustrate the entirety of those international flows in one graph. It depicts countries as nodes (i.e. circles) and volumes of transactions between buyers and sellers in those countries as edges (i.e. the lines connecting countries). Country nodes are shaded according to the world region that they are in and sized according to the number of buyer transactions originating in them. Edges are coloured according to the flow of services: with the line shaded as the colour of the originating/selling region. Edges are also weighted according to the total volume of trade.

odesk_net_4_no-numbers

The Geographic Network of Sales

We see not just a complex many-to-many relationship of international trade, but also the large role that a few geographic relationships take (in particular, India and the Philippines selling to the United States).

Back to the Executive Summary of the above report:

The report’s central question is whether online gig work has any development potentials at the world’s economic margins. Its motive is to help platform operators to improve their positive impact, to help workers to take action to improve their situations, and to prompt policy makers and stakeholders interested in online gig work to revisit regulation as it applies to workers, clients, and platforms in their respective countries.

It is interesting to see these marketplaces evolve, in terms of the international, distributed nature, issues such as taxation, intermediation, opportunities and risks. There are also entirely new forms of social networks forming, based on blockchain powered token systems convertible into crypto-currencies (such as Steem). The core concept here is to eliminate not just geographical distance, but also risks from exchange rate fluctuations and predatory intermediaries. It remains to be seen to what degree this can act as a counterweight to technology-induced increasing inequality.

 

 
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Posted by on March 26, 2017 in Industrial, Socioeconomic

 

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10 years of BI Magic Quadrant

Every February the Gartner group publishes its Magic Quadrant (MQ) report on the Business Intelligence segment. As covered in previous years (2012, 2013, 2014), its centerpiece is a 2-dimensional quadrant of the Vision (x-axis) vs. Execution (y-axis) space. When this year’s report came out about 3 weeks ago, it completed a decade worth of data (each year from 2008 to 2017) on about 20-25 companies each year. Here is the latest 2017 picture (with trajectory from previous year):

BI_MQ_2017.png

I have collected the MQ position data in a simple Google Docs spreadsheet here. The usual disclaimers are worth repeating:

  • Gartner does not publish the x,y coordinates as they caution against using them directly for interpretation.
  • To approximate the data, I screen-scraped them from images revealed by Google search, which introduces both inaccuracies and the possibility of (my) clerical transcription error.
  • Changes in the x,y coordinate for one company from one year to the next are a combination of how that company evolved as well as how Gartner’s formula (also not published) may have changed. For example, from 2015 to 2016 many companies “deteriorated” in the ranking as can be seen from the following graphic:

bi_mq_2016

  • It is unlikely that so many companies deteriorate in their execution in unison. More likely, the formula changed and shifted the evaluation landscape upwards, meaning companies that stayed the same on the previously used factors now slipped downwards. (I read somewhere that Gartner wanted to have only 3 companies in the leader quadrant – an instance of curve-fitting if you will.) Whatever the reason, this shift removed all but three companies from the upper rectangle on execution.
  • That said, relative changes between companies in the same year are still meaningful, as they are all graded on the same formula.

Naturally, it is of interest to study the current leaders – Microsoft, Tableau and Qlik. Here is the dynamic evolution of these three competitors MQ positions over the last decade as GIF:

mq_leaders
mq_leaders_allFrom the entire trajectory (left) one can see that all of them have been leaders for many years.

Tableau joined the leader board in Feb-2013 from the status as challenger. It went public in May-2013 (ticker symbol DATA) and has grown into a company with close to $1B in annual revenue and > 3000 employees. It has had particularly consistent ratings on Execution scores since then. Many of the visualization metaphors it has introduced are commoditized by now, with a desktop designer tool for both Windows and Mac, a robust server product as well as a free public cloud-based option. For any company of such size it is a challenge to grow fast enough and it needs to both stay ahead of the competition as well as diversify into adjacent markets. Its stock price reached lofty heights of $127 (roughly $10B market cap) by mid-2015, but saw a drop to ~$80 by year-end 2015 and then cut in half one month later ($41 on Feb-1, 2016), from where it has only modestly recovered to around $52. Most SW products nowadays are offered as a service, which Tableau still hasn’t transitioned to as much as others have. That said, it’s Aug-2016 hiring of Adam Selipsky from Amazon Web Services indicates this transition and focus on Tableau Online and scale.

Microsoft is in a unique position for many reasons: It has a very healthy and diverse product portfolio across Windows, Office, Server, Cloud, and others. Most of these help build out a complete BI stack, helping it in the Vision dimension. Furthermore, it can subsidize the development of a large product and offer it free to capture market share. Unlike Tableau, the Power BI price point is near zero, which has helped it acquire a large community of developers, which in turn provides a growing gallery of solutions and plug-in visualization components. Lastly, Power BI is very well integrated with products such as Excel, SharePoint and SQL Server. Many enterprises already invested in the Microsoft stack will find it very easy to leverage the BI functionality.

I don’t have personal experience with QlikView, but enjoy reading on Andrei Pandre’s visualization Blog about it. Qlik was always a bit different, focusing on complex analytics more than mainstream tooling, and it having been taken private in Mar-2016 seems to have reduced its leadership status.

Another Blog I quite enjoy reading is blog.AtScale.com. (such as the 2017 article on the BI MQ by author Bruno Aziza).

I would summarize various factors influencing the BI solutions over the last few years:

  • Visualization tools and galleries – BI tools have reached a high level of maturity around the  generation of dashboards with interconnected components as well as complex interactive visuals such as treemaps or animated bubble charts. Composing the visual presentation is often the smallest part of a BI project, with proper data-mining often taking an order of magnitude more effort and resources.
  • ETL Commoditization – the need to support data-wrangling as part of the solution, not a mix of add-on tools. Microsoft’s SSIS, Tableau’s Maestro, Alteryx Designer Tools, etc.
  • Hybrid Cloud and On-Premise solution – Most enterprises want a combination of some (often historically invested) On-Premise data store / analytics capabilities with newer (typically subscription-based) Cloud-based services.
  • Big Data abilities and Stream processing – Need to integrate popular data visualization tools (Excel, Tableau, Power BI, QlikView) with big data platforms such as Hadoop. Furthermore, ability to analyze data as it is ingested in real-time without the time-consuming post-processing for dimensional analysis (data cubes)
  • Predictive Analytics and Machine Learning – Move focus from reporting (past, what happened?) via alerting (present, what’s happening?) to predicting (future, what will happen?)

 

Two weeks ago I attended the HIMSS’17 conference in Orlando (HIMSS = Healthcare Information Management System Society). I was particularly interested in the Clinical and Business Intelligence track and exhibitors in that space. My overall impression is that adoption of BI tools in Healthcare is still somewhat limited, with the bigger operational challenges around system interoperability and data exchanges, as well as adoption of digital tools (tablets, portals, Electronic Health Record, etc.) by patients, physicians, and providers.

I did see specialty solution providers such as Dimensional Insight. While impressive, their approach seems decidedly old school and traditional. I doubt that any company can sustain a lead in this space by maintaining a focus on their proprietary core technology (such as their Diver platform / data-cube technology). Proper componentization, standard interface support (such as HL7 FHIR) and easy-to-integrate building blocks will win broader practical acceptance than closed-system proprietary approaches.

There are some really interesting systems being applied to healthcare such as IBM’s Watson Health or the new Healthcare.ai platform from Health Catalyst. But that is a story for another Blog post…

 
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Posted by on March 5, 2017 in Industrial

 

Magic Quadrant Business Intelligence 2014

Over the last two years we have posted some visualization and interpretation of Gartner’s Magic Quadrant Analysis on BI companies. The previous articles in 2012 and 2013.

A Blog reader contacted me about the 2014 update; he sent me the {x,y} coordinate data for 2014 and so it was relatively straightforward to update the public Tableau workbook for it. Here is the image of all 29 companies with their changes from 2013 to 2014:

Gartner’s Magic Quadrant for Business intelligence, changes from 2013 to 2014

Gartner’s Magic Quadrant for Business intelligence, changes from 2013 to 2014

With the slider controls for Execution and Vision as well as the changes thereof, it is easy to filter the dashboard interactively. For example, there were a dozen companies who improved in their execution score (moving up in the quadrant):

Subset of companies who improved execution over the last year.

Subset of companies who improved execution over the last year.

Most of the companies improving their execution are niche players, with SAP being the only leader improving its execution score.

Most of the leaders improved in their vision score (moving right in the quadrant), including Tableau, QlikTech, Tibco and SAS.

Subset of companies who improved vision over the last year.

Subset of companies who improved vision over the last year.

 

7 companies, most of them leaders, lost ground on both execution and vision (moving to the bottom-left):

Companies who lost ground on both execution and vision in 2014

Companies who lost ground on both execution and vision in 2014

 

Lastly, I have updated the Public Tableau workbook with the Magic Quadrant as originally published in 2012 with the data for 2013 and 2014. (Click here for the interactive drawing.)

Public Tableau workbook with 7 years of BI Magic Quadrant data.

Public Tableau workbook with 7 years of BI Magic Quadrant data.

 
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Posted by on September 28, 2014 in Industrial

 

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Apple’s Health App and HealthKit – Platforms for Next Gen Healthcare?

Business Analytics 3.0

mobile-applicationsGame on….I think we just witnessed a big next generation leap in Healthcare Data and Analytics.  Apple jumped into the health information business on June 2, 2014, launching both a new health app and a cloud-based health information platform with IOS 8.

The new App, called simply “Health”, will collect a number of body metrics including blood pressure, heart rate, and stats on diet and exercise.  Health will constantly monitor key health metrics (like blood sugar or blood pressure), and if any of them begin to move outside the healthy range, the app can send a notification to the user’s doctor.

The Health app will share all its information with a new cloud platform called “HealthKit.” The new health cloud platform is designed to act as a global repository for all the user’s health information. It will accept data collected by a variety of third-party devices and apps. For instance Nike is now working to…

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Posted by on June 2, 2014 in Medical

 

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