The company that perfects Data Visualization in Virtual Reality will be the next Unicorn

Fortune 500 companies are investing staggering amounts into data visualization. Many have opted for Tableau, Qlik, MicroStrategy, etc. but some have created their own in HTML5, full stack JavaScript, Python, and R. Leading CIOs and CTOs are obsessed with being the first adopters in whatever is next in data visualization.

The next frontier in data visualization is clearly immersive experiences. The 2014 paper "Immersive and Collaborative Data Visualization Using Virtual Reality Platforms" written by CalTech astronomers is a staggeringly large step in the right direction. In fact, I am shocked that 1 year later I have not seen a commercial application of this technology. You can read it here: http://arxiv.org/ftp/arxiv/papers/1410/1410.7670.pdf

The key theme that I hear at technology conferences lately is the need to focus on analytics, visualization and data exploration. The advent of big data systems such as Hadoop and Spark has made it.

Picture Source: VR 2015 IEEE Virtual Reality International Conference

possible - for the first time ever - to store Petabytes of data on commodity hardware and process this data, as needed, in a fault tolerant and incredibly quick fashion. Many of us fail to understand the full implications of this inflection point in the history of computing.

Storage is decreasing in cost every year, to the point where you can now have multiple GB on a USB drive that 10 years ago you could only store a few MBs. Gigabit internet is being installed in cities all over the world. Spark uses the concept of in memory distributed computation to perform at 10X map reduce for gigantic datasets and is already being used in production by Fortune 50 companies. Tableau, Qlik, MicroStrategy, Domo, etc. have gained tremendous market share as companies that have implemented Hadoop components such as HDFS, Hbase, Hive, Pig, and Map Reduce are starting to wonder "How I can I visualize that data?"

Now think about VR - probably the hottest field in technology at this moment. It has been more than a year since Facebook bought Oculus for 2Billion and we have seen Google Cardboard burst onto the scene. Applications from media companies like the NY Times are already becoming part of our every day lives. This month at the CES show in Las Vegas, dozens of companies were showcasing virtual reality platforms that improve on the state of the art and allow for a motion-sickness free immersive experience.

All of this combines into my primary hypothesis - this is a great time to start a company that would provide the capability for immersive data visualization environments to businesses and consumers. I personally believe that businesses and government agencies would be the first to fully engage in this space on the data side, but there is clearly an opportunity in gaming on the consumer side.

Personally, I have been so taken by the potential of this idea that I wrote a post in this blog about the “feeling” of being in one of these immersive VR worlds.

http://sarcastech.tumblr.com/post/136459105843/data-art-an-immersive-virtual-reality-journey

The post describes what it would be like to experience data with not only vision, but touch and sound and even smell.

Just think about the possibilities of examining streaming data sets, that currently are being analyzed with tools such as Storm, Kafka, Flink, and Spark Streaming as a river flowing under you!

The strength of the water can describe the speed of the data intake, or any other variable that is represented by a flow - stock market prices come to mind.

The possibilities for immersive data experiences are absolutely astonishing. The CalTech astronomers have already taken the first step in that direction, and perhaps there is a company out there that is already taking the next step. That being said, if this sounds like an exciting venture to you, DM me on twitter @beskotw and we can talk.

10 Minutes to Data Science

I just followed John Hopkin's Executive Data Science team. In the first chapter of the course they talk,

*In Data Science, the importance is science and not data. Data Science is only useful when we use data to answer the question.*
It actually true in some point. I've actually seen too many companies that brag how big they data are but they don't actually know how to pose a question. If the data can't be used to answer the question that makes the company growth, it's not a good idea. They press even more to the point of data, data, data but really is in the end, machine learning will just feed into it, to make a better prediction to answer the question. It's critical to pose a question first, then try to get/build data to answer the question. And more importantly, don't be afraid to get other data from sources outside your company.
When investigating a problem and communicating to the broader audience, it's important to find the right question to answer, and then find the data related to answering the questions. This how all data science work will look like. Even in A/B testing when we measure the confidence interval between control and experiment group, the real question is still about how we can use data to answer our question.
In that course, Jeff Leek continues in term of Money Ball. We can find some evaluation metrics to measure player's skills, but the key important to answer questions is, "Can we be a winning team with a small budget?" Creating the best predictive model is not the most important, in the case of Netflix prize, one-million-dollar algorithm can't be implemented because it's impossible to scale with all of the customer combined.


Statistics and Machine Learning

Statistics mainly divided into two parts, Descriptive Statistics and Inferential Statistics. Descriptive Statistics, as the name implied, is using statistics to better understand the data. This includes using summary statistics and visualization to explore the data. Jake Vanderplas, the author of Python Data Science Handbook, showed how to use this method to understand the pattern of Seattle's Bicycle Habits in his blog.
While Inferential Statistics let you use Hypothesis testing and Confidence Interval to make an inference about your assumption. Suppose you have two group with numerical variable (female age vs male age), and you want to found whether these groups is significantly different with each other. Statistical Inference need you to follow experiment design, so that your inference can generalize well to your population of interest, and also found correlation that suggest causation. This method is useful to get insight, whether the relationship of two variable is correlate with each other.
Machine Learning is one of the fields in artificial intelligence that give a machine a capability to learn about your data. Thanks to the modern era, where computers have grown computation power and hype of data science , machine learning has grown into broad area. Two of the interesting topic is Supervised Learning and Unsupervised Learning. Supervised Learning is where given a set of input and output, machine learning tried to predict future output given future input. Think of it as a student that given a bunch of papers of quiz where it has a wrong and right answer, he learns and will able to answer the quiz. While the example of Unsupervised Learning is clustering algorithm like we discussed earlier.
Machine Learning is a different use case when compared to Statistical Inference. Have you seen Kaggle competitions? take a look around at their leaderboard, and all that score is extremely close. Often top 100 hundred is already a winner. They're willing to go into 2-3 times more complexity to get 1% increase. This is not practically possible, and we saw from Netflix prize, they give up on the one million dollar code because it's computation is expensive. So if you in for the accuracy games, go ahead! Otherwise, a simpler model is better.
So what do you use when you want to make a prediction? use Statistical modeling to understand what your prediction is. Use machine learning to make your prediction better. Statistical modeling concerns about the complexity model because we have to understand it, but machine learning scale as complexity increase. Moreover, Machine Learning concern about parameter tuning to performance, statistics concern about parsimonious model (get understanding bett

Software Engineering

So why is software engineering is important in data science? Because often you will have to get data using programming language. Sure there is some reporting that you can be download from Google Analytics or any dashboard in your company, but it's only summary and aggregation metrics. Things will get harder if you need advanced or specialized metrics that would need you to create aggregation yourself. Log data will always have some messy data. And if you have human-input data, there's going to be a human error. In order to do that, you need engineering skills. Pulling data from database alone need some programming or SQL skills. In case you need additional data from open data in the web, you also need programming to get data through API.
So engineering skill is a critical part of data science. In fact, it's so critical that you won't get the fun part, analyzing and making inference/prediction if you don't have engineering skills. You don't even know how to get data and clean it. At least software engineer alone can still do something. They can get some data and validate through analyzing inconsistency, and make some descriptive statistics to analyze the data.


Toolbox

One of data science toolbox is often a debate between choosing R or Python. But they are two different things. Python comes from software engineer background. Since Python famous first at web development, gathering and manipulating data has becomes very important. On the other hand, R has statistician background. There is widely variant of statistical packages available. And as statistician also use visualization to get insight about data, it also rich with visualization packages. So when processing the data use Python, and doing R when you want to analyze. Of course, sometimes these two overlaps, and you can choose to stick to one language. You can manipulate string in R, or do statistical analysis in Python. You have few choices when you want to present your result of analysis. If narrative, you can use Jupyter or Rmarkdown to storytelling your analysis in a narrative way. Sometimes you end-up want to engage your audience based on your findings. In this case, you want to create interactive visualization. D3.js is great in this area.
So when you talk about data science, think more about the question that you want to answer. Can you get some information if you have to answer the question? Do you have useful data to answer your question? If you could even answer it, is the answer practically possible? By doing this series of questions, it will avoid your missteps in the long run.

KPI for Hospitality Business

Hospitality service in numbers

Key Performance Indicators (KPI) for Hospitality industry help remove the guesswork from managing the business by checking the numbers that tell what’s really happening.
There’s a business saying: ‘If you can’t measure it, you can’t manage it!’ Real, responsive management needs reliable and truthful figures on which decisions can be based. If there are problems, you can take corrective action quickly. If you are having success, you’ll know to do more of what you’re doing! Good figures also give you a wider understanding of your success – sometimes if it’s a quiet month (when your suppliers are telling you that ‘everyone’s quiet!’) you’ll see that some of your KPIs are actually improving (ex. sales per head).
KPIs in Hospitality industry can be categorized for functions like Reception, Housekeeping, Maintenance, Kitchen, Restaurant, Sales, Store, Purchasing, etc.
Staff KPI:
- Wage Cost %: wage costs as a percentage of sales
- Total Labour Cost %: not just wages but also the other work cover insurance, retirement and superannuation charges and other taxes that apply on your payroll
- Total Labour Hours: how many hours worked in each section. This is useful to compare against sales to measure productivity
- Event Labour charge-out: Hotels usually charge-out service staff at a markup on the cost of the wages paid. Are you achieving a consistent mark-up?
- Labour turnover: number of new staff in any one week or month
- Average length of employment: another way to look at your success in keeping staff. Add up the total number of weeks all your people have worked for you and divide this by the total number of staff
- Average hourly pay: divide the total payroll by the number of hours worked by all staff
Kitchen Management KPI:
- Food Cost %: measured by adding up food purchases for the week and measuring them against your food sales
- Total Food Costs: how much was total food bill? Sometimes a useful figure to show staff who think you are made of money
- Food Costs per head: see every week how much it costs to feed an average customer
- Kitchen Labour %: measure kitchen productivity by comparing kitchen labour against food sales
- Kitchen Labour hours: how many hours worked in this section? Compare against sales to measure productivity
- Stock value: food stock holding- It should be less than a week’s use, but can slip out if you are storing frozen food
- Main selling items: weekly sales from POS or dockets & know the best sellers and map these on the Menu Profitability
- Kitchen linen costs: cost of uniforms, aprons & tea-towels can be a shock! How many tea-towels are being used each day?
Front House Management KPI:
- Total Sales Per Head: total sales divided by number of customers. This may vary between different times of the day
- Number of customers: simple! A good measure of popularity
- Food, Dessert, Beverage Sales per head: how much your menu appeals to your customers (do you have all the choices they want), & how well your staff are selling.
- Seating Efficiency: how well are tables being turned over while still offering high quality customer service
- Basket Analysis: how many items do lunch customers buy? What else do morning coffee drinkers order? Grab a pile of dockets and look for ordering patterns
- Linen costs: uniforms, aprons etc.
- Front of House Labour %: how many hours worked in this section? Compare against sales to measure productivity
- FOH Labour hours: how many hours worked in this section? Compare against sales to measure productivity
- Customer satisfaction: Feedback forms, complaints and other methods that are hard to quantify sometimes but worth making an attempt.
- Strike rate: if 500 people came to hotel last night & only 100 ate at the bistro, your ’strike rate’ would be 1 in 5, or 20%
- RevPASH Revenue per Available Seat Hour: take the total number of ’seat hours’ and divide total revenue for a period by this number
Bar & Restaurant Management KPI:
- Sales per head: how much your beverage and wine appeals to your customers and how well your staff are selling
- Gross Profit on sales: difference between what you sold and what it cost you. The sales mix can influence this heavily
- Average Profit % on sales: useful to see if your sales are holding steady, although ultimately the actual Gross Profit (real money) will matter the most
- Stock value: It’s worth checking with your suppliers and seeing how much you can order ‘just in time’
- Stock turnover: how fast is your cellar stock selling?
- Carrying cost of stock: what is the cost of financing the stock?
- Sales / stock-take discrepancies: Alcohol is security problem, & keeping an eye on ’shrinkage’, staff drinks and stealing a constant problem
Banquet Sales Management KPI:
- Number of customers: simple! A good measure of popularity.
- Visits by your top 100 or 200 customers: they provide a huge proportion of your sales! Track their frequency and spending – these people are gold!
- Sales per head: across all areas
- Marketing and advertising costs: total value of spend, always trying to measure it against response
- Response rates: how many people responded to different campaigns and what effect did this have on profit?
- Press mentions: keeping your eyes open for favourable mentions
- Bookings: in the current week and month and coming up. Also in peak times, eg Christmas.
- Event inquiries: No. of inquiries about large bookings & functions, especially if a campaign to promote them is on
- Sales inquiry conversion rate: No. of inquiries that turn into actual sales. why so few people were ‘converted’ – was it the quality of the promotional material, skill of the sales staff, pricing or make-up of your function menus and facilities?
Finance & Admin Management KPI:
- Cash position at bank: how much do you have available after reconciling your cheque book?
- Stock-take discrepancies: measure of efficiency of each department, but also of administrative systems in place
- Total accounts due: how much do you owe?
- Total accounts payable: needs careful management if you have accounts, eg large restaurants
- Return on Investment: profit business makes can be measured as a percentage return on the amount invested in it
- Taxes owed: to know how much is owed at any one time so it is not ’spent’
- Sales & costs: actual figures compared to what budgeted for a period
- Administration labour costs: strong and skilful administrative support will be essential to manage the KPIs listed above!
- IT efficiency: how much down-time for IT systems? How accurate is the POS system?
Other KPIs:
- Revenue per available room
- Average daily rate of rooms
- % of occupancy of rooms
- Average cleaning costs per room
- % of reservation requests cancelled with / without penalty
- % of rooms with maintenance issues
- % of cancelled reservation requests
- Average number of guests per room
- Average length of stay of guests
- % of non-room revenue
- % of cancelled rooms occupied
- Kilowatt-hours (kwh) per room
- Number of hotel guests per employee
- Gross operating profits per available room
- % of guests who would rank stay as exceeding expectations
- Waste per night per occupied bed space