22 tips for better data science

These tips are provided by Dr Granville, who brings 20 years of varied data-intensive experience working with successful start-ups, small companies across various industries, and eBay, Visa, Microsoft, GE and Wells Fargo.

1.     Leverage external data sources: tweets about your company or your competitors, or data from your vendors (for instance, customizable newsletter eBlast statistics available via vendor dashboards, or via submitting a ticket)

2.     Nuclear physicists, mechanical engineers, and bioinformatics experts can make great data scientists.

3.     State your problem correctly, and use sound metrics to measure yield provided by data science initiatives.

4.     Use the right KPIs (key metrics) and the right data from the beginning, in any project. Changes due to bad foundations are very costly. This requires careful analysis of your data to create useful databases.

5.     Fast delivery is better than extreme accuracy. All data sets are dirty anyway. Find the perfect compromise between perfection and fast return.

6.     With big data, strong signals (extremes) will usually be noise. Here's a solution.

7.     Big data has less value than useful data.

8.     Use big data from third party vendors, for competitive intelligence.

9.     You can build cheap, great, scalable, robust tools pretty fast, without using old-fashioned statistical science. Think about model-free techniques.

10.  Big data is easier and less costly than you think. Get the right tools! Here's how to get started.

11.  Correlation is not causation. This article might help you with this issue.

12.  You don't have to store all your data permanently. Use smart compression techniques, and keep statistical summaries only, for old data. Don't forget to adjust your metrics when your data changes,to keep consistency for trending purposes.

13.  A lot can be done without databases, especially for big data.

14.  Always include EDA and DOE (exploratory analysis / design of experiment) early on in any data science projects. Always create a data dictionary. And follow the traditional life cycle of any data science project.

15.  Data can be used for many purposes:

·        quality assurance

·        to find actionable patterns (stock trading, fraud detection)

·        for resale to your business clients

·        to optimize decisions and processes (operations research)

·        for investigation and discovery (IRS, litigation, fraud detection, root cause analysis)

·        machine-to-machine communication (automated bidding systems, automated driving)

·        predictions (sales forecasts, growth and financial predictions, weather)

16.  Don't dump Excel. Embrace light analytics.

17.  Data + models + gut feelings + intuition is the perfect mix. Don't remove any of these ingredients in your decision process.

18.  Leverage the power of compound metrics: KPIs derived from database fields, that have a far betterpredictive power than the original database metrics. For instance your database might include a single keyword field but does not discriminate between user query and search category (sometimes because data comes from various sources and is blended together). Detect the issue, and create a new metric called keyword type - or data source. Another example is IP address category, a fundamental metric that should be created and added to all digital analytics projects.

19.  When do you need true real time processing? When fraud detection is critical, or when processing sensitive transactional data (credit card fraud detection, 911 calls). Other than that, delayed analytics(with a latency of a few seconds to 24 hours) is good enough.

20.  Make sure your sensitive data is well protected. Make sure your algorithms can not be tampered by criminal hackers or business hackers (spying on your business and stealing everything they can, legally or illegally, and jeopardizing your algorithms - which translates in severe revenue loss). An example of business hacking can be found in section 3 in this article.

21.  Blend multiple models together to detect many types of patterns. Average these models. Here's a simple example of model blending.

22.  Ask the right questions before purchasing software.

Interview with Jill Dychè on Data Management in the Era of Big Data

Guest blog: www.biblogg.no

 

Av

Jill Dyche

Jill Dyche, Vice President of SAS Best Practices, was keynote speaker at SAS Forum Norway in September, and was interviewed by Lars Rinnan 

Jill, you delivered a strong keynote, and the audience was really attentive.
You talked about big data and how to get the c-suite to listen. It sounds almost impossible. How do you get them to listen?

You need to meet executives where they are. In other words: figure out what’s important to them now, and then map big data as the answer.
Here’s an example: A large cable company sees an uptick in customer complaints in its call center. They have to add expensive headcount to the support staff. But they decide to incent customers to use social media interactions to ask for support or lodge complaints.  By adding social media transactions to customer profiles, the cable company can not only monitor valuable customers who may be at-risk, it can also “score” its brand reputation based on text analytics of social media interactions. They understand that over half of customer feedback comments are actually installation questions and not complaints. They develop customer support videos and post them on YouTube. Both questions and complaints are reduced, and support staff can be redeployed to cross-selling functions.

Data governance is essential, but how do you get the CXO interested?

Find the problem the CXO needs to solve, and explain how data enables the solution. Many senior executives don’t make the link between a business need and data. If you “deconstruct” the business problem into the data necessary to solve it, you can see the lights go on with executives.


How would you start a data governance process at a large company who has no clue of data governance?
We recommend starting with what I call a “small, controlled project.” Take a business problem, scope it down to a level where data can enable it quickly, and then implement a well-bounded process around data rules and policies necessary to address it.


How is data governance related to big data?

Big data is like any other data: It requires policy making and oversight. In that respect big data should be beholden to larger rules. For instance, data from sensors or devices that may be streaming into your company should be handled in a different way. Is it sensitive? Is it defined? Is it targeted to be consumed by a department? An individual? A machine? All of these factors, and others, should inform the policies around that data. And that’s data governance.

In your experience, are businesses giving data governance enough attention in terms of resources, technology and funding?

Only after they feel enough pain. Very few companies new to data governance actually say, “Hey! Let’s make sure we factor data governance into this new initiative.” Most have to experience the pain of not having the right data for the right business purpose. Go back to the days of Customer Relationship Management and remember how everyone thought they could just plug in a new tool? The validity of the data is directly proportional to the value of the resulting application. Data can no longer be considered an afterthought.

Thanks you for sharing these valuable insights with the readers of biblogg, Jill!

KPI for Hospitality Business

Important Calculations (BI) for Hospitality



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
Provided by: Maia Intelligence, edited by: Besim Ismaili