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Casual Encounters Archives

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If keyword arguments are given, the keyword arguments and their values are added to the dictionary created from the positional argument. This pushes marriage back, giving people a longer more about period of time to mingle with fuck buddies. Now you can meet for sex instantly with one ( or multiple ) people without spending countless hours on dates, dinners, movies, and bullshit. But, like Maya Angelou said , When someone shows you who they are, believe them the first time.” If someone you’re interested in says they just want casual sex, believe them.

Campbell also found that men had stronger feelings of being “sorry because they felt they used another person,” whereas women had stronger feelings of “regret because they felt used.” Again, both men and women had experienced some sexual regret, but women were more negatively impacted by some hook-up experiences. About the App: We recommend LesbianPersonals for those gay girls who aren’t looking for serious dating but casual and hot sexual encounters instead. Both women and men are more regretful about one-night stands that did not end in orgasm for them.

KPIs and Data Sciences

Performance shrinked

Key Performance Indicators (KPIs) are so commonplace in measuring business performance. It is not uncommon to find businesses using variety of KPIs to measure the health of the business. KPIs not only help businesses with something tangible to work with but also sets the direction for the entire organization. Its a known fact that, many executive level bonuses are tied to KPIs. Given that KPIs are so important to businesses, is there a way to improve the all pervasive and critical KPIs using Data sciences? I think there is.

To set the context, lets take an example of a Pizza Delivery and lets assume we are measuring the Order to Delivery time. (BTW, this is a good measure because it’s tangible, easily understood, easily agreed upon and difficult to manipulate. We rarely find such simple yet powerful measures in enterprises.)  One can easily and intuitively come up with variables correlated with the order to delivery time. I am listing some of them:

  1. What day and time was the order placed?
  2. From which location was the order received?
  3. What was ordered? (Size, variety etc.,)
  4. Who was delivering?

While intuition is good and works most of the time, your data may show a different story. At a minimum your data may validate your intuitions. But as a data science professional, I would be looking for more. I could answer questions like:

  1. If I move my location of the pizza store, can my delivery time be improved?
  2. Can I optimize my routes and schedules so that delivery time can be improved?
  3. Can I start a promotion by promising delivery time?
  4. Is my data showing a pattern or behavior which can be used as a competitive advantage?

Delivery Time as a KPI could be linear and can be shown in the visuals below (Very unlikely)

Linear model

3D Delivery time

Or Delivery time could be very complex (very likely) and could be represented something like this (source:


Whether the data is simple or complex, there is technology available today to analyze the data and make business sense of the data . Businesses are always in the pursuit of competitive advantage. Your business data is a gold mine and KPIs are a best place to start with for improving business performance.

Time Value of Data


“Justice delayed is justice denied,” is a statement we all have heard. Similarly, the concept of the “time value of data” says that the faster the business information is available to the business, the more valuable it is. In fact, the data or information might lose its value completely after certain period. This is not applicable for every situation, but it is for some.


There are many important business examples which come into this category, such as stock price trends, fraud detection, dynamic pricing, cyber security and bio sensors. Here is a common example we all might relate to. Have you noticed lately that credit card companies are able to alert you if your card is not present during the point of sale? Recently, I signed up for a new business credit card from AMEX and as soon as I do any Internet transaction (or over the phone transaction), I get an email with the subject “Account Alert: Card Not Present Transaction.” Similarly, I did an overseas transaction and I got a phone call from a representative within ten minutes of the transaction, asking me to verify if the transaction was something I authorized. How cool is that? Many such business time-value-of-data scenarios can be accomplished by leveraging today’s technology.



The past decade or so, with the availability of data modeling capabilities used especially in MapReduce by Hadoop, new possibilities and opportunities are made possible for business. Through Apache Spark’s open source innovation, it is becoming much easier for organizations to use these technologies and solve real-world problems. In other words, new unexplored frontiers are possible using these innovations. Similarly, SAP’s Hana technology was built in the same manner – through innovations such as in-memory computing. In addition to time processing improvements, what I like about Apache Spark innovations is the attempt to bring Data Science skills to everyone. It is Data Science made possible for the masses. Much complexity in Data Sciences prep-work is abstracted out, and Data Scientists can quickly start doing their real magic.

Trends, Anomalies, Opportunities, Patterns and Risks

Conceptually, using these technologies you can build systems for your business operations, so that you can recognize any of the following:

  • Trends
  • Anomalies
  • Opportunities
  • Risks
  • Patterns

You can easily put in thresholds and tie business actions to the data activity. For example, a retailer can automatically reorder from his or her suppliers based on a reorder point set in advance. Similarly, real time offers can be given to Internet shoppers based on the situation they are in. For example, dynamically giving an instant coupon for a screen protector once the retailer knows that the Internet shopper has bought a smart phone.


While the cost of doing such high impact technology projects is relatively much cheaper compared to the ROI, the cost of not doing such projects is much more. I think the call to action for businesses is to make a list of high impact pain points and start increasing business value through Data Science projects.

“Accelerate growth and expand margin” – Isn’t a buzz word statement anymore.

Accelarate your growth

BBusinesses love the statement “Accelerate growth and expand margin.” They also like statements like “reduce risk,” “improve market share,” “multiply shareholder value,”  “sustainable value creation,” and so on.   Many reputed consulting firms modify these statements in many ways to sell their expensive $400 an hour services.  Please don’t get me wrong, I am not diminishing the value of these services. What I want to point out is that with the innovations and disruptions happening in the field of Data Science, many of these blanket and seemingly “buzz word” statements can be mathematically validated before executing billions of dollars in investments. The more I think of it, the more it makes sense for organizations to combine business consulting and Data Sciences to unleash true value.

Are these Data science problems or Business Consulting problems?

According to IDC’s Big Data and Analytics Maturity Survey, the following areas have been identified as significant drivers of Big Data and Analytics initiatives:

• Product or Service Improvement and Innovation
• Customer Service and Support
• Market and Competitive Intelligence
• Pricing Strategies and Programs
• Process and Operations Optimization and Control
• Customer Acquisition and/or Retention
• Regulatory Compliance and Financial Controls
• IT Optimization and/or Modernization
• Supply Chain Management and Logistics
• Human Capital Management
• Design, Maintenance and Use of Plants, Facilities, and Equipment
• Operational Fraud and Malicious Risk Management

This is a list for Data Science, but one can also see that almost all of the above are of interest in business consulting. In many organizations today, Data Science teams work closely with engineering teams but they are part of business teams. This was not the case a few years back.

Decisions Based on Intuition Should be a Last Resort




Let us look at the core area of focus for many businesses. From our list, let’s take for an example, “Product or Service Improvement and Innovation.” Market success or failure in this area will probably define the future existence of the company. How does a company know if the new product or service will be a success?  Many companies end up taking big investment decisions intuitively, but this should be a last resort. Taking decisions intuitively and quickly makes sense when you are on a deadline and have no data to back you up. Looking at historical data is a good start but not good enough for big tick-et spending. If you are making a long term investment decision without empirical evidence, you are probably taking too much risk.  When technology is there to help you to validate your decisions, why not use it?
Organizations should not get bamboozled by big pitches and succumb to spending billions of dollars. There are many fancy names for the combination of business consulting and data sciences.  I like the term “Decision Sciences.”  Whether an organization is embracing technology or not, it has a clear choice. An organization can improve its decision making by exploiting both business consulting and Data Science offerings.  Theoretically, organizations can validate every major investment decision.

Don’t just join the Bandwagon… Accelerate in Data Sciences.

The ROI on sound Data Science investments is insanely high. Leaving money on table is never a good decision.  Building something valuable and sustainable needs high-quality implementation. Becoming a “me too” player without a competitive strategy is not going to help you. Faster and successful implementation of Data Science means faster to market and faster to money, but doesn’t mean you forget quality.

Here are a few strategies to accelerate quality Data Science implementation:

Hire a Chief Data Scientist

Finding the right leader is going to be a key factor to build your Data Science competency. Sounds easy, but the way this field is growing, it’s not going to be that straightforward. Here are the reasons why:

  • Your Chief Data Scientist should know your business extremely well.
  • He or she should be an expert in the statistics of Data Science, and should have already solved three or four big ticket business problems.
  • He or she need not be a techie, but should have exposure to various technologies in the market.
  • He or she should be a leader, with a proven history of managing high-performing teams, preferably in the field of Data Sciences.
  • Academic excellence, such as having a PhD in the field of Data Sciences, will definitely help.

Once you hire the right person, develop a business strategy with them and a roadmap of what areas to address to build a sustainable, competitive advantage. Your Chief Data Scientist also needs to think of tactical issues, such as quick wins, a hiring plan, a budget, ROI measurement, and so on. Build your strategy in such a way that it is foolproof, but also, if you fail, make sure you fail quickly and learn from the mistakes.

Get External Help

I am not saying to get external help as a provider of Data Science services, but I am saying this as an advocate of high quality Data Science implementations. Like any other evolving technology domain, the field of Data Sciences is complex, exhaustive and expensive. While the ROI is huge, hiring full-time, accomplished Data Scientists is not going to be cheap. There is an imbalance between supply and demand.  Going for external help will accelerate your implementation and mitigate the risk of failure. You have the option of starting with small investments, with limited scope and quick turnaround times (say 3 to 6 months). Expand your investments after ensuring that this is a good use of money and a good strategy. Make sure the provider has enough credentials to support Data Science competency.  They will be part of your organization’s holdings for its own competitive advantage.

Use a Solution Accelerator Approach

Solution Accelerators are quasi-products which are (as the name says) designed to accelerate a solution implementation. There is no off-the-shelf product in the field of Data Sciences, and I doubt if there is going to be a plug-and-play Data Science product in the future. However, with the help of Solution Accelerators, you achieve the following:

• Accelerate implementation
• Faster time to market and money
• Have proven and tested solutions that have already worked for other business cases
• Have open methods, because they are customizable


Hiring full-time employees and building in-house competency is a suitable strategy for a long-term approach. However, getting external help and using a Solution Accelerator approach will accelerate your Data Sciences and Big Data implementations. Combining all three strategies is probably the wisest way to approach building Data Sciences competency.

Big data’s transition from Hype to Hope

“Big data” has crossed the Hype phase and entered into the Hope phase. Today, both business decision makers and technology decision makers are aware of the basics of leveraging Big data. This is mostly true especially for the organizations that are already leveraging technology to run their core businesses.  Nevertheless, the quality of your business actions determine if you are truly leveraging the power of big data.

Before we add “big” to the “data”, we need to understand about how “data” is relevant to your business. Let’s start with one simple question you need to ask about your organization:

Is your company relying on data to take business actions?

For most of the organizations in today’s world, the answer would be “Yes”. However, there is a whole spectrum of organizations, which fall into this category. On one end, there are companies which take business actions but are truly transactional in nature. On the other end, there are companies, which take data based business actions which can drastically change the way they do business.


Now, whether you add “big” to the word “data” or not, the quality of the business actions you are taking determines how effectively you are using data for your organization. With the advent of the Internet, social media, and sensors, there is a ton of data out there, which is probably relevant for your business. Your organization will probably miss the bus, if you are not opportunistic in evaluating the quality and maturity of your business actions.

Undoubtedly, big data demonstrates hope, but organizations need to evaluate the quality of data based business actions to unleash its full potential.