Data Science in Business

 

In 1987, 30 years ago, the amount of all digital data in the world was 8 gigabytes. It can be saved several times by a modern flash drive. Nowadays the amount of all digital data is about 10 zettabytes or 10 trillion gigabytes. Take into account, that 1,5 Mb of information per person is creating every second in the world, the increasing all data amount over the next 5 years fourfold sounds plausible. And, if you could invest in the data just like in stocks, maybe it would have been a successful investment in your life.

At the same time, the cost of data storage fall down every year. For instance, in 1981 Apple presented hard drive with 5 megabyte with price tag at $ 3,500. It means that the cost per megabyte is $ 700. Today, the cost of storage is 35 thousand times lower – 0.02 cents per megabyte (without taking into consideration inflation).

The cost of data-processing has fallen down too. In 2002, $ 100 million was spent to decode the human genome. In 2008, the decoding of the genome cost $ 10 million. And today there are companies on the market that will completely decode your DNA for about $100, other words, cost become cheaper ago in million times than 15 years.

So, there are three main factors:

  1. increase of the amount of available data
  2. fall of data storage cost
  3. development of analytical tools for data-processing
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They together gave the growth of data science era. “The revolution of Big Data” brought technical advances in storage sector of data cloud computing. These things help business to compute similar patterns for future events. Today, Data Science algorithms help to predict everything from influenza outbreaks to crime-related deaths.

Boom of Data Science

The main reason is the hidden efficiency that data is contained. Every serious company collects its data and analysis, allowing company to open the road to new discovery: make better products, attract more target customers and keep them, improve business process, etc.

Why is data science perceived as a kind of “magic pill”? The reason of this is that data science helps you to draw objective conclusions from existing data, which are not available to person, because of human bias and prejudice.

“History doesn’t repeat itself but it often rhymes” — Mark Twain

Demand from the business area creates a great demand for specialists. Only in the USA in the next three years, expected a shortage of about 190,000 specialists. Interest of applicants also did not keep waiting.

Every industry needs Data Science

Data Science can be useful everywhere. No matter what industry your business operates. Let’s look at the popular industries and think how Data Science can help them:

Insuarance. Machine, which reason for being is forecast risk and use results to inform pricing, will better do analytical work than anyone else in case of providing enough historical and other information for learning.

Finance. With the strategies developed, using Machine Learning, you can create more profitable loans for people who will repay with higher probability.

Pharma and Biotech. In Medical area Data Science can help with many things, beginning with clinical research, ending of creating new more effective drugs.

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Organization Management. In company every employee have many tasks, that he should be done during his work time. For increasing productivity in company’s atmosphere, and better working hours optimization, you can use Data Science with data-driven improvement plans.

Information Technologies is the largest area of Big Data. You can improve everything in your business, beginning with analyzing existing customers’ experience for building new products and ending to the finding out the most risks investment strategies.

Oil & Gas. Using Data Science for predicting different things is very actual nowadays. Oil & Gas are the main energetic minerals in the world. You can do research in pricing or in creating new production technologies safer. 

Logistic. Transport issue is very important for retailers and factories. Big Data can do hard analytics job for finding more attractive supplier and the cheapest delivery. No extra money spending for special outsourcing teams.

Government. Smart cities will new trend for developer are in the nearest 5-10 years. Machine Learning will provide the help in cases, like traffic jams, high population density, optimization of finding work for application and so on.

Healthcare. Usual doctors can assess Machine Learning help, when they have difficult situation with making the correct diagnosis, for example.

Netflix Experience

There is no surprise, that Big Data can be used in business or finance, but guys from Netflix have decided to prove that this technology is useful in media business. Let’s look at three main parts in Netflix’s service: data collection, analysis of data and personal recommendations.

Data collection. Netflix doesn’t only distribute content, but also produces it. Usually, the companies film pilot series and show them to targeted focus group that say their verdict, or they let pilots go straight on TV channels. But Netflix was more twisted. It has collect all information about their users:

1)profile data; 2) favorite films / serials; 3) actors / directors users had looked for; 4) how often users watch a certain genre; 5) data on age, location, time of day and days of the week of viewing and so on.
Also, Netflix even knows if you skip over the titers, at what point they paused and when they returned to viewing. And now imagine how much data the company collects from one person. Nowadays, Netflix has more than 100 millions active users.

Data analysis. The perfect example is “House of Cards”. Netflix found out the number of requests with Kevin Spacey, the number of views of David Fincher’s films and the number of views of the original “House of Cards” series. That’s all – no focus groups, no pilot series, ready base. It remains only to find writers and begin production of the series. The next part – advertising. If you like Kevin Spacey, look the trailer with him. Are you a housewife? Here’s trailer to you with Robin Wright – a strong female character. Are you excited about quality thrillers? The director is David Fincher! I hope you understand how it works. Enormous attraction of the audience was guaranteed. The Netflix schema is as simple as it is ingenious. Everything was hiding in the open, it was only necessary to use the available data competently.

Rating grow
Rating fall

Personal recommendations. We have already talked about personalized trailers to involve a larger audience in each project. About 75% of the activity in Netflix is based on recommendations. But the company has done one interesting thing, for each subscriber it selected a personalized cover of the content. For example, for those who love romantic movies, Netflix will show a romantic cover, for comedy fans – something fun.

Diffirent covers for different

More information look here.

Our teams did the following projects in Data Science

Well-tested process of work

At Eulerr our main goal is to deliver the results of work ASAP. As the results, you give the report, which  will include:

  1. assessment of current situation in your deal
  2. research of possible weakness
  3. upgrading and improving proposals
  4. forecast of future results, using Data Scientists’ suggestions.

You will have detail statistics of different testing hypothesis with visual addition. Here is the example of using different algorithms based on Machine Learning  for best sell optimization (Picture 1 and Picture 2).

Picture 1
Picture 2

During the process, we use sprints, each two weeks a team brings results. The sprints can easily be reorganized in accordance to your needs. More important here is that you don’t need to wait long to meet the product and you can follow the progress constantly.

No risk

The first two-weeks period is trial. Only after complete satisfying with the first results, we will charge you for the time and continue with our collaboration.

Start a new project. No risk, no fees. We practice only
performance payment.

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