What makes someone a data scientist? This question keeps coming up continuously, so we’ve finally decided to write a blog to make it clear.
It is difficult to come up with one, single definition, because data scientists perform a wide variety of activities and have a wide variety of skills.
Typically, a data scientist can be best utilized working alongside an engineering and product management team to help develop products, and to help provide valuable insights to the business. Some data scientists will even refer to themselves as “data detectives.”
Here are some of the values/services/skills that a data scientist can provide:
Product Analytics: To understand how users interact with the product through analysis of the logs
Data Engineering / Data Pipeline: To automate data collection, aggregation, and visualization to produce metrics that are actionable
Experimentation (A/B Testing): To establish causality that is otherwise too messy or not possible through observational study
Data Modeling: To build predictive models
However, it is important to note that each of these is an individual skill and job. A single data scientist cannot perform all of these job positions.
Here is a great podcast where a data scientist explains in her own words what a data scientist is:
Big Data is bringing new business opportunities to companies around the world. We have officially entered this new age of Big Data, and it’s helping business, education, healthcare and more. This Big Data is offering vast amounts of information at rapid speeds, even in real time. And this information is fueling insights, leading to better decision-making. These better decisions enable huge savings, huge increases in sales, and happier customers. But big business opportunities do come at a cost.
Big Data is not really a new thing. It has been used for years and years; but it seems new because it’s something that is just now becoming affordable enough for it to be within reach for most businesses. Despite the decrease in costs, it still remains pricey.
Many companies are turning to software, such as Hadoop, in attempts to reap the benefits of Big Data, without the Big Cost. Although Hadoop is technically a free software, there are many costs associated with it.
The costs begin right away. The cost to set up the software is estimated at $9,000 a month, and takes around 4 months to do so. But the software isn’t the solution itself; you’ll need to hire a developer to develop the solution. But before you hire this developer, you’ll have to hire a business analyst to determine what question needs to be answered, or what hypothesis needs to be investigated. Then, he will tell the programmer what to code.
Another cost gets added if you have data from other systems that you need to import to your Hadoop cluster. Which is likely, as most companies have over 6 systems. In this case you will have to hire a database specialist to do this for you.
Finally, if you’re trying to get all you can out of the data, you will have to hire more data scientists and analysts. A business analyst alone is not effective when it comes to Big Data analysis.
But, this isn’t exactly fair, or attainable for small or medium-sized enterprises. Even though using data analysis always improves ROI, it is sometimes too large of an up-front cost for some companies – even if they wanted to, they couldn’t.
Luckily, there are options for companies that cannot afford this cost of set-up, maintenance and ongoing analysis. There are some companies that offer end-to-end solutions, however they do not offer their clients the best deal, as they are not customized to the company itself, they are a one-size-fits-all solution. But, there are a select few companies that truly do offer customized, end-to-end solutions at far less of a cost than these others. We did our research and found one for you, that offers customized solutions- in fact, they don’t even list their services on their website in advance. They wait to meet with you, learn about your business, and then tell you how they can help you.
But what’s even better is that they know that some are weary of putting that much cash upfront without seeing on a first-hand basis how it effects/benefits their company. This is why they have created a “special.” They will analyze one of your data sets, for a greatly reduced cost, reflecting a problem area in your company, to see how that analysis alone can help you. You can find more details from this blog about it.
Major commercial buildings use the same amount of energy as over 500 houses. So trying to save energy in commercial buildings is key. But most building management systems (BMS) are trying to find a needle in the haystack, and wasting time doing so.
But new, cloud-based systems are finding the needle in the haystack much quicker than ever imagined. Many UK sites are starting to adopt new data analytics programs and systems, saving something like 1 1/2 million Pounds each year. So, those that have it are saving energy, saving the environment and saving cash.
There are high hopes for the future; thousands of sites will have this data capability. Thus, they will be able save the amount of energy of an entire power house.
There is a glass with water in it sitting on a table- is the glass half empty or is it half full?
The way you answer the question above may determine how you think about IBM’s new solution for the high demand for data scientists. Data scientists are people who can analyze and control analytical programs that work to sort through large masses of data.
As the amount of data the world produces increases, the need for people who can sort through that data and make meaning of it goes up as well. IBM believes that they can aid this problem with their natural language analytics program which is predicted to be able to do the job of a data scientist.
This provokes the question, is this a good thing? Is this a means to an end or is this the beginning of a war between humans and robots for jobs?
Cancer is a horrible illness that has been nearly impossible to figure out. Scientists have had a difficult time finding a cure for cancer because no two cases of cancer are the same. Everyone’s bodies are different and therefore, those who develop cancer do not have identical cancer cells. For researchers this means that it is just that much harder to figure out how to stop cancer because finding a link between cases is like finding a needle in a hay stack.
In the past it has been hard for researchers to gather data on cancer to study, but with the recent development of more advanced technology it is much easier to access information from many different cases. Now that there are easily accessible databases full of medical records concerning cancer, researchers have the resources to dig deeper into what could possibly take down cancer. The problem is that no matter how many researchers you have, it takes a lot of time to comb through large amounts of data in search of a tiny clue. This is where Big Data becomes a great tool for scientists and researchers. With the help of data scientists, Big Data analysis goes in and scans through large data sets to find connections and insights that we may not have caught. Not only can Big Data analysis dig more deeply, but it can do this searching much more quickly than we can.
Big Data allows researchers to focus on connecting the dots between significant findings from the data and apply it to a possible cure. Research can become very expensive the longer it goes on, which is why Big Data can be a valuable asset to potentially find a cure faster. Big Data is a great tool for speeding up processes that could otherwise be very tedious. To learn more about how Big Data is doing big things, check out these informational articles!
Even though certain tough situations play a big role in making us stronger individuals, people generally want things to be easy. Simple is good. That is why we are always trying to come up with new processes and devices to make everything we do easier. Big Data is a great way to make things less complicated and frustrating.
It can become very difficult for companies to try to get inside the heads of their customers. Without any feedback companies have no clue what the public thinks about their service or product. The only telling sign is how many sales or deals are being made, but this is not a very reliable form of evaluation. There are many other key factors that can play in the decrease of business. It may be a marketing deficiency or maybe even location. There could be a horrible review out there that you don’t know about or your customer service is bad. Big Data can seek out these issues and determine which one is the root cause for the lack of success.
With Big Data analytics and a savvy tech team, businesses no longer have to waste their time making and sending out surveys that people will more than likely use to make a cozy fire. Big Data analytics can quickly survey large sums of information and piece together the important connections that lead to hidden insights about what it takes to ensure customer retention. The best part about it is that Big Data is customize-able and can be used in almost any business setting.
To learn more about how Big Data can make YOUR life easier, check out CLIINTEL!