"Big data" has become a popular buzzword in a host of different industries. It's the cutting edge of technology and statistics. Without the technical know-how to chisel useful insights out of the copious amounts of data generated, though, big data is useless.
The appeal of conjuring up actionable intelligence out of data that an organization is already generating and collecting is extremely tempting for many companies. Today data scientists who know how to work with big data are finding their skills in high demand in many different industries, including the financial sector.
Of course, there is virtually no corner of the commercial world that has not felt the influence of modern information technology. Data is spread throughout every industry and niche, and today it can be put to work in ways that would have been unimaginable just a decade ago. As an example, look at the healthcare system in the United States. A recent McKinsey report estimates that embracing big data principles could cut healthcare spending by 300 to 450 billion dollars. This represents 12 to 17 percent of total US healthcare costs - a fraction that should be clearly significant to any observer.
Data issues strike the other side of the balance ledger, though. Experts estimate that bad data and data handling errors contribute to about $3.1 trillion in lost potential revenue every year in the United States.
Every day it gets clearer and clearer that collecting, processing, and analyzing data is a vital job in every kind of organization. This is the data scientist's time to shine. With the increasing popularity of big data these professionals are beginning to get recognition for all the hard work they do, the money they save, and the revenue that they generate. Although their role is becoming more well known, many professionals on the executive and managerial levels are still rather ignorant about the potential role and value of data scientists in their own organizations.
The Job Of The Data Scientist
Most professional data scientists have received high-level training in math, computer science, and statistics. Degrees in these fields are very common in the industry. Good data scientists also have extensive experience in working with big data. If you want to hire one, Harnham have a great selection of applicants.
This means they are skilled at managing information, visualizing data, and data mining. Closely-allied information management fields include cloud computing, infrastructure design, and data warehousing.
Situations that call for the insight of data scientists include:
* Organizing and using large amounts of operational / customer data
* Deriving value from credit data, social media streams, third-party data sets, or consumer research
* Manipulation of data on large scales
How Data Scientists Deliver Value To Commercial Organizations
While new roles for data science in business are being developed every day, the benefits that these professionals bring to the table are already well-established. Here are eight of the most important advantages of incorporating data scientists and their work into an organization:
1) Managers And Executives Get Better Insight For Decision Making
Experienced data scientists are good not just at juggling numbers but also at explaining the implications of their work. They translate their own analytical acumen into clear, actionable advice for institutional management. Good analytics produce insight at every level of an organization from the top to the bottom. Besides shring the benefits of what they learn with high-level managers and decision makers, data scientists are also adept at establishing procedures that make it easier to make smart choices at every level. Data scientists help establish and manage effective performance metrics for all of an organization's operations.
2) Identifying Trends And Establishing Goals
Data scientists uncover useable information that lies hidden in the depths of the organiztion's data and use it make recommendations and predictions about future performance. These insights can be harnessed to improve efficiency, refine the quality of the organization's work, and make it more profitable.
3) Training Staff Members To Use Analytics
As touched upon previously, data scientists are capable of building effective analytic tools for use at every level of an organization. Good scientists don't just create the tools, though; they also take the necessary steps to ensure that those tools get put to use. A large part of this job is demonstrating to staff members how the organization's new and existing analytic tools can help them do their jobs better. This vital support effort makes data scientists valuable members of the team.
4) Opportunity Identification
In the course of assessing an organization's analytical resources - and overhauling them, if necessary - data scientists become intimately familiar with the way data is collected and work is done throughout the system. This gives them a superb "top to bottom" perspective that allows them to spot opportunities that might otherwise go unnoticed.
5) Quantifying Risk
Because they frame an organization's choices in objective, quantifiable statistics, data scientists reduce the amount of risk involved in making key decisions.
6) Tracking Outcomes
It can take an enormous amount of work to implement the changes required after a major decision. How long will it take to determine whether or not the correct choice was made? Data scientists strive to minimize this time following key decisions. They can identify, quantify, and tracks the metrics which will reveal whether or not a previous decision was the correct one.
7) Audience Identification And Market Research
In the modern data environment, even organizations that aren't directly involved with the public need to devote more time to customer research. There are any number of different ways to collect information on customers, from handing out surveys to plugging into Google analytics.
Data scientists have the skills required to turn all of that customer data into useful insights. They can identify key groups by correlating multiple data sources and make actionable suggestions for serving customers better. This in turn leads to higher profits and a bigger share of the market.
8) Simplifying Staffing
Sorting through CVs and resumes to fill staffing shortages is the bane of the human resources department. Finding the right person to join the team takes time and costs money. Data scientists can help. By streamlining the way in-house applicant data is handled and culling useful information from external sources (e.g. social media, job-hunting websites), data scientists can make better staffing matches faster. Recruitment becomes less of a headache for organizations that make full use of all the data available to them.
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