Data can be put to use in many ways and it should be explored. Data should be used to its full potential, this is not about technology but management. A team of data scientists employ a series of various analysis to look into the entire series from data insight to profit. There are 7 methods to put data into work. 1) Use data to make better decisions along the chart. 2) Use innovations in products, services and processes. 3) Making existing products more valuable. 4) Improve quality, eliminate costs and build trust. 5) Sell or license richer data. 6) Asymmetries should be exploited. 7) Connect providers and those who need the data. These help to provide greater value to others. Read more at: https://hbr.org/2017/06/does-your-company-know-what-to-do-with-all-its-data

Yes, if companies need anything in 2017 they are Data Scientists.

But why , what is so special about them? And the answer is :

Data scientists tracks millions of data sets and provides concrete information for organizations looking to break their data into meaningful information that can be used at all levels in the organization.

As this is the data century , every company wants to recommend its users what they are most likely to choose and hence the need of Data Scientists to study the data and extract various pattern from it and hence creating a 360-degree view of their customers. This not only impresses the customers but also helps the companies in understanding their customers better and hence improving their services according to the customers.

So , in a nutshell yaa companies do need Data Scientists.

You can read more at  http://www.datasciencecentral.com/profiles/blogs/why-large-companies-need-data-science-experts-like-you

Any organization is hugely dependent on data. Either these data are raw in nature or coders has full authority to cluster and filter them. But the process of critical analysis of such data is not simple. So highly skilled data scientists are required, but unfortunately these job demands high specialization and its platform is not yet developed.

Read more at: http://www.pcworld.com/article/3067957/how-the-skills-shortage-is-transforming-big-data.html

In today's world Data Scientist is the most- desired workers in IT industries. Since the demand for such skills is high, this has become a desired job for the potential candidates as well. So here are some skills which will increase your chances of hiring, if you want to become a Data scientist - SQL, Hadoop, Python, Java, and R. Not only these, but MapReduce, Hive and Pig skills which will increase your desirability as a data scientist. So, gear yourself up and start working and learning these skills to become a data scientist. Read more at: https://adtmag.com/articles/2016/01/08/data-science-skills.aspx

In order to do away with bottlenecks in data science supply chains we need strategies by data scientists. Not only the productivity of the data scientists need to be improved, but also the increasing demands of data scientists. Technology solutions can be used in place of skilled professionals in this field which will present analytics to non-data scientists in familiar business terms. The models developed by the data scientists can be given to the business analysts then they can successfully get the work done without being technical.
The basic idea behind this is not to do away with data scientists but to keep them free for new challenges. Proper communication between data scientists and business analysts leads to a cultural shift which reduces the gap between these.
Read more at: http://www.forbes.com/sites/teradata/2015/07/08/sugar-spice-hadoop-and-everything-nice-a-recipe-for-more-data-scientists/

Healthcare Analytics Market has been predicted to have tremendous potential over time with low cost and improved population health. Survey report gives an increase in analytics adoption rate though many still don't use predictive modelling techniques. Data integration is required to derive analytical value which comes as a big challenge. Data Breeches and Data Security is a lesser factor than the former one since enterprises take their own data. But this factor works when external data is taken into account. People with a unique combination of statistics training, technology skills, and data knowledge are also required in large number. Also, infrastructure improvement for analytics must be done according as many healthcare decision makers. Thus analytics is getting much priority in healthcare in recent times. Read more about this article at:  http://www.cio.com/article/2904270/healthcare/healthcare-analytics-4-things-impacting-the-adoption-rate.html

A Survey, based on the responses from 111 data scientists in US, found that Hadoop is too slow according to 76% of data scientists as they believe that the open source software framework requires too much effort to program and isn't fast enough to keep up with big data demands. On the other hand almost 91% of the survey respondents claim that they are performing complex analysis of data on the basis of which 39% of overall respondents say that their job is getting tougher. However, Big Data is becoming highly important for all enterprises. According to a research commissioned by Dell and conducted by Competitive Edge Research, a big section of midmarket companies with 2,000 to 5,000 employees are embracing the rise of big data and almost 80% percent of the midmarket thinks they need to better analyze their data, as they believe big data initiatives provide a significant boost to company decision making. Read more at:http://analytics.theiegroup.com/article/53baa9d23723a81e1300007b/Survey-Finds-Hadoop-Is-Too-Slow-Big-Data-Is-Too-Complex

Generally people think that the role of a data scientist is just to examine the relationships between diverse sets of data as well as the disparate systems, processes and locations which store them. But the role is actually mature across certain sectors like retail. With the help of this, Amazon, for e.g., is able to analyze the behavior across multiple accounts, and knows exactly when and why to push a certain product to a customer. But the case is somewhat different in financial services where the role is not properly organized. Though Big Data analytics is used across the retail banking industry from fraud and sanctions management to improving account management processes, analysis of Big Data provides the potential for banks to create new income streams and the sector as a whole is benefitted when it comes to deriving value from vast quantities of information. Thus financial services, in spite of having people with good skills to do modeling and statistical analysis, need people who are able to spot key trends and focuses on looking for the relationships between data across disparate sources. Once these two skills are combined, the financial sector will start to see the rise of data scientists in it like other industries. Read more at:http://www.banktech.com/business-intelligence/piecing-together-the-data-scientist-puzz/240168604