{"id":34298,"date":"2020-04-07T10:38:09","date_gmt":"2020-04-07T02:38:09","guid":{"rendered":"https:\/\/zegal.com\/?p=34298"},"modified":"2021-09-23T14:02:05","modified_gmt":"2021-09-23T06:02:05","slug":"data-science-as-a-bridge-between-business-and-technology","status":"publish","type":"post","link":"https:\/\/zegal.com\/en-au\/blog\/post\/data-science-as-a-bridge-between-business-and-technology\/","title":{"rendered":"Data Science as a Bridge Between Business and Technology"},"content":{"rendered":"
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The influence of data on business is expected to increase dramatically. <\/span><\/p>\n

According to <\/span>Tech Jury<\/span><\/a>, the big data analytics market will reach $103 billion by 2023, and this year, the big data market has grown by 20%. <\/span><\/p>\n

This rapid growth of the data analytics market is also explained by how much data each person generates daily. Tech Jury reports that one person will generate 1.7 gigabytes of data in just a second. This data carries a lot of value, thus, prompting businesses all over the world to hop on this trend as fast as possible to remain relevant. <\/span><\/p>\n

Reportedly, in the U.S. only, <\/span>55% of all companies<\/span><\/a> are already adopting big data analytics. This indicates the importance of data science and the need for professionals, who would bridge the gap between data analytics technology and business.<\/span><\/p>\n

Sounds great, but there\u2019s one problem. <\/span><\/p>\n

There\u2019s a shortage of data scientists. <\/span><\/p>\n

What Is This Shortage Premised On?<\/span><\/h4>\n

Employers report the big gap between the high demand for data scientists and the low percentage of these specialists on the market. <\/span><\/p>\n

DataNami<\/span><\/a>, referencing KPMG, reports that 46% of companies experience a skills shortage in big data analytics:<\/span><\/p>\n

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Image credit: <\/span>DataNami<\/span>\/KPMG<\/span><\/a><\/p>\n

The shortage of professionals with big data analytics skills is followed by the shortage of professionals in AI and technical architecture, showing the distinctive gap between business and technology. <\/span><\/p>\n

According to <\/span>Inside Big Data<\/span><\/a>, there might be several reasons why the shortage of data scientists keeps growing. <\/span><\/p>\n

One of the reasons is exaggerated education and experience expectations. Although companies offer data scientists a rather attractive salary (starting from $91,000 a year), data scientists need at least 6 years of experience:<\/span><\/p>\n<\/div>\n<\/div>\n

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Image credit: <\/span>Inside Big Data<\/span><\/a><\/p>\n

As a result, Inside Big Data reports that companies already experienced a 50% to 60% gap between supply and requisite demand for data scientists. Thus, the source projects that there will be a need in 1.5 million more data scientists and analytics. <\/span><\/p>\n

Both sources, mentioned above agree on one thing. <\/span><\/p>\n

Companies should provide big data training to bring up in-house data scientists. <\/span><\/p>\n

However, judging by the statistics above, companies are reluctant to do that. <\/span><\/p>\n

So, here are several ways how data scientists can bridge the gap between business and technology.<\/span><\/p>\n

1. Prioritizing Cybersecurity<\/span><\/h4>\n

A business can lose a lot of money because of weak cybersecurity. There have been precedents in the past few years with big corporations as well as governmental organizations losing millions of dollars as a result of cybersecurity attacks. <\/span><\/p>\n

However, it\u2019s not just money that they have lost. They have lost terabytes of valuable data, which is one of the <\/span>risks of non-protected digital privacy<\/span><\/a>. Some say that data is more precious than money. So losing it can be a serious threat to your business\u2019s viability. <\/span><\/p>\n

What\u2019s the role of data scientists in increasing cybersecurity?<\/span><\/h5>\n

Data scientists employ machine learning to increase cybersecurity. Machine learning works on detecting and preventing threats by following the algorithms that identify unusual patterns and halt them. <\/span><\/p>\n

Thus, with the help of machine learning, data scientists are able to:<\/span><\/p>\n