Everyone in business seems to be talking about data science. For many, we believe data science is reserved for large enterprises; however, things are rapidly changing. In fact, it's predicted that data will grow at a compounded annual growth rate of 61 percent by 2025. That’s an immense acceleration of information and heaps of big data. This is one of the reasons why data science is growing in importance for businesses across size and industry.
As our world becomes increasingly digital and interconnected—from e-commerce, to electronic health records, to social media—businesses of all sizes are presented with a massive opportunity to leverage big data for enhanced insight into customers, operations, finances, and more. Even so, according to a recent study conducted by Oracle, 93% of executives believe their organization is losing revenue as a result of not being able to fully leverage their data.
Why Data Science?
The purpose of data science is to take a tremendously large volume of data, which cannot be suitably dealt with in traditional methods, and drive business decisions in order to make positive impacts. Properly utilizing big data to your business’ advantage can set your organization apart from its competitors in a radical way; after all, knowledge is power. Data science can drive business impacts through making evidence-based decisions on optimizing processes, improving products, day-to-day operations, overall efficiencies, marketing, sales, recruitment, and so much more.
What Do Data Scientists Do?
Data science practices include data mining, big data analysis, data extraction and data retrieval. Data science concepts and processes are derived from data engineering, statistics, programming, social engineering, data warehousing, machine learning and natural language processing.
As you can imagine, data science is a complicated discipline; however, that’s not to be confused with being unachievable within smaller organizations. Although most data scientists have a master’s degree or PhD, it is feasible to integrate skills with those who are not currently holding a data scientist job role. Data science professionals are well-rounded, data-driven individuals with high-level technical skills who can build complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy within their organization.
Successful Data Scientists can identify relevant questions, collect data from a variety of data sources, organize the information, translate results into solutions, and effectively communicate their findings.
Technology Trends Aligned with Data Science
When speaking about data science you’re unable to avoid also speaking of cloud computing, as it is expected almost 50% of that data will be stored in the cloud. An additional conversation aligned with data science is regarding Internet of Things (IoT). In fact, the International Data Corporation (IDC) predicts 90ZB of data will be created on IoT devices by 2025. Currently, organizations that incorporate IoT into their business successfully are able to profit from the investment in 94% of the cases. As a result, when organizations develop their data science strategy, it’s also equally important for businesses to define their cloud and IoT strategies. Artificial Intelligence (AI) and Machine Learning are also important when integrating innovation and business transformation.
New Horizons is here to help your business with our data science training courses. Our training is for data scientists who want to learn or add particular skillsets within Microsoft Azure, R Programming, or Python Programming.
Incorporating data science in your business can add a multitude of benefits, allowing your company to make evidence-based decisions in order to increase its success. Find how New Horizons can help your business learn how to transform data into value. Reach out to us!
Looking for more information on data science and other IT trends and technologies? Check out our New Horizons YouTube Channel to see recordings from past webinars.