Once upon a time, a barrel full of a mixture was made with statistics, programming language, artificial intelligence, scientific method, and data analysis, and the concoction was named data science. We know that sounds dramatic, but it is true.
Data science is a new field of study that has integrated knowledge from multiple disciplines and encompasses the activities of preparing, analyzing, and exploring data to gather meaningful information. Today data science can be applied to a variety of areas, starting from the medical field to the supply chain of any organization. Companies are embracing data science with open arms, and so should you.
How does data science add value to your business?
Each minute millions of data are generated in an organization, that when analyzed properly, can change its course of action for good. Data science enables the organization to make analytical decisions based on facts and figures. If you are still wondering do you need data science, we have a list of reasons that says a big yes.
- Ambiguity has no place in organizational decisions. Insights obtained from the data science process clear the air of uncertainty and let you take objective decisions aimed toward success.
- Data science tells you who are your target customers or what they want therefore letting you direct your marketing efforts in acquiring and retaining them.
- Data science can predict any breakdown in the supply chain network and alert the team to take precautionary measures.
- Data science has the potential to swiftly and efficiently detect financial frauds.
- Data science lets you optimize your sales and increase customer satisfaction through customized suggestions based on their previous purchases.
Who is involved in the data science process?
- Data science projects are more repetitive than linear and require continuous monitoring and evaluation. The data science project is mainly overseen by:
- Data scientists: Data science is a relatively new specialty that was born from the domains of data mining band statistical analysis. However, the field is proliferating as companies are integrating the agile technologies of data science into their operations. The rapid growth has given rise to the role of data scientists, who are responsible for generating meaningful insights from data using programming languages like Python and R. They prepare, analyze, explore and visualize data that drive organizational decisions.
- Business analyst: The business analyst closely works with the data science teams. They determine the problem at hand and also draw strategies for analysis. They are also responsible for defining the key performance indicators for different areas of business.
- IT managers: For data science projects to thrive, a secure and functional IT environment is a basic necessity. IT managers are responsible for ensuring that the available technologies work optimally and resources are allocated efficiently for the unhindered success of data science projects.
The benefits of an integrated data science platform:
The demand for data scientists is on the rise as more companies are hiring them to gather significant information from their data. Nevertheless, many organizations were quick to understand that their efforts are futile in absence of an integrated data science platform. Therefore, data science platforms were developed.
Equipped with a variety of technologies for advanced analytical uses, a data science platform is software that empowers data scientists to unearth actionable insights from organizational data and communicate it.
A robust data science platform benefits the users by:
- Making it easier to work with large sets of data.
- Producing error-free models.
- Deploying numerous versions of the same model for testing.
- Quick acceleration and delivery of the models.
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