The Difference Between Predictive Analytics and Data Science

Humanity wonders about tomorrow and predicting industry trends or seasonal consumer demand matters to clever business leaders. Predictive data analytics helps managers and their teams develop strategies for successful sales. Simultaneously, data scientists uncover hidden insights in business data. This post will explore the difference between predictive analytics and data science. 

Predictive Analytics in Data Science 

You want to understand the context of data science consulting services and the scope of predictive analytics solutions to learn the relationship between their business models. While some of their activities often overlap, both techniques have unique processes and outcomes/deliverables. 

What Is Data Science? 

Data science is the art of identifying patterns in structured, unstructured, and semi-structured data types to enhance the effectiveness of corporate growth strategies. A data science consulting firm also prepares and cleans the datasets while implementing machine learning (ML) models. 

Different data structures are always more demanding on computer systems because they primarily provide numerical calculations for structured data. Therefore, data science professionals (data scientists) develop several abstract models and adjust them for practical business applications. 

Data science models can integrate predictive data analytics solutions when testing different hypotheses/thought experiments corresponding to business decisions. Machine learning enhancements allow data scientists to teach their developed business models by re-testing business problems.  

What Is Predictive Analytics? 

Data analytics comprises objective investigation into structured datasets to acquire beneficial insights into how the business has been performing since its incorporation. So, data analysts deal with structured data to learn about the past and present dynamics of an organization’s business unit. 

However, you can extend the resulting trends or performance curves while expecting external variables to stay constant. Leveraging historical data models for financial and performative forecasts is the essence of predictive analytics solutions. 

The reliability of business predictions relies on the behavior of their performance curves and context. For example, in long-term planning, you want to consider the year-on-year growth rate in sales forecasts. However, predictive modeling for seasonal or festive sales will often require different data analytics solutions. 

Skills in Predictive Analytics and Data Science 

Data science jobs and careers that involve developing predictive models require technological, mathematical, and social skills, which you can find below. 

Skillset for Predictive Analytics Roles 

Mathematics involves the geometric visualization of equations and functions, and a predictive model requires these abilities to ensure high-quality statistical modeling presentation. A predictive data analyst must also be familiar with computing languages and the commands needed to develop insights. 

Database management principles and philosophies allow professionals to improve data structure and integrity. Therefore, the analysts can save tremendous effort and streamline operations. 

The lack of the latest information technology (IT) skills poses a massive challenge in getting and keeping jobs in firms that provide predictive data models and corporate solutions. So, continuous professional development (CPD) in statistical science and coding languages is essential. 

Requirements in Data Science Consulting Jobs 

Data scientists require advanced statistical tools and techniques, without which they cannot discover hidden patterns in the datasets. Also, machine learning skills are indispensable to the data science job description. 

You want to develop your analytical and social skills to work with data engineers, analysts, and business management leaders. Likewise, you must know the core competencies in business administration to optimize the ML models for real insights. 

Example: Predictive Model Analytics for Data Science Consulting Solutions 

You might test the following hypothesis, which depicts a potential business decision affecting the marketing, customer care, and finance departments. 

“If we reduce the marketing department’s budget by 20%, and allocate it to customer support systems, our customer churn rate (attrition) will decrease by 5%.” 

Remember, your team also wants to address how the decreased marketing efforts impact the sales target for the next quarter. While customer attrition is critical and requires immediate resolution, decreased sales often affect your revenue and profits. Eventually, the reduced profits will upset some investors, affecting your stocks. 

This example is one of the several cases where companies require predictive analytics solutions for quick answers and data science consulting partners to gauge a business decision’s system-level (macro) impact. 

Conclusion 

The difference between predictive analytics solutions and data science models lies in their scale and complexity. Predictive analytics leverages basic statistics for trend discovery in structured datasets to plot the forecast curves. 

Data scientists require advanced statistical skillsets to extract valuable insights from the datasets, including semi-structured (infographics) and unstructured (video/audio) data. Moreover, you can only acquire realistic business insights with an accurately configured and trained machine learning model.