Home Online Business Blog How Data Science Drives Business Decision-Making

How Data Science Drives Business Decision-Making

08 Aug
The businesswoman in glasses standing near monitor displaying business metrics and analytics.

In our increasingly connected society, we generate over 328 million terabytes of data daily. The fact that over 90% of the world’s data was created in the past two years is even more remarkable.1 While businesses have been making decisions based on statistics for decades, this vast amount of unstructured data—mined with sophisticated machine-learning techniques—allows businesses to uncover trends that would otherwise go unnoticed.

For example, as part of its expanded use of data science, Chick-fil-A used a combination of 3-D cameras, data analytics and edge computing to build an IoT (Internet of Things) platform to ensure its hot food stays fresh. It automatically alerts employees when they should discard food for safety reasons.2

Read on to learn more about how companies incorporate data-driven decision-making into business strategy.

What Is Data Science?

Unlike data analytics in previous years, modern data science can access consumer behavior at the most minute level. YouTube cannot only tell if a viewer quit watching a video but exactly when they clicked away.3 Data science uses math, statistics, computer programming, advanced analytics, artificial intelligence and machine learning to transform raw data, such as how long viewers watch videos, into actionable insights, such as market research on how to keep viewers watching longer.4

The exponential growth of available data has led to data science becoming one of the fastest-growing fields across all industries. Businesses of all sizes rely on data scientists to interpret data and recommend processes to improve outcomes.5

Predictive Analytics

Predicting future outcomes is enormously valuable when formulating a business strategy. Forecasting the future based on historical data isn’t a new concept. In 1689, The Lloyd Company used the data about past trips to estimate the risks of new sea voyages.6

Today, modern data science has tools, such as machine learning and statistical modeling, that have refined the process to be far more accurate and reliable. Statistical modeling creates mathematical constructs to represent a set of data. These models capture relationships among data, allowing predictions to be made based on input variables.7 The following are some of the types of predictive analytics models businesses use:

Forecast Models

A forecast model predicts future events based on past data. It's similar to the Lloyd Company’s method for predicting whether ships would safely reach their destinations or be lost at sea, only with much more advanced techniques. Forecast models can help businesses:8

  • Use resources more effectively
  • Predict future sales
  • Effectively time the launch of new services
  • Estimate recurring costs

Classification Models

Classification models are a type of supervised machine learning model that categorizes data and describes relationships within a given dataset. One of the best uses for classification models is answering binary questions.

Businesses frequently use classification models in fraud detection and evaluating credit risk. For example, they ask "Is this transaction standard for this customer?" and "Is this applicant likely to default on their loan?"9

Outliers Models

Outlier analysis focuses on finding anomalous entries within a dataset, either alone or as a part of a category. An outliers model can provide more context than classification models, which are best for answering binary questions.

For instance, when used in fraud detection, an outlier model can factor in variables such as location, time and transaction amount. It can use that data to determine that a $100 transaction in a customer’s hometown is less likely to be fraudulent than a $100 transaction in a foreign country.10

Business Intelligence

Business intelligence uses data analytics to answer questions to help an organization reach its goals. The primary purpose of business intelligence is to provide a comprehensive view of a company’s collective data and present it. Thus, stakeholders can leverage it to enhance performance.11

Business Intelligence Methods

Business intelligence methods encompass all the technologies, applications, strategies and practices used to collect, analyze and present business information. The data science process includes the following steps:12

  • Understanding and framing the business problem
  • Collecting relevant data
  • Cleaning data to remove irrelevant, duplicate or erroneous data
  • Exploring the data for trends and insights
  • Building and deploying models
  • Communicating your results with actionable steps

Data Visualization

Most non-technical people have difficulty understanding mathematical formulas and statistical models in their raw form. Data visualization is a graphical representation of data that makes it easier to understand patterns, trends and outliers in data.

Charts, graphs, maps and other data visualization methods allow data scientists to easily communicate their results to business stakeholders.

Data visualization can distill complex concepts into a shareable, interactive format. It brings data to life to tell a story and provide business insights at a glance. Far from being a casual afterthought, data visualization is one of the most essential skills for any data science professional.13

Effective data visualization incorporates the following elements:14

  • It’s targeted toward the audience and how they will understand and interpret the data
  • It creates a framework that clearly establishes what data is being communicated
  • It tells a compelling story that helps the viewer gain insight from the data

The Role of Data Science in Business Decision Making

The role of data science in business decision-making has been on a steady rise, given the vast amounts of data that modern businesses generate. This data, often referred to as 'big data,' comprises an amalgamation of customer data, operational data and market data. Data scientists play a pivotal role in extracting, processing and analyzing this data, transforming it into valuable business insights.

Data scientists employ advanced data analytics techniques, including machine learning algorithms, to dissect large and complex datasets. Their aim is to identify patterns, trends and correlations that can influence business decisions. By doing so, data science becomes a cornerstone of strategic planning, fostering a culture of data-driven decision-making across organizations.

Data-driven decision-making allows businesses to utilize data science findings to make more informed business decisions. These decisions can range from customer segmentation strategies to optimizing manufacturing processes, all supported by the critical data collected and analyzed by data scientists.

Harnessing Data Science in Business Strategy

The integration of data science into business strategy provides businesses with a competitive edge. Data scientists analyze customer data to glean insights into customer behavior, preferences and trends. This information is invaluable for shaping business strategy, from marketing campaigns to product development and customer service initiatives.

Data science also facilitates the prediction of future trends. By analyzing data, machine learning algorithms can project potential future scenarios, allowing businesses to make proactive decisions rather than merely reacting to changes in the market.

Data Collection and Analysis in Modern Business

Data collection and analysis have become central to modern business operations.15 Businesses routinely gather data through various channels, including customer interactions, social media and industry research. This data is then cleaned, processed and analyzed to produce actionable insights.

Data analytics, therefore, becomes a fundamental aspect of business analytics. By analyzing data, businesses can discover critical patterns and trends, enabling them to make data-driven decisions that align with their objectives. Data scientists often utilize various types of data in their analyses, including both qualitative and quantitative data.

Through this robust process, businesses can improve their data literacy, ensuring they are well-equipped to make informed decisions and shape their business strategies effectively.

Leveraging Predictive Analysis for Business Growth

For businesses seeking growth and stability, the power of predictive analysis cannot be overstated. By harnessing past data and performance metrics, predictive analytics allow for an evidence-based approach to business decision-making. This methodology, which relies on data-driven evidence, provides a robust platform for planning and shaping future strategies, covering areas such as sales, marketing and even project management.

Incorporating predictive analysis allows businesses to anticipate the dynamics of their target market, adjusting their strategies based on emerging trends and customer behavior. Moreover, it offers insights into possible challenges in production processes, facilitating proactive problem-solving measures.

Incorporating Artificial Intelligence in Data Science

The union of artificial intelligence (AI) and data science has ushered in a new era in the business world. AI's ability to analyze raw data rapidly and accurately translates into valuable insights that can significantly impact business outcomes. Such insights play a crucial role in unveiling customer behaviors, identifying operational efficiencies and spotting emerging trends—all vital components in achieving business goals.

Furthermore, AI simplifies the entire process of data analysis by automating tasks from data collection to insight generation. Such automation ensures speed, accuracy and efficiency, thereby improving the accuracy of the results.

The Significance of Analytical Tools and Technical Skills in Business

The growing complexity of data necessitates the use of advanced analytical tools and the development of technical skills in the modern business world.16 These tools, when wielded by skilled data scientists, facilitate the efficient and effective parsing of complex datasets, generating actionable insights for business decision-making.

The relevance of analytical tools extends beyond the realm of data scientists, with departments across the entire organization harnessing their power. From sales and marketing to HR and operations, each sector can use these tools to make decisions rooted in data, ultimately aligning more closely with the overarching business objectives.

To conclude, the significance of data science in helping companies develop successful strategies is evident. It provides the tools and insights required to make informed decisions, anticipate future outcomes and steer the course of the business. As such, it is an invaluable asset for any business seeking growth in today's data-driven world.

Data Sources: Turning the Right Raw Data Into Powerful Insights

In the field of data science in business, generating insights from various data sources is a fundamental process. Companies draw from a mix of individual data points such as customer behavior, internal processes, market conditions and internal finances to produce reports that guide decision-makers.

Yet, the mere presence of more data doesn't guarantee success. It's the capability to efficiently extract insights from this data that sets apart successful businesses.

Bad data, or inaccurate and inconsistent data, poses a substantial threat to businesses, potentially leading to ill-informed decisions and financial losses.17 Therefore, part of data science's role involves ensuring data quality, which aids in mitigating risk and building a robust foundation for further analysis.

Data Science: Guiding Businesses and Enhancing Customer Experience

Data science serves as a compass for most companies, guiding them through various business issues and opportunities. By turning data into actionable insights, data science helps companies understand their target audiences better, tailoring products and services to meet customer needs and ultimately enhance the customer experience.

Furthermore, the insights garnered through data science are instrumental in creating compelling marketing strategies, forecasting sales, improving operational efficiencies and informing many other strategic decisions. As a result, companies that effectively extract insights from their data are more likely to see improved performance and growth.

In conclusion, as the world continues to generate and collect increasingly vast amounts of data, the role of data science in effectively assisting businesses to make sense of this data cannot be underestimated. From improving internal processes to enhancing customer experiences, data science is undoubtedly an essential facet of modern business.

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Big data has transformed how companies do business. Data is often touted as the “new oil” and included in the valuation of many companies. Global professional services network leader Deloitte says data is the “engine driving the total value and growth of modern organizations.”18

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