What is Data Science? The Key to Modern Business Intelligence


Summary: Data science is the underlying set of processes that turn complex mashups of data from multiple sources into understandable, actionable insights. It includes processes such as data collection, processing, analysis, machine learning, AI, and data visualization. Data science now impacts all sectors of business and underpins data-driven decision-making.

Data science is a mashup of math, statistics, artificial intelligence, computer engineering, and other related fields all converging for one reason: to extract meaning and value from data. It involves analyzing large amounts of data-often complex data from different sources and in different formats-to find patterns and insights. Data science is how to move from information to action by finding the signal in the noise.

This is in contrast to a more traditional basic business intelligence (BI) strategy. Pure data science can dig deeper toward a more intensive, business specific bespoke solutions that are tailored to your business objectives. Data scientists are equipped to do different and more advanced work than your traditional BI team, and can especially boost your advanced analytics capabilities for use with predictive and prescriptive analytics solutions. High-complexity scenarios and data can benefit greatly from the guidance of a data scientist.

Key Components of Data Science

Data Collection

Data is everywhere, and so are potential insights. Sensors, surveys, and web scraping are all potential data collection methods. Data can come from internal company sources like financial documents and process manuals as well as public sources such as social media, product reviews, and public databases. Data also comes in all formats-text, audio, video, images. Potential data sources and collection methods are limitless, as long as there is some way to turn the “raw” collected data into a structure that lets it be compared with other, similar data. This brings us to our next point: data processing.

Data Processing

Data processing is how raw data (unstructured data) becomes structured data. Data processing utilizes techniques such as:

  • Data cleaning: Data cleaning removes data that is corrupted, incorrect, incomplete, formatted incorrectly, or duplicated within a data set.
  • Data normalization: Data normalization make data more usable by organizing it in a similar way and in similar formats to remove remaining unstructured data and duplicates, further boosting data integrity.
  • Data transformation: Data transformation is the process of changing the format of data. It includes deleting extra data fields, changing the naming scheme, combining columns, and otherwise standardizing data by adding, deleting, combining, or restructuring data.

Pandas, Apache Spark are tools commonly used during data processing. Three-step process Extract, Transform, and Load (ETL) is often used to bring together data from multiple sources into a central location-like a data warehouse-for analysis.

Data Analysis

Data analysis is the systematic application of a variety of logic / statistical techniques to actually evaluate the data. It is at the data analysis phase that insights emerge from the data. At this stage, predictive, prescriptive, diagnostic, and descriptive data analysis is applied, depending on the use case, employing machine learning, inferential statistics, etc. It’s this stage of data science that you’ll most often hear about supervised learning, unsupervised learning, and reinforcement learning and about tools such as R, Python, SAS, and SQL.

Data Visualization

This is when data science becomes “human readable” (including by non-data scientists). All of the complex data that has now been cleaned and analyzed is organized and visualized using tools like Tableau, Power BI, Matplotlib, and Seaborn to create charts, graphs, heat maps, and dashboards. It’s at this phase that data can be viewed from the perspective of taking action, because insights are presented in a clear and concise way.

Applications of Data Science

Data science is now used across all major industries. In healthcare, it is utilized for personalized medicine as well as predictive analytics across everything from patient outcomes to hospital staffing. In finance, it aids in fraud detection, algorithmic trading, and risk management. Across all industries, data science can improve customer satisfaction via customer segmentation and targeting to enhance customer experience, boosted operational efficiency, strategic planning, inventory management, and more. Data science and data-driven decision making is becoming increasingly integrated into nearly all facets of business.

Challenges in Data Science

Data science comes with a host of challenges to overcome. First, businesses must ensure data quality and availability, checking that data is accurate, complete, and timely. In collecting data, compliance with data privacy laws (such as GDPR) can complicate collection efforts, as can the changing regulatory landscape surrounding data privacy. Similarly, data ethics which help deter bias and discrimination are important during all phases of data science. Overcoming the challenges that come with disparities between sources and data siloes (data that “lives” in different locations) can increase the complexity of data science and data analysis.

Unlock Insights Within Your Data With Data Science Help From VisionWrights

If you’re already doing business intelligence with VisionWrights, then you have a huge head start on what it takes to do data science. And if you’re not working with us yet, we can help you build your foundation on solid fundamentals, so you get value at each step along the way.

No matter where you are in your data journey-from the first baby steps toward developing your strategy to enterprise-grade data science-having an expert along for the ride is invaluable for quickly making sense of your data and setting your business up for future success.

VisionWrights can help you on your path to a data-driven business intelligence strategy (including how to collect the right data). Support the future of your business with data and artificial intelligence, and stay ahead of other future developments on the tech horizon. Reach out to us today at VisionWrights to find success with your data.


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