Data Science vs Data Analytics: What Most Businesses Get Wrong

Data Science vs Data Analytics: What Most Businesses Get Wrong
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The digital age has introduced massive quantities of “big data” to the enterprise landscape. Businesses increasingly use this data to guide their growth, strategic decision-making, product development and marketing. Yet many enterprises fail to consider fundamental differences in data science vs. data analytics. This can result in underperforming investments in one or both areas.

To the uninitiated, data scientists and data analysts appear to occupy the same roles. In reality, subtle but important features differentiate these roles and the business insights they generate. Let’s explore the distinction between data science vs. data analytics. We’ll also look at best practices for each area so your business can gain maximum value from its “big data” investments.

Data Science: Unraveling the Complexity

In examining data science vs. data analytics, experts often describe data science as a multidisciplinary or interdisciplinary field. Data scientists combine applied principles of the scientific method with computer science capabilities.

Specifically, they use algorithms, systematic querying and structured processes to examine large quantities of data. The goal is to identify tangible patterns or extract actionable insights that their employer or clients can then use as a competitive advantage. That might be through data-driven decision-making that improves operational efficiency or previously unknown opportunities to solve challenges.

Data scientists work in a lifecycle that covers five main stages:

1. Collecting Data

At the outset of a project, data scientists collect and harvest data from a range of internal sources, such as financial reports, statistical tables and digital sources like text files and APIs. Depending on the scope of the project, data scientists may also assemble information from external sources like government reports and third-party market research or projections.

The data collection process is critical to project outcomes. Data-driven insights have the most value when they’re based on the deepest, highest-quality input sets.

2. Preparing the Data

Another differentiating feature of data science vs. data analytics relates to the preparation phase. This step tends to require significant time investments. But when performed properly, data prep generates bodies of reliable data that data scientists can work with systematically.

Preparing or “cleaning” data may involve:

  • Arranging the data in a format that facilitates systematic analysis
  • Normalizing data
  • Performing data-type conversions
  • Eliminating duplicate data
  • Accounting for inconsistencies or gaps in data

3. Exploring the Data

The data exploration phase frequently applies visualization tools that allow data scientists to look for patterns, trends and important revelations. Common visualization tools include heat maps, line charts and scatterplots.

Data scientists can also perform manual analyses of raw statistical data. However, they usually draw on digital and computerized tools designed to automate data exploration processes. Artificial intelligence and machine learning are also becoming increasingly integrated into data exploration and visualization.

4. Extracting Insights

At this stage of the data science lifecycle, professionals apply statistical modeling tools and algorithms to answer targeted questions. While these processes take many forms, they generally share a common set of objectives, such as:

  • Generating insights with a significant set of potential business applications
  • Identifying hidden or previously undetected trends or patterns
  • Classifying data in novel ways to advance strategic objectives

This stage highlights one of the clearest distinctions between data science vs. data analytics. Data scientists often use present data to make projections or predictions. In contrast, data analytics examines past patterns to guide decisions related to present or near-future courses of action.

5. Communicating Results

With respect to data science vs. data analytics, the final step of the data science lifecycle reflects a principle shared by both disciplines. At this stage, the data scientist presents their findings in a format that clearly communicates the importance and value of the insights. This may involve comparisons or emphasizing conclusions, with a particular focus on potential applications or impacts.

When communicating results, effective data scientists strive for simple, clear explanations. As such, they often use words and explanatory aids that laypeople can understand.

Data Analytics: Navigating Insights

Data analytics is the close, systematic examination of statistics and other raw forms of data to generate insights or draw conclusions about the information contained therein. Experts often explain the nuances of data science vs. data analytics in terms of differences in focus.

For instance, data analysts are generally more concerned with what the data itself reveals. But data scientists use data to model and make projections about the future.

Data analytics has a wide-ranging, rich and high-value set of business applications in nearly every industry. It’s particularly valuable in sectors such as financial services, healthcare and retail. For instance, data analytics:

  • Helps companies identify areas for operational and efficiency improvements
  • Can be a valuable guide through uncertain or challenging economic times
  • Can harvest strategic insights from past performances to improve in the future

Data analytics also involves data engineering. This is the process of building the computerized systems and tools used in data architecture. While data science also draws on such architecture, data scientists often seek to perform more targeted and focused analysis on bespoke data sets that they prepare.

The Four Main Types of Data Analytics

Professionals apply data analytics tools in four main ways:

  • Diagnostic Analytics: This approach uses data to understand why certain events or business conditions evolved the way they did.
  • Descriptive Analytics: With respect to a specific aspect of business operations, descriptive analytics uses data to describe precisely what happened, how the situation evolved and what its impacts were.
  • Prescriptive Analytics: Prescriptive analytics analyzes historical data to suggest a strategic course regarding a particular business decision related to the present or the impending future.
  • Predictive Analytics: Predictive analytics uses established statistical information to guide near-term data-driven decision-making.

In comparing data science vs. data analytics, note that prescriptive and predictive analytics generally display the most overlap between the two disciplines. Data scientists also draw heavily on prescriptive and predictive analytics principles when making projections about potential outcomes or suggesting future courses of action.

Bridging the Data Science vs. Data Analytics Gap: Best Practices

In assessing the differentiating features of data science vs. data analytics, bear in mind that both disciplines form integral parts of contemporary approaches to business intelligence. Extracting the maximum possible value from your investments in both data science and data analytics demands that you follow the established professional best practices specific to both disciplines.

Data Science: Best Practices

Data scientists often face challenges when creating an effective analytical approach or defining the precise problem to be analyzed or solved. But businesses can overcome such problems in the following ways:

  • Create a compelling proof-of-concept (POC) for the project to secure the necessary resources from management
  • Use the POC to define project plans and operations
  • Equip data scientists with effective tools, such as customized algorithms and analytical metrics
  • Focus on targeted bursts of data science work followed by performance reviews every two or three weeks
  • Include a specific set of strategic actions that business leaders can take in response to project findings

These best practices draw on an often-cited principle of data science, which favors a continuous progression from raw data to analytics to strategic insights to actionable suggestions.

Data Analytics: Best Practices

Data analytics projects frequently suffer from design flaws that negatively impact their scalability. As a result, their findings are too limited or littered with inconsistencies to offer compelling and actionable insights.

Addressing these challenges requires a proactive organizational approach:

  • Source the most reliable and complete data available to generate accurate insights and observations.
  • Commit most of your resources to developing systems and products that facilitate ongoing data analysis rather than overcommitting to single projects.
  • Invest in the most applicable and targeted analytics tools available.
  • Establish a company culture that recognizes and embraces the value of analytics as a core aspect of operational management.
  • Work with management-level stakeholders to establish a clearly defined and relevant set of key performance indicators for specific analytical tasks.
  • Update data analytics tools and analysis models as industry best practices evolve and as new forms of data and analysis become available.
  • Present results with a clear and compelling case that supports the project’s conclusions.

Finally, data analytics stakeholders and teams must ensure they’re following all applicable compliance guidelines. Data privacy and protection laws are in a constant state of flux, so businesses must be acutely aware of how changes impact their data collection and analysis practices.

Navigating the Data-Driven Landscape: The Imperative of Clearing the Path to Business Success

Businesses benefit by building their internal data literacy and equipping their data science and analytics teams with the right tools and resources. To this end, enterprises must consider the pivotal role API security plays in generating and safeguarding the data that both data scientists and data analysts use in executing their duties.

Growth Acceleration Partners (GAP) provides an elite suite of data engineering, consulting, protection and analysis tools. We help businesses implement customized data architecture solutions that enable them to extract the greatest possible strategic benefits from their data science and analytics investments.

Schedule a custom consultation with a GAP data engineer today.