The Role of Data Products in Data Analysis and Data Visualization


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Data products are the essential tools businesses need to make sense of the data they collect. They’re the unseen force behind business insights, orchestrating order from chaos. In a world that constantly bombards us with data, data products are like lifeboats, helping us find meaning, patterns, and narratives in the sea of information.

Data products differ from traditional data tools in fundamental ways, particularly in how they enhance data analysis and visualization processes for better decision-making. For example, businesses can use data products to identify patterns in customer behavior to improve their marketing campaigns.

Modern businesses use data products to facilitate interactive data visualization, making complex data easier to understand. Interactive data visualization allows users to explore data more dynamically, identifying patterns and trends that might not be visible otherwise. Data visualization is a valuable tool for businesses that want to make better decisions based on data.

Data products enhance predictive modeling and statistical analysis, transforming how organizations analyze and interpret complex datasets. In the banking industry, data products help banks identify patterns in the data that suggest fraudulent activity, such as multiple small transactions in a short time or transactions that occur in different countries. Data analysts use this information to help banks prevent fraud and protect their customers.

The synergy between data products and data analysis

Data products and data analysis exist in a symbiotic relationship. Businesses rely on data products to optimize data preprocessing, supplement predictive modeling, and illustrate the practical applications of enhanced analysis. By streamlining these processes, data products enable businesses to make more accurate decisions based on comprehensive data insights.

Data products are curated, consumable collections of raw data and data objects. A data product organizes various data formats, like PDFs or Excel sheets, into a user-friendly package, similar to curated playlist which combines individual songs for easy listening. It provides a consumable abstraction layer (e.g., downloadable files, APIs) on top of the underlying data. Creating a data product involves four steps: 

  • Sourcing the data 
  • Ensuring its quality
  • Implementing advanced algorithms
  • Packaging it in a user-friendly manner

Data productization steps ensure end-users gain meaningful insights with minimal friction. A data fulfillment platform like Revelate helps businesses automate these steps and deliver data products to their end-users quickly and easily.

Data analysis is the process of extracting insights from data. It involves cleaning, transforming, and modeling data to identify patterns and trends.

The interplay between data products and data analysis can revolutionize how organizations strategize their data approach. By integrating data products into their analytical frameworks, organizations can streamline operations, foster a culture of innovation and agility, and take the lead in their respective industries.

In 2018, telecommunications company AT&T used data products to improve customer service. AT&T analyzed customer call data to identify common customer problems. It then used this information to create a knowledge base for training customer service representatives. 

The result? AT&T reduced the average time to resolve customer issues by 20% and saw a decrease in customer complaints by 15%.

Facilitating better data preprocessing and cleaning

Data products can help businesses improve data preprocessing and cleaning. Many data products automate tasks, such as connecting to data sources, identifying and correcting errors in data, and creating a single view of data from multiple sources. This saves businesses time and effort, freeing data analysts to focus on more value-added tasks, such as analyzing data and creating insights.

Not all data products automate the process of data preprocessing and cleaning, however Some are raw data files that users must clean and prepare before data analysts use them.  Data products supporting data preprocessing and cleaning include:

  • Data wrangling tools to combine, transform, and clean data from different sources
  • Data cleansing tools to identify and remove errors from data
  • Data validation tools to check data for errors and inconsistencies

Implementing these tools equips organizations to address potential issues with data compatibility, data quality, and data governance. They also ensure a smooth transition when integrating data products.

Enhancing predictive modeling and statistical analysis

Data products offer advanced features and capabilities that enhance predictive modeling and statistical analysis. For example:

  • Response modeling predicts the likelihood of an event, such as a purchase or loan approval
  • Uplift response modeling predicts the impact of a marketing campaign on customer behavior
  • Churn modeling predicts the likelihood of a customer canceling a subscription
  • Churn uplift modeling predicts the impact of a marketing campaign on customer churn
  • Risk modeling predicts the likelihood of a negative event, such as loan default or fraud
  • Fraud detectionidentifies and prevents fraudulent transactions

By automating processes and providing interactive visualizations, data products enable organizations to dig deeper into their data and discover new insights.

Larger companies may use these data products to enhance their offerings: 

  • Netflix’s recommendation engine analyzes user behavior, ratings, and watching habits to predict which shows or movies a user might like to watch next. The end-to-end solution ingests user data, processes it through predictive algorithms, and presents recommendations to the user—all packaged as part of the Netflix platform
  • AT&T’s churn modeling system predicts which customers are likely to switch providers by analyzing data points such as call drop rates, customer service interactions, and billing history. The company may integrate its churn modeling system into a larger data product that uses insights from the model to drive customer retention strategies. They might embed it within a broader CRM (Customer Relationship Management) system, which qualifies as a data product
  • Mastercard and Visa use fraud detection models to monitor transactions. By analyzing patterns, geolocation, and purchasing history, these models flag suspicious activities in real-time. These components are part of a larger security data product. They’re integrated into transaction processing systems and provide immediate feedback on transaction validity.

Data visualization and data products

Data visualization is a critical aspect of understanding and interpreting complex datasets. Presenting data in visual formats, including charts, graphs, and maps, data visualization helps users quickly recognize patterns, trends,  and anomalies, and make predictions.

Data products not only offer dynamic visualizations, they frequently integrate with other advanced visualization tools. Combining such capabilities allows data analysts to create more engaging, interactive, and insightful visual representations of their data.

Data visualization’s importance in interpreting complex datasets

The role of data visualization in data analysis is essential for interpreting detailed assets because humans are inherently visual creatures.Data visualization makes detailed datasets easier for users to interpret, allowing them to recognize patterns, trends, and correlations that may not be immediately identifiable in the raw data.

Google Data Studio and Tableau stand out as exceptional data visualization tools for analysts and businesses alike.

  • Google Data Studio is a cloud-based visualization tool enabling users to craft interactive dashboards and reports using data from Google Cloud Platform. Its ability to integrate with various data sources, such as Google Sheets, BigQuery, and Salesforce provides versatility without requiring coding. Its pre-built templates ensure users can effortlessly produce polished dashboards
  • Tableau is a data visualization platform that allows users to create interactive dashboards and reports from a variety of data sources. Tableau is a popular tool for businesses and analysts because it is easy to use to create visually appealing and informative visualizations.

Data analysts can leverage data visualization in both platforms, enabling them to effectively communicate their findings and make informed decisions using data interpretation methods. The platforms offer interactive graphs, charts, and maps that identify patterns and trends in data. They also allow data analysts to use more complex visualizations, such as heatmaps and scatter plots, to identify correlations between different variables.

How data products streamline creation of interactive visualizations

Data products streamline the creation of interactive visualizations by automating data preprocessing and cleaning, predictive modeling and statistical analysis. They allow users to explore, analyze, and present data in a visually appealing and interactive way.

For example, Power BI and Looker are business intelligence and data visualization tools that help businesses make better decisions.

  • Power BI offers a variety of data connectors to connect users to a range of data sources, including Microsoft Excel, cloud services, and on-premises databases. Power BI also offers a variety of pre-built visualizations that users can customize to meet their needs. Finally, its associative data indexing engine allows users to explore data and create custom visualizations without IT assistance
  • Looker uses a data modeling layer to create a single view of data from multiple sources. This view makes it easy for users to explore and visualize data without worrying about underlying data sources. Its cloud-based platform makes it easy for users to share visualizations with others.  

Data visualization platforms like these offer a range of features for creating interactive and dynamic visualizations. They enable users to generate visualizations tailored to their specific requirements and offer deeper insights into their data.

Integrated tools for enhanced data visualization

In addition to the data products mentioned above, various tools and platforms integrate data products for advanced visualization techniques. Embracing these integrations allows users to generate more detailed and comprehensive visualizations, facilitating the discovery of deeper insights from the data and enabling more knowledgeable decisions. Python and SAS are two such tools.

  • Python is a versatile programming language with a rich ecosystem of data visualization libraries. Developers use these libraries to create interactive and informative visualizations, such as bar charts, line charts, and pie charts. They also use Python to create custom data visualizations
  • SAS is a leading analytics and visualization platform that offers a wide range of features, including dynamic data exploration, interactive reports, and self-service analytics. SAS’s Visual Analytics tool simplifies complex visualizations and dives deeper into data to promote informed decisions.

Developers use both Python and SAS to create enhanced data visualizations. The choice of which tool to use depends on the user’s specific needs.

Revelate adds data productization to the mix

If data users view data products as a lifeboat, data visualization is the oar that propels them toward actionable insights. Data products also enhance data analysis and visualization capabilities by:

  • Automating data preprocessing and cleaning
  • Improving predictive modeling and statistical analysis
  • Streamlining the creation of interactive and dynamic visualizations

As a result, data products revolutionize the way organizations approach their data strategies. Navigating this evolving landscape of data strategy is Revelate, a forerunner in the realm of data productization

Revelate prides itself on an optimized data productization process, which merges automation, predictive modeling, and enhanced visualization techniques. Their platform not only encompasses the aforementioned capabilities, but also amplifies users’ ability to derive actionable insights efficiently. Revelate is a testament to how modern data products can transcend traditional data analysis by offering a holistic solution tailored for the future.

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