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Data Sourcing for E-commerce Success: A Strategic Approach

Table of content

    Introduction

    In the sourcing and procurement field, professionals are actively seeking practical solutions for sustained efficiency and business growth. As the process of digitalization of business continues to progress, it opens up new opportunities and encourages development. Amid these changes, where the importance of data cannot be overstated, one significant trend is emerging— Data Sourcing.

    This article explains the importance of data sourcing, what is data source, the kinds of data sources that are accessible, and a successful data acquisition plan with a little help from Rubick.ai.

    What Is Data Sourcing?

    A strategic process through which organizations identify external datasets and incorporate them into workflows to gain commercial success is known as data sourcing. This helps organizations to have a clear idea of what data they need and what problems they want to solve. As per the Neudata survey, companies with specific use cases are more likely to use external data.

    The process of finding the right data varies across sectors. Common sources include word-of-mouth, advertising, industry media, and conferences, particularly in financial services. For the less experienced in the data world, various marketplaces with different approaches and fee structures are available.

    Data sourcing revolves around different objects of interest, such as specific datasets, metadata information, data catalogs, and agreements with data resellers. The data itself can be bought in structured or unstructured formats, with several cost drivers requiring careful consideration and strategies.

    • Simplifying API usage for better accessibility.
    • Enhancing data maturity through curation and cleansing.
    • Focusing on necessary data to avoid unnecessary complexity.
    • Digitizing and automating manual processes.
    • Simplifying the data environment and architecture.

    In essence, data sourcing is the process of extracting information from internal and external sources and converting it into valuable insights that support a firm’s data infrastructure and business objectives.

    Explore Rubick.ai to extend your data-sourcing strategies. Experience advanced capabilities in web scraping and competitor analysis for a competitive edge e-commerce world. Take the next step in data sourcing efficiency – book a demo session to get started, and let Rubick.ai be your partner in achieving data-driven success.

    What Is a Data Source?

    A data source is essentially the origin of a specific set of information. With businesses generating increasing amounts of data each year, data analysts rely on various data sources to calculate business success and provide strategic recommendations. Possessing data literacy means having the ability to identify, understand, and interpret crucial data and its outcomes.

    Data sources play a crucial role by organizing information into accessible formats and facilitating smooth integrations between different systems. This ensures that relevant information about a dataset is readily available while remaining cautious, allowing analysts to concentrate on data interpretation and analysis. Data analysts often deal with extremely large datasets known as big data, and these necessitate a framework that scales with their volume and variability.

    Types of Data Sources

    Let’s look at some common types of data sources:

    • Primary data: Also known as first-party data, it is obtained directly from the source by the individual or organization intending to use it. Surveys, questionnaires, interviews, and online searches are examples of primary data sourcing.
    • Secondary data: Also called second-party data, it originates from external sources and includes health records, web searches, GPS data, and census data. This type of data supports researchers and analysts in building comprehensive databases to address business challenges.
    • External data: Originating from outside an organization, this administrative data is sourced from other organizations or individuals. Analysts use external data to create detailed segment profiles on regions, trends, markets, or demographics.
    • Internal data: Sourced from within the organization, internal data comes from primary sources like CRM and historical records. It includes customer data, transactions, and other records collected by the company.
    • Other Sources: Modern data extraction goes beyond conventional channels. From credit card purchases to location data and even profile pictures, data can be extracted from diverse sources. With the rise of smart devices, alternative data has become a valuable resource.

    Challenges of Data Sourcing

    Although data sourcing has many potential advantages, there are also several challenges that businesses must overcome. Three primary challenges need to be overcome include:

    • Freshness of Data: Data freshness, a crucial aspect of quality data, refers to up-to-dateness. Today, where information unfolds every second, obtaining real-time insights is challenging. The constant need for fresh data demands significant resources, leading some companies to rely on outdated databases. Choosing data sources that are updated frequently is crucial to staying relevant.
    • Quality of Data: Data quality is critical for accurate analysis. Inconsistencies, duplications, and breaches in data can compromise the integrity of information generated. Opting for data sources that offer high-quality, substantial data points is essential for conducting thorough analyses. Investing in data providers that prioritize quality is a prudent long-term strategy, preventing potential costs associated with inaccurate data.
    • Legal Issues of Data Sources: Data sourcing involving individual data is a sensitive work. Despite global consumer data policies and privacy regulations, a common infrastructure governing data collection and usage is lacking. Companies must ensure their data sources comply with legal standards to avoid getting into legal issues. Prioritizing compliance safeguards businesses from potential legal traps associated with non-compliant data sources.

    Steps to Data Sourcing

    Effectively sourcing data is essential for organizations to make informed decisions and achieve a competitive advantage. Here are the key steps to data sourcing:

    1. Understand Your Business Needs

    To begin, it’s crucial to identify your business goals and objectives. Knowing what data is beneficial for your business is equally important. This knowledge helps in determining your business data requirements, which can be refined into specific statements suitable for analytical solutions.

    2. Decide Resources

    Once business goals are clear, the next step is to decide on resources for the data sourcing stage. Consider factors such as data sources that match your needs, the timeframe for data sourcing (recurring or one-off), the intended data provider, budget constraints, and the required data volume.

    3. Set Up Structures

    Setting up structures is vital for data cleaning and analysis in later stages. This includes creating a framework for data optimization, which is crucial for reporting and analysis. Decisions regarding the data format and how it will be used in subsequent steps should be made at this stage.

    4. Look For Data Sources

    With structures in place, the focus shifts to finding data providers that address the challenges identified in the data-sourcing process. Rubick.ai’s Data Sourcing tool, for instance, offers API-powered data sourcing, reducing manual preparation and minimizing the risk of compromising data quality.

    5. Start Collecting

    Now, you’ve reached the stage where actual data sourcing begins. With a suitable data provider identified, it’s time to initiate the collection process. This step marks the transition from planning to action, bringing your data-sourcing strategy to life.

    6. Monitor And Rectify

    The final step involves continuous monitoring. After securing a data source, it’s crucial to closely monitor and periodically check in with your data provider to ensure the expected outcomes are met. Early detection of issues, lapses, or irregular data points can prevent potential project failures and save valuable time and resources.

    Mastering Data Sourcing

    In the data-driven era, the success of your e-commerce venture depends on effective data sourcing. Whether it’s primary or secondary, internal or external, the right data holds the key to business opportunities. Data sourcing plays a key role in data analytics. When managed effectively, it becomes a powerful catalyst for the data analytics process, translating into business success. Accurate raw data generates insights that can significantly propel your business growth.

    For those ready to take the next step in data sourcing the right way, consider Rubick.ai’s data sourcing. The platform presents a transformative solution that redefines the way businesses interact with data, driving online ventures to heights of growth. Employing advanced web scraping techniques, it lets businesses extract essential insights, offering a seamless approach to data interaction. From extracting product details to understanding market trends, Rubick.ai ensures a strategic approach. Start your journey towards informed decision-making and sustainable growth.

    Monica Gangadrapal

    Monica Gangadrapal

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