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Best Practices for Maximizing the Value of Big Data

Best Practices for Maximizing the Value of Big Data

A recent survey highlighted the challenges faced by Hadoop adoption, with a majority of respondents showing no immediate plans for investment due to hurdles around business value and skills. This trend reflects broader challenges in the overall adoption of Big Data technologies. Addressing the scarcity of resources with the right analytic skills is a primary focus for new-generation, business-friendly Big Data tools. However, extracting business value remains a fundamental issue. To unlock the full potential of Big Data, organizations need to follow emerging best practices.

Achieving maximum value from Big Data involves excelling in six key areas:

  1. Start with a Business Problem in Mind:
    • Rather than aimlessly navigating through extensive data sets, it is crucial to begin with a specific business problem in mind. Collaborating with business experts to understand challenges and opportunities facilitates successful projects. Focusing on a particular problem streamlines the identification of relevant data sources and the selection of appropriate tools and techniques.
  2. Look Ahead to Operationalizing Insights:
    • To realize genuine business value, insights derived from analysis must be operationalized. Many projects languish because their findings are not seamlessly integrated into day-to-day business operations. Ensuring that results can be practically applied is essential for success. Factors such as data availability, cost, and compliance with industry regulations play a significant role in operationalizing insights.
  3. Take Advantage of Analytic Innovations:
    • Innovations in Business Intelligence and Business Analytics are transforming how businesses extract value from customer data. Moving from periodic snapshots to continuous analysis for real-time prescriptive insights is essential. Big data tools and infrastructure facilitate the application of machine learning techniques to explore vast datasets, encompassing structured and unstructured data.
  4. Embrace Analytic Diversity:
    • The proliferation of analytic tools demands an embrace of diversity in the toolbox. R, Python, Hive, Groovy, Scala, MATLAB, SQL, SAS—adopting multiple tools becomes necessary. Leading analytic teams should create a flexible infrastructure supporting diverse tools to operationalize models effectively. Decision management systems simplify this challenge by enabling the integration of analytic services and business rules seamlessly.
  5. Leverage the Cloud and Productivity Platforms:
    • Big data analytics no longer necessitate substantial investments in infrastructure and specialized skills. Leveraging cloud services allows organizations to focus on solving business problems while a third party manages underlying systems and services. This approach offers scalability and cost-effectiveness.
  6. Give Control to Business Experts:
    • Ultimately, the highest value arises from empowering business experts with new insights that drive differentiating strategies. Interactive dashboards, visual reports, business rules authoring services, simulation, and data visualizations empower experts to quickly incorporate new models and insights into their strategies, facilitating faster decision-making.

Addressing these six areas collectively positions organizations to derive significant value from their Big Data analytics initiatives.

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