For businesses looking to grow, data is a key driving force behind innovation and success. The term "big data'' has emerged to describe the sheer volume of data available to us, which is often too large and complex to be analyzed through traditional approaches and platforms. So in order to effectively harness the potential of big data, you need a comprehensive strategy and system to support.

Why Big Data Matters

Big data has a myriad of benefits for organizations. It enables data-driven decision-making, enhances customer insights, improves operational efficiency, supports innovation, and helps identify new business opportunities

Beyond that, the quantity of data is continually increasing: creating more potential  insights available than ever before. Collectively, humans produce 2.5 quintillion bytes of data every day, and it’s estimated that 1.7 MB of data is created every second for every person on earth. Statista projects that by 2025, global data creation will grow to more than 180 zettabytes. 

However, managing big data is not without its challenges.

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Challenges of Managing Big Data 

When you consider the complexity of large volumes of data which change quickly and  must be pulled from a variety of sources, it’s no surprise that managing all of that can be tricky. 

The primary challenges enterprises face when managing big data include:

  • Storage and Processing: Large quantities of data require a comprehensive storage infrastructure and efficient processing capabilities. Fortunately, the emergence of cloud computing and artificial intelligence has significantly aided in overcoming these challenges. 
  • Data Integration: Big data is pulled from a wide array of sources and formats, making integration a complex task. Data integration tools and data virtualization can help consolidate different sources. 
  • Data Quality: You’ve heard the saying “garbage in, garbage out.” Ensuring data is accurate and consistent is crucial for successful analysis. This is yet another area where automated tools can minimize human error, and having governance guidelines, data auditing, and other best practices in place help maintain data integrity.
  • Security and Privacy: Managing sensitive data presents significant security and privacy concerns. Organizations must implement robust security measures and adhere to relevant data protection regulations, particularly in an era where users are increasingly hesitant to share data and distrustful of how shared information may be used.

The other challenge that leaders face is in determining how to successfully invest in big data. A 2023 article from the Harvard Business Review summed it up nicely:

“It’s time for Fortune 1000 companies to rethink their investments in data, analytics, and AI. Of course, companies should be investing in these critical business capabilities and differentiators. What they need to take a hard look at is how they’re investing, and whether these investments are leading to the kinds of gains and the levels of business value that companies are aspiring to achieve.”

This is where executives can benefit from utilizing big data experts, who not only address the technical requirements, but are able to identify what will deliver immediate value, and outline a change management strategy to ensure that teams understand and embrace data-focused initiatives.

Best Practices for Big Data Management

Aside from being aware of the challenges associated with big data management, it’s important to also understand the best practices that can ensure success.

  • Defined Goals: Start with clearly identifying what outcomes you hope to achieve through data management. By understanding business needs, data scientists can more effectively identify appropriate data sources and elements. Business leaders must build a rapport with analysts and establish an environment of clear communication, so that data can be properly leveraged to inform business strategy. 
  • Simplicity & Scalability: As previously noted, the volume, velocity, and variety of big data makes its management complex. So as much as possible, streamlining data sources and honing in on the ones that truly matter, rather than trying to incorporate every piece of data available, is often a better path to success. And when simplicity is paired with a scalable infrastructure that can evolve alongside your business, your big data management strategy is sustainable.
  • Consistency: From creating a data governance framework to ensure data quality, integration, and compliance, to consistent practices for data validation, your data protocols must be clearly defined and communicated. Without consistent, accurate data and processes, there is no confidence in the outputs. 
  • Security: As Forbes writes, “It is incumbent upon the data scientist to remind their stakeholders that the organization is not the owner of all information that they can gather. Consumers own their data, and the organization is merely a steward of this data on behalf of the consumer.” Privacy and security measures must be part of your data governance protocols. With ever-increasing threats of data breaches, and consumers who are often unaware of the data they are sharing, and 
  • suspicious of doing so when they are aware, companies need to treat the personal data of consumers as if it were their own.

In Conclusion

As big data continues to shape industries and transform the way organizations operate, understanding and effectively managing it is critical for success. By embracing best practices in big data management, organizations can improve efficiencies and decision-making, better understand and serve their customers, and more quickly innovate. 

Interested in learning more about big data and how it can help your organization? Reach out to the team of experts at Object Edge for a free consultation. 

About the Author

Blue dotted circleVinny Maurici

Vinny Maurici

VP of Data Engineering

As Vice President of Data Engineering, Vinny is accountable for the growth, success and thought leadership of the Data Management business at Object Edge. He brings 15+ years of master data, merchandising, and governance experience; and has launched several successful enterprise and Fortune 500 global product data programs in B2B Manufacturing and Distribution, Retail, and Food Services.

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