In the fast-paced world of technology, trends that were once considered unfamiliar can quickly become integral components of our IT landscapes. The year 2018 has brought to light a transformational shift in the realm of big data, where innovative technologies like Hadoop and Spark have taken centre stage, accompanied by the rise of data lakes, altering the landscape of traditional ETL processes. However, with these advancements come unique challenges, especially in the realms of data governance and data quality, essential for reaping the full benefits of big data analytics.

Attention: The Evolution of Unfamiliar Technologies

As we reflect on the past four years, it’s remarkable how certain technologies that were once seen as uncharted territory have now become indispensable tools for IT professionals across diverse industries. Syncsort’s 2018 Big Data Trends survey, which engaged hundreds of IT experts, unveils a striking revelation: what was unfamiliar just four years ago has seamlessly woven itself into the fabric of modern IT landscapes.

Over 40% of the surveyed professionals proudly state that they have successfully integrated Hadoop or Spark into their production processes, while an additional 30% are actively experimenting with these technologies in various proof-of-concept initiatives. The allure of Hadoop and Spark lies in their exceptional data manipulation capabilities, paving the way for data-driven decision-making and enhancing the efficiency of data integration.

Interest: Data Lakes – Reshaping Traditional ETL Paradigms

At the forefront of this transformative journey stands the concept of data lakes, heralding a revolution in the field of Extract, Transform, Load (ETL) processes. Traditional ETL approaches are gradually making way for the flexibility and scalability offered by data lakes. These reservoirs of raw and unprocessed data present an innovative solution for managing vast quantities of diverse data types, serving as a repository for both structured and unstructured data.

The proliferation of data lakes promises a paradigm shift in the way businesses manage and harness their data. As a result, enterprises are poised to witness a remarkable increase in business user productivity through streamlined access to enterprise data. The advantages extend beyond productivity, encompassing cost savings, heightened responsiveness, and the empowerment of data-driven decision-making.

Desire: Navigating the Stormy Waters of Data Governance and Quality

While the potential rewards of these emerging trends are immense, they do not come without their fair share of challenges. It’s evident that data governance and data quality have emerged as the principal hurdles in the successful implementation of big data initiatives. The once-dominant concern over a shortage of big data skills has now taken a backseat to the pressing realities of poor governance and data quality issues plaguing data lakes.

For businesses to harness the true power of big data analytics and earn the trust of executives and regulators, the establishment of robust data governance processes is imperative. The ability to easily identify, comprehend, and trust data sources is non-negotiable. The influx of questions surrounding data origins, access privileges, manipulation history, and data quality levels underscores the need for comprehensive data governance frameworks.

The significance of data governance transcends the boundaries of data lakes, extending its reach to encompass traditional Business Intelligence (BI) and operational data spaces. To fortify the foundations of data-driven decision-making, businesses must ensure that data governance capabilities seamlessly span the entirety of their data landscape.

Action: Seizing the Opportunities, Enhancing Data Quality

The emerging trends in big data present a tantalizing array of opportunities for enterprises willing to embrace change and innovation. To fully capitalize on these trends, businesses must heed the call to action and embark on a journey towards enhanced data quality and governance.

Investing in technologies like Hadoop and Spark is just the first step; organizations must also prioritize the establishment of comprehensive data governance processes that foster trust, transparency, and accountability. A well-structured data governance framework not only enhances the quality of data within data lakes but extends its reach to ensure the integrity of data across all operational facets.

As the digital landscape continues to evolve, those who master the delicate interplay between cutting-edge technology, data governance, and data quality will emerge as the true frontrunners in the race to harness the full potential of big data analytics. The dawn of 2018 has marked the beginning of a new era, one where data integrity, governance, and quality are the cornerstones of success in the dynamic world of big data.

In conclusion, the trends that have emerged in big data over the past four years have brought about a transformational shift in the IT landscape. Technologies like Hadoop and Spark have become integral tools for data manipulation, while data lakes have revolutionized traditional ETL processes. However, the journey towards harnessing the full potential of these trends is not without challenges, particularly in the realms of data governance and quality. Enterprises must rise to the occasion, embracing robust governance processes and prioritizing data quality to unlock the true value of their data assets in the modern era of big data analytics.