As I have stated in previous articles that surround the data topic, the world is generating more data than ever before. IT professionals in the contemporary landscape face an exponential surge in data generation, shifting from the era of Big Data to an even more expansive data paradigm. This new reality entails grappling with data at hyperscale proportions, characterized by its volume, intricate intricacies, and escalating expectations for exceptionally high performance.

As a result, IT experts are required to equip themselves to handle these unprecedented challenges in data management and processing. Ocient wanted to dive into how IT experts are doing that, especially through hyperscaling, in a recent report. They also wanted to get a better feel about how IT leaders view AI, gauging their interest in and preparedness for incorporating AI into their businesses.

Here is what the hyperscale data analytics platform found.

Data quality is an immediate priority. The accuracy, completeness, and reliability of data are paramount, as businesses increasingly rely on it to make informed decisions. Organizations are investing heavily in data quality initiatives, such as data cleansing, validation, and governance, to ensure that the data they collect and analyze is trustworthy and free from errors. Poor data quality can lead to misguided strategies, missed opportunities and financial losses.

Data and IT leaders recognize the transformative power of data warehousing and analytics in shaping their overall IT strategies. The growth of data volumes and the need for real-time insights are compelling organizations to allocate significant budgets to data-related initiatives. Whether it's adopting modern data storage solutions, implementing advanced analytics tools or expanding data infrastructure, organizations are prioritizing these investments to stay competitive in the data-driven age.

As organizations embrace hyperscale data volumes, they fight with a growing gap in terms of the resources required to manage and extract value from their data. The demand for data professionals with expertise in data engineering, data science and machine learning is outpacing the available talent pool.

Additionally, organizations are often burdened with a complex mix of data tools and technologies, making it challenging to streamline their data operations efficiently. This talent and tools mismatch hampers innovation and the ability to leverage data effectively, driving the need for talent development and technology consolidation efforts to keep pace with the data explosion.

As for AI, it is on the radar of almost every organization, promising to unlock new opportunities and efficiencies. However, there are concerns about AI's security, accuracy and trustworthiness. Organizations are eager to harness AI's potential but are cautious about potential biases, data privacy, and the ethical implications of AI systems. Ensuring AI models are robust, transparent and accountable is a challenge, requiring a balance between innovation and responsible AI development.

“Data volumes and the importance of understanding your data continues to grow, yet challenges around data quality, tool proliferation, and staffing constraints continue to hold the industry back,” said Chris Gladwin, co-founder and CEO, Ocient. “The frontier beyond big data is here, and Ocient’s annual report showcases the challenges – and opportunities – driving the enterprise data strategies of tomorrow.”

With this report, leaders recognize the need to build a strong data foundation, allocating budgets to data and analytics, optimizing IT tools and refining cloud strategies. Managing the surging data volume requires specialized talent and cutting-edge tools for advanced analytics, data science, AI and ML, prompting data leaders to expedite modernization efforts.

Hyperscale data is here to stay, offering the potential for organizations to excel in analytics and AI-driven advancements.