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By Inbal Aharoni
According to the 2021 NewVantage Partners Big Data and AI Executive Survey, 99% of enterprises are actively investing in big data and artificial intelligence in their quest for superior, data-driven business decisions. Two-thirds (65%) of the firms have appointed a Chief Data Officer, and 96% report that they have enhanced their competitive edge and achieved measurable business outcomes.
Innovative data analytics use cases are being explored every day across a wide range of verticals, from manufacturing and supply chain management to financial services, healthcare, ecommerce, and retail. However, there are still major challenges in extracting the full value from an organization’s data: huge volumes and varieties of data, costly and time-consuming ETL, and complex hybrid infrastructures, not to mention making data analytics part of your organization’s DNA rather than the specialized domain of data scientists.
In this blog post, we look more closely at the issues surrounding data analytics and discuss how industry watchers expect those challenges to be addressed during 2022.
Organizations used to aspire to be data-centric. But today, we know that for an organization to get the full value from its data, it must take the next step and become information-centric, i.e., gain timely and reliable insights from the data.
Below, we describe the key barriers to achieving this next level of data analytics maturity.
According to Statista, the volume of data created and consumed worldwide will rise to 181 zettabytes by 2025, up from an estimated 79 zettabytes in 2021. Most of this data is unstructured, coming in a wide range of forms, sizes, and shapes. And ingesting, storing, protecting, and making this unstructured data available for analytics and insights is both challenging and costly.
Clean, high-quality data sets are more important for getting insightful results than algorithms. But the sheer volume, variety, variability, and velocity of the data being collected can affect the veracity of an organization’s data assets. As in many domains, so too in data analytics: Garbage in, garbage out.
Many organizations continue to keep their most sensitive data on-premise. In addition, they maintain data stores across multiple public clouds. Maintaining data pipelines at scale across complex hybrid data infrastructures is a key data engineering challenge.
Deep learning models are what stand behind the real-time, interactive applications that are shaping the 21st century, from self-driving cars to natural language processing, virtual assistants, visual recognition, and much more.
In order to achieve human-level performance, these models make extensive use of hyperparameters. Choosing, optimizing, and tuning these hyperparameters is yet another major data science challenge.
The new metaverse focuses on reliable real-time insights using time-sensitive data. Working on data that is stale or produces obsolete insights just won’t cut it in many verticals such as manufacturing, retail, and finance. But ETL is slow and costly, as is running the analytics. Data engineers themselves often become a bottleneck as they juggle an incessant stream of data pipeline requirements from a variety of stakeholders.
In a recent survey of data engineers, 91% reported that they frequently receive data pipeline requests that are unrealistic or unreasonable. Data lakes and ELT as well as direct querying of raw data have emerged to accelerate time-to-insight, but there are still major data engineering barriers to be overcome to meet the holy grail of real-time, interactive analytics.
In this section, we summarize the data analytics trends foreseen for the coming year—trends that seek to address the challenges outlined above. We have organized them into three categories: data management and governance; operationalization and collaboration; and accelerated time-to-insight.
More than a decade ago, forward-looking data scientists started experimenting with GPU-accelerated databases in their quest to achieve real-time interactive insights from very large and varied datasets. The advantages of the massively parallel architecture of GPUs over CPUs quickly became clear, and today, GPU’s are utilized for their:
As we can see, the massive data computing capabilities of the GPU have played an integral role in speeding digital transformation, and enabling a host of previously impossible use cases – from OFSAA financial reporting, to network QoS, to always-on-marketing, and many others – in industries whose very survival is contingent upon being able to rapidly analyze and gain insights from their growing data.
The GPU (working together with technologies such as AI and ML), has gone from being the gamer’s graphics chip, to being the go-to tool in the arsenal of the enterprise organization, helping telecom operators, manufacturers, bankers, etc. achieve reliable insights where and when they need them, optimizing performance, cutting costs and increasing revenue.
You can learn more about the power of GPU architecture data-base by clicking here