How to make sense of your enterprise data with AI

From the rise of digital transformation to the now-prevalent use of smart devices, there has been a rapid growth of data over the past decade. This has granted enterprises with more access to data than before. In fact, 64% of organizations are already managing at least one petrabyte (PB) of data, while 41% are overseeing at least a staggering 500 PB of data.

Recognizing the role data plays in powering key decisions, as well as the rising trend of artificial intelligence (AI), many business leaders are hoping to unearth more insights from this gold mine. The contrary, however, has taken place. Even as they are awashed in data, many enterprises are faced with an insights deficit. Unfortunately, this is caused by the sheer abundance of data to sift through, which has limited many businesses’ capacity to analyze and extract the right insights.

The costs of data cleansing

That said, data analysis has become increasingly challenging. For one, poor quality data in the form of inaccurate, incomplete, or duplicate data can hamper efforts. Such data, which can include unstructured data, would require thorough data cleansing. This can be a costly investment, with the resources required growing exponentially alongside the size and complexity of the data set. Meanwhile, enterprises are struggling with determining which data they should keep. What’s also holding them back from retrieving crucial insights is the myriad difficulties in synthesizing internal and external data sources.

Given the inextricable relationship between AI and data, it makes sense that the quality of any AI initiative is only as good as the data that fuels it. AI is heavily dependent on vast amounts of data, be it training machine learning and large language models, or generating high quality content with large data sets for powering generative AI (GenAI). To translate the potential of AI to tangible value, it’s imperative that enterprises tap on high quality data for their AI investments.

Getting your data AI-ready

The first step is understanding how to make sense of their data. Harnessing the right AI factory, which has the capacity to transform enterprise data into actionable insights, can enable companies to power their AI investments effectively.

With data typically residing in disparate locations, from on-premise to the edge, the Dell AI Factory with NVIDIA can bring AI as close as possible to where their data resides. On top of minimizing latency, lowering costs, and maintaining data security, this also provides a way for businesses to leverage quality, accurate data for the AI factory.

In addition, enterprises can tap on services and tools that automate data cleaning, transformation, labelling, and augmentation. Built-in data governance processes, such as classifying and tagging data sources, help businesses ensure that their AI models are trained on data that stay within regulatory boundaries, while minimizing leaks of sensitive data. These are all part of the features that Dell AI Factory’s data pipelines deliver, which integrate, optimize, filter and aggregate these data for fuelling AI use cases.

To extract the full potential of enterprise data, the final piece of the puzzle is to deploy a full stack AI solution, comprising infrastructure, software and services. As the foundation of every AI factory, the infrastructure layer in the Dell AI Factory can deliver the demanding performance and flexibility that AI workloads require through a broad AI portfolio.

New Dell AI Factory advancements help to further ease AI adoption. These include the Dell PowerEdge XE9685L, a dense, 4U liquid-cooled server designed for AI, machine learning, high performance computing and other data-intensive workloads, and the Dell PowerEdge XE7740 servers, which uses dual Intel Xeon 6 with P-cores and up to 8 double-wide accelerators, including NVIDIA H200 NVL, or up to 16 single-wide accelerators, such as the NVIDIA L4 Tensor Core GPU. Such updates to the Dell AI Factory will deliver accelerated performance and reduced time-to-outcomes for AI operations and use case deployment.

The key to keeping the data explosion under control—and leveraging the most suitable data for your AI initiatives—lies in adopting effective data management. And with Dell Professional Services, enterprises can look to Dell consultants for additional guidance in refining their data and power their AI outcomes. Data management services by Dell Technologies can offer organizations an AI-ready catalog that can help simplify access to their data, while accelerating time-to-value for data analytics in AI use cases.

Find out more about getting your data AI-ready with Dell AI Factory.