Commissioned: The importance of data has never been more salient in this golden age of AI services. Whether you’re running large language models for generative AI systems or predictive modeling simulations for more traditional AI, these systems require access to high-quality data.
Seventy-six percent of organizations are counting on GenAI to prove significant if not transformative for their businesses, according to Dell research.
Organizations teem with sales summaries, marketing materials, human resources files and obscene amounts of operational data, which course through the data center and all the way to the edge of the network.
Yet readily accessing this data to create value is easier said than done. Most organizations lack a coherent data management strategy, storing data in ways that aren’t easy to access, let alone manage. For most businesses, anywhere and everywhere is just where the data ended up.
Think about how many times employees have tried and failed to find files on their PCs. Now multiply that experiences thousands of times daily across an enterprise. Finding information can often feel like looking for a virtual needle in a data haystack.
You probably tried to centralize it and streamline it to feed analytics systems, but without structure or governance, the monster has grown unwieldy. And don’t look now – with the advent of GenAI and other evolving AI applications, your organization craves access to even more data.
Accessible data in the AI age
Maybe you’ve been tasked with activating AI for several business units, with partners in marketing and sales collateral to product development and supply chain operations looking to try out dozens or even hundreds of use cases.
Given the years of data neglect, affording these colleagues access to the freshest data is a great challenge. How do you move forward when these tools require data that must be cleaned, prepped and staged?
As it stands, IT typically spends a lot of time on the heavy lifting the comes with requests for datasets, including managing data pipes, feeds, formats and protocols. The struggle of tackling block, file and other storage types is real.
What IT doesn’t tackle may get left for others to wrangle – the data analysts, engineers and scientists who need high-quality data to plug into AI models. Asking the folks who work with this data to take on even more work threatens to overwhelm and capsize the AI initiatives you may be putting in place.
But what if IT could abstract a lot of that effort, and make the data usable more rapidly to those who need it, whether they’re running LLMs or AI simulations in HPC clusters?
To the lakehouse
Organizations have turned to the usual suspects, including data warehouses and lakes, for this critical task. But with AI technologies consuming and generating a variety of structured and unstructured data sources, such systems may benefit from a different approach: A data lakehouse.
The data lakehouse approach shares some things in common with its data lake predecessor. Both accept diverse – structured and unstructured – data. Both use extract, transform and load (ETL) to ingest data and transform it.
However, too many organizations simply let raw data flow into their lakes without structure, such as cataloguing and tagging, which can lead to data quality issues – the dreaded data swamp.
Conversely, the data lakehouse abstracts the complexity of managing storage systems and surfaces the right data where, when and how it’s needed. As the data lakehouse stores data in an open format and structures it on-the-fly when queried, data engineers and analysts can use SQL queries and tools to derive business insights from structured and unstructured data.
Organizations have unlocked previously siloed data to make personalized recommendations to customers. Others have tapped lakehouses to optimize their supply chains, reducing inventory shortfalls.
Democratizing data insights
While a data lakehouse can help organizations achieve their business outcomes, it shouldn’t be mistaken for a lamp. You can’t plug it in, switch it on and walk away. That’s where a trusted partner comes in.
Dell offers the Dell Data Lakehouse, which affords engineers self-service access to query their data and achieve outcomes they desire. The solution leverages compute, storage and software in a single platform that supports open file and table formats and integrates with the ecosystem of AI and ML tools.
Your data is your differentiator and the Dell Data Lakehouse respects that by baking in governance to help you maintain control of your data and adhere to data sovereignty requirements.
The Dell Data Lakehouse is part of the Dell AI Factory, a modular approach to running your data on premises and at the edge using AI-enabled infrastructure with support from an open ecosystem of partners. The Dell AI Factory also includes professional services and use cases to help organizations accelerate their AI journeys.
How is your organization making finding the needle in the haystack easier?
Brought to you by Dell Technologies.
