Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Building a Cohesive Enterprise Data Storage Strategy for 2024

Data is the DNA of the modern enterprise. It’s the informational lifeblood that informs an organization about its customers, partners, transactions, employees, assets, campaigns—everything that constitutes a business. These factors are at the heart of production, but they’re also the source of your competitive advantage. In today’s world, generative AI is an emerging technology that, when paired with intelligent automation capabilities, can help augment and streamline how you observe, govern, and leverage that data.

With the burgeoning popularity of both consumer and enterprise-facing generative AI systems that illustrate the vast potential for the technology to transform huge areas of our lives and workstreams, enterprises need clean, trustworthy data now more than ever.

The reality is that many organizations do not yet have a data strategy in place that can effectively account for the massive amounts of storage needed, the many varieties and sources of structured and unstructured data, and the access policies needed for the democratization of data. Yet a recent report finds that 66% of leaders in generative AI adoption and data-led innovation say their digital transformation initiatives will not succeed without an integrated data and AI strategy. There has been such a massive explosion of the variety and volume of data that humans struggle to understand or analyze it all without using AI tools to augment our own capabilities.

However, if you’re relying on siloed data repositories with various databases, data warehouses, and data lakes, both on-premises and on cloud, you’re not alone. Many organizations are grappling with a lack of interoperable data storage solutions and having issues with cost, data accessibility, as well as the ability to effectively conduct real-time data analysis. These problems can feel almost insurmountable for those who are early in the journey to digital transformation or beginning to assess the organization for AI readiness.

Re-envisioning the Data Storage Foundation

You may now be asking yourself —

What is the best data storage strategy in 2024 and beyond?

What kind of data storage is the most flexible and cost-effective for building powerful AI with a strong data foundation?

How do you decide between proprietary encoding formats or a more open system?

Related Posts
1 of 8,534

In the past few years, data architectures have been evolving from proprietary and monolithic architectures to open and composable architectures.

However, many organizations fear that they will need to sacrifice performance because typically, the more tightly coupled and proprietary a system is, the more optimized it’s assumed to be.

The Emerging Open Data Lakehouse: How open-source technologies are closing the gap

Enter the open data lakehouse, a data store that combines the qualities of a data lake and a data warehouse. This data storage solution offers the flexibility and low-cost storage advantages of a data lake with the high performance and powerful functionality you would find in a data warehouse. It’s the best of both worlds, bundled and secured underneath a protective governance layer.  Moreover, open data formats and storage standards enable maximum flexibility and control over your data management and storage options. This is part of the solid foundation that enterprises need to access data across a hybrid infrastructure to train and tune AI models.

The open-source building blocks in a data lakehouse capitalize on the contributions from a broad community of vendors and users of the technology while providing superior security and project governance to ensure the long-term roadmap balances the interests of the community and not just those of a particular controlling vendor. You can also break free from the restrictive nature of proprietary technologies as your data grows and requirements evolve while also having the freedom to connect to your existing systems.

Test the Lakehouse Waters in 2024

As you’re thinking about revamping your data strategy in 2024 to accelerate and scale the impact of AI on your business, consider an emerging option in the market that delivers an optimal level of performance with the flexibility to extract value from all sorts of data sources, formats, and storage tiers.

An open lakehouse architecture will enable your teams to fully unlock the value of your enterprise’s DNA and give you a competitive advantage in 2024.

[To share your insights with us as part of the editorial and sponsored content packages, please write to sghosh@martechseries.com]

Comments are closed.