What is the #1 Reason AI Projects Fail?

For enterprise AI, data collection is not a one-time thing – it’s a continuous process, and this is why AI projects need to begin with a modern data collection and curation strategy

Clayton Dukes, CEO


What is the #1 Reason AI Projects Fail?

Tuesday, April 14, 2020

No matter how compelling the business case is, or how talented the teams are, without high-quality data, many AI projects are doomed to fail.

Why do Most AI Projects Fail?
Collecting, curating, and tagging data accounts for ~80% of the effort in today’s AI projects, and until now, there have no vendors in the market to present a new way of collecting data at its core, different than traditional models.

Most AI projects need ‘training data’ on massive scale, and that data often needs to be pulled from multiple sources – mining everything from traditional databases to text documents. Additionally, business requirements change quickly, making continuous model training and validation critical for corporate AI applications.

For enterprise AI, data collection is not a one-time thing – it’s a continuous process, and this is why AI projects need to begin with a modern data collection and curation strategy.

LogZilla’s Network Event Orchestration platform is the only available solution to address all the characteristics to prepare for AI that are then used to ingest, cleanse, transform, and validate ‘training data.’

Extensible metadata: Metadata refers to “data about data.” While some metadata is system generated, an effective training data infrastructure needs to support system-generated metadata from diverse data sources (object stores, file systems, cloud repositories, etc.)

Multi-protocol data access: Since data can come in many forms and from many sources, the data infrastructure needs to be flexible. Data can range from large binary objects to small files. You can avoid expensive and inefficient data duplication by leveraging LZ NEO’s pre-duplication platform to accelerate the execution of your data pipeline.

Multi-temperature storage: The data infrastructure must support auto-tiering and multi-temperature storage. Pay attention to any SIEM that is automatically placing your data into ‘cooler’ storage and the fees associated with pulling that data back out.

Scale and Performance: Data bandwidth requirements are increasing, and multiple training models frequently run at the same time, demanding ever-higher levels of throughput from parallel file systems and objects stored both on-premises and in the cloud.

Managing data assets is critical to the success of modern AI projects.

To help simplify the infrastructure behind your projects, develop a comprehensive AI plan that includes LZ NEO to maximize the volume of data needed now, and in your business’ future.

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Clayton Dukes

Clayton Dukes

CEO

4819 Emperor Boulevard Suite 400
Raleigh, NC,27703

About Clayton

Clayton Dukes leverages over two decades of experience in network systems design, implementation, and management. Early years included designing an open source solution to solve network event management challenges as a Datacenter Lead Engineer at Cisco, which and ultimately led to a later-creation of the LogZilla Network Event Orchestrator platform. Dukes has co-authored the CCIE SP OPS certification and resides in North Carolina
Tags: data collection , LogZilla Network Event Orchestrator , LZ NEO , AI , SIEM , Pre-duplication