With data firmly established as an enterprise crown jewel, artificial intelligence (AI) and machine learning (ML) have become superpowers, steering organizations to better decisions, greater efficiencies, and even new business models. 

While AI initiatives are fueled by the same operational and historical data in existing systems, the scale of what’s required is like nothing organizations have experienced before. The first wave of AI capabilities harnessed operational data found in enterprise systems like enterprise resource planning and customer relationship management to orchestrate day-to-day business activities—an exercise that involved manageable data capacities of relatively low value. With the second wave came increased data volume and variety to enable dashboards, graphs, and other business intelligence (BI) assets to aid forecasting and identify trends.  

Today, the data deluge is unstoppable. And organizations are hungry to take advantage of both structured and unstructured data to power new AI/ML-driven workloads. “Machine learning fundamentally impacts every aspect of the business, whether top-line revenue or bottom-line cost issues,” explains Bhavani Rao, product marketing manager at HPE. “It’s relevant to 360 degrees of what the business does, and the more data you have and the better the data, the better the machine learning models.”

To facilitate AI at scale requires data preparation—a colossal problem for enterprises which lack the talent, resources, and expertise to continually process petabytes of changing structured and unstructured data to make accurate predictions. According to a report from Anaconda, data professionals spend about 38% of their time on data preparation and cleansing. And as the scale of data increases, so does the preparation burden, hampering data science teams’ ability to complete higher-value modeling work.

Conquer the data preparation challenge with one tool 

HPE Machine Learning Data Management Software helps enterprises conquer the data preparation problem by standardizing on a single tool for business analytics and ML workloads at scale. This reduces the learning curve and maintenance burden for enterprise teams, enabling them to quickly turn petabyte-scale workloads, including ML models, into insights that were previously hidden. These in turn help drive better decision-making and achieve desired business outcomes. 

HPE Machine Learning Data Management Software transforms large-scale data initiatives through:

  • Incremental processing which calculates what data needs to be processed and only processes new or changed data. This ensures workloads involving large, complex data sets can be completed in days, not weeks, requiring less storage and compute resources. In one example, an insurance company processing 68 million subscriber records every month for input into a BI system reduced its processing cycle from three weeks to less than 24 hours given that only 5% or less of the records changed between processing cycles.
  • Immutable data versioning and data lineage for pipelines, code, and data.  The software automatically captures unchangeable versions of data processed, code, and pipeline runs. This creates auditable data lineage—no other software can guarantee reproducibility in this manner.
  • Data-driven pipelines that are automatically triggered by changes to data, code, or pipeline steps. Automation eliminates manual steps and reduces processing time, speeding time to insights and eliminating potential errors. Consider a self-driving car application where a manufacturer is using the automated pipeline feature to continuously build and update granular, high-definition maps as new satellite and sensor data are added—all without the drudgery of manual operations.
  • Flexibility to use any data type or code on any platform, either on premises or in the cloud. The software works well with both structured data (database/data warehouse records) and unstructured data (images, video, audio, and text). Users can write data transformations in any coding language or library (Python, Rust, R, etc.), opening access to the broadest and most diverse data set for AI-driven workloads.

The heavy lift of data preparation work shouldn’t stand in the way of finding hidden insights. HPE Machine Learning Data Management software gives organizations a robust foundation for turning data into competitive advantage. “Our end-to-end approach reduces processing cycles from weeks to days, provides complete reproducibility, and has a high degree of flexibility,” Rao says.

To learn more about how HPE can help businesses uncover hidden insights in data, go to HPE.com/data.