Unified Analytics is accelerating AI adoption in the enterprise

New research indicates that Unified Analytics could be the key to unlocking the true potential of enterprise AI

Unified Analytics is accelerating AI adoption in the enterprise

Although many enterprises recognise the importance of artificial intelligence (AI), most struggle to implement the technology. According to new research from Databricks, however, Unified Analytics could accelerate AI initiatives in the enterprise.

Foundational barriers

Despite enterprise AI adoption becoming more widespread, companies still face major challenges. While data is an integral element of AI models, the preparation process for deep analysis is often incredibly complex.

In addition to this, the siloed nature of enterprise data is a major concern for companies. The separation of data science and data engineering across different systems and organisations can also cause problems.

Organisations therefore spend a significant amount of time preparing data for analysis. This involves cleaning data from “errors, duplicates, and missing fields and combining different data types into new groups for added understanding.”

Next, enterprises need to verify that this data is correct and usable. Then, companies often need to enrich data with additional attributes in order to provide context for analysis.

These processes are evidently a hindrance to AI implementation schemes. However, Unified Analytics could transform the way that companies interact with data and ultimately overcome the “inefficiencies of scattered IT environments.”

Accelerating AI initiatives

Unified Analytics is “a concept that brings together solutions that unify data science and data engineering.” As a result, this makes “AI much more achievable for enterprise organisations” and enables the acceleration of AI initiatives.

The first key element of Unified Analytics is its ability to unify siloed data assets. As a result, enterprises can “speed up the process to explore, prepare, and ingest massive datasets for best-in-class AI applications.”

Data management is also simplified, while it is easier to connect data pipelines with machine learning (ML) to “quickly fit the models to the data.” By separating compute from storage, companies can benefit from better performance at lower costs.

Unified Analytics also enables companies to “continuously train, track, and deploy AI models on big data faster, from experimentation to production.” In addition, it provides a collaborative workspace to unify data engineering and data science organisations.

Finally, Unified Analytics helps companies reduce the complexity of infrastructure. In this context, the approach also creates an efficient infrastructure that supports DevOps – which is crucial to monitoring software creation.

Artificial intelligence has had its equal share of innovation and challenges over the past year, what will 2019 bring? Take a look at Forrester’s 2019 predictions to find out