Companies are making more decisions based on data. However, the ability to intelligently process the growing volume of data is a bottleneck to extracting actionable insights. This is where machine learning (ML) and deep learning (DL) have a role to play, facilitating data-centric decision making without relying solely on human intervention.
Findings from 451 Research’s latest Voice of the Enterprise: Data Platforms and Analytics survey reveals the extent to which enterprises see AI and ML as critical aspects of their data platform and analytics initiatives. Two-thirds of all respondents agree that AI and ML are an important component of their data platform and analytics initiatives, but this figure increases to 88% among the most data-driven companies (i.e., those at which nearly all strategic decisions are data-driven).
The survey highlights the business impacts of bringing AI and machine learning to data platform and analytics initiatives:
Improving Operational Efficiency
Enterprises often struggle to ensure that database systems are running efficiently. Queries that overload the system, consume excessive resources or impact other running jobs not only impact performance but also require manual resources to rectify. AI can help by automating the management of queries based on their likely resource consumption, providing a more stable and reliable system that can prioritize queries, reducing manual governance and monitoring of the database.
[Also learn the importance of planning your AI architecture correctly.]
Improving Query Performance and Accuracy
AI-enabled database querying can have a dramatic impact on increasing the overall accuracy of – or confidence in – the query result. By executing queries in a more efficient manner, enterprises can lower the time taken to generate insight and improve business decisions.
Empowering Business Analysts
One of the primary challenges when doing analytics has been to ‘democratize’ the technology to enable a broader range of people to be able to make analytics-driven decisions. Accelerating the development of AI-based applications can enable the output of machine learning models to be placed in the hands of domain experts and business decision-makers.
Accelerating Data Scientist Productivity
451 Research survey results indicate that accessing and pre-paring data is one of the three most significant barriers to ML adoption. An AI-enabled database can help overcome this barrier to insight by accelerating data exploration and lowering development times though the integration of developer tools and frameworks.
Automating Database Admin Tasks and Changing the Role of the DBA
Through the automation of mundane database administration tasks such as database provisioning and performance tuning, DBAs can focus their time on higher-impact tasks such as architecture planning and data security.
[Read how AI resources are too valuable to squander.]
Discover the importance of AI and machine learning to data platform and analytics initiatives by reading the full Business Impact Brief by 451 Research, Voice of the Enterprise: Data and Analytics, 1H 2019.