Data is an incredibly powerful tool when harnessed and used correctly. So what are some of the current most-important trends to be aware of?
1. Data-centric Artificial Intelligence (AI)
There can be a number of issues with data quality that can impact AI. Therefore it is very important to formalise AI-centric and data-centric data. Doing this will help to address anomalies, bias, diversity and labelling in a systematic way. Data management is also key.
2. Adaptive AI
Because there are so many constantly changing variables and data sources, using adaptive AI systems is important. These offer more flexibility than traditional forms of AI and can modify their behaviour based on previous experiences. They adapt and improve as they evolve – and can also offer faster decisions/outcomes.
3. Metadata-Driven Data Fabric
Data fabric is a type of data architecture that can unify disparate data sources in a way that improves efficiency and enables greater data protection. It utilises data pipelines and automated/intelligent systems. It can use the metadata to learn, predict and make recommendations. Using this architecture can reduce data management tasks and also increase confidence in the data.
Looking for a Data Analysis Company?
If you feel that your data is not being utilised as effectively as it could be, there are several specialist companies that can offer advice and assistance. Many of these firms also have online resources such as the examples seen here: https://shepper.com.
4. Context-Enriched Analysis
This trend builds on existing graph technologies and enables deeper analysis which looks at both the data points and the relationships between those data points. This type of analysis helps create additional context based on constraints, similarities, communities and paths.
5. Increased Data Sharing
Data sharing is regarded as key to digital transformation, but there is some uncertainty about how to do it safely and at scale. Collaboration is key (across a business and within the industry). Investment in architectures such as data fabric can boost data sharing and deal with both external and internal data sources (no matter how different they may be).