“In just migrating to AWS, organizations experience substantial improvement in application availability, reducing both planned and unplanned downtime by 29% and 69%, respectively, and decreasing application latency by 38%.”
Modern Data Works Best in the AWS Cloud
Most modern businesses find themselves in a position where they are able to collect and store more data than ever before. This can be everything from unstructured data—large collections of files which may be of different types that aren’t stored in a structured database format—or it could be extremely granular information about a specific user’s purchasing habits.
No matter what type of data your organization has, it is important to build a strategy in order to make your data actionable. Because data is dynamic and comes in so many different forms, it can be challenging to get real value out of it.
What is data analytics and why is it important?
Data analytics can help shape business processes, inform better decision making, and help uncover new activities. Analytics, generally, is the “the systematic computational analysis of data or statistics.”[1] Data analytics helps lead to the understanding of meaningful patterns in data. These patterns can be used to find trends that may otherwise be unknown.
Data analytics is important because it helps organizations have a more sophisticated understanding of their processes and services.
Critically, the processes of data analytics helps get business data out of siloes. For example, different departments across an organization may have different data sets that could be useful to cross functional teams, if the data could only be accessed.
Yet, organizations often find barriers to being able to actually use their data because storing and processing it can both slow and costly. AWS solutions can help you collect, clean, and consolidate data, both accelerating the pace of the processes and helping to mitigate against spend.
Managed services help you ensure that your data analytics on AWS is configured to appropriately define, support, and deliver on your desired business outcomes
Using data to eliminate incidents
Nucleus research found that in just migrating to AWS, organizations experience substantial improvement in application availability, reducing both planned and unplanned downtime by 29% and 69%, respectively, and decreasing application latency by 38%[2]
Pairing the resiliency and scalability of public cloud services like AWS with a data strategy leads to even better outcomes.
For example, predictive and Machine-Learning (ML) powered analytics and Artificial Intelligence (AI) technologies use data analytics to identify issues that could interrupt normal business operations.
Some of the advantages of a data analytics strategy:
- Solve your business problems with factual data rather than speculations
- Enable better decision-making by breaking down silos and expanding data access.
- Improve customer experience by providing personalized experiences or responding to customer feedback.
- Realize strategic goals faster and compete with data using analytics, business intelligence (BI), AI, and ML.
- Use improved privacy, security, and governance practices when handling customer data.
- Predict business outcomes, optimize and reimagine processes, and reduce costs.
[1] https://en.wikipedia.org/wiki/Analytics
[2] https://pages.awscloud.com/rs/112-TZM-766/images/the-value-of-improved-availability-security-and-performance.pdf
