Unlock the potential of your data with AWS and Altostratus
In today’s business landscape, data is essential to every application, process and decision. Using data effectively accelerates innovation and helps companies achieve organizational goals. According to Forrester Research, organizations that leverage data-driven insights are 140% more likely to gain a competitive advantage and 78% more likely to increase revenue. (“Half of AI models never make it to production,” EnterpriseAI, August 23, 2022).
The increasing amount of data generated daily requires companies to have a clear and effective strategy for managing it. A well-designed data strategy not only optimizes decision making, but also helps improve operational efficiency, drive product and service personalization, and ensure regulatory compliance.
Business leaders are recognizing that the key to innovation lies in a comprehensive data strategy. This approach, already successfully adopted by several organizations using the Amazon Web Services (AWS) platform, unlocks the value of data and delivers sustainable advantage.
Challenges of data management
The main challenge for many organizations is that they have a lot of data, but don’t know how to make the most of it. Here are the main challenges and key considerations to take into account when developing a data strategy:
The value of your data with AWS
AWS solutions can help your organization implement a comprehensive strategy that facilitates data management at every stage of the process, from ingest and storage to analytics, visualization, and running machine learning models.
To successfully address business challenges, a data strategy is critical:
Comprehensive
A complete data strategy requires a toolset that takes into account the scale, variety of data and multiple purposes for which you want to use it. In addition, it must offer an optimal price-performance ratio for any user, data type and use case.
In this context, AWS solutions provide broad capabilities to support any workload, from databases for applications to storage for data lakes, analytics, machine learning and end-user tools.
To scale applications and quickly adapt to business needs, AWS offers modern infrastructure and advanced analytics services. For example, Amazon Aurora guarantees high performance at low cost. In addition, tools such as Amazon EMR and Amazon Kinesis Data Streams facilitate big data analytics and real-time decision making, without the need for complex ETL operations.
Integrated
An effective data management strategy must facilitate the integration and analysis of information stored in various tools and systems, allowing you to better understand the business and anticipate future events. With data dispersed across multiple departments, services, local databases and third-party applications, it is critical to be able to integrate this data seamlessly across silos for a complete and accurate view.
In many cases, organizations adopt different approaches to unify their data, such as data grids, lake houses or data structures, but data lakes stand out as a key solution. They allow valuable data from various silos to be collected, stored, organized and processed, making the information accessible to analytics, visualization and ML tools.
Integrating data between these silos often requires complex ETL pipelines, which are slow and unsuitable for modern decision making. In this regard, AWS has technologies that automate or eliminate ETL, enabling automated data integration. With SageMaker integrated into Amazon Redshift, Aurora, Athena y Neptune, and real-time integration between Amazon S3 and Redshift, organizations can perform analytics without moving data.
Governed
It is crucial that users can access data where and when they need it, with the right control. As more data migrates to the cloud, enterprise data governance models must evolve. IT managers and enterprises need up-to-date policies to protect data as it moves between repositories and to comply with ever-changing privacy and security regulations. Without a proper data governance strategy, organizations will face difficulties in focusing on data management and staying competitive.
AWS is investing across the entire data lifecycle to facilitate end-to-end governance with less effort. For example, AWS Lake Formation simplifies the control and auditing of actions performed on data in AWS S3, as well as data sharing in Amazon Redshift. Amazon DataZone enables cataloging, discovery, sharing and management of data, improving its accessibility across the organization. Finally, Amazon SageMaker provides capabilities to control and audit the full development cycle of machine learning models.
How does Altostratus help you?
At Altostratus, we specialize in managing the full cycle of your data with state-of-the-art AWS technology. From initial extraction to processing and final visualization in customized dashboards, we ensure that your data is accurate, reliable and available according to your specific business requirements.
Our services include implementing robust policies and strict controls to monitor access, maintain data quality and ensure data integrity at all times. With a focus on security and operational efficiency, we help optimize your data processes to drive informed, strategic decision-making.
For more information on how to unlock the full potential of your data, contact us at www.altostratus.es and find out how we can help you develop a comprehensive data strategy with AWS.
