Business leaders and their organizations have more information at their disposal than ever before. Not only do we create enormous volumes of data every day, but we also can store, process, analyze, and apply valuable findings with tremendous speed. The problem is that many companies today have immature or archaic data management practices that prevent them from taking full advantage of all the information available.

For some, the problem is a simple capacity and scaling issue. These organizations need to move away from on-premises data centers, rigid licensing contracts, and self-maintained hardware that can’t flex with real-time utilization. For others, the challenge is transforming and enriching raw data so that it’s useful across many contexts. Data is rarely ready to use as soon as it’s generated.

Even agile, tech-forward businesses struggle with data. Enterprises may be able to collect, process, and store any amount of information, but if they fail to extract insights quickly, they miss out on opportunities to strike while the iron is hot with customers and the market. So, on the one hand, we have more data than ever. On the other hand, it becomes obsolete faster.

Finally, the gold standard in data-driven decision-making is being able to use data near-instantaneously and over the long term through cutting-edge AI/ML. This takes exceptional data management processes, infrastructure, talent, and awareness of the latest technologies available. Only best-in-class organizations today can deliver timely, data-based interventions while sustaining high-quality machine learning models.

The good news? The tools are out there for companies in every industry to achieve this level of data mastery. It all starts with the cloud.

End-to-End Data Control on AWS

Amazon Web Services (AWS) has an answer for every step in the modern data pipeline. Organizations can now collect and process data at the edge with services like AWS IoT Core, IoT Greengrass, and IoT Device Management. Companies can also ingest massive volumes of data from a variety of sources and process it as it comes in using services like Amazon Kinesis and AWS Glue.

When it comes to storing data on the cloud, AWS has purpose-built databases for a wide range of use cases, including Amazon Neptune for a graph database service, DynamoDB for a NoSQL database, and Amazon Elasticache for an in-memory data store. Enterprises can also run advanced analytics and big data workloads with solutions like Amazon Redshift and Amazon EMR.

For those who have deeper machine learning and data science capabilities, AWS also provides all the tools needed to train, deploy, and manage complex models through Amazon SageMaker. And now, with Amazon Bedrock, leveraging Generative AI using today’s most powerful foundational models is easy.

Data leaders have everything they need on AWS to extract maximum value from data and make better decisions in real time and over the long term. The hard part is knowing how everything fits together and designing the right architecture for specific industries and problems. This is where working with an AWS Premier Tier Services Partner like ClearScale can make all the difference.

Unlock Data-driven Decision-making with ClearScale and AWS

Since 2011, ClearScale has completed over 1,000 AWS projects with clients in many industries, from education to energy. Our team of AWS architects and engineers has collectively earned 100+ AWS technical certifications and 12 AWS competencies, including the Data & Analytics and Machine Learning competencies. As a result of this experience, we know how to guide organizations through data and analytics and machine learning projects that create value and impress customers.

For instance, we worked with PBS to implement a powerful and personalized recommendation engine for app users. Our team built a sophisticated MLOps platform and overall data ecosystem on AWS, with crucial services such as Amazon SageMaker Studio, Amazon Aurora, AWS Lake Formation, Amazon Athena, and AWS Step Functions. With this setup, our client was able to launch a new recommendation engine that will continue to improve over time with more data.

We tackled a similar problem in the eCommerce sector for an online flower business. Our client wanted to make intelligent recommendations to new and previous site visitors using a variety of data points. We used past purchase history, cart additions, previous page visits, and more to determine what products to recommend. Our solution also included a notification service, search engine prototype, transfer learning prototype, and Infrastructure-as-Code (IaC) processes. Since the conclusion of this project, our client hasn’t had to maintain a data science team to deliver higher-quality shopping experiences to every customer.

These are just a couple examples of how businesses can use data and AWS to create better products that boost profitability and engagement. If you’re ready to get started on your cloud data project or just want to bounce ideas off an AWS expert who understands data-driven decision-making, contact us for a consultation.