How to Use the AWS Analytics Stack to Fulfill the Promise of Data-Driven Decision-Making

Most organizations gather data these days, but that doesn’t mean their business leaders get the full value that data can provide. Many companies collecting data and are just starting to make it available to employees. Enterprise companies often have silos of data that only some workers can access. When these silos include sensitive data, its presence can prevent workers from accessing insights and benefitting from their application.

A company could be at any of these positions on the data optimization sliding scale:

• Capturing data and storing it in a data warehouse, data lake, purpose-built database, or in Amazon S3
• Democratizing the data across the organization
• Processing data and applying the insights to take cutting-edge, forward-looking actions

Where is your company on that continuum, and how can you take it to the next level? A range of easy-to-use Amazon Web Services (AWS) analytics tools can empower your organization to ingest, clean and manage data effectively. By embedding the resulting advanced analytics throughout your organization, you can deploy your data, fulfill the promise of data-driven decision-making and achieve better enterprise performance.

Deploying Your Data Volumes

The tech industry has been talking about big data for over ten years. Statista estimates that global data volumes will reach 97 zettabytes by the end of 2022, and this number is projected to grow to 181 zettabytes by 2025. But simply continuing to amass data won’t get you anywhere. Companies must find ways to use data constructively to reap the benefits and increase ROI.

The AWS analytics stack gives you the tools you need to obtain an in-depth analysis from your past data. With these analytics, you can then build machine learning models that predict alternative futures under various circumstances. This empowers you to employ an “if this, then that” mindset, develop actionable directions for multiple scenarios, and factor these considerations into your strategic plans.

When most of your data collection is automated, you can amass it in a central repository and make it available to everyone in the organization who has access to use the data, all while masking the personal identifiable information (PII). These days, analyzing data is not just about predicting the future but developing actual plans to apply big data analytics in a way that benefits your organization’s strategy.

Applying Big Data Analytics

Every business leader wants to be seen making data-driven decisions. You have the data; you just need to apply it in a usable fashion to achieve the following results:

1.     Optimize Your Operations

Insight from data can be used to fine-tune operations and help you achieve maximum efficiency across many different industries. Whether you're developing software, manufacturing physical products, or conducting inventory management, there is a slew of data available about how your process works. This data will help you determine what normal operations look like so you can tell when (and where in the process) anomalies arise. Using that data to identify issues such as bottlenecks or fluctuations from sensor readings enables you to anticipate problems before they occur and develop actionable plans to avoid or minimize them.

2.     Perform Proactive Strategic Planning

Companies have used analytics to inform their strategic planning for a long time. Usually, they looked backward to understand enterprise performance and the factors impacting it. For example, by identifying where the highest sales revenues came from, companies could determine where to focus future marketing efforts to maximize market potential. Alternatively, they could focus on less active markets to increase awareness and boost sales.

Whatever they did, it required looking backward and making guesses about the future—albeit informed ones. The AWS analytics tools enable business leaders to perform proactive strategic planning. With the right expertise, companies can develop specific strategies to implement in any given situation.

3.     Implement Automation and Machine Learning

By using data, companies can now develop models to automate operations or augment human activities. Machine learning (ML) algorithms can detect fraud in financial services, generate insights from call center interactions and deliver real-time e-commerce recommendations. With enough data available, machine learning models can seek out the anomalies without human intervention. You can potentially even implement corrective measures automatically, based on the algorithm’s recommended solution to a specific problem.

For example, ML can be used to predict when equipment will fail by analyzing data from the machine’s sensors. Your company can automate maintenance schedules to avoid breakdowns, unnecessary repair costs and damaging downtime. A 2021 report on the true cost of downtime shows large multinational industrial and manufacturing companies lose an average of $172 million per plant annually. With an effective predictive maintenance strategy in place, companies can benefit from longer equipment life, greater efficiencies and less unplanned downtime.

The AWS Analytics Stack

The AWS analytics stack contains a range of solutions enabling organizations of every size to handle processing, warehousing, operational analytics and visual data preparation. AWS provides the following solutions that can help you manage your data:

• Amazon Athena: Provides an interactive query service to analyze data using standard SQL to analyze large-scale data sets
• Amazon Elastic Compute Cloud (EC2): Delivers secure, resizable computing capacity in the cloud
• AWS DataSync: Automates and accelerates migration of data from on-premises to AWS services
• AWS Database Migration Service (DMS): Transfers databases to AWS while remaining fully operational
• Amazon EMR: Supplies a big data platform to support petabyte-scale analysis using open source tools
• AWS Glue: Delivers a fully managed ETL service to prepare and load data for analytics
• Amazon Kinesis: Delivers a managed service that scales elastically for real-time processing of streaming data
• AWS Lake Formation: Permits rapid set up of a secure data lake, eliminating manual management and monitoring
• AWS Lambda: Facilitates the running of code at scale without a server for virtually any application or backend service
• Amazon Redshift: Provides a scalable cloud data warehouse to run business intelligence tools using standard SQL
• Amazon QuickSight: Produces interactive business intelligence dashboards publishable on any device that can also be embedded into applications
• Amazon SageMaker: Provides a scalable platform to build, train and deploy ML models
• Amazon S3: Provides a range of data storage classes with scalability, security, and performance for virtually any use case

The Take-Away

Wherever you are in your analytics journey, you can benefit from learning how to implement data-driven decision-making. The AWS stack provides all the tools you need to do so, and its integration capabilities mean you don’t have to abandon your current programs.

Discover our AWS-authorized Data Analytics courses that will empower you to manage your data comprehensively and cost-effectively.