Unlock powerful insights by building a strong data foundation

We offer 5 best practices to ensure data analytics are built atop a solid base.

In 2006, British mathematician Clive Humby famously predicted that “data is the new oil.” Just as oil powered the industrial revolution and transformed economies, data is now the driving force behind the digital age. The rise of artificial intelligence has sparked a data-collecting gold rush. Fortune 500 companies are in a race to gather as much data as possible to train their generative AI models and capitalize on the innovations these models can produce.

To avoid falling behind, companies must transform their raw data into actionable insights. Establishing a data lake is a strategic move that empowers organizations to collect and aggregate data, turning it into valuable information that can drive innovation.

Data lakes offer a powerful solution for managing vast amounts of data from diverse sources: Internet of Things (IoT) sensors, website traffic, customer data, and social media activity. By breaking down silos among various platforms and departments, data lakes provide a unified repository for all organizational data. They offer many benefits:

• Storage flexibility. Data lakes allow organizations to store data in its native format without requiring any schema or transformation. This flexibility ensures that all data assets are readily accessible for analysis.
  
  
• Unified data view. Cloud-based data platforms, such as Microsoft’s Azure Synapse, offer a single platform for data ingestion, preparation, management and analysis. This unified view enables IT teams to query, analyze and visualize data using a single web-based portal.
  
  
• Scalability and cost-effectiveness. Data lakes, when paired with cloud-based platforms, eliminate the need for in-house servers. Cloud storage allows organizations to scale up or down on demand, ensuring performance and availability without significant capital expenditure. The pay-as-you-go model provides greater cost flexibility.
  
  
• Enhanced security. Data lakes and analytic platforms come with robust security features, including data encryption, access controls and threat protection. These features safeguard sensitive data and simplify compliance with industry standards and regulations, reducing the risk of data breaches and legal penalties.
  

Data into insights

The full benefits of a data lake can be realized when integrated with centralized data analytics platforms. These platforms enable various analytics and visualization scenarios, such as:

• Automated data pipelines. Building automated data pipelines streamlines data processing and ensures that data is continuously updated and available for analysis.
  
  
• Advanced analytics capabilities. Implementing AI and machine learning technologies provides a rich and diverse data set for training and testing models. These capabilities, integrated with various data science tools and frameworks, enhance the depth and breadth of analytics.
  
  
• Interactive dashboards. Centralized data lakes and warehouses feed data analytic platforms, allowing users to create and consume interactive dashboards. This connectivity facilitates data-driven decisions and actions at speed and scale.
  

Before pooling the organization’s data, it’s critical to effectively plan out a data foundation to build transparency and accountability within the data architecture. This will ensure that all shareholders trust the insights garnered from the raw data collected.

Best practices: ensure a strong foundation for data analytics

When designing and implementing data storage and analytics solutions using data platforms such as Microsoft’s Azure Data Analytics Storage, it’s important to follow some best practices. Through experience with global clients, we’ve developed the following recommendations.

1. Assess your data and workload requirements and choose the appropriate data analytics storage components and services. This decision will be based on factors such as data type, volume, velocity, variety, access frequency, query complexity, and performance expectations.
   
   
2. Establish policies for data management, access control and compliance to ensure data integrity and security. Design your data lake and data warehouse schemas and structures in a way that follows best practices for data partitioning, compression, encoding and indexing, to optimize data storage and query performance.
   
   
3. Implement data security and governance policies to enforce identity and access management, data classification and protection, data lineage and catalog, and data quality and validation. Consider using software tools to protect data at rest and manage encryption keys. Also, implement Role-Based Access Control to restrict data access to authorized users only.
   
   
4. Monitor and optimize your data storage and analytics performance and costs to track and analyze data usage, resource consumption, and billing. Continuous monitoring and optimization ensure the data analytics storage remains efficient and cost-effective. Also consider planning for future growth by designing the storage architecture to scale seamlessly with increasing data volumes.
   
   
5. Leverage centralized data platforms to simplify and streamline your data ingestion, preparation, management, and analysis processes.

In the digital age, companies collecting the right data and knowing how to use it will continually innovate and improve the customer experience. By establishing a data lake, your organization can transform raw data into valuable insights; drive innovation by creating a holistic data platform that powers highly personalized customer experience; and easily scale and optimize performance as you continue to grow.