Are You Building Your Data Strategy to Scale?
Teresa Wingfield
March 28, 2023
A data strategy is a long-term plan that defines the infrastructure, people, tools, organization, and processes to manage information assets. The goal of a data strategy is to help a business leverage its data to support decision-making. To make the plan a reality, the data strategy must scale. Here are a few pointers on how to achieve this:
Infrastructure
The right infrastructure is necessary to give an organization the foundation it needs to scale and manage data and analytics across the enterprise. A modern cloud data platform will make it easy to scale with data volumes, reuse data pipelines and ensure privacy and regulations are met while also making sure that data is accessible to analysts and business users. The platform should use cloud-native technologies that allow an organization to build and run scalable data analytics in public, private, and hybrid clouds.
People
The talent shortage for analysts and data scientists, particularly for advanced analytics requiring knowledge of artificial intelligence, is a big challenge. With the U.S. Bureau of Labor Statistics projecting a growth rate of nearly 28% in the number of jobs requiring data science skills by 2026, the shortage will continue to grow.
To cope with the shortage, businesses will need to invest more in training and education. The more teams know about advanced data analytics techniques and how to use and interpret data, the more value an organization can derive from its data. Also, with demand for analytics skills far exceeding supply, organizations will need to make of the talent pool they already have.
Tools
A cost-optimal solution should not only process data analytics workloads cost-effectively, but also include data integration, data quality, and data management that add more costs, and complexity when sourced from multiple vendors. However, there is no such thing as a one-size-fits-all tool when it comes to analytics. Increasingly, organizations are adding many types of advanced analytics such as machine learning to their analytics tool portfolio to identify patterns and trends in data that help optimize various aspects of the business.
Businesses will also need to devise strategies for users to easily access data on their own so that limited technical staff doesn’t bottleneck data analytics. Embedded analytics and self-service help support the needs of data democratization. Self-service gives users insights faster so businesses can realize the value of data faster. Analytics embedded within day-to-day tools and applications deliver data in the right context, allowing users to make better decisions faster
Organization
For a data strategy to scale, an organization needs to build a data driven culture. Transitioning to a data driven approach requires a corporate cultural change where leadership views data as valuable, creates greater awareness of what it means to be data driven and develops and communicates a well-defined strategy.
Processes
There are many processes involved in a scalable data strategy. Data governance is particularly critical to democratizing data while protecting privacy, complying with regulations, and ensuring ethical use. Data governance establishes and enforces policies and processes for collecting, storing, using, and sharing information. These include assigning responsibility for managing data, defining who has access to data and establishing rules for usage and protection.
Get Started With the Actian Data Platform
The Actian Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full benefits of cloud native technologies. It can quickly shrink or grow CPU capacity, memory, and storage resources as workload demands change. As user load increases, containerized servers are provisioned to match demand. Storage is provisioned independently from compute resources to support compute or storage-centric analytic workloads. Integration services can be scaled in line with the number of data sources and data volumes.
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