Getting the most out of data requires a holistic, unified approach that can be impeded by the stumbling blocks listed here.
As volume and variety of data increase, and data sources proliferate, we can tap on the data to deliver superior customer experiences, drive better business decisions and enable greater agility and resiliency.
However, four common barriers can prevent or impede the gathering of insights from data. Here they are, and how they can be overcome:
- Data discovery challenges
Data discovery is difficult when businesses have unknown data sources, poor data quality, data silos and compliance restrictions. These issues arise when data that is generated is stored in a siloed data platform.
Data scientists and analysts need to attain a holistic view and understanding of data estates and a modern data architecture that makes data accessible so that they can make data discovery and utilization a more natural part of DevOps processes and culture.
- Excessive costs
When a company’s infrastructure is not structured for utility and elasticity, talent is expensive, and businesses are facing large, ongoing investments with no guaranteed return—costs can run out of control.
By moving data platforms to the right public and private clouds, businesses get the benefits of multi-cloud—including elasticity, self-service, optimized economics and cloud native services—so they can develop modern applications and host a modern data architecture.
Choosing the right mix of technologies, identifying architectural best practices for deployment, and integrating cloud, on-premises and edge—these are all complex responsibilities. Yet they are made even more difficult if data platform mix is not optimized.
By putting data into the right data platforms in the right cloud platforms configured into a modern data architecture, data can be more readily used, more cost effective and it can set the foundation for modern analytics and superior business insights, regardless of complexity and nature of data variety, velocity and volume.
- Skills gaps
To create a modern data fabric, businesses need specialized education, training and experience not organically available in typical IT teams. This skills gap also contributes to a data integration architecture that is scattered and opportunistic, preventing applications from getting the right data at the right time and leading to less-than-optimal experiences, results and insights.
Outsourcing to specialists is one way to acquire the right skills and work experience quickly and free up internal resources to focus on core business skills and/or build internal expertise progressively across many different industries and use cases.