Overcoming the top four barriers to actionable data insights
According to IDC, the amount of data created over the next three years will be more than all the data created over the past 30 years. And the world will create three times more data over the next five years than it did in the previous five.
As data volume and variety increase and data sources proliferate, new opportunities will arise — opportunities to deliver superior customer experiences, drive better business decisions and enable greater agility and resiliency. New technologies and approaches — such as the Internet of Things (IoT), cloud native development, AI and machine learning, and the modern data fabric — offer a path to this intelligent business vision.
Despite these opportunities and new approaches, businesses are struggling to manage data and generate meaningful analysis. They’re weighed down by issues like dirty data and misaligned data collection and governance policies. These companies risk falling behind competitors, who are using data intelligence to adapt to their customers’ needs quickly and proactively.
To gain actionable insights from your data, you’ll need to address some common barriers. Let’s take a look at each of these, as well as what you can do to overcome them.
Barriers and solutions to running a smarter and faster business
In a recent study of 1800+ IT leaders, we explored why businesses either fail to move their data analytics projects into production or experience quality and availability issues when they do. The study revealed several key barriers that are common to most businesses:
1. Data discovery challenges
Data discovery is difficult when you have unknown data sources, poor data quality, data silos and compliance restrictions. These issues can trace their origin to data used or generated by a specific application stored in a siloed data platform, typically found in an early 2000s web application architecture or in 1990s UNIX applications.
Additionally, incomplete views of customers and other business entities, duplicated data and a general lack of understanding around what data is available (for building new applications or updating existing ones) results in less-effective services, insights and customer experiences.
With a holistic view and understanding of your data estate, plus a modern data architecture that makes your data accessible, you can make data discovery and utilization a more natural part of your DevOps processes and culture. DevOps drives speed and quick turnaround. And your data — if it’s known and accessible and in a useful format — can be fully incorporated into your DevOps culture, development and deployment processes.
2. Excessive costs
When your infrastructure isn’t structured for utility and elasticity, your talent is expensive, and you’re facing large, ongoing investments with no guaranteed return, costs can grow out of control.
Costs also become excessive as you continue to rely on on-premises data solutions for your worst-case scenarios — as you’re stuck servicing older virtualized applications and data infrastructure. And your on-premises data platforms servicing cloud-based applications may incur higher than needed ingress/egress fees.
By moving data platforms to the right public and private clouds in a multicloud architecture, you get the benefits of multicloud — including elasticity, self-service, optimized economics and cloud native services — so you 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’re made even more difficult if your data platform mix isn’t optimized.
For example, your data may have been forced into a traditional relational data management system, or worse, into unstructured files — even though that is not the optimal place for data use and analysis. This makes developing applications using this data more difficult and less effective.
IoT dramatically increases the data coming into your business. But it must be analyzed and intelligently separated into data flows that support the business — such that your applications get the data they need when they need it. Many organizations either do not leverage the IoT or do so in a manner that overly restricts their data being used from the IoT. While these approaches result in preventing data flooding with its reliability, security and availability issues, they eliminate the benefits of using all of the appropriate data in their business ecosystem.
Dealing with the explosion of data variety, velocity and volume is complex. But by putting this data into the right data platforms in the right clouds configured into a modern data architecture, your data can be more readily used, be more cost effective and set the foundation for modern analytics and superior business insights.
4. Skills gaps
Most organizations don’t have the skills necessary in-house to optimize their data architecture for modern AI/ML use cases and cloud native applications. To create a modern data fabric, you need specialized education, training and experience not organically available in typical IT teams. This skills gap also contributes to 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.
Work with a partner whose team has the right skills, career paths and continuous work experience — where they’re always busy solving problems and building expertise across many different industries and use cases. This helps ensure that they’re able to attract and retain the best data people.
Achieving actionable data insights
With a modern data architecture, your data can help drive better business processes, experiences and decisions. And with a fully integrated data environment supported by DataOps and MLOps, your business and IT teams can make intelligent business and IT decisions that will drive the most value to your customers and have the greatest impact on your business’s bottom line.
Modern data architecture — coupled with AI and machine learning — enables your business to get the right data to the right application at the right business moment while delivering a new level of business insights and intelligent applications.
To learn more about how organizations are managing and modernizing their data, check out our report, “Data modernization: Removing the barriers to AI and machine learning.” You’ll discover more about the widespread challenges businesses are facing with data quality and infrastructure, as well as critical insights into the role of people and operations in modernizing your data.