Overcoming the Top Four Barriers to Actionable Data Insights

Overcoming the Top Four Barriers to Actionable Data Insights

By: Gene Tang – Head of Professional Services, Asia and India at Rackspace Technology

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. Also, 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. Local governments such as Singapore and Malaysia have also called for businesses to accelerate their digitalisation efforts across various key industries such as healthcare and manufacturing sector, as part of their smart nation plans through Singapore’s Research, Innovation and Enterprise (RIE) 2025 plan. 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 the data they collect and generate meaningful analysis. They’re weighed down by issues like dirty data (inaccurate, incomplete or inconsistent 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 the data which businesses collect, they need to address some of these common barriers.

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 businesses 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.

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 data estates and a modern data architecture that makes data accessible, data scientists and analysts can make data discovery and utilisation a more natural part of DevOps processes and culture. DevOps drives speed and quick turnaround. And data — if it’s known and accessible and in a useful format — can be fully incorporated into DevOps culture, development and deployment processes.

2. Excessive costs

When a company’s infrastructure isn’t structured for utility and elasticity, talent is expensive, and businesses are facing large, ongoing investments with no guaranteed return, costs can grow out of control.

Costs also become excessive as they continue to rely on on-premises data solutions for worst-case scenarios — as businesses stuck servicing older virtualised applications and data infrastructure. In addition, the 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, businesses get the benefits of multicloud — including elasticity, self-service, optimised economics and cloud native services — so they can develop modern applications and host a modern data architecture.

3. Complexity

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 data platform mix isn’t optimised.

For example, an organisation’s 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 a business. But it must be analysed and intelligently separated into data flows that support the business — such that applications get the data they need when they need it. Many organisations 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, data can be more readily used, more cost effective and set the foundation for modern analytics and superior business insights.

4. Skills gaps

Most organisations don’t have the skills necessary in-house to optimise their data architecture for modern AI/ML use cases and cloud native applications. To create a modern data fabric, businesses need specialised 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.


Organisations can 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, collected data can help drive better business processes, experiences and decisions. With a fully integrated data environment supported by DataOps and MLOps, business and IT teams can make intelligent business and IT decisions that will drive the most value to customers and have the greatest impact on a business’ bottom line.

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