5 Factors That Impact the Performance of Your Big Data Project

By Published August 6, 2015 Updated July 6th, 2017


The drive to make the most out of big data is in full swing, with companies eagerly looking into big data analytics tools designed to get the most out of the valuable information they are collecting. The insights gained from proper analysis of big data can lead to big dividends later on, but getting to that point can be a challenge. For that reason, many businesses are investing in new big data projects with ambitious goals they hope will lead to greater success for their respective companies. Put simply, big data projects have a lot of moving parts and numerous factors that can greatly influence if they will succeed or fail. Considering how costly a failed big data project is, organizations will want to pay heed to these factors and ensure their own big data projects will have what it takes to be successful.

1. Data Movement

When enacting a big data project, many organizations may consider transferring their data from one database to a different one. Others may choose to transfer their data to a new cloud platform or new subscription within the cloud. In either case, doing so could impact performance. Moving data to a new location, whether before a project has begun or in the middle of it, creates an extra step in what is already a complex process. Requirements to move data during a project also creates a large interruption in the analytics process. The end result could be a significant bottleneck that holds up progress in the project.

2. Internal Networks

Even if an organization is only moving data internally, they still need networks that connect their main database to their analytics platform. These internal networks have to be built to handle the increased data flow, which is a challenging task to complete successfully. Poorly constructed internal networks can be easily overwhelmed by rapid data growth, especially when that growth rate reaches reaches 50 percent annually. With growth rates so high as the matter of collecting large amounts of data becomes easier, organizations need to plan ahead to ensure networks can keep up with the new workloads.

3. Scalability

Requirements for a big data project can change after the project is already underway. When such changes occur, organizations need to have a scalable system that can expand to meet new demands as they crop up. If a system lacks scalability, it won’t be able to support the demands, and the project is likely to fail. This is often seen in the importance of having the right storage systems. Storage systems that are easily scalable can be used to prevent the occurrence of bottlenecks before they happen. They also can lead to easier and faster access to vital data, a necessary component for big data project success.

4. Data Focus

Even if all the right systems and equipment are in place, if the organization is focusing on the wrong data, a project can quickly reach a dead end or be swamped with irrelevant information. Companies in the midst of a big data project need to be able to identify what data matters most and focus on that specifically. If an organization knows what it is going for, time and resources will be saved.Figuring out which data is relevant is only half the job. Companies must determine which data are relevant to which teams, and then make sure those teams have access to that data. ​Too little data access prevents useful analysis, and too much can be an unnecessary distraction for teams. This is a balance that is both difficult and mission critical.

5. Organizational Structure & Culture

Insights gained from a project need to be acted upon quickly to have them be of much use to the organization, but such action may be impeded if a business doesn’t have a data-driven culture already in place. Without quick action, organizations stand to lose whatever competitive advantage they stood to gain from unique big data insights.

Having the right technology and equipment is only part of the equation to having a high performing big data project. Organizations also need broad data knowledge and a culture that upholds the value that big data brings to the table. These factors all play major roles in how well a big data project performs in the long run. As long as organizations pay close attention to the factors outlined above, they’ll be in a better position to capitalize on whatever big data projects they undertake.

Are you researching big data adoption or struggling with your current implementation? Read this blog post to learn more about big data challenges.

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