5 Ways to Make Big Data Investment Work For Your Organization
Start small by focusing on what you hope to accomplish with your data and work backwards.
Big data is gradually percolating every technology from AI to autonomous cars to IoT, with companies continuing to invest heavily on Big Data Analytics. However, if you blindly spend on building infrastructure and buying tools, without really thinking about how to derive value from your data, then it will only do more harm than good for your organization. Here are 5 ways to ensure that your big data investment achieve the desired result and deliver the promised value for your organization.
1. Avoid adopting solutions in search of a problem
Many times, people rush to the latest ‘hot thing’ without thinking about the end goal, or even the business problems to be solved by it. This is not only specific to Big Data but also the technology industry in general.
Before adopting a technology, spend some time thinking what problem you aim to solve with the technology. If you’re unable to find a precise answer, you should re-evaluate its need in the first place.
Identify a business problem to be addressed and only then look for tools and technologies that can help you solve it. This will help you make the most of your big data tools and projects.
2. Be careful with free tools
One of the deceptive aspects of big data technology is that the tools and infrastructure required to manage vast pools of data are available for free. Here’s the catch. Just because the software is free, it doesn’t mean the technology is cheap, free, easy to install, configure and use.
Many of these tools require highly specialized skills to manage and it can be quite expensive to hire the right people for it. Also, many open source solutions tend to lack the operational support and maintenance capabilities provided by the traditional solutions for decades. As a result, it increases the workload of your IT department, who has to manage and monitor these tools on their own.
Before going for an open-source technology, take a step back and compare the total cost and benefits of ownership of an open-source solution with those of an enterprise-grade software. You may find that enterprise solutions are more cost-effective and less painful in the long run.
3. Integration is the key
It’s important to understand that big data tools are most valuable only when they’re a part of your overall data infrastructure. It means they need to play well with other systems and processes in your organization, so that data and insights can easily flow from one part of your organization to another.
If you try to use them in isolation, or see them as a replacement to your traditional data warehouses, then you may end up with a big data mess.
Sometimes, you may even need to adopt a mix-and-match approach, whereby you combine a set of open-source tools with enterprise ones to build a functional architecture. So, while evaluating big data tools, it’s essential to pay special attention to their integration capabilities. This will help you implement big data projects smoothly, without disrupting your existing processes and systems.
4. Be prepared for the ‘last mile’ stretch
Many of the new technologies such as Hadoop and Spark are easy to implement and work great in a sandbox, development or even a test environment. However, when it comes to transitioning them to production environments, things become fairly difficult. This is because many large enterprises have data governance, auditing and control requirements that these tools are unable to satisfy due to their limited data governance and DevOps capabilities. As a result, shifting to production requires careful planning and detailed strategy.
Spend adequate time to chart out a roadmap that will help you identify the skills and investments required to successfully put your big data infrastructure into production.
5. Plan for resource crunch
With new types of data being used in systems and applications, it will be difficult to apply the traditional methods of analyzing data and getting insights. As a result, organizations need to adapt their processes to accommodate the new technologies.
Also, over the past decade, many IT teams who have grown a mature technology infrastructure, have downsized and reduced their expertise in integration and architecture. Asking such teams to integrate new technologies into their work can be a recipe for disaster. Such organizations are not in a position to handle the rapid change in their underlying technology.
Organizations that have limited resources and money, will find it expensive to manage big data projects, especially at large scale. In such cases, you should look for new innovative ways, such as metadata-driven solutions that will not only reduce the cost but also mitigate the risk.
Wrapping it up
By following the above steps, organizations can gain useful insights from their data, and transform them into commercial benefits, while others are looking for ways to move the needle.
Today, big data has become more understandable for consumers, and more manageable, thanks to cloud-based technologies. Start small by focusing on what you hope to accomplish with your data and work backwards. This will help you cut through all the noise around Big data and invest in the right tools and technologies that will help you get the desired results.