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Big Data Investments: Making Them Pay Off

Companies are making substantial Big Data investments today. Today, it's permeating every technology from driverless cars to AI and IoT.
Companies are making substantial Big Data investments today. Today, it’s permeating every technology from driverless cars to AI and IoT.

Companies are making substantial Big Data investments today. Today, it’s permeating every technology from driverless cars to AI and IoT.

It’s not wise to spend money blindly on infrastructure and tools without considering how to extract value from your data. If you do, you will wind up doing more harm than good to your company. Here are five techniques to ensure that your big data investments yield the best results and add value to your company.

1. Do not embrace Big Data solutions in the absence of a problem.

Many times, people rush to the latest and coolest thing without considering the final purpose or the business challenges that technology will solve.

This is true not only for Big Data investments but also for the technology industry as a whole.

Spend some time thinking about what problem you want to tackle with the technology before you implement it.

If you can’t come up with a specific problem, you should reconsider the need for it in the first place.

Therefore, identify a business issue that needs to be addressed before looking for tools and solutions to assist you to solve it.

This will help you in getting the most out of your big data investments, including the projects and technologies.

2. Use caution when using free big data tools.

One of the misleading elements of big data technologies is that the tools and infrastructure for managing large amounts of data are available for free.

However, there’s a catch. The fact that the program is free does not mean that the technology is also free or easy to install, operate, and use.

Many of these tools necessitate highly specialized management abilities. In addition, finding the right people for it can be costly.

Furthermore, many open source systems lack the operational support and maintenance capabilities that traditional solutions have given for decades.

As a result, your IT department’s burden grows as they are responsible for managing and monitoring these technologies on their own.

Therefore, take a step back and compare the entire cost and advantages of ownership of an open-source solution. Look at them in comparison to those of business-grade software before deciding on an open-source solution.

Business solutions may prove to be more cost-effective and less uncomfortable in the long term.

3. The key is to integrate.

It’s critical to realize that the technology in big data investments is most useful when integrated into your broader data architecture. It implies they must work well with other systems and processes in your company.

That way, the data, and insights can flow freely from one department to the next. If you don’t, you may wind up with a massive data mess when you try to use them in isolation.

Additionally, if you use them as a replacement for traditional data warehouses, it may cause even more problems.

To construct a functional architecture, you may need to use a mix-and-match method. This means you combine a collection of open-source tools with business solutions.

As a result, when analyzing big data solutions, it’s critical to focus on their integration capabilities. This will make it easier for you to undertake big data projects without disturbing your current procedures and systems.

4. Be ready for the Big Data last mile.

Many modern technologies are easy to set up and use in a development sandbox or even test environment. However, transitioning them to production environments, on the other hand, is a bit more difficult.

Because of their poor data governance and DevOps capabilities, these solutions are unable to meet the data governance, auditing, and control requirements of many major companies.

As a result, transitioning to production necessitates meticulous planning and strategy. Therefore, spend enough time developing a roadmap that will help you identify the skills and resources needed to successfully launch your big data infrastructure.

5. Prepare for a resource shortage after Big Data investments.

With new types of data being used in systems and applications, traditional methods of data analysis and insight will be challenging to use. As a result, businesses must change their business processes to suit new technologies.

In addition, many IT teams that have a mature technology infrastructure have downsized and diminished their competence in integration and architecture during the last decade.

Inviting such groups to incorporate new technologies into their work can be a disaster waiting to happen. Such businesses are unable to cope with the quick changes in their underlying technologies.

Managing big data initiatives, especially at a large size, will be costly for organizations with limited resources and funds. In such circumstances, you should seek out fresh and innovative approaches. These might be things such as metadata-driven solutions, which will not only save money but also reduce risk.

Bringing things to a close.

Organizations can get relevant insights from their data and turn them into commercial benefits. They can do this by following the processes above. Thanks to cloud technology, big data has become more intelligible and controllable for users.

Begin by concentrating on what you want to achieve with your Big Data investments and working backward. This will enable you to cut through the clutter around Big Data and invest in the tools and technology that will help you achieve your goals.

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