Effective Methods To Reduce Costs in Data Analytics

Reduce Costs in Data Analytics

Reduce Costs in Data Analytics: From tech titans like Netflix to local e-commerce stores, data runs the 21st century. Data empowers businesses to make better decisions, communicate information to shareholders, and create more positive customer experiences. With its expansive selection of applications, data can become a core component in every operational movement, team, and strategy in modern business.

Yet, without an effective system to manage the capture, processing, and analysis of this data, businesses can begin to hemorrhage capital. According to Gartner, poor-quality data can cost businesses nearly $10 million USD each year, creating a needless drain of resources that impacts the bottom line.

Without effective methods that reduce the cost of data analytics, businesses will lower their ROI from any operation empowered by data. Especially for businesses that want to base a large part of their strategy on data-driven decision-making, even a small reduction in the cost of analytics will translate into a major increase in ROI.

In this article, we’ll discuss the importance of data costs, demonstrating how they impact the bottom line and can lead to operational inefficiencies. We’ll then turn to three strategies that help organizations reduce the cost of their data analytics. Let’s dive right in.

Why Should I Focus on Minimizing the Cost of Data?

For many businesses, especially those that are only just beginning to use data analytics in everyday processes, data costs may not yet be particularly high. As you continue to incorporate data into the decision-making process and functions, the cost will steadily rise. Instead of waiting for the cost of data to outweigh its benefits, businesses should focus on minimizing data costs in order to fully streamline their management of data.

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Currently, global economies consider the world’s most valuable resources to be data. The extent to which the world of businesses uses data is only going to keep growing as we find new platforms, integrations, and data-driven systems. Yet, an organization is unable to take advantage of these innovations if it doesn’t first have a stable data architecture and infrastructural system in place.

In order to get the most value from data, organizations need to know where data comes from, how they transform it, and where they store it. At the very least, they should focus on creating cloud data warehousing to cover the storage of their data. Without a comprehensive, centralized system, businesses will suffer from data silos as they continue to expand.

Beyond just helping with organization and management, sustainable data practices like using data warehouses and improving data quality will reduce the overall amount of money a business spends on its data. Establishing effective processes may initially require a larger degree of capital, but will pay itself back over the years due to your better management of information.

If data is our most valuable resource, we must learn to structure our infrastructure to allow it to flourish. That all begins with effective architecture, end-to-end management, and compliance policies in place.

How To Reduce the Cost of Data Analytics

Understanding the importance of reducing the cost of data is one thing; actually minimizing its cost is another. Creating a streamlined system where you can capture, process, analyze, and create visualizations from data won’t happen overnight.

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Without effective architecture in place, the final stage of data analytics will always come with a higher charge. If you’re wanting to convert your organization into a data-driven business, then minimizing this cost should be an absolute priority.

Here are a variety of leading methods that businesses can use to minimize the cost of data analytics and architecture:

  • Move to the Cloud
  • Clean Data First
  • Establish a Program Owner

Effective Methods To Reduce Costs in Data Analytics

Move to the Cloud

Especially for businesses that are scaling, the cost of producing on-site data infrastructure can be unstable and deeply unpredictable. Local data architecture has a high upfront cost and can create a bottleneck as infrastructure can take weeks to install. Instead of facing these upfront costs and scaling them as your business grows in size, we recommend you try cloud data architecture.

Cloud architecture allows businesses to scale their warehouses and other related services with the click of a button. By hosting data storage with another company, you also access a highly available form of data, reducing the chance of downtimes where you could lose insight from data.

Making the switch to cloud-first data architecture can lower the costs of your entire data infrastructure, helping to minimize the cost of every analytical process you run.

Clean Data First

Whenever data analysts process data that includes inconsistencies, duplicated records, or other issues, they waste capital and reduce the overall ROI of that data. In order to avoid conducting any analytics on data that will not produce the desired outcome, businesses should endeavor to construct a comprehensive method to clean data.

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Data hygiene is a small part of the end-to-end movement of raw data, yet one that can have a tremendous impact on the bottom line. By cleaning up your data, removing duplicate records, and structuring information in the most effective format, you can reduce wasted analytics and promote the effective use of data.

Establish a Program Owner


All of the above tactics won’t happen without coordination and an ongoing effort. Instead of leaving the minimization of data costs up to chance, businesses should select one or many individuals to take ownership of this improvement. By giving people accountability, perhaps to managers of different data science departments, you ensure that all minimization objectives are being carried out effectively.

Don’t just leave data costs up to chance; select individuals to hold ownership over the process and see it through all of its stages. Accountability will increase the likelihood that positive changes come out of your pursuit of better data analytics.

Final Thoughts

At this point in the game, we all understand the importance of data in the modern workplace. From optimizing overall operational processes to improving each customer touchpoint with a business, data can drive us toward a more effective way of working. Yet, if businesses don’t also consider the costs of data, they will begin to lose the cutting-edge that data-driven decision-making offers them.

By incorporating the strategies that we’ve outlined in this article, organizations will be able to minimize the cost of data, empowering them to get more from every plot point. When the costs of data are minimized, the return is maximized, allowing organizations to take their use of and insight from data to the next level.

As we progress further into this digital age, businesses that are smart about their use of data will survive higher competition, bring in more capital, and provide winning customer experiences.

Also Read: 6 Tips to Learn Data Analytics As a Beginner
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