Data has always played an important role in business decision-making, but it reaches its full potential only with digitized technologies. The amount of daily information online is hardly understandable and is expected to continue to grow.
New software, faster computers, and the global World Wide Web allow instant access to data, benefiting many growing businesses. But before using it for data-driven decision making (DDDM), they need to take care of data collection, storage, and security issues. In this article, we’ll go over technological strategies that solve these problems.
What is data-driven decision making?
DDDM is a decision-making method based on factual and verifiable information to improve business initiatives, objectives and development strategies. Their goal is to eliminate guess-based choices and reduce human error as much as possible. DDDM became very popular with the evolution of digitized technologies that enable unprecedented data access, storage, and sharing.
The benefits of data-driven decision making
Businesses that rely on strong data are more consumer-focused and resource-friendly than their counterparts. Below are some of the most significant benefits of DDDM.
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Less human errors
Making a crucial decision based solely on intuition is risky. Of course, self-confidence is mandatory for executives, but verifiable data is your best foundation. Instead of making rough assessments, you can use concrete correlations backed by facts. Additionally, data analysis tools like Tableau enable intuitive data visualization to help you consider hundreds of different details simultaneously.
Task automation
In one way or another, most companies collect certain information, even if they do not use DDDM strategies. Manual data collection is a slow process and susceptible to human error. Mis-entered data distorts crucial results for a successful investment. DDDM relies on automated data collection solutions such as APIs to prevent input errors and delays.
Communication between teams
Medium and large companies know all too well how difficult it is to communicate a cohesive strategy between departments. Software developers, testers, and marketing strategists must keep pace. If all of your departments implement data-driven solutions, it will be easier for them to align goals using the same data.
Risk evaluation
Successful companies know that failure is a valuable lesson. If something goes wrong, you can be sure that those strategies don’t work and avoid them. However, how do you know the exact reasons for failure? Since DDDM is based on statistical data, it will describe where its algorithms did not produce the required result. Perhaps your price was too low compared to the market average, or you misread consumer sentiment and chose the wrong ad keywords. Data analysis software will highlight these points for further reassessment.
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Technological strategies for DDDM
Most data analytics operations relate to the collection, storage, and access of data. As an added challenge, all three must comply with data privacy and security regulations, such as GDPR.
Online data collection is more complicated than it seems. There is so much that doing it manually is almost impossible. First, consider the difference between structured and unstructured data. Structured data is stored in a specific format that can be easily used with data analysis tools. It is organized and searchable. Unstructured data takes up a lot of space, is stored in the original format, and requires additional aggregation for further analysis. Structured data is further used with machine learning algorithms to optimize and automate data-related tasks.
APIs are very popular tools for collecting structured data. API stands for Application Programmable Interface, a frictionless and consensual information exchange tool. Businesses that rely on data production or consumption agree to share data via API. They set the rules for what kind of data is transferred and structure it according to business interests. Real-time APIs are extraordinarily valuable as they enable analysis and visualization of data once it appears.
Medium and large companies must also take care of data storage. Some decide to build a large server infrastructure. While undeniably beneficial, it requires enormous resources to ensure data integrity, access, and recovery. Cloud storage services rent the server infrastructure to help save money. They manage virtual and physical server security and 24/7 data access, streamline migrations, and ensure compliance. Make sure your cloud storage provider uses at least industry-standard encryption to prevent data leaks.
Finally, what good is information if it cannot be accessed? We advise to investigate data engineering if you haven’t heard of it. It is a relatively recent profession created by terabytes of unstructured data. Data engineers ensure that stored information is accessible, searchable, and ready for future use. They handle data migrations between different platforms and cloud servers.
Conclusion on data-driven decision making
Many entrepreneurs stress the importance of data for business longevity. People are still getting used to the new reality that the rapid expansion of the Internet and electronic commerce opens up. Companies that get to the data analytics shop early get an early advantage, as Big Data and data-driven decisions are here to stay forever.
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