Tech Strategies for Data-Driven Decision Making

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 online information is hardly comprehensible and is expected to keep growing.

New software, faster computers, and the global World Wide Web enable instantaneous data access, which benefits many growing businesses. But before they use it for data-driven decision-making (DDDM), they must take care of data gathering, storage, and safety issues. In this article, we’ll overview tech 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, goals, and development strategies. It aims to eliminate intuition-based choices and reduce human error as much as possible. DDDM became widely 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 solid data are more consumer-centric and resource-friendly than their counterparts. Below are some of the most significant DDDM benefits.

looking at data

Fewer Human Errors

Making a crucial decision based solely on intuition is risky. Of course, trusting yourself is mandatory for executives, but verifiable data is its best foundation. Instead of making approximate evaluations, you can use concrete correlations backed by facts. Furthermore, data analytics tools like Tableau allow intuitive data visualization to help you simultaneously consider hundreds of different details.

Task Automation

One way or another, most businesses gather some information, even if they don’t use DDDM strategies. Manually collecting data is a time-consuming process susceptible to human error. Badly input data distorts the results crucial for a successful investment. DDDM relies on automated data-gathering solutions such as APIs to avoid input errors and delays.

Cross-team Communication

Medium to large businesses know very well how hard it is to communicate a cohesive strategy between departments. Software developers, testers, and marketing strategists must march to the same drum. If all your departments implement data-driven solutions, they will find it easier to align goals using the same data.

Risk Evaluation

Successful enterprises know that failure is a valuable lesson. If something goes wrong, you can be sure those strategies do not work and avoid them. However, how do you know the exact reasons for failure? Because DDDM is grounded in statistical data, it will outline where your algorithms did not produce the required outcome. Maybe your price was too low compared to the market average, or you misinterpreted the consumer sentiment and chose the wrong ad keywords. Data analytics software will highlight these points for further reevaluation.

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Tech Strategies for DDDM

Most data analytics operations relate to data gathering, storage, and access. As an additional challenge, all three must comply with data privacy and safety regulations, like GDPR.

Gathering online data is more complicated than it sounds. There’s so much of it that doing so manually is nearly impossible. Firstly, consider the difference between structured and unstructured data. Structured data is stored in a specific format that can be easily used with data analytics 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 streamline and automate data-related tasks.

APIs are widely popular tools for gathering structured data. API stands for Application Programmable Interface, a frictionless and consensual information-sharing tool. Businesses that rely on data production or consumption agree to share it over API. They set the rules for what kind of data is transferred and structure it aligned with business interests. Real-time APIs are extraordinarily valuable as they enable data analysis and visualization once it appears.

Medium to large businesses must also take care of data storage. Some decide to build a vast server infrastructure. Although undeniably beneficial, it requires enormous resources to assure data integrity, access, and recovery. Cloud storage services rent the server infrastructure to help save money. They handle virtual and physical server safety, and around-the-clock data access, optimize migrations, and guarantee compliance. Be sure your cloud storage provider uses at least industry-standard encryption to prevent data leaks.

Lastly, what good is information for if you cannot access it? We advise looking into data engineering if you haven’t heard of it. It’s a relatively recent profession called into being 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

Numerous entrepreneurs outline the importance of data for business longevity. People are still getting used to the new reality opened by the rapid expansion of the Internet and e-commerce. Businesses that arrive early at the data analytics workshop gain an early advantage, as Big Data and data-driven decisions are here to stay for good.