Data-Driven and Data-Informed Approaches to Strategic Decision Making
In this article, we will look at data-driven and data-informed approaches to data analysis.
In the Information Age, data has become a key driver of business success. It is the fuel that enables companies to make informed decisions while gaining a significant competitive advantage. However, it is important to recognize that not all companies are harnessing the potential of data in the same way.
There are different types of companies in terms of how they work with data. There are so-called data-resistant companies that deliberately act against data outcomes. In addition, there are data-aware companies that only partially use data. And at the top of that chain are data-driven companies where data is a key factor in decision making.
The data-driven approach embodies a management philosophy that relies on the analysis and interpretation of data to make strategic decisions. Essentially, decisions are based on concrete data rather than simple assumptions, subjective thoughts, or personal preferences.
Suppose a company generates many reports. Does this automatically classify it as data-driven? The answer is a resounding no. Firstly, the sheer existence of countless reports alone does not indicate data-driven practices. Secondly, we lack insight into how these reports impact decision-making.
If, for example, last week’s revenue fell and a report was prepared on this occasion, can we say that the company is guided by the data? Actually the answer is no. While the existence of the report is commendable, we still do not know if it was actually read and understood by those involved in the revenue changes.
Moreover, simply mentioning of revenue decline in the report does not automatically imply a data-driven approach, as it fails to address the underlying reasons for the decrease. The report does not delve into the specific metrics that were influenced, resulting in the revenue decline.
To truly qualify as a data-driven company, foresight and predictive capabilities are crucial. Presently, we find ourselves receiving a weekly report stating that revenue has decreased at the beginning of the following week. This signifies a seven-day delay, suggesting that the report could have been generated earlier. Ideally, it should have been anticipated at least seven days ago or, better yet, predicted in advance. With this proactive approach, we could have taken measures such as promotional activities or events to avert the revenue decline. Only then can we confidently declare the company as truly data-driven.
Data-Driven Processes in a Gaming Company
Let’s take a look at how data-driven processes are shaping the landscape of gaming companies and revolutionizing game design and LiveOps strategies.
In game design, a data-driven approach involves careful analysis of each feature before implementation. Market intelligence services provide valuable insight into market trends and competitor behavior to help you make informed decisions. In addition, monitoring the impact of specific features on performance metrics, including project revenue, enables data-driven decision making.
Сareful evaluation and prediction are crucial before implementing a feature. Questions arise: how profitable will the feature be? How will this affect key metrics over time? To ensure reliable results, the trustworthy interval must be estimated to determine whether the effects of a function are sufficient or not. Importantly, the analysis of implemented features should be documented and archived, serving as a foundation for future actions and decisions.
In LiveOps, a data-driven approach drives the planning and execution of promotions and events. Understanding the key metrics you can rely on becomes paramount when when developing successful promotional strategies. Before launching a promotion, it is crucial to have a clear idea of its purpose, whether it be discounts or other types of promotions. This understanding allows for careful planning and accurate evaluation of the expected impact.
To ensure long-term success, careful consideration is given to the calculation of a promotion’s effect. In free-to-play economies, where inflation poses significant long-term risks, it is essential to avoid scenarios in which short-term gains from a promotion are followed by a substantial drop in revenue. Similar to the principles discussed earlier in feature analysis, each promotion and event can be viewed as a unique feature in itself. Consequently, a comprehensive analysis is conducted, and the findings are documented and archived for future reference and iterative improvements.
When considering the broader context of the gaming industry, a data-driven culture becomea an important factor. Owners and decision-makers must possess data literacy, comprehending the nuances of data and be patient while waiting for results. A/B testing becomes a fundamental practice, ensuring that unknowns are explored, tested, and iteratively improved with each product iteration.
It is important to note that analytics in a data-driven culture goes beyond the role of a mere SQL operator. Analysts play a proactive role by actively participating in decision-making processes. Their expertise contributes to meaningful discussions about the future direction of the game, as insights from data analysis guide strategic decisions. By integrating the valuable perspective of analysts into the decision-making process, companies can leverage data-driven insights to optimize their products and drive overall success.
Data-informed decision making
While the data-driven approach has been widely praised, it is essential to recognize that it is not the ultimate stage of utilizing data within a company’s development. A new concept, known as “data-informed,” has emerged, signifying a more holistic approach to decision-making.
The data-informed approach involves considering data as one of many factors influencing decisions. It is evident that decisions rely not only on data but also on intuition and expertise. It is crucial for a company to first establish a data-driven foundation before transitioning towards a data-informed approach. By understanding how data precisely influences their operations, organizations can strike a balance between data and subjective vision.
