How To Make Better Tech Decisions Through Analytical Levels
When we consider data trends, we think of catchy expressions, such as big data, machine learning, AI,, etc. But at its core, data is about helping you make smarter and better-informed decisions.
What would be the point of things like big data and predictive algorithms if it did not lead organizations to make better, smarter, well-informed decisions? But it’s not just data access that helps you make smarter decisions, but how you analyze them. Therefore, it is imperative to understand the four levels of analysis: descriptive, diagnostic, predictive, and prescriptive.
1. Descriptive analytics
Descriptive (observation and reporting) is the most rudimentary level of analysis. Often, organizations spend most of their time at this level. Imagine dashboards and why they exist: to create reports and presentations about what happened in the past. This is a critical step in analysis and decision making, but it is really only the first step. It is important to go beyond the initial observations and immerse yourself in the information.
2. Diagnostic analytics
The diagnostic analysis is where we come to the why? We go beyond an observation (such as whether the chart is trending forward or backward) and come to “what” makes this happen. Here is the most important aspect of asking questions about data and linking them back to business goals and imperatives.
3. Predictive analytics
Predictive analytics permits companies to anticipate different decisions, test them for success, locate areas of weakness, make more predictions, and so on. This allows organizations to see how the previous levels can flow together.
The predictive analysis involves technologies such as algorithms, machine learning, and artificial intelligence, which gives it power. This is where data science comes into play – with this level of analysis combined with the first two levels, companies can achieve actual success with their data and analytical methods.
4. Prescriptive analytics
Prescriptive analysis thrives at a very advanced level and is the most robust and final phase, and truly embodies the “why” analysis. At this stage, the data itself prescribes what should be done. Data-based decision making is most closely linked to a predictive and prescriptive analysis, even if they are the most powerful.
Think about the first three levels of analysis: you have a description of what happened, followed by a diagnosis of the reason, and then you end with a prediction of what might happen. Now imagine that you permit the data and analysis to tell you what action to take. This is powerful and why it is crucial to companies.
All four levels generate an analytical puzzle: describe, diagnose, predict, prescribe. When all four flow together, you can really succeed with a data and analytics approach. If the four do not work well together or one part is totally missing, the organization’s data and analytics strategy are not complete.
These four levels of analysis must penetrate the entire organization for data literacy to be effective. Besides, teams must have better skills that allow them to access each level as well as possible. The final hope is that these decisions are linked to the most important goals and business objectives.