Modern corporate strategies rely heavily on data analytics to power important decision-making procedures. But there are frequently many obstacles in the way of turning raw data into useful insights. Integration, interpretation, quality control, and application of data frequently obstruct advancement. They conceal data’s actual worth. Working your way up the data analytics hierarchy and making sure data governance is in place at every turn is how you want to master each of those components.
To get the most of your data, you must understand the data analytics hierarchy, which consists of data, descriptive, diagnostic, predictive, prescriptive, and proactive analytics. Your data’s potential can truly be unlocked when you begin to incorporate generative artificial intelligence.
Pain points in data analytics
All of us gather data, but do we do it in the right way? Are we gathering it in order to use it for something? The issues brought on by a lack of data governance are listed below.
Data overload
It is simple to gather data. As soon as we have software, we simply plug it in and let it operate. Companies are left with a mountain of data to sift through as a result. It’s like strolling into a hoarding scenario.
There is data everywhere, and organizing it and extracting the important information will need a great deal of effort and patience. Businesses struggle to organize and find pertinent information among massive amounts of data.
Problems with data quality
Inaccurate data results from an abundance of data. You’ve gathered so much information that it’s hard to decide which to use. Businesses find it more difficult to separate the wheat from the chaff. Data that is erroneous or inconsistent impairs analysis and decision-making.
Integration difficulties
Each and every vendor wants you to stick with them and their software lineup. In actuality, the majority of businesses use a range of tools to gather and export various data sets for various purposes. Insufficient data governance makes it difficult and time-consuming to merge data from multiple sources.
Delayed insights
Businesses that have inadequate data governance are forced to operate reactively. They have a hard time moving forward and are constantly curious about what transpired. Making judgments in a timely manner becomes impossible as a result.
By using the data analytics hierarchy to address these issues, businesses can enhance their data-driven decision-making. Let’s investigate each stage and how generative AI may improve it.
The hierarchy of data analytics
The hierarchy of data analytics is a structured technique. It makes sure that the data is fully understood and used. It is divided into six stages, each of which builds on the one before it to offer more comprehensive understanding and useful results:
Data: Unprocessed, unrefined information gathered from multiple sources.
Descriptive analysis: answers the question “What happened?” by condensing historical data to reveal trends and patterns.
Diagnostic analysis: Investigates the fundamental causes of noted patterns, providing an explanation for “Why did it happen?”
predictive analysis: This method answers the question, “When will it happen?” by utilizing past data to predict future events.
Prescriptive analysis: Provides explicit recommendations for responding to the question “What should we do about it?” based on predictive insights.
Proactive analysis: Uses AI agents to carry out suggested actions on their own, responding to queries such “Can the machine do it for me?”
Every level in this hierarchy is essential for creating data-driven decisions that work. Let’s examine each stage in more depth and discover how generative AI improves it all.
1. Data: the foundation
Particular pain point: Organizing the vast amount and diversity of gathered data is daunting.
Solution: Gathering, cleaning, and storing data from several sources is necessary to build a solid data foundation. Prior to beginning any type of data collecting, you need establish the necessary conditions.
Describe your user stories, KPIs, and business objectives. Your system configuration will be guided by your goals for the data you wish to gather and your intended uses for it.
Generative AI applications
Creation of synthetic data: Generative AI can create synthetic data to augment real-world data, guaranteeing strong and varied training datasets.
Data normalization: To guarantee correctness and consistency across datasets, AI algorithms automate data normalization.
2. Descriptive analysis: What happened?
particular pain point: It’s difficult to gain valuable insights from large amounts of unstructured data.
Solution: Using a summary of past data, descriptive analytics finds patterns and trends. Usually, your CRM, web analytics, and marketing automation tools provide quantitative data for this.
Instead of taking the effort to properly configure these systems, businesses usually just “set it and forget it” when they first set them up. Starting from the foundation up allows you to know what information to gather, how to extract it, and what insights you can draw from it.
Generative AI applications
Code development: AI can help you with code development so you can write faster code for data extraction and analysis.
Automated data exploration: AI uses machine learning to automatically investigate data relationships, revealing patterns that manual analysis frequently misses.
Data visualization: Generative AI produces visually appealing representations that draw attention to important findings and facilitate the data understanding and communication.
3. Diagnostic analysis: What caused it to occur?
Particular pain point: figuring out what drives trends and anomalies at their core.
Since data analysis might be intimidating yet data collection can be simple, many businesses omit this step. All they have left is a pile of difficult-to-analyze unstructured qualitative data.
Solution: The goal of diagnostic analytics is to identify the causes of observed patterns.
The next logical step is to figure out why it happened after you have an understanding of what happened. This is obtained by tracking trends, gathering consumer input, and doing market research.
Generative AI applications
4. Predictive analysis: When will it happen?
Particular pain point: Correctly predicting future trends in dynamic settings.
As marketers, we have a tendency to plan our campaigns and activities based primarily on intuition and anecdotal evidence.
Solution: Predictive analytics forecasts future events by utilizing historical data.
A useful yet underutilized tool is forecasting. It functions best when you have a solid base of quantitative and qualitative data with sound data governance.
Generative AI applications
Enhanced forecasting models: Artificial Intelligence creates and improves forecasting models by simulating different situations and offering a variety of possible futures.
Code generation for custom models: Artificial Intelligence (AI) reduces development time and skill requirements by writing and optimizing code for complicated predictive models.
5. Prescriptive analysis: What should we do about it?
Particular pain point: It takes time to decide on concrete actions based on insights and data analysis.
Solution: Based on predictive insights, prescriptive analytics suggests particular courses of action. This is how you want things to go.
It takes time to compile the data from the earlier stages. Marketers want to go right in and begin doing things.
Applications of Generative AI
Practical suggestions: Artificial intelligence (AI) determines the optimal course of action by evaluating past facts and predictive insights to generate comprehensive action plans. Plans are available without utilizing your data. Nonetheless, you’ll be able to develop extremely customized programs if you can master the descriptive, diagnostic, and predictive processes.
6. Proactive analysis: Is it something the machine can handle?
Particular pain point: It is difficult to apply insights in a timely and efficient manner. With a little assistance, marketers may do more as they are constantly juggling multiple tasks.
Solution: AI agents that carry out suggested actions on their own are referred to as proactive analytics.
To get to this stage, your data governance needs to be precise and stringent. Since AI is acting and performing on your behalf, it is imperative that you provide the systems with precise data.
Applications of generative AI
Autonomous decision-making: AI-powered autonomous decision-making systems, such as those that modify marketing tactics on their own, are able to make and act upon decisions instantly.
Continuous learning and adaptation: AI agents are always learning from fresh data, which helps them perform better and adjust to changing environments without the need for human assistance.
