Why is it important to prepare data for AI, and how to organize this process

*digital marketing data

Effective data management explained

It is always better to go from the bottom to the top, from the basics to the nuances, and our case is not an exception. We will start with the types of data analytics, move to data management, and finish with a discussion on how to do that effectively and why it is so essential for AI.

Data analytics and its main types

It is wrong to start the discussion about how to manage data effectively without peeking into the data analytics domain. Ask yourself, how can you manage something if you cannot gather and analyze it? Sounds wrong, doesn’t it?

So, there are 4 types of data analytics, and it is important that you have at least a basic understanding of all of them to be able to plan your data management strategy successfully.

1. Descriptive analysis. It summarizes certain information and gives a conclusion on a matter. Mastering descriptive analysis techniques will help you ensure the high quality of gathered data, which will lead to more accurate and insightful summaries.
2. Diagnostic analysis. This type of data analytics is responsible for answering the question “why?’. With its help, you can dig deeper into the roots of something and understand the initial reasons why something happened and why it happened in the way it did.
3. Predictive analysis. It is used to predict future events based on already existing knowledge and experience. Data enrichment, which is required for such planning, is a vital tool that helps to enhance the accuracy of any analytical model.
4. Prescriptive analysis. A logical stage after predictions – suggestions on the further actions and steps based on the predictions and forecasts.

Although these 4 are equally important and independent types of data analytics, it is always highly recommended to combine all of them and use them in sequence, as this will guarantee significantly better results.

Data analytics and data management

While you may have heard a lot about data analytics (the internet is full of digital courses and experts), and not much about data management, both are equally important and closely linked.

Without a proper strategy, all analytics results can potentially be inconsistent, incorrect, outdated, or all at once, and the worst part is that there is no alarm that will inform you that something is going wrong. If you do not organize all the processes from the beginning, you will bury yourself under the layers of inconsistent conclusions and keep making wrong decisions, with every new one being much worse than the previous ones.

Why are effective data management practices important for AI

We allowed ourselves a bit of drama picturing how cumulative the effect of poor data management can be, but let’s return to our cakes: why are effective data management strategies important for AI?

Forbes reviewed the AI topic more extensively and came to the conclusion that artificial intelligence is involved in almost all aspects of human life in one way or another. AI needs data to learn, and the better the provided data is organized, the better it learns. Basically, we can end it here, as this only reason is more than enough.

However, let’s explore the question in more detail. Not that AI models won’t be able to operate at all without a proper data management strategy. It would rather provide low-quality and mostly unsatisfying results, making it senseless to use this technology in our everyday lives. Proper approach, on the other hand, ensures that you get the maximum profit from the AI and use it to the fullest.

1. Data quality is improved. The better “study materials” you provide the AI models with, the more reliable and effective they will be. With a proper data management plan, you will be able to address all the major errors and inconsistencies in the early stages.
2. Model performance reaches another level. The better the data is structured and delivered to the machine learning model, the more effectively and faster it can learn.
3. The ethics and objectivity grow. Diverse data delivered to the AI model in a structured way decreases the level of dependency on the operator and promotes the development of fair and ethical AI.
4. Focus on compliance and security. AI is often used in projects that involve sensitive and personal data that belongs to other people. This, in turn, requires a solid data management strategy that will ensure compliance with privacy policies and provide proper access controls.
5. Reusability and scalability. With the proper data organization and storage, businesses can reuse datasets across different projects, speeding them up and reducing redundancy.

In short, without proper data management strategies, the usage of AI in any type of project would be accompanied by inaccurate insights, biased reports, and issues with privacy policies and regulations. We guess you agree with us that no one needs such results.

An effective data management plan for an AI project

Just as all the data for AI should be well prepared and structured, the process of initial data preparation should be as well. Companies and businesses that ignore that stage and hope to start using and learning AI models “on the go” often end up using underperforming and inaccurate models with unrealistic costs. Here are the main steps you need to go through if you want to manage things effectively.

1. Define when and how you will use the AI and data requirements. If you do not have a clear idea of how you are going to use the AI model and what problems you want to solve with it, there is no way you can manage all the processes effectively. It will also help you to understand the potential data sources better.
2. Assess the data landscape. Audit all the data you already have in your possession and identify what is missing. You need to create a clear representation of all the existing sources and formats, and know what is needed for further or better development and learning of the AI model.
3. Set the priorities. Not all the data is equally useful. Focus only on the data sets that are clear and have a high business value exactly for your project. This is essential for effective data management and planning
4. Create and implement the governance framework. It is needed to ensure that all the teams involved use the data consistently and responsibly. You need to clearly define all the ownership roles, access permissions, and compliance protocols.
5. Set up data pipelines. Be ready to prepare solid pipelines to work on, clean, and transform the data before it is “fed” to the AI. If you are not familiar with the Extract, Transform, Load process, learn it and implement it in your team. Do not hesitate to automate repetitive and clear tasks – it will save a lot of time and budget. It is crucial to “feed” the AI with the best resources possible, that is why we recommend using a server-side tracking setup combined with the needed analytical platforms.
6. Add metadata. Enrich all your work with metadata to make it easily searchable and accessible. Use cataloging platforms to structure everything you gather.
7. Use, check, reuse. Do not aim for a perfect result from the first iteration. It is advisable to launch a demo project first, evaluate its efficiency, fix all the problems that may (and, most probably, will) arise, and only after that start using it “in the field”. However, even after that, be ready to implement some minor adjustments and make decisions “on the go”.
8. Monitor and improve. When the project is launched, constantly monitor its effectiveness. You need to observe the data freshness and pipeline health. Automation tools can come in very handy at this stage. The main thing, however, is to always stay alert and nurture your models with constantly improving and enriching data and strategy.

You can hire a consultant and pass all the responsibilities to them. This, however, does not mean that you do not need to understand all the processes at least superficially, as otherwise, your project can start moving in the wrong direction.

Conclusion

No one will tell you how to create an effective strategy that will work perfectly in 100% of cases and would not require any additional time, money, or personnel investments. We, however, did our best to provide you with basic steps everyone must consider and follow as the bare minimum. Good luck for now, and don’t forget to share all the insights you get when preparing data for AI when you attend the summit.

Stape is an all-in-one platform for server-side tagging. They provide a powerful infrastructure packed with custom scripts, tags, gateways, plugins and apps to make server-side tracking easier for marketers and website owners alike. Stape is a Sponsor of the DDMA Digital Analytics Summit 2025 on October 9th. Get your tickets here.