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Implementing AI on centralized data for adopting a data-driven strategy





Implementing AI on centralized data for adopting a data-driven strategy

On the 28th of September, our Senior Account Manager, Zacharias Siatris, spoke at the Data Conference 2022 organized online by BOUSSIAS and netweek.

Zacharias tackled the subject of how to implement Artificial Intelligence on centralized data for adopting a data-driven strategy! The article below summarizes his presentation on that day:


Centralized and good quality data is a basic building block of an AI/ML pipeline. Gathering, training and test datasets in one place, is the true asset behind Artificial Intelligence (AI), more so than when it comes to trained models.

Most importantly, establishing a single source of truth helps organizations build trust in data and make it readily available to all the relevant stakeholders and interested parties. Consolidating information in one place will cultivate an AI adoption culture and therefore, lead an organization to develop and achieve an efficient and powerful data-driven strategy.

The greatest barrier to AI adoption is the lack of centralized data

Over the past few decades AI technology has evolved and has been adopted by a wide range of sciences and fields. Computers and AI algorithms (e.g. Machine Learning) use a massive amount of data to be able to teach themselves and make predictions based on specific models. Such a model is Natural Language Processing (NLP) which deciphers human language (both oral and written) into signals which thereafter are used to develop virtual communications systems such as chatbots.

Even though AI/ML technology is becoming more and more popular, it has a few key challenges that we need to overcome to be able to support businesses and organizations achieve their digital transformation. By utilizing massive chunks of data, AI enables organizations to achieve outstanding results. However, AI is inextricably linked and dependent on the consistency, quality and integrity of the data.

Training data is the foundation of AI initiatives. A prerequisite for the development of AI/ML models is the existence of an adequate amount of high-quality training data in order to be trained and built. Centralized and validated data can guarantee improved outcomes and performance for AI algorithms. Nevertheless, it is important to highlight that when it comes to data, quantity and quality must go together, more so for enterprises and large-scale organizations.

It can be incredibly challenging to acquire accurate and consistent data. Surveys highlight that the most significant barrier for enterprises in their AI adoption process is the shortage of insightful, usable and relevant data. This can lead to poor insights and forecasts and leave businesses incapable of cultivating an AI culture. To mitigate such barriers, organizations should employ a strong data governance framework along with quality management processes to safeguard their data. All pieces of information within the enterprises should be gathered, processed, and stored centrally, correctly, securely and efficiently. Therefore, organizations should carefully consider establishing a centralized storage system (single source of truth) to establish data integrity and consistency.

Establishing a single source of truth will help an organization improve reporting and monitoring procedures by automating and enhancing them with more consistent information and developing more transparent operational activities on the identification of problem areas.

Transparent and Explainable AI

Organizations need to understand how Artificial Intelligence works, to avoid the “black box” phenomenon. With AI and ML models, it’s - indeed - not clear how scores are generated or what factors lead to specific results. Artificial Intelligence explainability, or “white box” initiatives, solve the riddle by providing transparency and reasoning for the outcomes AI models produce.

Explainable AI (XAI) and transparency allows interested parties to understand how an AI system works, by focusing on topics such as methodologies of training and evaluating models, decision boundaries , specifics of data “fed” to a model, and last but most importantly, the reason they may predict specific values. Developing explainable AI models and/or using specific libraries and frameworks, organizations can retrieve more actionable insights that they can exploit and therefore, achieve their goals and objectives.

In order to better understand what is described in this article, we would like to provide an example: Suppose there is a model named “Explainable AI Churn Model” which apart from helping organizations identify the customers that will quit or stop using their products and/or services, it also gives tremendous insights on the reasons behind their behavior. More specifically this model pin points and highlights which features of data used as input for the AI model, play a crucial role in making customers churn. As soon as stakeholders observe these patterns they may take the necessary actions to mitigate such customer behaviors and improve the engagement of their customers. As a result,organizations can improve their customer experience and satisfaction, ensure much better quality services and therefore, increase revenue and experience higher ROI.

Adopting a Data-Driven Strategy

Consolidating and centralizing information and data from various systems and sources, ensures data integrity, consistency, quality and an opportunity to integrate a data governance framework in a significantly more convenient way. However, the benefits are not limited to the data and the processes themselves. Good data improves all data-driven use cases, including business intelligence, analytics and most importantly data driven decision making.

There are many advantages in understanding the factors (features) that have led AI models to specific outcomes. For example AI transparency and explainability ensure that the algorithm is working as expected. Alternatively, implementation of these principles may be obligatory for an organization to follow regulatory standards. However, the most important benefit of XAI is that it allows for better and more effective decision making by acknowledging which feature affects mostly the model’s results. By obtaining such valuable information, organizations can develop advanced analytics and arrive at more detailed explanations and enriched insights which may lead to a more sophisticated and efficient strategy, or a better implementation of the existing one.


Even though the core elements of establishing a data-driven strategy remain the same, the way of achieving it has dramatically changed. In this new “AI era” we currently live in, the most successful organizations are the ones that may identify changes via data driven approaches and are thus quicker and more efficient to adaptIt does begin with a principle though which can be summarized as follows:

“Before you worry and start thinking about implementing data-driven strategies and adopting an AI culture in your organization, make sure you do data right.”

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