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Leverage your data to start making smarter, more impactful decisions.
Data is a valuable asset that is key to maximizing revenue, operational efficiency and profit. It is also the key enabler for Artificial Intelligence (AI). Having a data management strategy and approach to managing your data assets is therefore critical to becoming a truly data-driven organization, unleashing the full potential of your data assets and unlocking all advantages of AI and business intelligence.
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Knowing your business's churn rate is essential if you want to ensure profitability and sustainability in the short and long term. Predictive churn models can help you control it and take actions to effectively retain and foster customer loyalty.
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In an increasingly competitive and multichannel world, it is essential to place the customer at the center of everything you do in order to effectively attract, engage, and retain them. Throughout this process, data becomes indispensable for understanding and better knowing the consumers, thereby enabling you to customize their experiences to the fullest.
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There is no single "correct" way to approach the data preparation workflow, as each industry, project, or situation may require slightly different approaches. However, we have attempted to group the most recurrent tasks that occur in the process into six fundamental phases.
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Without proper data preparation, any analysis or decision-making would be based on incomplete, inconsistent, or erroneous information, meaning that anything you build on top of it is likely to be flawed.
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Data alone has no value: it needs an organizational model and common guidelines.
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Any organization looking to take solid steps using its data as a strategic asset must employ standard techniques to carry out data lineage within its organization.
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Without demand, there's no business. And without a deep understanding of demand, companies can't know how to operate properly. This is where demand forecast comes into play.
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While AI is a broad field that encompasses a wide variety of techniques, XAI is a subset focused on making models more understandable for people who do not have experience in data science.
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If all users had access to the data, they could extract insights to make better decisions and improve their company's performance.
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Why do organizations struggle so much with the data preparation phase? Because it's a rather costly process, and carrying it out correctly involves facing and overcoming a series of challenges, which, we won't deceive you, can be somewhat tedious, and it's easy to fall into their traps.