How to prepare data for the AI era?

How to take care of a company’s data in the age of AI? This is one of the most important challenges for IT teams in organizations large and small. Today’s processes rely on data more than ever, so ensuring its hygiene, correctness and scalability are critical to the success of the entire company.


Table of Contents

 The use of AI systems depends on the data you have, and most of today’s data architectures are not designed for generative artificial intelligence. We all talk about data architectures, but it is AI that forces companies to really examine their conscience: where is my data, what am I doing with it, how am I storing it, how can I use power generative AI with it? As a result, companies are looking at their infrastructure and starting to make a digital transformation. Now they have a real incentive to do so. What can they do to take care of their data to prepare it for the AI era?   


Data hygiene


Database hygiene is how data is handled. It is important because it enables AI to use it. Data hygiene is another way of taking care of data cleanliness and quality.  Data cleanliness defines how accurate, consistent and complete the data is in a particular collection. A broader concept is data quality. It encompasses a number of characteristics, including usability, accessibility or comparability. They determine the suitability of data for specific purposes. It can be said that data cleanliness is one of the components of data quality.

You may have incomplete, incorrect, outdated, or unstructured data. Itcan also contain duplicate entries or special characters, commas and dashes, causing challenges in data transfer and interfaces. Inaccurate or low-quality data undermines business performance and can lead to failed deployments. So what is the solution?Solution: Structured data models, data validation, quality control and cleaning processes can help ensure data accuracy and reliability. To succeed in these areas, you first need to define clear processes for data collection, management and maintenance. Attention to data hygiene is also fundamental to the security of, among other things, the personal data a company processes on a daily basis.

You can read more about data hygiene in our article: Clean data, clear path: mastering data hygiene in your organization. 


Data integration


Data integration is a process that enables information from different sources to be combined into a coherent and unified whole. It is important to eliminate data silos that can lead to inefficiencies and errors. Having a single, consistent version of data that is accessible to the entire organization is essential for effective business management.  Critical thinking and a thorough assessment of data sources are essential to gain valuable insights from data.

Companies often use a variety of software and systems for different functions, but integrating these systems can be difficult due to incomplete interfaces or incompatibility.  Data is processed either at intermediate stages or at the end, such as in Excel, to get the desired reports and analysis.

How do you create a centralized solution that reduces the manual work involved in data management and reporting, while flexibly handling changing business needs in the future? First, designing and implementing a high-quality data management solution requires vision, experience and expertise. Second, the technology should be flexible enough to adapt to the company’s processes without overburdening it. Third, it should be easy to use. Actionable insights can be drawn more effectively when systems are well integrated, making it easier to identify areas for improvement and adjust to tech and market trends.

Scalability of data


The system should be capable of efficiently processing increasingly large data sets. The amount of data can grow exponentially as the company grows. Therefore, it is important for the system to be able to cope with this. The costs associated with data management, including infrastructure, software licenses and human resources, can be significant.

The solution: Investing in the right technology and automation can help save costs in the long run. The cloud is a flexible solution that makes it easy to scale resources to meet growing needs.

It is important to assess which areas of data management require the most resources and prioritize them. Data management processes can also be streamlined by outsourcing services.

Data interoperability

Data interoperability is the ability of different systems to exchange and process information. This is especially important as companies rely increasingly on collaboration with external partners.

It is important to choose solutions that are based on proven standards. This will make it easier to exchange data with other systems.

Data interoperability will soon play an even greater role with Digital Product Passports. Tire manufacturers, such as Michelin, will have to agree on a common data structure with bicycle retailers, such as the Decathlon store.


Data security

Data security is a set of processes and technologies that protect data from unauthorized access, use, modification or destruction. Collected data is a valuable resource, so taking care of its security is important for the organization that collects and processes it.

Protecting data and ensuring data privacy are of paramount importance in the age of AI. There are already regulations in place that impose requirements for protecting the personal data of customers and employees. At the same time, companies must do everything in their power to secure critical corporate secrets.

The solution: organizations must invest in reliable and secure technologies and processes and establish clear data protection policies and practices. Proper procedures, such as data encryption, can effectively protect against threats. Training employees and making them aware of data security and privacy is also crucial.


Summary


Taking care of data is an investment that pays off, especially in the field of AI. Securing data from leakage, taking care of its quality and correctness allows its safe use in artificial intelligence systems. Companies that invest in proper data hygiene, integration, scalability, and security can unlock valuable insights from their data, leading to more informed decisions which in turn helps them stay uptodate with market and tech trends.

Problem with data integration? Set up a meeting with Jacek. 



/by