Unlocking the remaining 99% of your data requires a radical transformation of your approach. Between the underutilization of unstructured data and distrust in their reliability, many organizations are stagnating. The stakes are colossal: unused data holds treasures of insights necessary to shape informed strategic decisions. Access to this wealth of information fosters unprecedented creativity, excelling under the aegis of effective and tailored artificial intelligence. Gaining a comprehensive understanding of your data becomes essential to fully manifest their potential.
Understanding Enterprise Data
Businesses, whether small or large, are beginning to grasp the significant value of their data to optimize user and customer experience while developing strategies based on tangible evidence. The emergence of artificial intelligence (AI) reinforces the importance of easy and pragmatic access to available data, multiplying their exploitation potential.
The Challenges of Successful AI Adoption
To leverage AI, businesses must engage in significant efforts in data collection, curation, and data preprocessing. Issues such as data governance, privacy, and regulatory compliance require particular attention from the start of the process. Integrating AI cannot be done without meticulous preparation and a rigorous framework.
The Complexity of Data
Henrique Lemes, head of the data platform for the Americas at IBM, highlights the complexity of enterprise data. Information is often classified into structured and unstructured data. The former is organized in a standardized format, while the latter encompasses various formats such as emails, social media posts, or videos, which are rich in information but difficult to exploit.
Exploiting the Potential of Unstructured Data
Less than 1% of enterprise data is used by generative AI, while over 90% of this data falls into the unstructured category. This has a direct impact on the trust and quality of the information used for decision-making. Decision-makers must be assured that the data is complete and reliable.
The Importance of Automated Ingestion
To transform the unstructured flow of data into a true decision-making tool, automated ingestion is essential. This approach significantly increases the volume of data available for AI while adhering to governance rules. Companies must strive to automate this process to maximize its benefits.
Methods for Data Optimization
Three key processes emerge to enable companies to leverage their data:
Large-Scale Ingestion
Automating the transfer of data to analysis systems is the first crucial step. This facilitates the transition from raw data to AI-ready data.
Data Curation and Governance
Once the data is ingested, curation and the establishment of governance rules become paramount. These practices ensure the quality of the data and its compliance with current regulations.
Availability for Generative AI
Once the previous steps have been completed, it is possible to make the data available for generative AI. This conveys the added value of organized and ready-to-use data.
IBM’s Data Transformation Approach
IBM adopts a unified approach. This method is based on a deep understanding of the company’s AI journey, integrated with advanced software solutions and sector expertise. This enables the effective transformation of structured and unstructured data into AI-ready assets, while adhering to governance and compliance standards.
Growing Need for Data Governance
The increase in volume and diversity of data complicates management. Companies face obstacles when expanding their AI solutions, often designed for specific tasks. As they seek to broaden their scope, managing unstructured data becomes a necessity that fuels the growing demand for data governance solutions.
Options and Tools for AI in Regulated Industries
IBM offers a range of solutions tailored for businesses, including tools to facilitate the operation of AI workloads, even in the most regulated sectors. Institutions like international banks and multinationals trust Big Blue’s capability to transform data into actionable resources.
For more information on optimizing data pipelines for AI, leading to tangible business outcomes with a quick return on investment, you can refer to this article on the partnerships between SAP and Databricks for data management.
Apple also emphasizes data privacy with synthetic and anonymized information, reinforcing the growing interest in data governance aligned with AI. French companies are adopting global data sharing strategies, illustrating a broader trend towards the efficient exploitation of data. Potential biases in datasets remain a concerning issue for the academic and professional community.
Artificial intelligence positions itself as an undeniable lever to optimize customer data and drive growth. This demonstrates the importance of unlocking the remaining 99% of available data for businesses seeking to capitalize on the power of AI.
Frequently Asked Questions
How can I unlock the remaining 99% of my unused data for AI?
To unlock unused data, it is essential to establish automation systems for data ingestion, as well as robust data governance to ensure their quality and compliance. Using advanced analytics technologies can also help extract relevant insights from unstructured data.
What types of data are considered unstructured?
Unstructured data includes various formats such as emails, social media posts, videos, images, documents, and audio files. Although they are more complex to analyze, they often contain valuable insights for the business.
Why is it important to manage unstructured data?
Managing unstructured data is crucial as they account for over 90% of the available data in many businesses. Ignoring this data can lead to missed opportunities for innovation and decision-making based on incomplete information.
What is the first step to make my data ready for AI?
The first step is to automate large-scale data ingestion. This allows for easy and efficient collection and structuring of data, making it accessible for advanced analytics.
How can I ensure the security and compliance of my data when using it for AI?
It is important to establish data governance rules from the outset, complying with regulations specific to your industry. This includes security protocols to protect sensitive data as well as anonymization processes.
What tools are available to facilitate the processing of unstructured data?
Many advanced tools and software are available to facilitate the processing of unstructured data, including those that integrate artificial intelligence and machine learning to extract insights and optimize their use.
What is the role of data governance in leveraging data for AI?
Data governance plays a fundamental role in ensuring that data remains accurate, reliable, and compliant with regulations. It establishes the necessary regulations for data usage, thus assuring quality and integrity in the analyses performed.
How cost-effective is a strategy for integrating unstructured data?
Adopting an effective strategy for integrating unstructured data can generate significant returns on investment. According to some studies, businesses can achieve more than 40% ROI when the integration is implemented correctly.
How can I unify the management processes for structured and unstructured data?
Unifying these processes requires an integrated governance approach that combines structured data management with unstructured data analytics. This often involves powerful tools that provide an overview of all available data.
