Are you planning to invest in Artificial Intelligence (AI) to make your business more efficient and competitive? Or is your investment in AI not delivering the expected results? 

AI is making a huge transformative impact across industries. But most enterprises miss a crucial point. Knowledge of the technology or the latest trends will not suffice. Effective information management is just as important for AI success.

In today’s digital age, enterprises generate tons of data.  AI-powered systems deliver insights from such data. 

But the efficacy of AI models depends on the quality and integrity of data. Poor quality or incomplete data distorts insights. 

Consider an e-commerce company training the algorithm with incomplete or inaccurate data. The recommendations will not align with customer preferences. The business will lose sales and customer trust. In finance, insights based on faulty or incomplete data lead to huge losses. Flawed insights in healthcare lead to faulty diagnoses and can even cost lives.

A 2023 Moody’s study underscores the widespread prevalence of low-quality, inconsistent data. Two out of every three enterprises suffer from such poor data, which is not suitable for AI.

Information management (IM) comes to the rescue.

IM involves collecting, organising, managing, and leveraging data in a systematic and organised way. These activities help enterprises improve data quality and use the right data for AI.

The increasing stakes associated with AI bring Information management to the centre stage. IM becomes a key enabler of AI-powered growth and innovation.

Ensuring Data Quality and Integrity

Information management boosts data quality and integrity. The specific interventions to such ends include:

• Data cleansing by identifying and removing errors and inconsistencies and filling missing values.
• Data validation by crosschecking the data for accuracy and reliability. IM systems apply data validation rules such as length and format checks, range checks, ensuring fields are not empty, and so on. Pattern matching validates complex patterns such as postal codes.
• Standardising data formats to ensure seamless integration of data from various sources into the AI engines.
• Identifying and removing potential sources of bias. For instance, historical data may reflect societal biases. Flawed sampling methods may exclude certain groups.

IM systems also automate most of the above data-related tasks. Automating routine workflows leaves knowledge workers free to focus on higher-value tasks. They can, for instance, focus on developing data models and innovation.

Implementing Data Governance

Data governance is an indispensable component of information management systems.

Effective data governance improves data management and increases user trust.

Data governance involves:

• Instituting rules, protocols, and guidelines for access, storage, sharing, and deletion.
• Ensuring all data marked for analytics go through the cleansing process.
• Assigning ownership and accountability for data assets.
• Ensuring compliance with all relevant rules and protocols. IM systems embed compliance into business processes to meet regulatory requirements and manage reputational risks. 
• Implementing security measures and access control protocols to protect data from various threats.
• Maintaining data catalogue and metadata management. These resources detail the location, format, usage, and other data-related information.
• Ensuring seamless data integration and interoperability between diverse systems and applications.

The best information management systems, such as OpenText, make data governance unobstructive. These platforms ensure robust compliance without impacting productivity.

Creating and Using Synthetic Data

Most enterprises grapple with data overload and complexity. While enterprises generate tons of data, not all data is useful or analytics-worthy.

Many times, there is not enough organic data to train AI models. The available data sets may not cover all eventualities.

Enter synthetic data or artificially created data that mimics the actual data.

Creating synthetic data covers data gaps for AI training and analytics.

It also improves data security. Enterprises may create synthetic data equivalents of actual data to mask and protect sensitive data. 

Algorithms trained on relevant data create synthetic data that mimic real-world scenarios. Information management systems:

• Compare synthetic data with historical data. Such comparisons validate if the synthetic data can represent real-world scenarios.
• Classify synthetic data from organic data to maintain data integrity.

Incorporating Explainability

Many AI models operate as black boxes and suffer from trust issues. Users do not understand how the algorithm works or makes its decisions. ExplainableAI (XAI) makes the algorithm’s decision-making process transparent. It links the decisions back to the underlying data.

Information Management systems aid in XAI implementation by:

• Ensuring traceability of the data used by the algorithms. Traceability makes explicit the data that influenced the decision.
• Streamlining version control. Version control makes it easy to track changes, enabling data scientists to explain the model behaviour.
• Offering visualisations, making complex data and model outputs more understandable.

Enabling Data Orchestration

Enterprise data resides in multiple and often disparate sources. Many of the data sources are inaccessible silos set up at the user or department level as the enterprise grew organically. Such a complex data landscape makes data inaccessible to AI algorithms.

Information management systems:

• Consolidate data from various sources into data warehouses or data lakes. The IM system may also provide robust APIs to connect to various repositories. Such interventions streamline data flows.
• Visualise complex data patterns and trends, making data insights even more accessible.
• Structure and organise all data to facilitate efficient access and analysis.

Enterprises use IM systems to structure their data assets and enable streamlined access.

The applications are many. For instance, data orchestration makes supply chains more agile and efficient. Identifying relevant data actualises real-time insights. Businesses can respond to demand fluctuations quickly to optimise inventory.

Likewise, ready access to the latest customer interactions and preferences improves personalisation.

As technology advances, information management systems have also undergone big-time changes. Enterprises that modernise their IM tools and practices drive better value from AI.

State-of-the-art platforms such as OpenText make information management seamless. OpenText offers scalable and intuitive tools to organise, integrate, and manage large data sets and complex information flows. The platform integrates people and systems with data and provides context. Users can search and organise information using natural speech and textual descriptions. They can exchange information in a seamless and secure environment to improve collaboration, reimagine operational models, improve customer journeys, and much more. 

Extracting the right insights from the right data sets at the right time provides enterprises with the information advantage.