Top Use Cases and Steps for Success

Key Takeaways

• Trusted data is critical for AI success. Data integration ensures your AI initiatives are fueled by complete, relevant, and real-time enterprise data, minimizing errors and unreliable outcomes that could harm your business.
• Data integration solves key business challenges. It enables faster decision-making, boosts efficiency, and reduces costs by providing self-service access to data for AI models.
• Follow five essential steps for success in making your data AI ready with data integration. Define clear goals, assess your data landscape, choose the right tools, ensure data quality and governance, and continuously optimize your integration processes.

Artificial intelligence (AI): it’s the ultimate example of garbage in/garbage out technology.

When done right, its truly transformative – enabling benefits like faster decision-making, personalized customer experiences, and unprecedented operational efficiency. But on the other hand, when initiatives are handled improperly, the damage can pile up – fast.

The success of your AI initiatives hinges on a critical factor: trusted data. AI models rely on high-quality, relevant data to generate actionable insights. Without it, you risk flawed predictions that contain AI hallucination or bias and cause you to miss valuable opportunities.

That’s where data integration comes in. Data integration breaks down data silos by giving users self-service access to enterprise data, which ensures your AI initiatives are fueled by complete, relevant, and timely information.

Let’s dive into some of the top real-world applications of data integration for AI – including how it addresses pressing business challenges, where it shines in practical use cases, and the five essential steps to prepare your data for AI success.

Solving Top Business Challenges with Data Integration for AI

Every business is different, and you may face your own unique hurdles that can minimize your AI’s effectiveness. Data integration plays a pivotal role in addressing many of these challenges. Here are just a few examples.

Challenge: Slow decisions in a fast market
Markets continue to evolve faster than ever – and your ability to keep pace is everything.

If you can’t use predictive analytics and make quick, confident data-driven decisions, you risk falling behind to your competitors that can. Delays can lead to missed opportunities, sluggish responses to market shifts, and the inability to capitalize on emerging trends.

• Solution: Ensure real-time insights and predictive analytics are both accurate and actionable with data integration. To enable smarter decision-making and operational efficiency, your business users, analysts, and data scientists need real-time, self-service access to data from across the business.

Challenge: Unreliable AI results
Poor data quality – characterized by data that’s inaccurate, incomplete, or inconsistent – renders even the most advanced AI models ineffective.

Errors stemming from bad data can harm customer relationships, expose your businesses to compliance risks, and waste both time and money. Not to mention, if internal trust of these technologies is damaged, it can obstruct further adoption.

• Solution: To provide AI with the full spectrum of correct and relevant information, you need to integrate your most comprehensive datasets. When your AI has access to all this high-quality data, you gain more relevant insights that help you power better decision-making and foster trust in AI outputs.

Challenge: Pressure to reduce costs and boost efficiency
As your competition becomes more efficient, there may be pressure to streamline your operations and cut expenses. AI offers a path forward by automating manual processes, identifying inefficiencies, and detecting anomalies that can drive up costs.

• Solution: Ensure that your users have self-service access to data from all of your mission-critical systems. Having this access and visibility means that they can discover previously hidden inefficiencies, minimize downtime, and allocate resources more effectively – helping you cut costs across your operations.

Use Cases: How Data Integration Drives AI Success

Integrated data is a foundational component to successful AI initiatives across industries. What are your goals with AI? Looking for more inspiration? Here are four use cases to consider:

• Personalized recommendations
  Integrated data from customer browsing behaviors, purchase histories, preferences, and beyond enables AI to create highly tailored recommendations that resonate with your customers and prospects.

If you’re an e-commerce platform, for example, you can increase revenue and reduce customer churn by suggesting products based on combined data points. Similarly, if you’re a streaming service, you could foster stronger user engagement by offering personalized content suggestions based on analysis of viewing habits and preferences.

• Fraud detection and prevention
  Fraud detection demands fast and accurate analysis of complex datasets. When successful, this means better protection of your company assets, stronger compliance, and boosted consumer trust.

Data integration ensures that AI is able to consume disparate, complex data from across the enterprise – allowing for real-time identification of subtle anomalies that might otherwise go unnoticed. The result? An overall approach that’s proactive rather than reactive.

• Legacy system modernization
  It can be costly and time consuming for you to access, integrate, and migrate data from your complex legacy systems – like mainframe and IBM i – into your cloud environment and applications. You should be able to release data from your complex, on-premises platforms while reducing the operational costs and performance bottlenecks associated with maintaining these systems.

Breaking down data silos and enabling real-time, self-service access to this complex data can help to reduce bias and improve the accuracy and reliability of your AI models. Integrating data from supply chain management systems, for example, can help improve your forecasting and reduce inventory costs.

• Chatbots
  Customer service chatbots can help your business reduce costs by automating inquiry responses. But, these chatbots must be trained on large amounts of accurate data to deliver consistent and relevant responses.

Data integration ensures these AI systems are fed up-to-date customer profiles, purchase histories, and past interactions so they can resolve customer queries faster, provide personalized recommendations, and reduce the workload for your support teams.

Ready to Start? Five Steps to Data Integration for AI

So far, we’ve covered some of the top use cases and challenges where the power of data integration for AI truly shines. Now’s the time to act and develop a foundation for AI success with data integration.

It comes down to five essential steps:

1. Define your goals and scope
2. Assess your current IT landscape and identify data sources
3. Choose the right approach, tools, and partners
4. Ensure quality and governance throughout the process
5. Monitor and optimize performance and outcomes

Let’s break each of those down.

1. Define your goals and scope

No one seeks to build a data integration pipeline for the sake of it. Start by aligning your data integration efforts with your higher-level business and AI objectives so that you can tackle high-priority, low-effort use cases. What specific business problems are you solving? What outcomes do you expect? These answers will help you define the scope of your project and set clear expectations with team members and stakeholders alike.

Action items include:

• Identify business and AI objectives so you can align your integration efforts to your organization’s goals.
• Assessing potential challenges, like resource constraints or existing data silos.
• Determining success metrics, like improved efficiency or revenue growth.

Taking this first step ensures your integration efforts are focused and achievable.

2. Assess your current data landscape and identify data sources

Once you know the goals and scope of your project, map your current IT landscape to your project requirements. This is how you’ll identify key data stores and repositories where your most critical and relevant data lives.

Action items include:

• Inventorying data sources: Document all relevant datasets, including their locations and formats.
• Assessing data quality and completeness: Identify gaps or issues such as duplication, missing entries, or inconsistencies.
• Evaluating accessibility: Determine whether data silos or privacy concerns hinder access.

3. Choose the right approach, tools, and partners

Data Integration is a very complex space, and it will only become more complex as enterprises of all sizes try to accelerate their journey to the cloud. So, choosing the right approach, tools, and partners that will be a part of your team – as opposed to just a vendor – is critical.

This consideration will ensure that you can design and implement an architecture that will scale to meet your needs today and in the future, while also meeting any compliance regulations that you may have.

Factors to consider include:

• Techniques: Choose methods like ETL (extract-transform-load), ELT (extract-load-transform), CDC (change data capture), or data virtualization.
• Environment: Opt for cloud, on-premise, or hybrid solutions based on your scalability needs.
• Partners: Make sure that you’re choosing vendors that see you as a partner and will work with you to ensure that you have the right solution – rather than those that are just looking to get their products out the door.

4. Ensure quality and governance throughout the process

AI applications rely on complete, relevant, and real-time data – which means that data quality and governance can’t be overlooked at any point in the data integration process. This applies to both the development quality and performance characteristics of your data pipelines as well as the data quality and overlay governance for this process.

Maintaining quality and governance from data source to data destination enhances the reliability, usability, and value of your data, and boosts compliance with data regulations and standards.

Best practices include:

• Ensuring that your data pipelines are well defined and tested so they can operate at scale when put into production.
• Defining data quality and governance roles and responsibilities, including data owners, stewards, and analysts.
• Implementing data quality and governance tools and techniques, like data profiling, cleansing, enrichment, validation, and monitoring.

5. Monitor and optimize performance and outcomes

Data integration is an ongoing journey that will evolve along with your business needs. That’s why it’s crucial to regularly review your architecture and data pipelines to identify bottlenecks, improve efficiency, and ensure that it’s delivering the value you need.

Then, you can also identify and leverage more enterprise data that can enhance your AI capabilities, reliability, and competitiveness.

Action items include:

• Tracking metrics around latency, table-level statistics, data reconciliation – making sure to identify errors or anomalies along the way.
• Reviewing, evaluating, and refining as needed – as your business grows and needs change, ensure that you’re reviewing your progress and adjusting your goals and scope accordingly.
• Documenting and communicating learnings and best practices, to provide resources including documentation, training, or knowledge sharing.

Ensure Trusted AI with Data Integration

From tackling business challenges to unlocking transformative use cases, data integration empowers you to leverage the power of AI to make smarter decisions that fuel innovation, faster. When your data is complete, relevant, and delivered where you need it in real-time, you improve the reliability of your AI results and reduce the risk of harmful errors and biases.

What are your goals with AI, and how can self-service data access help you meet and exceed those objectives?

The use cases and steps for success covered here equip you with plenty of inspiration and actionable insights, but if you’re ready to learn even more about data integration for AI success, read our eBook Unlocking AI Success through Data Integration – Transform your AI initiatives.