10 principles for building your data foundation

Augmented intelligence is the future of decision-making, as artificial intelligence's (AI) analytical power and speed take over most data processing tasks and produce insights that enhance human abilities.

Through machine-generated insights, teams can streamline processes, unlock ideas to create new products and services, improve the customer experience, and boost revenue growth. In short, augmented intelligence has the potential to revolutionize the business world.

But before you realize the benefits of augmented intelligence, you must build a solid data foundation for artificial intelligence in business to deliver data-driven insights across the organization.

Here, we'll show you how to build a data foundation and the best practices to make your aspirations a reality. The result? Empowered employees that work better and smarter.

There are 10 fundamental principles – adopted by many of the leading Fortune 500 businesses – for unleashing the power of augmented intelligence across the enterprise:

1. Prioritize data integration: Bringing enterprise data together requires a tailored approach that considers internal and external data. If you begin with data integration and automatic data processing, the following steps become more manageable.
2. Focus on what matters most: Rather than creating independent use cases – for example, one data foundation for marketing and another for sales – build a single data repository first. Then, focus on the use cases that will have the biggest business impact for less rework and an easier way to scale your efforts later down the line.
3. Seek diverse talent: Employees with knowledge of both industry-specific business processes and emerging digital technologies are in high demand. And for a good reason. This talent can often spot the most impactful business use cases for your data foundation.
4. Change the culture: The general perception of data – as a source for reporting – needs to change. By generating data-driven insights at scale, enterprise leaders will understand why they need a data-driven culture and are more likely to support your project.
5. Make your data traceable: It's essential to trace how you push and pull data into the data foundation. When you keep an active catalog of your data processing, it enables data lineage and reduces reconciliation. This approach establishes trust and accountability for data audits.
6. Aim for speed: Real-time, automated data processing allows you to turn data into insight and then act with speed. It also provides an opportunity for course corrections – something nearly impossible with stale or incomplete data.
7. Head to the cloud: Build your data foundation in the cloud, powered by machine learning. This technique will give you plenty of data samples for machines to learn from and improve. Then, they can deliver predictive insights and recommend the best next course of action in real time.
8. Lead with architecture: Spend time carefully crafting the architecture of your data foundation. Keep data close to where you need it using AI, operating systems, and input from analytical and digital experts. This approach delivers efficiency, context, and accountability among all parties.
9. Remember governance: Governance is critical in every development process, so you must ensure your data is high quality and handle it securely. By embracing governance principles early on, you'll comply with legal frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
10. Work holistically: Building a solid data foundation is about more than having the latest AI and analytics capabilities. Instead, design and build your data foundation so that everything – including people, processes, data, and technology – works together toward the best business outcomes.

Remember, your data foundation supports decisions that can make or break your business. By building a strong data foundation early on, you can make your data more accessible, consumable, manageable, and actionable.

Let's explore how a financial services firm uses its data foundation to power artificial intelligence for accelerated credit decisions.

With operations across 35 countries, the firm needed to manage and extract data quickly from more than 45,000 accounts in different formats and languages while adhering to regulatory requirements for each region.

By establishing a data foundation, our team could use natural language processing and computational linguistics to handle and automate data processing across multiple locations and formats.

Here, our industry and business knowledge proved essential in creating a data foundation that could sensitively process information from public and private companies. We also built a big data library for better auditability and risk management.

With our support, the firm automated 80% of its financial statement processing, freeing employees to focus on more strategic credit decisions. As a result, application-to-funding cycle times decreased from eight days to 48 hours, and customer satisfaction improved. The firm also expedited its on-time credit decisions with an AI-powered financial spreading solution.

If establishing a data foundation feels overwhelming, begin with a hard look at where your data sits today. Then, select your business goals for data automation. And don't discount the value a partner with expertise in process, technology, and industry nuances can bring.

Ultimately, if you follow the 10 principles we've shared, you'll create a data foundation that lays the groundwork for augmented intelligence. You'll usher in a future where machines can uncover predictive insights and make intelligent recommendations that drive better decision-making and innovation across the enterprise.