Corporate finance and accounting teams are highly tuned to the importance of data integrity and security in a regulated industry. For finance teams embarking on AI adoption, investing in a robust data environment is equally important for mitigating risk and maintaining accuracy in AI-enabled decision making. However, the top challenge for surveyed businesses today is a mistrust in their data, which greatly hinders data analytics. Clean, reliable and accessible data is key to using AI for data analytics. Thus, businesses must heavily focus on data preparation for machine learning models.

Data Is the Underpinning of AI Success

Data preparation is often one of the most time-consuming steps in the AI adoption process. This stage involves handling missing data, correcting errors, standardizing formats and normalizing data sets to ensure that the data fed into AI models is accurate and consistent for analysis. A deep understanding of your data sources, labels and lineage will also better assist your team in understanding the drivers behind a model’s recommendations or outputs, as well as data-related areas of potential risk or bias. 

Before getting technical, however, the first step in data preparation for machine learning or generative AI models is identifying the data most relevant to your needs. 

First, Match Your Data to Your Challenge

Having well-defined AI objectives lays the groundwork for determining the data you need to collect. Identify the data you’ll need by using three important indicators:

  • Problem: Consider the problem your AI algorithm will answer or solve and consider the data that’s needed to supply that algorithm. 
  • Granularity: How specific does your data need to be? Do you have enough data to analyze and lead to accurate outputs? Are you taking only the data that’s most relevant? 
  • Stakeholder input: Speak to your stakeholders and data owners to determine what data is available, its quality and vulnerabilities, as well as how the data can be used to solve their challenges. 

Assess Your Data Infrastructure & the Flow of Information

Once you understand your data needs, you’ll need to understand how your data will be collected and what that lineage looks like. Data lineage is the lifecycle of your data, from its origins or source to its final destination—the AI algorithm. 

Be prepared to define and document your data’s lineage, not just for the purposes of collecting data, but to achieve greater results overall.

  • Identifying the origins of your data will help you verify accuracy and reliability and hold data owners accountable when auditing quality. 
  • Tracing data flows for any gaps, errors or anomalies will help you detect them prior to model training. 

Businesses will then ensure that the raw data can be standardized and formatted in a way that’s easy to translate for machine learning and other AI models. Remember, data can come in many formats, including numbers, text and images.

Data Storage

When data inputs are needed, how easy can they be retrieved? Data storage solutions can range from simple Excel spreadsheets for small businesses to more specialized databases for larger, more complex datasets. Cloud-based storage solutions are also popular, offering scalability and flexibility for businesses of all sizes. 

The volume, accessibility and complexity of your data are major factors in assessing the AI solutions that are most reasonable for your business. Your data infrastructure and storage should enable:

  • Accessibility to data scientists and engineers to train and validate models.
  • Scalability as data volume grows and models are retrained.
  • Security compliance with relevant access controls, encryption and safety measures for sensitive data.
  • Quick integration of new data sources as needed.

Data Security & Governance

Establishing a strong data governance framework ensures that data across the business is managed with clearly defined policies and procedures around usability, integrity, security and availability. Data governance best practices should include compliance with data privacy laws, which vary by state but generally aim to protect personal information. This is especially important to follow for maintaining public trust in highly regulated industries like finance.

What Is Data Modeling?

To perform and learn at their best, AI systems require data that is logically organized. Data modeling makes it easier for AI algorithms to interpret and make sense of the data. The process includes steps like standardizing the data, selecting important features or choosing data to train versus validate the model’s accuracy. While data scientists and engineers may lead this process, finance professionals will provide their domain expertise to provide context to the data.

Cleaning the Data

Flaws in data can propagate through AI systems, undermining reliability. Inconsistencies, gaps, biases—these can all yield inaccurate or unethical model outcomes if not addressed with the help of data scientists. Diligently auditing and cleansing data is thus a crucial data preparation step.

  • Fill missing values: Identify gaps and work with data scientists who can use various techniques to input missing values that can’t easily be filled.
  • Remove outliers: Data scientists will often remove or adjust anomalies to improve accuracy.
  • Normalize the data: This is the process of adjusting the values in the dataset to a common format to make it comparable and usable across multiple calculations—or to standardize data from different sources. 

What Are Features?

Feature engineering is a process that plays a pivotal role in enhancing the performance of AI models. It involves working with data experts to determine the most important indicators or signals from the data that are useful for making predictions.

Consider an algorithm that will predict quarterly revenue. Original data might include basic financial metrics such as past revenues, expenses and market trends. By employing feature engineering, finance teams can build more insightful features like revenue growth rate, expense-to-revenue ratio or market sentiment scores derived from external data. These new features encapsulate complex financial dynamics and allow the model to uncover deeper insights into what drives revenue changes, leading to more accurate forecasts.

The Continuous Cycle of Data Preparation for Machine Learning Models

Data preparation for machine learning is not a one-time task but an ongoing process. As data evolves and business environments change, AI models must adapt to maintain their relevance and accuracy.

Continuous testing involves regularly evaluating AI systems to ensure they perform as expected with current data. This identifies any model drift or degradation in performance that may occur as the characteristics of the data change over time. By frequently monitoring AI models, businesses can ensure that their AI models remain effective tools for decision making, capable of adjusting to new data and evolving business needs. 

The Role of Data Engineers & Scientists in AI Preparation

The journey from raw data to actionable insights is complex and requires a nuanced understanding of both technology and business needs. Businesses adopting AI will need the support of data experts. Data engineers and scientists play pivotal roles in data preparation for machine learning and AI models. 

Data Scientists: From Data to Insights

Data scientists apply statistical analysis, machine learning and data mining techniques to clean, model and interpret data. They are instrumental in feature engineering, model selection and fine-tuning so that AI applications are accurate and aligned with business objectives.

Data Engineers: the Architects of Data Infrastructure

Data engineers are the architects that build the infrastructures that support the lifecycle of AI modeling. They design and implement data pipelines that efficiently collect, store and process data. Their work involves ensuring data quality, selecting the right storage solutions and maintaining the infrastructure to handle the scale and complexity of data operations 

Consulting with Experts: When to Seek External Expertise

One of the greatest barriers to AI adoption is the shortage of AI-specific talent. External or independent experts, however, are a cost-efficient workaround that can significantly accelerate AI adoption and work with business professionals to optimize their data preparation steps. Seeking external expertise is especially beneficial when:

  • Scaling AI initiatives to meet growing data demands.
  • Navigating regulatory compliance and data privacy considerations.
  • Embarking on complex AI projects that require specialized knowledge.
  • Implementing new technologies or methodologies for which in-house expertise is limited.

The journey to leveraging AI within your business is a continuous cycle. Take the next step towards AI transformation by working with experts who have decades of combined experience in AI. Schedule a consultation with Paro today, and let’s secure the full potential of your data together.