How to do Data Optimization for AI software in 2024

With the rate at which AI is evolving and improving, we’re seeing new software being developed at rapid speeds. While development may have accelerated significantly, this also poses a question mark over the quality of our products.

Why? As AI models become more complex and AI integrated software is developed more rapidly, the need for high quality data is becoming increasingly more important for well informed iteration cycles.

This is where data optimisation plays a key role in making sure the data we have can not only maximise the efficiency and effectiveness of our AI model’s performance, but also reduce costs and development time as well.

In this article we’ll be covering a simple but comprehensive 6 step guide for data optimisation in AI software in 2024. We’ll also mention high leverage tools you can utilise such as Phospho to extract real-time insights, continuously monitor performance, and its automated processes that enrich data quality.

Step 1: Data Collection – Gathering Relevant and Diverse Data for AI

Before we start analysing or optimising data we first need to collect it, and a lot of it at that. The volume and quality of data we have access to relevant to our AI model’s needs is the foundation for market competitive AI products.

Diversity in our data helps our AI model generalise better and ultimately improve overall accuracy in its outputs. But the need for diverse data means we’ll need a balance of both qualitative and quantitative data. For this we’ll need to leverage multiple different data sources e.g:

• User interactions
• Public datasets
• Proprietary data
• Direct feedback

We mentioned relevance as well because we don’t want any irrelevant or outdated data which can skew our model’s outputs. So how can we make sure that the data collected is directly relevant to our AI model’s function?

Traditional methods dictate you can set clearer objectives and manually audit the data yourself periodically. But this is much easier with tools like Phospho where you can:

• Continuously track your AI model’s performance by monitoring user interactions to identify when data relevance might be slipping
• Set customisable KPIs for Phospho to automatically ‘flag’ for specific interactions so you can investigate and refine quickly
• Even A/B test separate versions to compare accuracy and performance of AI models trained with different datasets

These are just a few smart strategies you can employ to streamline the entire process of collecting and gathering high volumes of data whilst maintaining relevance for the best AI model training.

Step 2: Data Cleaning – Ensuring Accuracy and Consistency

It goes without saying, ‘dirty’ data can significantly reduce AI model performance. In many cases the data we collect can still contain anomalies, inconsistencies, and redundant information.

So we need to clean our data we collect to maximise their efficiency and effectiveness. In fact it was mentioned in a 2019 Experian report that:

“69% of Fortune 500 companies cited poor data as having a negative impact on their business. Furthermore, 30% of companies also named poor data as a significant roadblock to creating a positive customer experience.”

It’s not always easy though, as vast sets of data will come with their challenges such as inconsistent formats and managing large volumes.

Thankfully, we have tools and techniques available to help us achieve this. Automation can really speed up the process of removing duplicates, correcting errors, and filling in missing values. This is specifically something you can delegate to Phospho’s automated data cleaning process.

Best practices dictate that you conduct data cleaning regularly by setting schedules and using validation checks to ensure ongoing data accuracy.

Step 3: Data Enrichment – Enhancing Your Dataset for Better AI Performance

How does enriching your data improve AI model performance? Think of data enrichment as adding context and depth to your AI system. This matters because richer data leads to a smarter AI - it’s the difference between an AI that can identify a software bug, and one that can pinpoint the exact line of code that’s causing the issue, suggest a fix, and predict potential impacts or dependencies that need addressing elsewhere in the codebase.

There are 3 key techniques to enriching your data:

1) Data augmentation

Augmentation is essentially maximising and streamlining your existing data. For smaller teams or startups working with limited datasets, this is vitally important. Augmenting data is best explained with an example. Let’s say you’re developing an AI to detect cybersecurity threats. You could take your existing data and create variations (change IP addresses, vary packet sizes, modify attack signatures).

This teaches your model to better recognise more threats even while leveraging the same data, making it more robust against new attacks.

2) Integrating external datasets

By bringing in external datasets we can broaden our AI model’s knowledge base. Let’s take an example, say you’re building an AI model for market predictions. You wouldn’t just use stock prices, you would integrate datasets on economic indicators, social media sentiment, and industry reports. This provides a more comprehensive view of the market.

External datasets help to bring diverse perspectives and reduce bias for more accurate responses (and predictions). It’s specifically high leverage for startups with larger, more data rich competitors.

3) Leveraging Metadata

Metadata basically provides more context which helps your AI model’s understanding. For example, if your AI model’s use case is providing personalised content recommendations, metadata such as user location, time, and device type helps the AI understand other factors to take into its decision making. This leads to more personalised and accurate recommendations which are more likely to improve user engagement and retention, two critical metrics for any product.

Properly applying data techniques like the above with tools like Phospho can seriously provide a competitive edge for startups building AI products. When leveraged fully you can provide more accurate and less bias AI products at a faster rate and position yourself as leaders in the market.

Sign up here and try out Phospho for free to see for yourself.

Step 4: Data Transformation – Preparing Data for AI Model Training

This step is what takes our collected and enriched data, and turns it into a format suitable and usable for training our AI model. Let’s look at some common techniques for data transformation:

1) Normalisation

Simply put, normalisation of your data is important because without it, data points with larger scales can be interpreted as higher priority and dominate the model’s decision making, leading to biased or inaccurate decisions, predictions, and conclusions. Normalisation puts parameters on a relative scale so they are considered equally.

2) Encoding categorical variables

When you have factors or parameters based on wording it’s hard for AI models to interpret scale properly as AI models work with numbers not words. For example, encoding variable might convert words such as “poor”, “average”, “good” into 1, 2 and 3 respectively.

3) Feature scaling

Similar to normalisation, feature scaling basically makes sure that all features contribute appropriately to decision making. Again, we don’t want our AI model to overlook or ignore any features because its numerical values are any lower than another and risk skewing any accuracy or reliability in responses.

Before applying these techniques for data transformation, it’s important to note the need for consistency when transforming our data to avoid introducing any biases or anomalies that could affect the AI model’s performance.

For this reason, we recommend using automation tools instead of transforming data manually as it’s tedious and more error prone.

Step 5: Data Validation – Ensuring Data Quality and Reliability

The next step in the process of data optimisation is validation. When we go through data validation, we are ensuring that the data we feed our AI for training is actually meeting our model’s requirements (a lot of which we’ve mentioned above).

One of the ways in which we can check this is by creating tailored validation metrics that align with the specific needs of the AI model. For this step we’ll use a practical example with Phospho as the easiest way to explain.

Hypothetical Example:

Let’s imagine you’re a startup developing an AI driven fraud detection system. Here, custom validation metrics might include checks for unusual transaction patterns or suspicious user behaviour.

1) Custom Event Detection

Phospho’s custom KPIs and automatic event detection let’s you define specific criteria to ‘flag’ for while continuously monitoring user interactions. You could specify the above metrics as events to trigger immediate investigation and analysis, Phospho will alert you when these occur and you can even customise how these alerts are configured such as through a slack message, in the interaction logs, or on Phospho’s dashboard.

2) Real-time logging

With Phospho’s real-time logging of every user interaction you can have complete oversight on any flagged interactions that have been detected against your custom metrics. By annotating and providing feedback on each log without the need for code, this accessibility invites more perspectives and potential fixes as it’s not restricted to developers.

3) Automated Evaluation

Finally, Phospho can automate the evaluation of your AI model’s performance by classifying detected events as success or fail based on your own criteria. In this fraud detection example, this could mean automatically categorising transactions as potentially fraudulent or legitimate based on your parameters. You can also classify these flagged interactions or patterns yourself, and by doing so you are also training and fine tuning your patterns for better ongoing automated classification. In other words, with Phospho the automated detection and evaluation gets better over time.

Step 6: Continuous Monitoring and Iteration – Ongoing Data Optimization

Most companies, let alone startups or scale-ups, struggle to convert data into actionable insights due to the lack of tools or know-how. In fact, according to a Forrester analysis, on average, between 60-73% of all data within an enterprise goes unused for analytics.

But it’s important to understand that proper AI data analytics is not about finding a silver bullet that leads to an explosion of revenue or users, it’s about continuous monitoring and iteration to adapt to new data and changing circumstances.

Phospho is open source and specifically designed for developing AI products faster by extracting real-time insights from user interactions with continuous monitoring. It’s accessibility towards non technical team members also encourages more perspectives to leverage on product data for better iteration cycles to constantly improve AI model performance over time.

With the features we’ve already mentioned in this article above, using Phospho positions teams to confidently and proactively detect issues and suggest optimisations with as much degree of flexibility and control as they need to ensure their AI models are performing at their best (and better than market competitors).

This is ultimately the vision we keep as our north star here at Phospho, we believe open source tools tailor made for AI products like ours will help with faster product market fit, and faster adaptation to the evolving needs of your users.

You can try testing Phospho with your own app/data with our free credits by signing up here. We integrate really easily into popular tech stacks.

Conclusion: AI Data Optimization with Phospho

Why does data optimization matter? Simple. Better data means better AI performance, lower costs, and faster development. The challenge has always been expensive pricing met with low performance and high maintenance from traditional tools.

Therefore, we can’t overstate the value in using cost-effective data optimisation tools native to AI products like Phospho to fully leverage your data, because it goes so much further than optimisation. Effective and efficient data collection, optimisation, and analysis can be the biggest difference between startups with an AI model that merely works, and one that truly excels in its use case.

If you want real user aligned iteration and scale faster, try Phospho for free by signing up here.