How to Navigate the New Data Science and Machine Learning Landscape
We’re living through unprecedented times. Until this moment, organizations were successfully using data science models, machine learning, and other types of AI across the funnel to gain visibility throughout their business and sharpen their competitive edge. Suddenly, many of these organizations find themselves stumped. Without warning, the data — and in many cases the models — they relied on can no longer provide the answers they need to weather the crisis. This is a critical moment for data scientists and leaders to step up to the plate and lead the way through this strange new landscape.
Here are three of the most pressing data science challenges facing data leaders — and what you need to do to tackle them.
Over the past few years, there’s been growing concern among regulators that Big Data (and machine learning tools that use this data) undermine individual privacy. These fears are far from unfounded: just look at the Cambridge Analytica scandal, which showed how easily personal data could be harvested from Facebook without consent and used for truly nefarious purposes.
Cases like this led to some pretty sweeping regulations — most prominently GDPR, which restricts how much identifiable data a site or platform can collect on users and demands that companies obtain explicit or implicit consent to use, sell, retain or transfer people’s data for each new purpose.
Although this is great for boosting individual privacy, it does mean that companies who use data must jump through many more hoops and ensure they’re on the right side of the law. In the current COVID-19 crisis, as private companies and governments scramble to collect and analyze more data than ever to battle the pandemic, the task becomes especially complex. Therefore, finding the right tools to help you is vital.
New technologies are helping to ensure that data is anonymized and encrypted to prevent privacy breaches while still making it available for machine learning purposes. The decentralization of data reduces the risk that storing multiple datasets together in one place makes it easier to identify individual data that has been anonymized. For companies anxious to future-proof their machine learning models, a smart strategy is to use platforms that manage the process of connecting to external data sources. The best of these will take responsibility for keeping up to date with these changing demands and ensure that the data you’re connecting to is already compliant with all relevant legal standards and regulations.
Using new data to improve our existing models
It’s always been important to question and evaluate the scope of datasets you feed into your models. Data science in business is only successful when you are alert to external factors like emerging trends and changing market conditions that impact sales performance, audience perception, conversion rates, and other aspects of your business.
However, right now, the need to tap into real-time data is more intense than ever before. On a global scale, we’re entering the most extreme period of economic volatility and recession since WW2. In such rapidly changing circumstances, in-house data from last year, last quarter, or even last week are already irrelevant. This means that to survive the downturn, you need to feed your existing models with up-to-the-minute external data that can help you make sense of the chaos. In turn, you can swiftly identify patterns, adapt your marketing and sales strategy, adjust production runs, and seize opportunities that may only last a short time.
Because there are opportunities out there, so long as you have the right data to see them coming. Hand sanitizer manufacturers are doing just fine right now. So is Amazon. So are many takeout food outlets. But so, too, are the factories that saw this coming fast enough to switch production to essential items before the worst of the crisis hit. And restaurants who didn’t previously offer delivery, but predicted the lockdown and managed to adapt and recruit drivers just in time.
As a data scientist, you’ll be intimately acquainted with the frustrations and dilemmas involved in handling anomalies and outliers. Now, we find ourselves in the midst of one giant, unpredictable shock to the market. The global pandemic is a black swan event that constitutes a spectacular anomaly in data patterns in just about every sector you can imagine, from stock market performance to air quality data.
This poses the question: what happens when the worst of the crisis is over? How will you handle this period in your historical data?
After all, an anomaly like this may scramble your predictive models, leading algorithms to suggest patterns that make no sense in a normal year, undisrupted by a virulent global pandemic. You may think the answer is simple: just remove this whole section from your datasets. The trouble with that, of course, is that it’s only appropriate for those datasets that you use to track trends. You still need to understand the impact of the underlying data on the future of your business. If you simply ignore this whole nasty mess, your sales forecasts, growth models, and other machine-learning-backed predictions simply won’t make any sense or be grounded in reality.
Instead, you’ll need to find ways to balance out internal anomalies with external data sources that minimize and correct these issues in your machine learning models. External data on market performance and sales trends from outside the organization provide context that prevents your models from extrapolating false meaning from in-house data, reducing the risk of peculiar, misleading results.
Final thoughts: adapt or perish
By accessing the right data in the right way at the right time and feeding this into your existing data science models, you can make your own luck. You’ll stay ahead of the curve, anticipating setbacks and supply chain issues, while spotting emerging opportunities and figuring out how to capitalize on them before your competitors do. Navigating the fast-evolving landscape of machine learning and data science in business is tricky. Ultimately, it all comes down to your ability to source quality, accurate, up-to-the-minute data from sources you can trust to stay on the right side of rapidly changing regulations.