Data scientists are a hot commodity, as demonstrated by the 56% increase in demand from 2018 to 2019. However, the flip side is that data scientists are also under escalating pressure. With the rise of artificial intelligence (AI) and machine learning (ML), organizations are demanding faster insights to remain competitive.
Remarkably, the same technology advancements that drive this urgency are also the key to unlocking better efficiency in data science work. Here are five tips for how data scientists can shift from data wrangling to developing data insights by automating ML and implementing better data management techniques.
Tip #1: Boost productivity with AutoML frameworks
For most data scientists, it takes approximately a month to perform feature engineering, manually run a data set against three to five algorithms, and produce a few models.
Now imagine running that same data set against 100 different algorithms in parallel and creating 80 to 100 models in less than half an hour.
That’s one of the productivity boosts provided by automated machine learning (AutoML) platforms. When AutoML manages the modeling process, data scientists shed time-consuming busy work and can focus instead on using business intuition to improve models.
Tip #2: Operationalize AI by adopting MLOps
Did you know that only 47% of ML models actually go into production?
The underlying challenge is a historical disconnect between data scientists and operations teams. A new practice called MLOps, or machine learning operations, addresses this challenge by operationalizing the joint management of the ML data pipeline.
When these two teams focus on their respective strengths and work towards measurable KPIs together, data scientists are freed to focus on business issues while ops teams manage the deployment of AI in production.
Tip #3: Experience workflow efficiencies with data consolidation
The first thing data scientists do when starting a new project is obtain data. While this step should be simple, most data scientists cite it as the most frustrating for two reasons:
- Data is stored in silos because it comes from disparate sources.
- Data exists in formats that are tough to combine.
Data platforms exist today that address these challenges by consolidating all data (structured and semi-structured) into a single source so it can be queried at the same time. Isn’t it time for data scientists to advocate for better data management?
Tip #4: Produce stronger insights by unlocking data
The unexamined and unused data challenge is a familiar one for most organizations, especially when data lakes are used for long-term storage. There’s real value in huge data sets, but data must be unlocked so it’s easily discoverable, accessible, and usable.
Setting up a self-service data refinery platform for internal use is one way to derive value. Another is to share data with outside organizations (partners, suppliers, customers, and vendors) or even monetize anonymized, aggregated data in a marketplace.
Tip #5: Encourage fresh ideas by collecting more data
If you think data scientists can use only the data collected by their organization, think again.
If mature data companies such as Netflix and Uber can do it, so can others. “More data from more sources” means proactively asking other companies if they want to share, exchange, or sell data. There’s a whole world of data out there, and gathering data from outside sources should be part of every organization’s data strategy.
To learn more about each of these tips and how to take the lead with AutoML, MLOps, and other best practices for data management and usage, download our ebook, Five Things a Data Scientist Can Do to Stay Current.
