Data science and the cloud go hand-in-hand. These are five straightforward ways that the cloud accelerates your ability to perform more advanced analytics to generate deeper insights and edge out your competitors.

As business leaders and innovators, we ponder how we can interpret a seemingly infinite sea of data to make intelligent decisions to grow our companies and increase revenue. Companies have become increasingly more analytical and expect deeper insights about organizational direction, requiring much more than Excel and PowerPoint for data analysis and presentation. More recently, many companies have adopted data science and machine learning to not only automate repetitive tasks, but also make data actionable through predictive modeling, segmentation, and anomaly detection beyond the realm of human interpretation alone.

Data science and machine learning capabilities are enhanced when they are done in the cloud. Here are five ways the cloud accelerates your ability to perform more advanced analytics.

Focus on data science rather than infrastructure management

Machine learning projects with an on-prem architecture means setting up localized copies of each program. Alternatively, the cloud provides native integration, makes sharing resources easy, and fosters the collaborative nature of data science. Cloud-based platforms like Microsoft Azure or AWS allow for easier expansion and use of open source frameworks, and they have an extensive catalog of native tools to help execute machine learning. Whether its R, Sci-Kit Learn, TensorFlow, or using wrappers inside existing business intelligence (BI) tools like Power BI or Qlik, the cloud provides access needed to manage your infrastructure and minimize overhead. The cloud also reduces time and effort to manage resources.

One of our clients, a veterinary association, wanted to incorporate their hard-to-manage survey data with their transactional data. Since they already had their data and analytics platforms in the cloud (built on Azure), it was easy to use Azure Machine Learning Studio and transform their unstructured responses into interpretable information. The results captured insights from a Natural Language Processing (NLP) analysis of the surveys and joined it with current membership information to uncover deeper insights about their membership.

Without the cloud, this would have been very challenging. Sourcing the data would require pulling local copies onto a laptop/on-prem server, which may not have the capacity to handle the analysis of large datasets. This may have forced smaller sample sets, which becomes an issue later in the data science life cycle. The cloud alleviated these concerns with higher performance and capacity.

Get cost-effective computing power

Data science projects require a high level of processing power, and this GPU computing can be expensive if a data science project is run locally. The cloud pay-as-you-go model saves you money on services that might otherwise require a significant spend for local installs. With these more cost-effective computing options, small and medium-sized organizations can easily get started with data science, leveling the playing field to compete with much larger companies.

With more cost-effective computing options provided by the cloud, small and medium-sized organizations can easily get started with data science, leveling the playing field to compete with much larger companies.

Use more big data

Data science projects often require:

  • Integration of structured and unstructured data—something many analysts and scientists find difficult to manage with large datasets; and
  • Inclusion of transactional data, which is not only large, but constantly churning.

To alleviate this, cloud-native tools like Databricks capture streaming data so a machine learning model can operate with real-time data. Incorporating streaming data allows for a larger data set which means a more accurate model. Trained models with a larger sample set is almost always better than having “the perfect model”. So even if your company has an army of data scientists, machine learning efforts may not succeed without access to adequate data.

Gain access to one true source

Locally sourced data does not provide “one true source”. Working from locally downloaded copies of datasets is problematic because data quickly goes stale, and individuals may be building models off different data. All of this leads to inaccurate predictions. With access to standardized and more accurate datasets found in a cloud-based storage, data scientists can generate results that are more trusted, which will result in increased adoption throughout the organization.

Be nimble, and make quicker, better decisions than your competitors

The cloud enables access to data anytime and anywhere, giving a huge competitive advantage over those that don’t have remote access to their data and analytics platforms. Whether your company is changing to a work from home model, or your business needs quicker and more agile decision-making, the cloud gives more people in your organization access to data for advanced analysis. Data scientists and data engineers can deploy machine learning models seamlessly into the cloud and integrate with existing business intelligence tools, giving leadership the confidence to make essential business decisions.

If you’re already enabled on Azure, AWS, or any platform in the cloud, take advantage of your capabilities to make better decisions with data science and machine learning. If cloud isn’t part of your data architecture, we can help you get there. Now is the time to untap the potential of your data!

Matt Levy Matt Levy is a Managing Consultant at Analytics8. Practicing what we call “ethical data science,” Matt specializes in making sure that our customers avoid bias when building machine learning models so that their projects bring real value to their organization. Matt wrote his Masters Capstone thesis on fantasy golf analysis, and is a consistent winner of A8 fantasy sports competitions.
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