Data Science as a Service: Navigating Post-COVID Uncertainty

As businesses look to a post-COVID world, uncertainty remains a significant challenge. Will consumer behavior return to normal, and when? What’s the outlook on consumer confidence? How long will COVID trends last, and what does that mean for business performance?

Answering these questions can be the difference between a growth-oriented, proactive strategy and floundering, reactive decisions.

With advances in AI and machine learning (ML) technology, the answers themselves are more achievable than ever before. Yet, how to answer these questions (and the myriad others), is outside the scope of most businesses internal resources. They simply don’t have enough data scientists, enough technical capabilities, or enough time, to develop and deploy the right ML models to decision-makers.

Data science as a service can fill the gap.

What is Data Science as a Service?

Data Science as a service, or DSaaS, refers to outsourcing data science skills and capabilities to suit the needs of a business.

Businesses usually opt for DSaaS to boost their internal data science resources— whether to build ML models or to fill a hiring gap for these in-demand professionals.

DSaaS, however, is more than a supplement. For many companies, it’s a means of scaling their analytics capabilities to meet the critical needs of the business.

Data science itself is the act of modeling specific problems and synthesizing an understanding of the data adapted to the business. Through this process, ML models investigate options, predict outcomes, and find solutions.

As such, the DSaaS provider must closely align with the business on the desired outcomes, as these outcomes drive the development of the model.

The actual implementation of the model will also vary based on need.

In practice, DSaaS consultancies can provide data scientists to:

  • Perform advanced analysis for specific projects.
  • Build ML models to deploy to the business.
  • Enable self-service analytics so business users can access the models and output (i.e. insights and visualizations).

The value of DSaaS is growing in tandem with rising analytics needs— now, more than ever.

Why is DSaaS Critical Now?

Even before COVID-19, data analytics has evolved to meet the growing needs of end users.

DSaaS is the latest phase of analytics maturity, broadly following this order:

  1. Database Tools — provide access to data
  2. Business Intelligence Tools — allow users to create canned reports and dashboards
  3. Self-Service Analytics Platforms — enable data exploration, performs complex metric calculations, and produces visualizations and conditional insights
  4. Data Science as a Service — model data to solve business problems and achieve critical outcomes, leveraging tools like automation where necessary

DSaaS fills the gap between the needs of the business and the data that can inform decisions.

It’s no longer a competitive advantage to simply see the results of the previous quarter, to see bar graphs and pie charts demonstrating basic computational and visualization skills.

To make informed decisions that impact performance, businesses need more advanced skill sets that typically fall to data scientists.

In the midst of COVID-19, data science capabilities are necessary to make realistic predictions about the impact of external factors, such as stimulus checks. Less mature analytics solutions will struggle to make sense of pandemic behavior, especially for industries that saw significant growth or loss. In fact, most solutions will be completely incapable without data science at the core.*

These forecasting needs fall beyond the scope of reports that simple analytics platforms churn out on schedule.

Inadequate technology coupled with urgent need and scarce data science resources has led to the rise of DSaaS.

For many businesses, the question is no longer about the value of these services, but: how can we leverage DSaaS for a competitive advantage post-COVID?

*Even advanced analytics solutions can hamper data science efforts with proprietary software that needlessly restricts access to the source code. Only open source solutions with open extensibility have the right foundation to operationalize data science models within their platforms.

How to Successfully Implement DSaaS

While DSaaS will not necessarily require implementation à la software, it will require focused effort on part of the business and the service provider.

A good provider will follow these essential steps to maximize the value of data science across your organization.

1. Identify key analytics questions

The provider should connect with the business to gain a thorough understanding of the problem the business is trying to solve. Common analytics questions include:

  • How might COVID impact demand?
  • How are new products contributing to growth?
  • How would a pricing change impact sales?

2. Develop the machine learning model

Next, the provider will develop, train, and test the model to achieve a high degree of accuracy. Once ready, the DSaaS team will deploy the model and continue to fine tune it. (For more information on ML models in analytics, check out this blog post).

3. Design output for end users

In many cases, end users are business people who need insights and visualizations to make decisions. DSaaS can put ML models in their hands, but the output must be consciously and carefully designed. Insights should be served in natural language that’s immediately understandable to a non-technical person. Likewise, visualizations must render with intelligence and options for customization.

4. Integrate analytics tools for self-service

Operationalizing data science across the business enables end users to tap into advanced analytical capabilities regardless of their role. A good DSaaS provider will understand the value of pairing ML models with self-service analytics and will work with the business to meet cascading needs at every level.

The end result of these efforts should be faster insights delivered directly to the business people who most need them. To see an example of DSaaS in action, check out the next section.

Data Science as a Service Case Study — Modeling the Impact of COVID-19 Stimulus Checks

One of the top snack companies in the world, with $26 billion in annual revenue, needed to better understand the potential impact of COVID-19 stimulus checks on demand.

This company’s sophisticated forecasting system was unable to predict extraordinary demand created by fiscal stimulus at the local level. They needed a new model that could learn from the previous stimulus and make educated predictions — enter RocketScience, AnswerRocket’s DSaaS solution.

Working with AnswerRocket, the customer built a scenario modeling tool that would learn from 2020 and adapt to 2021 as the year evolves. As a result, the customer identified hundreds of millions of dollars of opportunity. They would be able to meet this opportunity by fulfilling customer demand, instead of disappointing them with empty shelves.

This case study is just one example of the significance of DSaaS in the post-COVID world. Every business will have its own unique problems to solve amid the impact of the pandemic. DSaaS can meet this need and help companies grow.

Do you have a business problem to solve with data science? Are you unsure where to start? RocketScience can help! Request a free consultation.

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