Unlock Your Employees' Potential to Grow Your Business

machine learning
Jo Ann Stadtmueller
machine learning
May 4, 2021

The Need for Data Science 

Business strategies and technology strategies are becoming inseparable, even indistinguishable. Companies across the globe are making rapid digital transformations to accelerate their business journey. At the spearhead of the journey is data – a tool that enhances productivity, can mitigate risks, builds trust, and predicts future trends. 

Becoming 'Future-Ready'

As organizations continue to measure and work with vast amounts of data, they now realize they need to keep in step with leveling up staff with ever-changing techniques and technologies. “The need for Data Science grew 5x during the last 5 years”, according to PwC, “there will be more than 2.9 million job postings for data scientists and analytics roles in the US alone.” Training or leveling up your already vetted staff is increasingly important to organizations wanting to remain competitive."

Understanding how to become more ‘future-ready' is paramount as we have all learned during the last year of this pandemic. Basic necessities like toilet paper and disinfectants became scarce, PPE equipment at scale was needed and was not available, meat prices rose for a time, economies were stretched and many jobs were lost. What we did learn is that organizations can be better prepared. Data can be used to model or predict the future, test and implement strategies and mitigate scenarios like the ones we witnessed. 

Methodologies for using data are changing quickly, and needing personnel and resources to wrangle and manipulate data is mission-critical, but so should be your commitment to making sure employees have the most up-to-date training to support your company’s mission. Staying ahead of these new technologies and capabilities needs vigilance. For instance, over the last year organizations embraced a less common but often more powerful approach – using “small” but “wide” data. Gartner stated, “the pandemic changed everything. In turn, forward-looking data and analytics teams are pivoting from traditional AI techniques relying on “big” data to a class of analytics that requires less, or “small” and more varied data.

Transitioning from big data to small and wide data is one of the Gartner top data and analytics trends for 2021.” This approach enables the cross-correlation of a variety of data sources to understand more intricate connections, and transfer patterns learned from one domain to power a model in a different domain – a methodology called transfer learning.

data engineering

As the world changes, not everyone is ready. 

Being a data-driven company allows all domains within the organization to harness and monetize the value of data and to view data as a strategic resource that powers a competitive advantage. Being data literate will enable all stakeholders to present data science projects for exploration. Once trained these stakeholders can realize shifts in trends, understand spikes in graphs and explore new opportunities for business success. McKinsey states “data-driven organizations are 23 times more likely to acquire customers, 6 times more likely to retain customers and 19 times more likely to be profitable.” 


data engineering

Key Skills for a Data-Capable Workforce

It’s hard to stay on top of the shifting trends, tools, capabilities, and applications of data collection, storage, and analytics. Yet key skills that form the foundation for a data-capable workforce are: 

  • Programming (Python/R): a core skill to turn unprocessed data into useful information 
  • SQL: the ability to extract and handle raw and untouched data from disparate sources, enabling various manipulation techniques
  • Writing production code: learning to follow well-defined coding standards to ensure reproducibility and modularity
  • NLP, Neural Networks, and Deep Learning: used to manage and automate human and computer interactions i.e. chatbots and email filters, solve complex problems like predicting market price fluctuations, face recognition, and actions like fraud detection.
  • Machine Learning: training your system to perform actions based on identified patterns or to even make predictions.
  • Data Visualization: effective ways of telling stories with data so that all stakeholders within an organization can derive applicable insights that drive decisions and actions.

Organizations that want to be resilient should proactively examine how to leverage training resources. And finally, remember that data is an important team member, without knowing how to work successfully with it, you won’t realize its vast potential.

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