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Mastering Data Analytics: Seven Proven Strategies for Your Firm

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Softlab360

In today's high-tech world, it's no secret that the volume of data generated and collected by companies continues to skyrocket. According to recent surveys, business data storage requirements are expanding at an astonishing rate, with an average growth rate of over 40% annually. For the top-performing firms, this figure can reach an astounding 100% or more. 

Managing and harnessing this deluge is paramount to business success. How can this data flood be converted into actionable insights? To shed light on this crucial question, Henry Zelikovsky, CEO of Softlab360, shares his insights into seven invaluable tips for enhancing a firm's data analytics capabilities.

 

  1. Prioritize Data Reliability

Reliable data is the bedrock of effective analytics. Shockingly, less than half of business leaders rate their data as "very good" in terms of reliability. Some even rely on gut instincts, a practice admitted by 36% of business leaders. Zelikovsky emphasizes the importance of learning from historical data to enhance reliability. By continually refining and relearning from new data, firms can achieve a level of consistency that supports reliable decision-making and predictive modeling.

 

  1. Master the Art of Data Slicing

Data slicing is a critical technique for breaking down extensive datasets into manageable segments. Softlab360 primarily deals with government data, which often comes in unorganized and unnormalized formats. Zelikovsky compares this process to examining an Excel spreadsheet from various angles, such as rows, columns, or diagonally. This approach helps extract different types of information from the larger dataset.

 

  1. Seek Nuanced Answers

Effective data analytics doesn't always yield a single "correct" answer. Instead, it often reveals multiple statistically significant results, painting a complex and nuanced picture of the situation. For instance, Softlab360's team discovered during a project for Robert Kirk, CEO of Intergen Data, that people tend to reach their peak income at either age 47 or 51, depending on specific factors. This multifaceted insight can help classify individuals in different ways.

 

  1. Ensure Proper Data Structuring

Data collection and storage must adhere to proper structure and format. In many cases, data collected over decades may not be suitable for machine learning due to suboptimal structuring. Correcting these issues can take months before data analysis can commence effectively.

 

  1. Embrace Surprises

Sometimes, unexpected discoveries lead to valuable insights. Zelikovsky recounts a project where the team stumbled upon data points and patterns unrelated to the initial query. These surprising correlations often hold the key to predicting future events and uncovering hidden relationships.

 

  1. Ask Questions Beyond the Obvious

Successful data analysis rarely stops at finding answers to initial questions. Instead, it often triggers new inquiries. Zelikovsky emphasizes the importance of continuously evolving the analytical process and seeking deeper insights, perpetually adding value to the business.

 

  1. Harness Data Patterns

Patterns in data aren't isolated; they reinforce and influence each other. Tracking correlations across physical locations or life events is increasingly crucial in AI analysis, particularly in retail and finance. Zelikovsky and Kirk found that expenses not only related to specific events but also interconnected with one another, forming predictive pockets of expenses. This intricate web of patterns opens doors to mapping out various life events.

 

In an era where data reigns supreme, mastering these seven data analytics strategies can significantly enhance your firm's decision-making and predictive capabilities. Embrace the power of data and leverage it to unlock new opportunities and insights for your business.

 

"In God we trust. All others bring data." 

– W. Edwards Deming, statistician, professor, and author


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