OneStream Organizational Analytics

Using Machine Learning with Operational Analytics

According to Dresner Advisory Services’ 2020 Data Science and Machine Learning Market Report, although ML use is not yet as widespread as the transition to the cloud for financial close and preparation processes, it is on the rise. 

The ongoing economic uncertainty, as well as quickly evolving market conditions, are expected to push the number considerably higher in the coming years. 

Although IT and Business Units have been well ahead of Finance in embracing machine learning (ML), the COVID-19 crisis shows that Finance teams must now have rapid-response forecasting and decision-support capability to help them work across complexity. 

Combining organizational analytics and machine learning (ML) provides very realistic use-cases for companies only starting out on their applied analytics quest, and does much more than increase prediction accuracy. Operational analytics driven by machine learning facilitate cross-functional communication by supplying decision-makers with new ideas and creative ways to challenge why and improve results. 

Here are a couple of the most popular use-cases for companies considering incorporating machine learning into their financial and organizational planning processes: 

  • Assist in strategic strategy, annual operational plans (AOPs), and rolling forecasting target-setting. 
  • Create baseline statistical prediction scenarios to compare to divisional Finance or Operational partners’ bottom-up forecasting. 
  • Predictive models may be used to seed any or parts of new predictions automatically. 
  • Adjust baseline statistical predictions to account for known market shifts including new customers or goods, plant closures, acquisitions, and so on. 

CFOs and finance departments also face an increasing need to analyze, model, and forecast potential situations to help agile decision-making as organizations become more competitive. Indeed, integrating financial analytics and machine learning (ML) offers substantial benefits in assisting Finance departments in driving operational efficiency and rising business profitability. 

The economic crisis is expected to hasten the implementation of these innovations, which would aid organizations in moving away from siloed decision-making. Beyond the hype, ML-enabled organizations will be able to adapt quickly and easily to the rate of change in their environment, no matter what the future holds. 

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