How data science can enable better demand forecasting and planning

| August 3, 2018

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In a previous post, we talked about how tough it can be to get the various parts of a business — operations, sales, finance, etc. — to “sing in harmony” to achieve better demand forecasting and planning. It’s like getting a group of singers to perform together with each one singing the right part, at the right time, in the right key. Using a mix of advanced analytics tools, backed by machine learning, is crucial for achieving that. But let’s extend the metaphor: if those singers are joined onstage by an orchestra, who supplies the instruments and keeps them in tune? Answer: the data science team. Data science duties for better demand forecasting and planning. Whether they work within a line of business or as a standalone, centralized function, data science teams are responsible for making sure that internal customers can optimize demand planning and forecasting. That applies no matter which data sources are used — even when the data is siloed, and regardless of whether it lives on-prem, in the cloud, or both. Oh, and it all must be done while explicitly demonstrating adherence to any requirements for security or regulatory compliance.

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Pythian is a global IT services company that specializes in designing, implementing, and managing systems that directly contribute to revenue and business success. We help companies adopt disruptive technologies to advance innovation and increase agility. Our highly skilled technical teams work as an integrated extension of our clients’ organizations to deliver continuous transformation and uninterrupted operational excellence using our expertise in cloud, DevOps, big data infrastructure, advanced analytics, infrastructure management, and databases…

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