Demand Forecasting: 4 Ways to Improve It

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Learn how to develop a comprehensive understanding of the data available across your enterprise and used for demand forecasting.
Companies interact with their customers across multiple channels – physical, web, mobile, social media and IoT – generating a wealth of digital data that improves demand forecasting . Companies have realised that unlocking the value of this data is the key to transforming customer experience and refining strategy. But achieving this requires more than just setting out to do so.


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From accurate demand forecasting to fulfilled promises
As businesses embark on their digital transformation journey, customer transformation experience has become the benchmark that guides this journey and ensures focus on the right set of priorities.

There are many channels, even more data, available to organizations that need to forecast demand , and when huge computing and storage resources are used to process this data and generate canada number dataset information, you need to be able to justify the investment.

With more data being collected on more customers across more channels, the goal of understanding the customer in order to deliver the right experience at the right time seems to have become more attainable than ever. Unfortunately for many companies, this remains a distant promise rather than a reality. Can you imagine why?



First, there has been an explosion in the volume and variety of data. This is big data and dynamic customer data from an ever-growing set of data sources: transaction data, machine sensor data, website clickstreams, social media feeds, logs, and location data.

Second, there is relentless business pressure to use this data from different types of users to address a host of customer analytics needs, ranging from real-time personalization of the web experience to weekly campaign planning to long-term demand forecasting. And they all want fast access to relevant data in a self-service model without relying on IT to provide the data for them.

Finally, there is increasing scrutiny around data privacy and security and the need to comply with new regulations such as GDPR. Developing trusted relationships with customers requires businesses to be seen as ethical custodians of all their personal information. A difficult balancing act is required to successfully manage this at enterprise level without compromising business agility.






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The first step in addressing this confluence of forces is to develop a comprehensive understanding of the data being used, processed, collected and stored across the enterprise.

Today’s data landscape is complex and constantly evolving with information assets of different types spread across cloud data warehouses, on-premises databases, various business applications, and unstructured documents.

Businesses need the ability to scan these data assets across the organization and intelligently catalog, tag, and classify that information to gain visibility and understanding. To do this, they need to make relevant data easily discoverable for data analysts. At the same time, organizations should be able to improve data trust for analysis by helping users understand where the data is coming from and providing proper business context, all while providing visibility into data quality and accuracy.