“Data Guru has enabled us to decrease our model build time. We can now connect directly to our data sources and extract data more efficiently, integrating our data extraction and modeling building processes.”

The most common challenge faced by supply chain analysts is data. Multiple source system access is restricted. Various formats and structures lost suspicious quality information. An enormous amount

Up to 80% of the analysis projects can be used to handle data problems, spend valuable time and endanger important decisions.

Data Guru was created to solve these issues, and Data Guru has been tested and certified to greatly improve the process of verification and mixing of enterprise data. Most importantly, Data Guru is seamlessly integrated with Supply Chain Guru, accelerating and further automating the model building process.

In modern supply chains, continuous design processes based on the latest data are needed to ensure that the design model is a clear representation of the supply chain. Data Guru builds a repeatable data conversion workflow that connects users directly to the system running the company and updates model data. Integration with the LLamasoft supply chain design tool enables users to automatically build and run supply chain models

"With Data Guru you can shorten model construction time by integrating data extraction and modeling processes so you can extract data more efficiently by connecting directly to the data source."

"Our approach to business development means that data is everywhere.DataGuru can connect, get data, clean up, adjust to the appropriate level, and extract data to Tableau It is a very good framework for Data Guru, so the biggest advantage of Data Guru is that you can spend a lot of time analyzing data because you can save a lot of work by pressing a button. "

The data is the King. As a product manager, one of our most important roles is to become a data expert. Undoubtedly, PM extracts usage condition data and analyzes it so that we can build better products. Without King's data, we do not understand how people use our products and there is no baseline to guide our development process. It is often said that data and indicators are not mixed with intuition and creativity, but this is a limited dualism. Although these methods may look like two poles, each method has its own advantages, if you support excluding one method from the other, what is in the process Worthless.

Derived data products We tend to overlook the fact that our engineering characteristics are themselves and valuable data of itself. As part of model building and engineering, consider not deploying these new data as unique, but as an API and integrating them into appropriate data assets. For example, if the data science team designed a combination of customer data, product data, and financial data, deploy the new function as an API and have the corresponding model use the new API.

How does data science improve products? In Windfall Data, our products are data, so the purpose of data science is well harmonized with the company's objective of building the most accurate model for estimating capital. In other organizations such as mobile gaming companies, the answer may not be as simple, and data science may be more useful to understand how to operate a business rather than improving it. However, in such an early stage, it is a good idea to start collecting data on customer behavior. Then you can improve the product in the future.