Part 3 | Hammer Goldmine | Usability
Last week we discussed how we implemented a data warehouse architecture at Hammer: The Hammer Goldmine. This week we will explain how we expect to get the most usage from Goldmine and how we want to facilitate the whole team/company.
The first important consideration when discussing the usability is the platform on which you want to promote your application. A few years ago, the answer would have been a desktop application. A desktop application operates in a completely controlled environment with a lot of development flexibility. However, application development is moving increasingly towards a browser-based environment. The benefit of this implementation is a seamless cloud integration and workplace flexibility. The flexibility of the controlled environment is making place for the accessibility of a web environment. This shift in focus can also be seen in computationally demanding applications like SAP and Tableau.
The goal of the data warehouse is to make our workflow as efficient as possible.
We have therefore decided to merge the active learning pipeline into the browser environment as well. At this point, Goldmine supports the workflow of the team at Hammer by making the process of gathering the right intelligence needed fora specific project easier. At the same time, Goldmine facilitates experts in labelling data to increase the quality of the database.
As described before, the idea behind Goldmine was to set up a robust framework that might have substantial implementation costs, but once it is implemented it runs efficiently and is flexible in its applicability. This means that we can pivot Goldmine towards different use cases with ease, and where the different use cases can have vastly different application uses.
We are experiencing the first benefits of this flexibility internally, where we are increasing the data input and adding new labels to the data structure. The increased data input means it is less likely that we must venture outside of Goldmine to find all information we need for our analyses. Adding more labels to the data structure means that we are finding the relevant snippets faster, with clearer segmentation between data entries.
At the same time, we are also developing Goldmine applications for partners where we specifically cater Goldmine to the data needs of a client. For example, we can shift the focus of a Goldmine to intelligence about new products/services in a market or tracking personnel changes in a specific industry. Over the next few years, we want to explore the flexibility of Goldmine by applying it to the use cases of our clients, where we want to adapt Goldmine to their goals.