Get Started with an Example Work Flow

Follow the example work flow to familiarize yourself with using "Magellan" to improve your business.

Context

Procedure

  1. Log in to "Magellan". Your credentials are managed via SAP Identity because "Magellan" is a SAP HANA Cloud Platform (HCP) project application. When you are logged in, you are brought to an exploration home screen that has samples and previously built explorations. Explorations are the primary structure that users interact with in "Magellan". Each exploration consists of a dataset and all of the analytics associated with the dataset.
  2. Create a new exploration (or delete or move existing explorations). To create a new exploration, you upload a wide dataset in a CSV file into "Magellan". When the file is uploaded, "Magellan" displays all of the fields that are included in the dataset, as well as information about the dataset size and scope.
    Note To ensure that you upload suitable data, read the section, How do you achieve accurate results?
  3. Define the business question using the following process:
    1. Choose the part of the data that you want to explore. The part must be present in your dataset and can be a field, multiple fields or a range of values. This is known as the target. For example, you might want to increase revenue. In this case, you must know the factors that influence revenue and to explore the best way to increase it. Therefore, you would click the Revenue field. This action asks the application to build a data model based on the revenue field.
    2. Examine the data that "Magellan" returns. The application runs Advanced Analytic algorithms over the data against the target and displays Contributing Variables, which show the data that has the most impactful relationship with the target. The key influencers display prominently also. In the case of a revenue exploration, a key influencer could be the amount of services sold. What’s more, the accuracy of the model and its robustness in the event of receiving additional data is given an Insight Quality Value.
      This is "Magellan" main screen which displays key influencers for the revenue field of a Travel Booking Dataset exploration:
    3. Interact with the output to see if anything interesting has emerged. Select the circled Contributing Variables to see the nature of their relationship with the target across a number of Variable Categories. Variable Categories are columns that are grouped together because they behave the same; for example, the dataset columns Hotels and Excursions can be grouped together as they attract tourists.
    4. Explore and discover insights. Delve deeper into the data by running more analyses against interesting parts of the information. For example, you can run an analysis on two related influencers to understand their impact. The results are visualized in various charts and maps, including a data heat map to view a range of numbers.
  4. Create a What-If Analysis. This enables you to adjust influencer variables to observe the impact on your business. Traditional analytical tools that comprise basic business intelligence (BI) examine historical data, whereas tools for advanced and exploratory analytics focus on forecasting future events and behaviors, which enables businesses to conduct what-if analyses to predict the effects of potential changes in business strategies.
  5. When you end your session, "Magellan" stores the information with which you were working until it is explicitly deleted.