What Makes Data Analysis so Important in Decision Management?

Data Analysis in Decision Management is a very important activity. Data analysis is the act of looking into the data, grouping, filtering, joining and etc., and making sense of what data means.
The data analysis is usually done by a data engineer or anyone who has the knowledge to create a data set and then look into the data by applying some data operations.

There are some business decisions that require looking into data and making the decisions. For instance, let’s say we have some stores across stats, and we need to understand which one is more profitable in order to select the best performing store to announce the winner of the quarter.

As you see in this example, “which is the best performing store in terms of revenue in the last quarter?” is the business decision we want the answer to. However, despite all the other business decisions we talked about, there is a simple rule for this decision which is “Maximizing the sales revenue” is the rule that can find us the winner of the best performing store.

With traditional decision management, you cannot solve this problem simply because it is not an operational decision in the traditional sense of business decisions. But as we discussed, the boundary of the strategic, tactical, and operational decisions is blurring. As for this example, as the CFO of the book chain, I’d like to announce the best-performing branch in our headquarter every month. This makes the decision repeatable; it requires data access and operation and has a simple rule, i.e., the store with the most sales is the winner.

Traditional Solution

Traditionally, for these scenarios, we need to ask our IT team (e.g., the software development team) to automate this for us by building systems around the requirements. That includes building full-fledged information systems and custom reporting around our requirements. Or making database queries and using Excel to do the data analysis.

In any case, these kinds of requirements are critical to business operation. In this instance, encouraging other branches to perform better, we probably are going to do many trial and error to get the criteria right, and further down, we’d like to send a thank you email to the branch managers and send a special email to the winner and so on.

As you see, based on the nature of these decisions, traditional decision management platforms cannot help you here.

End-to-End Decision Management and Data Analysis

FlexRule provides end-to-end decision management and automation, meaning we empower our users to make any type of a decision, including data analysis and data-driven decisions.

Step 1: Create the Query

Using visual data query builder you simply can connect to your operational databases and start building the query you need visually.

data query for data analysis in decision management

The visual query builder enables you to simply drag and drop for building complex queries containing:

  • Table joins
  • Filters
  • Parameters
  • Complex calculation and custom fields

Therefore, as the leader of an operation, you can start building the data you need and use the “Preview Results” to ensure you have all you need.

Step 2: Data Analysis Phase

In the latest version of our authoring platform, we have incorporated a very easy-to-use data viewer that enables you to simply start analyzing your dataset and make sense of the data you collected in the previous step.data analysis

 

This advanced Data Viewer allows data analysis and inspection visually:

  • Multi-Level Columns Group
  • Sort
  • Search
  • Filter on Columns' values
  • Advanced filters based on Rules
  • Highlight based on Rules
  • Manipulate and Fix data
  • Group and Footer summary based on aggregate functions (e.g., Count, Min, Max, Average)
  • Clone a View in multiple Windows
  • Support Missing Values
  • Export to Excel, CSV, and JSON

This empowers users to not only use FlexRule for operational decisions using rules and other techniques such as ML and statistical analysis but also for tactical and strategic decisions that are purely based on data analysis and exploration.

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Conclusion

Not all the decisions are based on predefined algorithms, e.g., rules or ML for that matter! Many decisions are based on data analysis. Traditional decision management platforms are not able to handle many data-driven scenarios, in fact, they only handle rule-based scenarios. But end-to-end decision management and automation must empower leaders to manage and automate all kinds of business decisions whether it is rule-based, AI/ML, process, robotics, or based on data analysis and exploration.

Last updated April 5th, 2022 at 04:05 pm, Published March 31st, 2022 at 04:05 pm