Agile Data Driven Roadmap

Overview

Architecting an organization that is agile and responds to data can be challenging task. The following roadmap delineates the various steps in achieving the agile data driven organization.

Data

The first step to being data driven is to obtain or collect the data. Without data, nothing further can be achieved. For some industries, the needed data can be purchased, but more often than not, a firm will have to have a robust data collection processes.

• Data Collection - data is collected at the finest grain from the point of origin.
• Data Quality - collected data loses its value if it is of poor quality. The next step in the process is to try to fix any issues that leads to poor quality data. If it is unfixable, the firm may still rely on statistical tools to deal with the quality issue. It is recommended that all data is retained, even if is of poor quality.
• Accessible and Queryable - the data needs to be accessible and queryable to the analysts who are to analyze the data. This typically means that it hosted on a server, and in a easy to use format.
  
  In addition, the data needs to be secure. That is, the data should be accessible to the people who need to analyze the data, and only them. A robust security system should include the ability to customize the data and fields accessible to any user.

In addition to creating an architecture that achieves the above goals, the architecture should be designed to be simple and flexible enough to easily evolve as conditions change.

Reporting

Once collected, data needs to processed in order to turn it into actionable insight.

• Reporting - tools that provide summary reports and charts, including dashboards.
• Alerts - flag current conditions when an outlier event has occurred. Can indicate risk, or even opportunity.
• Models and Forecasts - the firm's analytics team needs to be able to model the data and turn it into actionable knowledge.
• Ad-Hoc Analysis - the truly agile data driven organization must be able to efficiently engage in ad-hoc analysis. That is, analysts need the ability to create and run new analytics and models in response to changing conditions, and publish those models to production dashboards.

Decision Making and Execution

Once a company understands its environment, it then has to decide how to proceed. This task can be complex in and of itself. Once a decision has been made, companies need to continually test their assumptions and alter course as needed.

• Decision Process - even good quality data and modeling can lead to poor outcomes, in particular is the decision process that utilizes the data is poor. It is often worthwhile for a company to analyze its decision processes to assess their strengths and weaknesses.
• Execution - once a decision has been made from the data and models available to the firm, the decision needs to be efficiently executed.

Full Automation

In order to be agile and efficient, an organization needs to achieve full automation that achieves the following goals.

• Fully Automated Data Capture
• Fully Automated Models, Anaytics and Reporting
• Flexible and Extensible
• Easy and Agile Collaboration

Ongoing Education

Davinci Resources

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• davinci design group - provides business leaders with customized solutions and a path to success. we can provide the skills and resources needed to power your vision. Our goal is your success, plain and simple.