Technique Summary: BABOK 10.13, 10.14, and Agile 7.7

 By Grant Warden

Below is a summary of section 10.13 - Data Flow Diagram and 10.14 - Data Mining  from BABOK® version 3 and Agile Techniques 7.7 - Personas from the Agile extension to the BABOK guide version 2.
 
Technique 10.13 – Data Flow Diagrams
Purpose - Data flow diagrams show where data comes from, which activities process the data, and if the output results are stored or utilized by another activity or external entity.
Description - Data flow diagrams portray the transformation of data. They are useful for depicting a transaction-based system and illustrating the boundaries of a physical, logical, or manual system.
Elements

  1. Externals - An external (entity, source, sink) is a person, organization, automated system, or any device capable of producing data or receiving data. An external is an object which is outside of the system under analysis.
  2. Data Store – A data store is a collection of data where data may be read repeatedly and where it can be stored for future use. In essence, it is data at rest.
  3. Process – A process can be a manual or automated activity performed for a business reason. A process transforms the data into an output.
  4. Data Flow – The movement of data between an external, a process, and a data store is represented by data flows. The data flows hold processes together. Every data flow will connect to or from a process (transformation of the data).

Usage Considerations
Strengths

  • Are excellent ways to define the scope of a system and all of the systems, interfaces, and user interfaces that attach to it. Allows for estimation of the effort needed to study the work.
  • Most users find these data flow diagrams relatively easy to understand.
  • Helps to identify duplicated data elements or misapplied data elements.
  • Illustrates connections to other systems.
  • Helps define the boundaries of a system.

Limitations

  • Using data flow diagrams for large-scale systems can become complex and difficult for stakeholders to understand.
  • Different methods of notation with different symbols could create challenges pertaining to documentation.
  • Does not illustrate a sequence of activities.

 
Technique 10.14 – Data Mining
Purpose - Data mining is used to improve decision making by finding useful patterns and insights from data.
Description - Data mining is an analytic process that examines large amounts of data from different perspectives and summarizes the data in such a way that useful patterns and relationships are discovered.
Data mining is a general term that covers descriptive, diagnostic, and predictive techniques:

  • Descriptive: such as clustering make it easier to see the patterns in a set of data, such as similarities between customers.
  • Diagnostic: such as decision trees or segmentation can show why a pattern exists, such as the characteristics of an organization's most profitable customers.
  • Predictive: such as regression or neural networks can show how likely something is to be true in the future, such as predicting the probability that a particular claim is fraudulent.

Elements

  1. Requirements Elicitation – The goal and scope of data mining is established either in terms of decision requirements for an important identified business decision, or in terms of a functional area where relevant data will be mined for domain-specific pattern discovery. This top-down versus a bottom-up mining strategy allows analysts to pick the correct set of data mining techniques.
  2. Data Preparation – Data mining tools work on an analytical dataset. This is generally formed by merging records from multiple tables or sources into a single, wide dataset.
  3. Data Analysis – Once the data is available, it is analyzed. A wide variety of statistical measures are typically applied and visualization tools used to see how data values are distributed, what data is missing, and how various calculated characteristics behave.
  4. Modeling Techniques – Some examples of data mining techniques are:
  • classification and regression trees (CART), C5 and other decision tree analysis techniques,
  • linear and logistic regression,
  • neural networks,
  • support sector machines, and
  • predictive (additive) scorecards
  1. Deployment – Data mining models can be deployed in a variety of ways, either to support a human decision maker or to support automated decision-making systems.

Usage Considerations
Strengths

  • Reveal hidden patterns and create useful insight during analysis—helping determine what data might be useful to capture or how many people might be impacted by specific suggestions.
  • Can be integrated into a system design to increase the accuracy of the data.
  • Can be used to eliminate or reduce human bias by using the data to determine the facts.

Limitations

  • Applying some techniques without an understanding of how they work can result in erroneous correlations and misapplied insight.
  • Access to big data and to sophisticated data mining tool sets and software may lead to accidental misuse.
  • Many techniques and tools require specialist knowledge to work with.
  • Some techniques use advanced math in the background and some stakeholders may not have direct insights into the results. A perceived lack of transparency can cause resistance from some stakeholders.
  • Data mining results may be hard to deploy if the decision making they are intended to influence is poorly understood.

 
Technique 7.7 –Personas
Purpose – Personas are used to understand and empathize with an intended stakeholder in order to align the solution with the stakeholder need.
Description – Personas are fictional characters or archetypes that exemplify the way that typical users interact with a solution.
Elements

  1. Templates – both long and short can be used.
  2. Persona Name and Image – Business Analysis practitioners give personas a realistic name and attach a fictional image in order to increase its relatability with the intended stakeholder.
  3. Traits and Characteristics – Personas include unique, distinguishing, and differentiating characteristics or traits regarding the intended stakeholder.
  4. Motivations – Personas include a representation of the underlying motivations regarding how and why the intended stakeholder interacts with the solution.
  5. Needs – Needs for the persona address specific needs, which can be basic such as safety, trust, or access to food. They can be higher level needs such as the need for validation and acceptance.
  6. Differentiators – Differentiators identify specifically why this persona is different and unique from another persona.

Usage Considerations
Strengths

  • Personas facilitate the shared understanding ofspecificrequirements for different sets of users, which can be used to develop user stories.
  • Proposed solutions canbe guided by how well they meet the needs of individual user personas. Features can be prioritized based on how well they address the needs of one or more personas.
  • Provide a human “face” so as to focus empathy on the people represented by the demographics.
  • If the data is available, using demographic (or anthropomorphic) data about the intended user population is a good way to start building personas.
  • Personas help stakeholder from projecting individual values and biases onto the solution.

  Limitations

  • Personas are fictional
  • Personas may not be a good substitute for a real user.
  • Personas need to be regularly reviewed and updated.