ICSAR Model for Data Use - User Guide

Introducing the

ICSAR Model for Data UseUser Guide

By Chris DowsettT: @chrisdowsettW: http://www.designingdata.co

v1 April 2015

The ICSAR Model is a step by step framework that helps business leaders ensure they are using data effectively.

It is the result of 4 year Doctorate research project, which found many business leaders were not using data appropriately and organizations were not getting the full value of different data sources.

See http://www.designingdata.co for the Doctorate research summary findings.

http://www.designingdata.co/

A lot of businesses have invested in data architecture and analytics but they have not invested in the business processes to ensure data is used correctly and most effectively.

The ICSAR Model for data use helps solve that problem by giving structure to data use in decision making.

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The ICSAR Model is free to use under the Creative Commons license (by non-commercial).

Citation when used: ICSAR Model © Chris Dowsett // www.designingdata.co - Use under Creative Commons license (by NC)


The ICSAR Model for data use

This is the ICSAR Model. It is a step by step process to help businesses use data more effectively.

The ICSAR Model has been designed as a structured model but remains flexible enough that it can support any organization.

There are 5 steps in the model. Each step has an input and an action. The input and the action are equally important.

The upcoming slides will explain the model and show how each step overcomes influences and challenges that are impeding better data use. Those influences and challenges were identified from the research with senior business leaders.

Step One

Step Two

Step Three

Step Four

Step Five

Inputs Actions

Measurement framework

Apply to business activity or project

Synthesize finding with organizational

knowledge

Compare with inputs from each data category to

validate

Initial insight from data input

Feedback process for activity

improvement

Measurement or review framework

Organizational learning hub,

experts or insights library

Data Categories Table: Behavioral,

prompted, unprompted data

Review outcomes and list new knowledge

Rep

eat l

earn

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Step one: Initial insight gathered

Action: Initial insight from a data source prompts an investigationInput: Measurement framework – see guide on creating a measurement framework @ http://www.designingdata.co

Making this step work Example Business challenges solved

Draw up a measurement framework and put it in place to measure key areas like – social media, web metrics, purchase metrics, product usage data, brand metrics and other relevant areas.

Put in place Key performance indicators (KPIs) to monitor each area as part of the framework.

The measurement framework is key because it means managers know when a business area improves or gets worse through KPIs.

When managers are alerted to a change in a KPI, this becomes the ‘initial insight’ to investigate.

A software company has started investing more resources in their social media presence.

As part of that, the analytics team sets up a measurement framework for their social media channels including KPIs such as social mentions, social sentiment and engagement.

Their social sentiment tracking starts to pick up some negative mentions around a software ‘bug’ on Twitter.

The Twitter mentions provide some clues on the problem but more information is needed. This data becomes the initial insight and the research team looks to investigate.

This step ensures businesses have a measurement framework in-place to track changes in key metrics.

Measurement frameworks encourage:• Transparency in analytics and

reporting• Consistency of measurement• Objective measurement• Clear key performance indicators

and metrics

You can follow

this example

throughout the

entire user-

guide.

ICSAR Model © Chris Dowsett // www.designingdata.co – Use under Creative Commons license (by NC)



Step two: Compare across data categories

Action: Compare the initial insight to data from each of the three data categoriesInput: Data categories table - see data categories guide @ http://www.designingdata.co

Making this step work Example

After the initial insight, the next step is to gather data from at least one source within each of the three data categories.

The data categories are: behavioral, prompted and unprompted data.

Triangulating across data sources from each of the three data categories ensures a holistic approach to reviewing the initial insight.

The aim is to use this step to understand the problem or business issue in more detail.

Following the mention of a software ‘bug’ by some of their customers on Twitter, the software company decides to investigate.

A product manager is alerted. They initiate a survey (data category: prompted data) and ask an analyst to look at product usage data (data category: behavioral data) for further evidence.

This exercise serves to triangulate across all three data categories by combining the survey and usage data with the Twitter mentions (data category: Unprompted data).

This step encourage businesses to triangulate across different sources.

The benefits of triangulation are:• Overcomes personal bias towards

familiar data sources• Overcomes organizational bias

towards data that has been ‘used before’

• Overcomes bias toward ‘most recent’ data insight

• Holistic approach to validating insights


Business challenges solved




Step three: Synthesize knowledge Action: Synthesize the insights with organizational knowledgeInput: Organizational learning library, experts or internal social network


Following the comparison of insights across data categories, the next step to synthesize the insights with existing organizational knowledge.

The goal here is to avoid repeat projects whilst also gathering additional evidence found in past experiences and leveraging previous bits of analysis.

Some companies have an internal business intelligence library, others rely on internal social networks or teams meeting with business experts.

Whatever the method, this step is important in ensuring teams maximize the value of a organization’s data assets.

After triangulating across the three data categories, the product manager finds there is a problem in their software that needs an urgent patch fix.

The product manager speaks to colleagues and uses the engineering forum on the organization’s internal social network to find previous bug fixes.

The product manager finds there was a similar bug in previous release and uses the solution to that problem as the starting point for the current issues. This saves time and resources.


This step encourages businesses to also leverage existing pieces of analysis and knowledge within the organization.

The benefits of this step are:• Leverages existing data insights• Continues to maximize past data

investments• Holistic approach to validating

insights• Overcomes personal bias towards

familiar data sources



Step four: Insights to action

Action: Put the insights into actionInput: Organizational action plan or management process


Step four is focusing on using the data insights and the learning gathered so far in the ICSAR process.

Once the issue has been identified and intelligence sought from the organizational knowledge library or elsewhere, the next step to make a plan and take action.

Some organizations have established processes for product development or organizational action plans so those will need to be used at this stage.

The product manager now has the evidence to show this is a significant problem with the software that needs a fix.

The product manager also has a good foundation to start working on the solution thanks to previous learning from a similar problem.

The product manager enters the information in the product development scrum process and business leaders urgently free up resources to fix the issue.

They create a working team to address the problems and communicate customers. They also set up a measurement framework with KPIs to identify how the project will be evaluated.


This step is really about action. The focus is on ensuring data is used once it has been validated.

The benefits of this step are:• Dedicated step to put insights into

action• Focuses on measureable

outcomes early in project planning

• Focuses on evidence-based decision making

• Overcomes data insights not being used



Step five: Review outcomes Action: Review the outcomes and any new knowledgeInput: Measurement framework – see guide on creating a measurement framework @ http://www.designingdata.co


Step five is focused on reviewing the outcomes from any product updates or business projects that happened in step four.

The aim here is to review the goals and KPIs that were established in step four.

Using a measurement framework, the team should review the metrics and any insights from the update or project.

The product team releases a software fix and the team monitors new data, looking for any change in behavior.

The team also highlighted an organizational learning opportunity. They decide to use this experience to create a training package because bug was similar to a previous problem.

The team use this event to improve the organization-wide knowledge base.

The model is cyclical so after step 5, the process starts over again. This ensures ongoing learning and development from data insights.

The process is

complete.

What happens

now?


The final step ensures the ICSAR Model is cyclical and feeds value back into the organizational knowledge bank.

The benefits of this step are:• Dedicated step to review the

outcomes• Ensures new insights are fed

back into the organizational knowledge systems

• Encourages ongoing development of business areas

• Reinforces measureable outcomes and evidence-based decision making




Don’t forget …

This ICSAR Model user guide has two supporting documents:1. “How to create a brilliant measurement framework in 5 steps” guide2. “Data Categories Table” user guideBoth of these are available from http://www.designingdata.co

Already tried using the ICSAR Model? We’d love to hear any feedback, tips or suggestions on how to keep improving the model. Please visit www.designingdata.co



Thanks for reading.Enjoy using the ICSAR Model.

By Chris DowsettTwitter: @chrisdowsett

Leadership & Management

ICSAR Model for Data Use - User Guide