Perceived Value Framework for Data Centric Solutions

A lot of my time is spent discussing enterprise and end user value of software solutions. Increasingly over the last few years the solution focus has moved from being first about specific application and business processes to being data centric. People start with thinking and asking about what data that is collected, displayed, manipulated and automated instead of what is the task (e.g. we need to better understand how our customers make buying decisions instead of we need to streamline our account managers daily tasks). I have been working on a mental model for how to think about these different types of solutions and one that would give me a better framework when discussing product, technical and marketing topics with clients or friends in the industry.

I came up with the following framework as a 2×2 matrix that uses two main axis to define the perceived value of data centric solutions. These are the Volume & Complexity of Data Integration and the Completeness & Flexibility of Data Analytics.

The reason for these definitions is that one very real change is that most clients that I work with are constantly dealing with distributed applications and business processes which means having to figure out how to bring that data together either in a new solution or in a analytics solution that can work across the various data sets. There is no single right answer to these issues but there are very real patterns of how different companies and solutions approach the underlying issue of growing distributed data inside and outside the control of the company.

Data Centric
Perceived Value Framework for Data Centric Solutions

 

Solution patterns:

1. Personal Productivity. These are solutions that collect and present data mostly for individual use, team data sharing and organization. They tend to be single task oriented and provide data reporting functions.

2. Business Productivity. These solutions usually span multiple data sources and are focused on either decision support, communication or collaboration.

3. Business Criticality. Theses solutions provide new value or capabilities to an organization by adding advanced data analytics that provided automated response or secondary views across distributed data sources.

4. Life Criticality. These solutions are a special subset which are aimed at either individual, group or social impact solutions. Traditionally these have been very proprietary and closed systems. The main trend in data-centric solutions is coming from more government and business data being exposed which can be integrated into new solutions that we just never could even do previously let alone think up. I do not even have a good example of a real one yet, but I see it as the higher level solution that evolves as at the juncture of real-time data meets analytics and distributed data sets.

Some examples of current solutions as I would map them on the perceived value of data centric solutions framework. Some of these are well known and others you probably have never heard. Many of these new solutions were not easy to create without technology that provides easier access to data from distributed resources or compute power for supporting decision support.

Data Centric
Perceived Value Framework for Data Centric Solutions

 

What I really like about this value framework is that it allows us to get beyond all the buzzwords of IoT, BigData, etc and focus on the real needs and solutions that are needed and that cross over these technical or singular topics but on their own are not actual high value business solutions. Feedback welcome.

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