5 years, 4 months ago

Big Data Analytics – Unlock Breakthrough Results: (Step 4)

Link: https://pragmaticarchitect.wordpress.com/2016/01/05/big-data-analytics-unlock-breakthrough-results-step-4/

wieghted_foodIn this step we look a little closer into defining the critical capabilities used across the four operating models discussed in an earlier post (Big Data Analytics – Unlock Breakthrough Results: Step 3). We are going to assign relative weights to each of the critical capabilities groups for each operating model uncovered earlier. This is done to assign the higher weighting to capability groupings most important to the success of each model. Having the quantified index means we can evaluate each platform or tool for optimization within quickly and produce meaningful results. We already know a set of tools and platforms which are ideal for Centralized Provisioning are usually unsuited for use within a Decentralized Analytics operating model. In contrast critical capability essential to Embedded Analytics is very different from Governed Data Discovery. Yes there are some capabilities that cross operating models (e.g. metadata), and some that are far important than others. So what we are doing in this step is just gathering and validating the relative importance of each so form truly does follow function. This will become increasingly clear when building the decision models to guide our actions.

What is a decision model?
A Decision Model is a new way of looking at analytics using business logic. A key enabler sandwiched between BPM and Business Rules, the logic is captured and knits both together to illustrate what drives the decisions in a business. Instead of trying to capture and manage the logic one business rule at a time, a Decision Model groups the information sources, knowledge, and decisions (including the rules) into their natural logical groups to create the structure that make the model so simple to capture, understand, communicate and manage. Using this method we will be using a proven approach for OMG_DMN_Imagesolving platform and tool optimization in the same way that proven practice suggests every analytic decision be made. DMN provides the constructs that are needed to model decisions, so that organizational decision-making can be readily depicted in diagrams, accurately defined by business analysts, and optionally use to specify and deploy automated decision-making. The objective is to illustrate a method to address the perplexing management challenge of platform and tool optimization. In this step we are simply using an organizing principle to continue grouping and categorizing our findings quantifying each capability in its complexity and nuance across several facets. For more on this see the OMG specification released in September 2015.

Relative Weights
The relative weights and further refinements should reflect your site specific needs so there is less of chance of friction or semantical confusion when the decision model and the findings are shared with the stakeholders. This is a collaborative exercise where the findings are shared and confirmed with both technical and business stakeholders for agreement and validation. This usually means you (as an architect) create the baseline and then iteratively refine with the subject matter experts and business sponsors to agree on the final results or weights that will be used. This work still remains platform, tool, and vendor agnostic. We are simply trying to identify and assign quantitative measures to evaluate which functional (critical capability) is most important to each operating model. A good baseline to begin with is the Gartner work published as Critical Capabilities for Business Intelligence and Analytics Platforms this summer (12 May 2015 ID:G00270381). With this we have a reasonably good way to think about form and function across the different operating models which Gartner refers to in their work as baseline use cases. Recall that across any analytic landscape (including big data) we are most likely to encounter one or more of the four operating models to include:

– Centralized Provisioning,
– Decentralized Analytics,
– Governed Data Discovery, and
– OEM/Embedded Analytics.

This seems to be a sensible way to organize the decision model we building. Thanks to Gartner we also have a pretty good way to describe manage the fifteen (15) groups of critical capabilities to use when comparing or seeking platform and tool optimization within each model. The baseline used includes the following groups of features, functions, and enabling tools:

– Traditional Styles of Analysis
– Analytic Dashboards and Content
– IT-Developed Reports and Dashboards
– Platform Administration
– Metadata Management
– Business User Data Mash-up
– Cloud Deployment
– Collaboration and Social Integration
– Customer Services
– Development and Integration
– Ease of Use
– Embedded Analytics
– Free Form Interactive Exploration
– Internal Platform Integration
– Mobile

The purpose in all of this is arrive at some way to quantify which capability within each operating model is more important than the others; weighting their relative importance in satisfying need. In this step we are simply starting at a baseline. We can refine the critical analytic capabilities from this baseline to meet site specific needs before moving on to the weighting in the next step. Note these are high level summary weights. Each capability includes a number of different values or characteristics you can refine to any level of detail you believe necessary. They should all sum to the groups value (e.g. 20% for Platform Administration within the Centralized Provisioning model for example) to retain the integrity of the results.

For each of the fifteen (15) groups of critical capabilities we assign weights to be used in later steps to evaluate the relative importance of each within each operating model.


Note: the weights used in this example are based on the Gartner work referred to above. I have changed the metadata weighting to reflect my experience, leave the balance of the work to the next step after you have tailored this baseline to your environment and are ready to apply your own weighting.

We have already seen there are very different needs required for each of the models presented. As the decision model is introduced and developed the data points for each can be used to develop quick snapshots and quantitative indexes when evaluating the form and function for each optimization in question.

The fifteen (15) critical capabilities are now assigned relative weights used within each of the four operating models. We are now at a point where the analytic community profiles can be compiled to arrive at a defensible approach to quantifying the data used in the upcoming decision model. This has also helped clarify and understand the key capabilities that drive each operating model which we see can be very different as illustrated in the following diagram.


If you enjoyed this post, please share with anyone who may benefit from reading it. And don’t forget to click the follow button to be sure you don’t miss future posts. Planning on compiling all the materials and tools used in this series in one place, still unsure of what form and content would be the best for your professional use. Please take a few minutes and let me know what form and format you would find most valuable.

Suggested content for premium subscribers:
Big Data Analytics – Unlock Breakthrough Results: Step Four (4)
Operating Model Mind Map (for use with Mind Jet – see https://www.mindjet.com/ for more)
Analytic Core Capability Mind Map
Enterprise Analytics Mind Map
Analytics Critical Capability Workbooks
Analytics Critical Capability Glossary, detailed descriptions, and cross-reference
Reference Library with Supporting Documents

Prior Posts in this series can be found at:

Tagged: Analytics, Big Data, Big Data Tools, Enterprise Architecture, Operating Model, Proven Practice