In 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 
solving 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.
Summary
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.
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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:
This post is part of a larger series to provide a detailed set of steps you can take to unlock breakthrough results in Big Data Analytics. The simple use case used to illustrate this method will address the perplexing management challenge of platform and tool optimization. This step is used to identify the types and nature of the operating models used within the analytic community. I’m using a proven approach for solving platform and tool optimization in the same manner that proven practice suggests every analytic decision be made. Here we are simply using an organizing principle to group and categorize our findings in what can quickly become a bewildering experience (much like herding cats) in its complexity and nuance.
accomplish its function. An operating model breaks this system into components, showing how each works together. It helps us understand the whole. In our case we are going to focus on the analytic community and use this understanding to evaluate fit when making changes to ensure the enabling models will still work after the recommended optimization is called for. Thanks to Gartner who published 
The classic model used for years in delivery of what has been referred to as business intelligence. Typically we would find tight management controls to push through centralized strategy and efficiency, usually at a high cost. Tightly managed processes for collecting and cleaning data before consumption can be found in the classic patterns associated with Extract, Transform, and Load operations into a data warehouse or mart. Most often characterized by formal processes where a developer or specialists collects business requirements from the users and then creates sanctioned reports and dashboards for them on trusted data. Centralized provisioning enables an information consumer to access their 
The opposite of centralized provisioning, this model or loose confederation encourages local optimization and entrepreneurial drive. Look for a community that rapidly and interactively explores trends or detects patterns in data sets often from multiple sources to identify opportunities or risks with minimal support from the IT development community. Interactivity and discovery in this model is NOT limited to what is designed in by the content authors we find in the Centralized Provisioning model. The users are the content authors. Users of platforms and tools that excel at the decentralized analytics model can explore data using highly interactive descriptive analytic (“what happened” or “what is happening”) or diagnostic analytic (“Why did something happen?”, “Where are areas of opportunity or risk?”, and “What if?”). Because of embedded advanced analytic functions offered by many vendors, users can extend their analysis to some advanced descriptive analysis (for example, clustering, segmenting and correlations) and to a basic level of predictive analytic (for example, forecasting and trends). They can also prepare their own data for analysis, reducing their reliance on IT and improving time to insight. As decentralized analytics becomes more pervasive, the risk of multiple sources of the truth and information governance itself becomes a real challenge. Six of fourteen most important capabilities important capabilities needed in this model would include:
A hybrid of centralized and decentralized this model is best characterized by offering freedom within a framework to enhance transparency and effectiveness. This model features business users’ ability to prepare and combine data, explore and interact visually with this data to enable discovery to be deployed and managed across the enterprise. With the success of data discovery tools in driving business value, there is an increasing demand to use data discovery capabilities for a broader range of analysis and an expanded set of users than previously addressed by traditional reporting and dashboards. Governed data discovery enables users to access, blend and prepare data, then visually explore, find and share patterns with minimal IT support using their own technical and statistical skills. At the same time, this model must also satisfy enterprise requirements for business-user-generated model standards, data reuse and governance. In particular, users should be able to reuse sanctioned and approved business-user-created data or data sets, derived relationships, derived business models, derived KPIs, and metrics that support analyses.
In this model analytics (decisions, business rules, and processes) are integrated into the organization to capture economies of scale and consistency across planning, operations, and customer experience. Most typically found where developers are using software development kits (SDKs) and related APIs to include advanced analytics and statistical functions within application products. These capabilities are used to create and modify analytic content, visualizations and applications and embed them into a business process, application or portal. Analytic functions can reside outside the application, reusing the infrastructure but should be easily and seamlessly accessible from inside the application, without forcing users to switch between systems. The ability to integrate analytics with the application architecture will enable the analytic community to choose where in the business process the analytics should be embedded. On example of a critical capability for embedding advanced analytics would include consuming a SAS/R or PMML model to create advanced models embedded in dashboards, reports or data discovery views. Six of the fourteen most important capabilities needed in this model would include:
Applied Enterprise Architecture 