Big Data Transparency-Integrate Data Initiatives, Maximize Investments, Make Smarter Decisions

Alex Paul Manders, Technology Business Management Practice Lead Ameri, ISG and Prashant Kelker, Director and Lead Evangelist, ISG

Many organizations today are aggressively pursuing IT cost-transparency by aggregating IT finance and IT operational data in the context of standardized costing taxonomies. They are then using the insights offered through aggregated data models to identify a mix of pure IT cost take-out opportunities or to identify opportunities to re-invest savings and fund IT innovation. Although there is an uptick in CFOs driving this type of program, typically this type of activity is led by the CIO agenda.

In terms of CIO use-cases for IT cost-transparency, common ones include benchmarking against peers, better understanding cost insights to drive sourcing strategies, evaluating the true cost of enterprise applications, aligning costs with IT services models and sunsetting legacy mainframes. All of this is well and good, and anyone on a cost-transparency journey knows it requires a lot of data from a lot of different systems and people.

The effort of aggregating data into a standardized model to achieve IT cost-transparency can be a time consuming effort, and the question often becomes: is this effort being duplicated by a big-data function, or should the two efforts actually be supporting one another for different business reasons? Consider IT financial data coming from various systems, labor data and all of the data associated with individual IT resource towers (servers, storage, network, end-user computing, etc.). We easily add a layer of complexity to the data when we add market pricing data to evaluate IT cloud providers against current costs, in addition to other layers of complexity with data related to benchmarking to a cost-transparency data model.

  ​The data and systems identified in the cost-transparency program are likely to benefit and support a broader data and analytics strategy  

Such a model would not only bring out cost transparency but would lend itself to making real-time purchase decisions. This is turning into a reality in the world of cloud orchestration in which businesses can now combine multiple IaaS stacks from different providers into a single pane of glass.

Organizations mustn’t forget the additional opportunity to align data source requirements to an even larger vision. Once systems and data have been aligned to drive a buy-versus-build decision within a hybrid cloud landscape, for example, the data model is now a powerful mechanism that can be used by all stakeholders to make non IT finance or IT related decisions. By applying data-blending strategies with non-IT/Finance data, business stakeholders are empowered to up-level the evolution of enterprise analytics strategies, which will drive future big-data requirements.

With all the data harvested by IT to drive cost-transparency, doesn’t it make sense to integrate the cost-transparency “use-case” into an enterprise analytics function? This type of thinking will reduce an organization’s duplication of efforts in data collection and position the data itself to be further integrated with non IT and Finance data. The most common challenge in cost-transparency modeling falls back on data quality, data availability and data defensibility.

One particular use case illustrates the value of a broader vision within a TBM framework. A large multi-national financial services client was actively evaluating IT cost transparency and TBM as a strategic IT transformation program to drive enterprise initiatives related to digitalization and mobility. In this use case, the power of big data applied to cost transparency within the TBM framework illustrated the need for a vision, a roadmap, and the right information to make the right decisions. With the TBM framework and cost transparency tools, the enterprise found it had more detailed information to make better decisions and to keep track of the right KPIs for tracking and adjusting the implementation as needed. This approach ensured a more successful implementation of its vision and the ability to quickly adjust with unexpected market evolution.

An additional benefit of aligning an IT cost-transparency initiative to an analytics initiative is that IT and Finance have a business case to justify program investments related to transparency and can bring value-add use-cases across the business that are not purely focused on the insights offered by transparency. 

If your organization is pursuing any type of big-data strategy, it’s safe to assume you are also developing enterprise analytics capabilities as the two go hand in hand. When making such an investment, consider understanding where your firm stands with IT cost-transparency projects. The data and systems identified in the cost-transparency program are likely to benefit and support a broader data and analytics strategy. An analytics strategy can then support requirements of your organizations cost-transparency efforts from a data perspective. The combination sets the foundation to rationalize investments into all three initiatives, each independently designed to support enterprise initiatives and, when combined, fully align to a powerful combination of data.



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