Earlier this month I attended IBM Impact in Las Vegas where the software vendor announced new enhancements and features for their BPM suite product. On the face of it, the announcement sounded a bit underwhelming, with heavy emphasis on updating core functionality that was needed to remain competitive in the market.  However, one bullet in the announcement stood out:

• “Significant enhancements to the IBM BPM built-in dashboards to help improve business process outcomes and enable the creation of custom dashboards.”

How can your firm deliver great, loyalty-inspiring customer experience – and achieve its efficiency objectives?

Firms that want to boost Return on Equity (ROE) or Return on Capital Employed (ROCE) must improve productivity.  And in a very real sense Productivity = value / resources.  But too often, the role of IT is to reduce the denominator – resources, and usually leave the numerator – value, to someone else to worry about. So many EA professionals are expected to deliver cost or risk reduction - reducing the resources required for delivery of that value, or the risk associated with that delivery. They usually take an inside-out view with a primary focus on efficiency; and struggle to engage with the value delivery side of the equation.

But if productivity = value / resources, then the challenge is to both reduce resources and deliver enhanced value.   The opportunity for Business Architecture and BPM professionals is to connect great customer outcomes and experiences (the value side of the equation) to scalable and efficient back office operations within the organization. That’s “both/and” – not “either/or.”

But how do you do that?

Generally speaking, business people don’t really care too much for efficiency. They come to work for the value they deliver to their customers; not the reductionist philosophy of cutting costs. If you want to engage them on a journey toward performance improvement, leading with the efficiency side of the equation can be a mistake.

There is a shift underway with master data management (MDM) that can't be ignored. It is no longer good enough to master domains in a silo and think of MDM as an integration tool. First-generation implementations have provided success to companies seeking to manage duplication, establishing a master definition, and consolidating data into a data warehouse. All good things. However, as organizations embrace federated environments and put big data architectures into wider use, these built-for-purpose MDM implementations are too narrowly focused and at times as rigid as the traditional data management platforms they support.

Yet, it doesn't have to be that way. By nature, MDM is meant to provide flexibility and elasticity to managing both single and multiple master domains. First, MDM has to be redefined from a data integration tool to a data modeling tool. Then, MDM is better aligned to business patterns and information needs, as it is designed by business context.

Enter The Golden Profile

When the business wants to put master data to use it is about how to have a view of a domain. The business doesn't think in terms of records, it thinks about using the data to improve customer relationships, grow the business, improve processes, or any host of other business tasks and objectives. A golden profile fits this need by providing the definition and framework that flexes to deliver master data based on context. It can do so because it is driven by data relationships.  

I met with a group of clients recently on the evolution of data management and big data.  One retailer asked, “Are you seeing the business going to external sources to do Big Data?”

My first reaction was, “NO!” Yet, as I thought about it more and went back to my own roots as an analyst, the answer is most likely, “YES!”

Ignoring nomenclature, the reality is that the business is not only going to external sources for big data, but they have been doing it for years.  Think about it; organizations that have considered data a strategic tool have invested heavily in big data going back to when mainframes came into vogue.  More recently, banking, retail, consumer packaged goods, and logistics have marquis case studies on what sophisticated data use can do. 

Before Hadoop, before massive parallel processing, where did the business turn?  Many have had relationships with market research organizations, consultancies, and agencies to get them the sophisticated analysis that they need. 

Think about the fact, too, that at the beginning of social media, it was PR agencies that developed the first big data analysis and visualization of Twitter, LinkedIn, and Facebook influence.  In a past life, I worked at ComScore Networks, an aggregator and market research firm analyzing and trending online behavior.  When I joined, they had the largest and fastest growing private cloud to collect web traffic globally. Now, that was big data.

Today, the data paints a split picture.  When surveying IT across various surveys, social media and online analysis is a small percentage of business intelligence and analytics that is supported.  However, when we look to the marketing and strategy clients at Forrester, there is a completely opposite picture.