Metaphorically, belief and disbelief in business analytics as a competitive edge, and not just a passing fad, meet at a door. On one side is passion, and on the other is fear. Passion always lives with fear. Those with passion for a methodology, like embracing analytics to support decision, have fear that others will reject them and their ideas. What position for analytics will come out ahead? Opposition or proposition? Rejection or acceptance?

Technology is no longer the barrier to analytics

What is it about accepting a new idea like applying analytics? There is a lot. And it mainly has to do with the natural resistance to change with people. People like the status quo. The main barrier to the acceptance of applying analytics is no longer technical but rather is behavioral and cultural. The software tools are proven. The use of analytics by casual users, not just a team of trained statisticians, has become widespread.

Consider these remarks from a consultant I have communicated with:

The executives I talk to every day are wrestling with business decisions where a better understanding of data at a very deep level can make all the difference. … Low-level analytics just won’t get you there. Work your way through the list of ground-shaking developments in business today – none are areas where companies can continue to shoot from the hip. Pricing. Workforce trends. Health reform. Even security and terrorism threats. These are all complex challenges where advanced signal detection capabilities are critical. … Analytics is no fad. It’s a serious competitive advantage.”

Analytics as the only sustainable competitive advantage

The popular Harvard Business School Professor Michael Porter’s defined accepted, generic strategies for a company (i.e., cost leadership, differentiation and focus), however they are all vulnerable today because competitors can more quickly take actions (such as reduce costs), imitate a company or invade a company’s market niche. An organization’s best defense against the competition is the ability to quickly make smart decisions – which can easily be accomplished by implementing business analytics. Organizations that achieve competency with business analytics are able to sustain a long-term competitive advantage. 

A similar case can be made for adopting analytics-based enterprise performance management methodologies. The early adopters are already well ahead. Their executives have properly communicated their strategy to employees through appropriate key performance indicators (KPIs) and achievable targets to align behavior. They have robust predictive analytics that reduce uncertainty and allow them to take smarter and quicker actions. These organizations understand their cost and profit margins by product, service, channel and customer – as well as optimal actions needed to retain and grow customers, and acquire the best target customers. They have driver-based budgets and rolling financial forecasts using modeling techniques. I suspect that passion (and common sense) was present to overcome the fear of trying something new, like advanced analytics, to improve organizational performance.

The traits of a good analyst

Good analysts possess more than just passion. Good analysts are curious, a common trait among analysts; and they ask lots of questions. They query data to answer the questions, and then use analytics to ask further and more robust questions. And better yet, their analytics can eventually answer their questions.

Analysts typically love what they do. If they are good with analytics, they infect others with enthusiasm. Their curiosity leads to imagination. Imagination considers alternative possibilities and solutions. Imagination in turn sparks creativity.

After analysts produce results they then provide an important ingredient needed by decision makers – confidence. Confidence is a feeling and belief that one can rely on someone or something to make a decision and perform at some known time in the future. Effective analysts create confidence and trust with their stakeholders.

So opposition to or proposition for analytics? Rejection or acceptance? I am optimistic that the latter to each question will prevail.

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