Seven Principles of Sustainable Analytics

Published
  • March 01 2011, 10:08am EST

Business intelligence is not enough anymore. Data volumes, global sourcing models and an ever-changing regulatory environment have combined in a perfect storm to render traditional BI capabilities inadequate at capturing deep insight into business performance. Deep analytic insight comes from looking deeper and further out toward the horizon to help answer the most challenging, even unknown, questions, and to build those answers into the business itself.

Most companies probably already have at least a subset of the capabilities they need to engage in deep, sustainable analytics. The challenge is to push those analytic capabilities even deeper into the organization - from business executives to the front lines - in a coordinated fashion. This requires changing the way companies look at the business and at information itself.

To meet the challenge of implementing deep, sustainable analytics - and capture more value from their analytics investments - leading companies often focus on seven key principles.

1) Start where you are. The first step in understanding your current capabilities and the gaps you'll need to close in order to get more value from your analytics investments is an honest self assessment. This extends to all your analytic capabilities, from both a technical and organizational depth perspective. Focus on how well (or not) you're employing those capabilities, and on which capabilities you need to acquire. Set goals for improvement based on what you find. For example, if your analytic capabilities are divided into business function-specific silos, set a long-term goal of having them integrated across the business functions, and develop a roadmap to get there.

2) Ask crunchy questions. Crunchy questions are the ones that help you find out what really drives value in your business processes. They focus on finding out what happened in the past and what the root cause of outcomes might have been. They help you understand what's happening now and why, and whether these events are consistent with forecasts and expectations. They also help you watch for expected events and predict what might happen in the future, based on past and current experience. They drive hindsight, insight and foresight.

3) Enhance signal strength. The data in your structured databases is not enough to fully understand your business performance. With the volume of unstructured data on pace to overwhelm traditional, structured data, it's critical to develop capabilities that leverage the unstructured content that inundates you on a daily basis. Email, video, customer interaction data and social media all provide a depth of understanding that traditional data simply can't. You need to capture it and exploit it, or fall behind.

4) Accelerate insights. You can do this by embracing automation, where it's practical. Implementing deep analytics requires automation across all dimensions of your analytics operations. In short, automate information delivery to people and processes and automate responses as much as is practicably possible, so that action can be taken with certainty, and at its lowest cost. Examples of automation include the use of price optimization models, developing compensation plans aligned with profitability data and optimizing vendor contract terms based on volumes and margins.

5) Engage the users and visualize. Give people power. Design data visualization capabilities and interfaces that help business users ask complex questions and get answers quickly. Then, give them the authority to act on the results, based on role and responsibility levels. Involve users in the design of the interface. They'll tell you what they need to do their jobs and when you've got it right.

6) Build a fact-based culture. Shooting from the hip is no longer in vogue. There is simply too much complexity in the business environment and too much competitive pressure to base decisions solely on gut feelings. Instinct is valuable, but it needs to be supported by data. Start at the top. For example, a CFO who says, "These are the inputs I use to make decisions," will go a long way to facilitate the adoption of those inputs throughout the organization.

7) Practice right-fit analytics. There's no silver analytics bullet that's right for every business. Businesses are unique entities that require unique analytics infrastructures. Just because a toolset is out there doesn't mean it's the right fit for your needs. Therefore, it's critical to set an overall analytics strategy that's right for your particular business and assemble a disciplined, experienced team to implement the strategy.

Sustainable analytics is not a project with a nice deliverable that you put on a shelf. It's a continuous cycle of understanding and improving business process performance. It should focus on high-impact areas and ask hard questions. It requires embarking on a journey to help move your organization beyond the hindsight of traditional business intelligence towards deeper insight and distant foresight. Are you ready to go?

This publication contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of this publication, rendering business, financial, investment or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates and related entities shall not be responsible for any loss sustained by any person who relies on this publication.

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