In my last column, I talked about deep analytics and covered core implementation principles. This month, I'd like to continue that theme and delve deeper into the technical and cultural changes that must take place at a company considering a deep analytics implementation.

I'm not going to draw a bright line between cultural and technical implementation principles here. There isn't one. They tend to feed off each other. Put the right culture in place and you'll know what technology you need. Once the technology is in place, the information culture will change over time to take utmost advantage of the technology.

The first change that should take place lies in the questions you ask about business. Over the last couple of decades, companies have been investing heavily in ERP systems to streamline their business processes. This has resulted in significant growth of organized data. Add that to the avalanche of unstructured data that has buried most companies, and you've got a data problem. ERP systems are fine for measuring the performance of business functions such as finance, CRM, supply chain and HR, but they lack the ability to help companies predict future performance.

Looking forward and predicting future performance requires a deep level of business analytics across the enterprise. It takes finding - and properly using - the right combination of skills, technologies, applications and techniques to conduct continuous and iterative exploration. It gathers insight from past performance and predicts how the company will perform in the future under a variety of likely scenarios and environments.

It requires a change in how management looks at information. Traditionally, management has focused on questions such as, "How did we do in a particular period, region, line of business, etc.?" in hopes of understanding past performance. However, because competitive pressures and data volumes have increased, managers must begin to form a holistic picture of their data in order to ask, "What do I need to know to improve future performance?" The shift is subtle, but profound. Instead of asking what happened, managers are asking, "What will happen, and how can we use this knowledge to improve performance?"

The next change required is the construction of an information management framework built with a consistent technical approach and founded upon consistent data governance and standards. This framework has several characteristics:

  • Corporate, well-defined data oversight and standards,
  • Data stewardship embraced by all users,
  • Explicitly assigned data ownership,
  • A well-defined data dictionary,
  • Clear and consistent data quality metrics,
  • A dedicated scalable analytics technology environment, and
  • Common data management and reporting standards and tools.

Finally, the big change: culture. The environment to implement a deep analytics solution is foreign to many companies. It requires cooperation and collaboration on an enterprise level. It unites the best resources, both human and technical, with the most critical projects, regardless of who "owns" those resources. It also shares information about, and responsibility for, data across business functions and lines of business.
This doesn't come easily to many companies where managers have built knowledge-power bases founded upon their access to, and understanding of, how to use corporate data. To affect this change, managers and workers at all levels must be made to understand that they will ultimately gain by sharing their knowledge.

We can document the critical success factors for creating this cultural change.

A value-driven rationale: Develop a roadmap, linked to key business questions and key performance indicators, that delineates and resolves challenges. This document should clearly show opportunities associated with implementing an information-sharing analytics environment.

An integrated structure and measurable value: Integrate any projects required to implement the deep analytics environment into a common structure for managing progress, as well as measuring and showing value across the enterprise.

A coherent enterprise end-state: Create a single, shared end-state architectural and data governance framework to underpin the delivery of improved access to high-quality information across the enterprise. Encourage information sharing and collaboration wherever possible.

A living implementation: Don't quit after the implementation. The roadmap should be designed to evolve and will require regular updates and maintenance as business needs and competitive pressures dictate.

Changing the way you do business is never easy. It is, however, sometimes necessary for survival. The inundation of information that most companies are faced with, coupled with uncertain economic times and increased global competitive pressures, has made it essential for most businesses to improve their information analysis capabilities.

Implementing a deep analytics solution will help, but it requires a sea change in how companies think both about themselves and about their information. It requires shifting from a siloed information environment, where data holds the key to knowledge and power for individuals, to an environment where data holds the key to corporate success. That's an enormous change - both technically and culturally. Are you ready?

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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