Why do executives sometimes fail to act on proposed ideas that could save a company substantial amounts of money? Is it due to their complacency or incompetence? I am unsure of the correct answer. I want to give executives the benefit of the doubt. Most executives were promoted to executive positions because they are smart and are effective leaders. They have made good decisions in the past.
But is there change in the wind? I sense that an explanation for less risk-taking by executives, such as they’re not acting on a proposed idea, involves the emergence of business analytics and big data. It can be explained with a pyramid depicting how a shift in power and influence from executives to employees is increasingly affecting various types of decisions.
A Power and Influence Pyramid
Executives can no longer hoard decisions at the C-suite level. Savvy executives are realizing they must now delegate and distribute decision rights deeper into their organization to empower their managers and employees. This is because of the exponentially growing mountain of data, both structured (numbers) and unstructured (text) data including social media, and a sped-up and volatile world. In my imagined pyramid, the executives are at the top, just like in an organization chart. Their decision types are strategic ones. These decisions are few in number but can have large impacts. For example:
- What is our organization’s mission?
- What products and services should we offer to maximize value to our constituents?
- What altered strategic direction should we navigate our organization toward as change occurs?
In contrast, at the lower levels of the pyramid are operational decisions that should be made by employees who ideally have had their organization’s strategy communicated to them by the executives via a strategy map, balanced scorecard and dashboards. Operational decisions are daily, even hourly, and often affect a single transaction or customer. In the sales and marketing functions, operational decisions maximize customer value much more than policies do. Examples may be what deal to offer a particular customer or whether to approve a bank loan. The daily decisions are arguably what actually move the dials reflecting an organization’s performance. Further, although much is now written about enterprise risk management , the reality is that an organization’s exposure to risk does not come in big chunks. ERM deals more with reporting. Risk is typically incurred one event or transaction at a time.
Big data can be described as a collection of data sets so large and complex that it becomes difficult to process using standard database management tools or traditional data processing applications. The challenges include data capture, validation, storage, search, sharing, analysis and visualization. With the emergence of big data, the base of this pyramid is widening, and executives realize it is futile for them to try to explore, investigate and comprehend this massive treasure trove of data. This is why the role of the analyst (think “data scientist”) is emerging as being mission-critical. Executives cannot do it all. They must now delegate decision-making and provide analytical tools and capabilities for their workforce to gain insight and foresight to enable better decisions.
Operational decisions scale from the bottom up, and in the aggregate they can collectively exceed the impact of a few strategic decisions.
Performance Improvement Levers
In contrast to strategic decisions operational performance improvement actions are the consequence of thousands of daily decisions made by employees. There are two powerful levers for performance improvement and, more specifically, for the execution of the executive team’s formulated strategy: as mentioned, clarifying decision rights, and designing effective information flows.
1. Clarifying decision rights. As organizations grow in size, the approval process gets complex and foggy. Employees become unsure where one person’s accountability begins and another’s ends. Workarounds then subvert formal hierarchical reporting relationships. Clarifying who has what decision-making authority and empowering decentralized decisions lower into the organization brings mission-critical agility – as long as trust is given by the executives and second-guessing by supervisors is minimized. But with more decision rights must come more accountability with consequences. This is the domain of performance indicators against targets and of motivational methods (e.g., bonus incentives).
2. Designing effective information flows. Decisions are based on information. Too often information flows are blocked by organizational silos. Information does not sufficiently flow horizontally, thus hindering the spread of best practices. Collaboration is important and enabled by cross-functional information flows. To complicate matters, logical and judicious decisions can be constrained by the type and quality of information available to employees. Some organizations simply have inconsistent and poor-quality data. Even with a new transactional business system, such as an enterprise resource planning or customer relationship management system, organizations drown in oceans of data but starve for information in a form that business analytics can mine and that can be quickly interpreted in the context of a problem, opportunity or needed decision.
Business Intelligence Does Equate to an Intelligent Business
Executives may be brilliant strategists, but strategists need foot soldiers to carry out tasks. The higher the executives are, the less they can know about what is happening. Yes, there can be summarized reporting and executive scorecards and dashboards. But monitoring the dials is not the same thing as moving the dials. Moving the dials comes from actions, projects and initiatives. Those things involve decisions.
The era of widespread use of analytics is in its earliest stage. If competency by the workforce with analytics is not now a top five priority with an organization, just wait a couple of years. It will be. It is a competitive edge.
About the author
Gary Cokins (Cornell University BS IE/OR, 1971; Northwestern University Kellogg MBA 1974) is an internationally recognized expert, speaker, and book author in business analytics and enterprise performance management systems. He is the founder of Analytics-Based Performance Management LLC, an advisory firm located www.garycokins.com. He began his career in industry with a Fortune 100 company in CFO and operations roles. He then worked 15 years in consulting with Deloitte, KPMG, EDS, and SAS. He has authored several books, and continues to write articles and blogs on EPM, managerial accounting including activity-based costing (ABC), and business analytics. His next book is “Predictive Business Analytics” due out in October 2013, published by John Wiley & Sons.
Article originally published on Information Management