- Poor Decisions Impacted by Bias in Software Development
- Organizational Culture Changes and Impacts on the Individual & Decision Making
- Using Data Analytics to Improve Heath Care
- Is Your Organization Using Evidence-Based Decision Making?
- Exploratory Data Analysis with Watson Analytics
- Simple Predictive Modeling with Watson Analytics
- Rapid Reporting and Visualization Development with Watson Analytics
There are more ways to access data through a large amount of software tools than there has ever been in the history of analytics. We have access to a wealth of information that, if used properly, can help drive decisions and bring about success within organizations. Why then are so many top-level executive decisions driven by intuition and gut feel?
There have been countless examples over the last decade of commercial failures that could have been avoided if a greater analytical approach had been adopted and used in evidence based thinking and decision making. Unfortunately adopting and integrating analytics into the operational and strategic decisions has it’s challenges. Two of the largest hurdles to successful integration of evidence-based decision making fall within the organizational culture and awareness or education.
Culture and Awareness
The biggest hurdles in adopting a more ‘evidence-based’ approach are cultural change and education. Changing an organization’s culture is one of the most difficult leadership challenges. Denning (2011). In any organization, the culture is the heart of what drives innovation and attitudes towards change. In larger firms, cultural change can be even more challenging than smaller nimble organizations. It can remain a hurdle in driving attitudes towards analytical thinking and decision making. Yet culture has been shown to be one of the biggest factors in fostering evidence based decision making.
Having the not only the right tools and resources available to build knowledge around analytical thinking and evidence based decision making can also be a major challenge. Education and awareness need to be promoted from within and the right training programs and resources need to be made available within the organization. Without the support of management for investment of such tools and resources, limited access to analytical education and training will be a huge challenge in moving to and promoting evidence based decision making.
The Analytics Talent Dividend is an MIT study that points out where deficiencies often lie within skills. The study highlights three main inhibitors: Turning analytical Insights into Action, Aggregating multiple data sources, Lack of appropriate analytical skills. The biggest skill gaps include translating analytics into strategy, asking the right questions, and knowing what data to analyze.
There are of course other obstacles that need to be overcome, and adoption of analytical approaches does not happen overnight. It is however important to begin accessing yourself and your own organization. Are you on the path to a better evidence-based decision making process? What stakeholder buy in is needed to get there? Ultimately, those organizations who learn to adopt analytical approaches and realize the benefits of integrating data and evidence into their strategy will be the ones that deliver higher earnings growth, higher revenue, and higher return on invested capital.
Assessing Analytical Maturity
So how do organizations measure how well they are moving towards analytical maturity? First and foremost, a methodology is needed in order to measure overall progress. Secondly, Key Performance Indicators (KPI’s) need to be measured to determine overall success. There are three popular models that can help:
The assessment uses a likert scale, which measures readiness, ability, and capacity to locate and apply insight, resulting in better decisions and outcomes. Results are grouped into novice, builder, leader, and master ratings. Also measures ability to act based on understanding of history and context from the past and ability to make insightful forecasts. In their findings, IBM discovered that analytical mature organizations are more likely to:
- outperform non-mature competitors
- rely on analytics rather than intuition
- yield higher earnings growth, revenue, and ROI
This model provides category scores and factor scores ranked at the beginning, developing, or advanced level. It helps set new analytics goals and achieve them, showing results in easily understood graphs and charts. The model will also let you track progress over time.
This model focuses on four challenges:
- Data governance and data management
- Skill sets
- Cultural and political issues
The assessment places results into five categories or stages. These include nascent, pre-adoption, early adoption, corporate adoption, and mature visionary. TDWI stresses that a chasm exists between early adoption and corporate adoption, which is where much of the challenges lie in promoting subscribing to evidence-based decision making featuring analytics.
No matter where you are in analytical maturity and how your organization manages their decision making process, there is no time like the present to begin the journey. The benefits are clear and the tools and resources are available to help get you there. The models above provide a good starting point in assessment. Where you go from there will determine your organizations position in the data-driven environment we all work and thrive within.
Image Credits: Photo by rawpixel on Unsplash.