The field of data analytics is dynamic with rapidly evolving innovations. To realize the potential of an enterprise-wide analytics program, leaders and managers at all levels need strong data literacy. Providing opportunities for continuous learning and sharpening of skills is necessary for a robust analytics enterprise able to reliably deliver measurable value to the organization.
Why is Data Literacy Important?
To adopt a data-driven culture, executives and top employees must be familiar with the capabilities as well as limitations of technologies in data science, artificial intelligence, and machine learning and be able to relate them to their unique business’ challenges.
One consistent theme that we have heard from executives was that their people struggled to communicate the meaning and business value of insights derived from data. Being data literate means being able to synthesize information, draw conclusions from the data, and convey the key findings to non-specialists.
Organizational leaders who are data literate can recognize opportunities to derive value from their data while avoiding the hype. They can define and effectively communicate the business value of Data Science or Machine Learning initiatives to key stakeholders to gain support for these programs.
Business leaders need to make well-informed decisions to support their goals and grow their business. They often have multiple data sources available capable of helping assess the value and risks of different options, if they can be understood. Data can reveal limitations or biases in our thinking, so it is important to ensure that the data accurately captures the behavior of a representative sample of the population (e.g. customers or other stakeholders) about whom insights are required. Data literate leaders understand these hazards and can avoid them to make more effective decisions.
Data literate managers understand their operational data and the information they convey about the health of the business. They know which metrics matter and learn how to manage them to drive future success. Choosing the right metrics matters because those are the goalposts used to measure the success or failure of analytics initiatives.
Becoming more data literate – at any level in an organization – can be achieved through self-study, training, and from trusted expert consultation.
Resources for Executives
The mission to become a more data-driven organization will succeed if it is broadly evangelized and supported financially by the executive leadership team. Yet, many executives find it difficult to effectively communicate their analytics strategy to employees who are not used to working in a data-driven environment, and who don’t understand how to implement that strategy in their day-to-day work. Fortunately, there is a wealth of resources available to help executives become more data literate. Our recommendations follow.
Executive Courses
These seven online courses successfully provide executives with immersive primers on (different aspects of) practical applications of data and business analytics.
- Coursera
- Elder Research workshops for executives:
- Data is the New Currency – Discusses the strategic importance of a data strategy governing enterprise data. The lessons contrast the positive impact of trusted, readily available data with the negative impact of unmanaged, siloed data. Most enterprise data is not readily suitable to answer critical analytic questions. Done right however, analytics can harness its enormous value.
- Data Science 101 – Helps executives identify where to best apply data science to inform decisions and support corporate strategy. It shows how to translate model results into financial terms, explains how building data science models differs from traditional project execution, and covers the key analytics teams roles and responsibilities necessary to achieve success.
- Machine Learning and AI – Explains machine learning and artificial intelligence and how they differ from with traditional analytical and statistical methods. The concepts of supervised and unsupervised learning and other powerful and relevant techniques are discussed, supported by actual business examples.
- Model Accuracy and Interpretation – Explores model accuracy and how to quantify it. This is necessary to achieve and communicate realistic expectations of model performance. It covers the common biases and pitfalls that breed overconfidence in model predictions, highlights ways that machine learning can go wrong, and considers important aspects of model validation and interpretation that are essential when discussing model performance with regulators, compliance managers, and business leaders.
- Data Governance and Delivery – Explores the connection between the data strategy and governance of data and models. The goal is to demonstrate how to increase the quality and velocity of data through the organization while demonstrating system control to manage operational risk. It also explores how to deliver key analytics findings in intuitive ways using interactive graphics, custom software applications, and creative visualizations to facilitate rapid adoption.
Books
These five books are especially useful for executives to learn from analytics experts who are also business experts. They skillfully navigate the intersection and integration of analytics applied to business decision making:
- Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics by Jeff Deal and Gerhard Pilcher
- Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, and Marlene Jia
- Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage by Douglas Laney
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel
- Data Analytics Basics for Managers by Harvard Business Review
Articles
These three short journal articles are very efficient resources for analytics managers and executives:
- Achieving Business Impact with Data (April 2018, McKinsey.com)
- Advanced analytics: Nine insights from the C-suite (July 2017, McKinsey.com)
- What’s Your Data Strategy (May 2017, HBR.org)
Resources for Staff
An organization’s best employees are eager to increase their capabilities and provide more value through data-driven decision making. Providing continuing education opportunities can be a strong motivator and increase employee satisfaction. Course topics range from project management to deep technical dives and are available through respected online resources such as coursera, datacamp, and udemy. Statistics.com, an experienced training company owned by Elder Research, provides collaborative, expert online instruction from leaders in the field of data analytics and statistics.
Elder Research greatly respects the value of continued learning and makes many resources available to its employees. Each week, Elder Research hosts a company-wide “tech-talk”, where internal or external speakers share presentations, and projects, techniques, challenges, and new ideas are discussed. Allocating regular, scheduled time to employee development promotes comradery and facilitates knowledge transfer across the organization. Focused analytics training helps develop a core foundation and enhances a team’s technical and non-technical knowledge base. Also, we have found that with clients, colleagues, and other stakeholders as well, a deeper understanding of the technology and data can greatly help with the adoption of analytics insight in their decision making.
Data literacy matters. Starting with the executive leadership, it must flow through an organization and become engrained in every business process. As understanding grows, so too does the use of data as an asset to improve decision-making and deliver business value.
