Categories: Agile, Benefits Realization, Best Practices, Calculating Project Value, Change Management, Complexity, Innovation, Leadership, Leadership, Nontraditional Project Management, Portfolio Management, Program Management, Project Delivery, Project Planning, Project Requirements, Roundtable, Strategy
By Peter Tarhanidis
Artificial intelligence is no longer a tool we’ll use on projects in the future. Right now, many organizations are formalizing the use of advanced data analytics from innovative technologies, algorithms and AI visualization techniques into strategic projects.
The maturity of advanced data analytics is creating an opportunity for organizations to unlock value. The McKinsey Global Institute estimates AI’s global economic impact could climb to US$13 trillion by 2030.
As an example, in the healthcare industry, Allied Market Research reports rising demand for data analytics solutions due to the growth in data from electronic health records, among other factors. The global healthcare analytics market was valued at US$16.9 billion in 2017, and the report forecasts it to reach US$67.8 billion by 2025.
The Evolution of AI Maturity
Gartner describes four growth stages of analytics and value activities. The first is descriptive analytics, which gains insight from historical data on what occurred in the firm or a project. This includes key performance measure reports and dashboards. Second, diagnostics analytics allow you to learn why something happened and the relationship between events. Third, is the use of predictive analytics to develop viewpoints into potential future outcomes. Finally, prescriptive analytics allow you to provide users with advice on what actions to take.
Everyday examples of these solutions range from simple automated dashboards, remote check deposit, Siri-like assistants, ride-sharing apps, Facebook, Instagram, autopilot and autonomous cars.
Tips on Successful Transformation
Leaders must consider advanced data analytics as a transformational journey—not a complex project. Without thoughtful consideration of the implications of managing AI projects, one may create chaos in adopting these new services.
As a project leader, take these steps to avoid key pitfalls:
- Develop your understanding of data science tool kits and technologies and identify any centers of excellence. Start with basics such as descriptive statistics, regression and optimization techniques. You’ll also want to familiarize yourself with technology such as machine learning and natural language processing.
- Determine how these AI initiatives integrate into the organization’s mission and vision. This may require a new strategic business plan, optimizing an organization, culture change and change management.
- Establish a data governance body and framework to ensure accountability, roles, security, legislative and ethical management of consumer, patient, customer and government data.
- Develop strong multiyear business cases that clearly indicate cost versus revenue or savings.
- Maintain an agile mindset and leverage design thinking methods to co-create the pilots into products alongside stakeholders.
Please comment below on what approaches you have taken to enable advanced data analytics in your role or in your organization.