Enterprise Design connects customer-centric product and service development to the enterprise transformation required to deliver. And no, this is not about running another Design Thinking workshop.
Looking back on 10 years of Design Practice experience applied at scale to challenging enterprise environments, Milan will show how to use Design to give shape to enterprises.
Applied in a holistic and systemic fashion, Enterprise Design can help us deal with challenges of innovation and transformation. It acts as the glue between Customer Experience and Business Architecture, and enables us tackle the complexity that makes ambitious endeavours so ambitious – and worthy of applying our collective intelligence and creativity.
Learning Objectives:
- How to scale design practice to the enterprise level using a framework going from the Big Picture challenges to the tangible Rendering (and back)
- Achieving traceability between the Customer Experience Journey, Operational Business Architecture, Processes and Organization
- Take home a framework and approach to drive innovation and transformation projects by design in your own or client organizations
Predictive Analytics, Machine Learning and Artificial Intelligence are hot topics everywhere you go. Every organization wants to turn its digital data into gold. Data scientists are being hired, software is being purchased, clouds are being provisioned. Lots, and lots, of money is being spent to move to a new era of algorithmic, data-driven business!
And yet… not much is actually happening. Most data science projects are still experiments, trials or pilots – some have become shelfware. Outside of big-name internet firms, demonstrated success at scale is remarkably hard to find. Most companies make decisions the same way. Data science is over-promising and under-delivering.
The problem is that data scientists don’t know how to talk to business people or IT professionals. They can talk to the data just fine. They can’t talk to the business to find out what problems need to be solved. They can’t talk to IT to get their algorithms across the last mile of operational adoption.
They can’t talk to the business or to IT – but you can.
Learning Objectives:
- Why decisions matter to ML and AI – and you
- Business rules AND machine learning not business rules OR machine learning
- Why and how to define decision requirements
We all know, by now, that siloed approaches to managing ‘work’ are, at best, suboptimal and at worst a nightmare. With all the changes and pressures organizations face today, getting processes right is more important than ever. Customer experience and value delivery demands it. Information management and data quality is impossible without it. Business performance management and cross-functional business governance needs its aligned framework. Operational risk and compliance programs start with a process structure to establish controls for auditors to assess. Defining SOPs, roles and work instructions for workers are derived from process designs. Software design and development must know the requirements to connect the end to end dots. A digitalized process still follows a business workflow needed to assure data integrity. Agile software development needs a clear process scope and purpose. Culture change requires a framework to enable it to stick.
No matter if you are dealing with enterprise-wide concerns or detailed development of specific business capabilities, a process-oriented approach just delivers better results.
This ‘back to the future’ session will visit the most effective ways to use business processes to deliver better outcomes in all your efforts.
- Enterprise/Business level process practices
- Process design practices for customer value and an agile business
- Process modeling choices for business and software design
- Governance and ownership myths and realities
- The growing usage of processes for concurrently managing performance, risk and compliance
When many teams learn agile ways of working, they focus on the new processes, teamwork, and metrics like velocity.
Teams also struggle with Product Owner availability, carrying over stories, and getting quick and regular feedback; often finding out too late what they built is not what the user really needed. Teams are stuck with a backlog of enhancement and fix requests; innovation and the strategic agenda seems like a leadership fantasy, rather than reality.
Let’s put the customer back into the product and our team’s work! We will discuss analysis from the vision and big picture, to the details of the acceptance criteria. There are so many techniques and practices that teams are missing out on to get great products that customers will love!
Come discover what practices you can put to work on the team to get better visioning, better backlog refinement sessions, deeper conversations about the customer, and a more relevant product.
We will highlight analysis best practices, helping teams connect the agile analysis to the agile values of quick feedback, measuring what matters, limited WIP, transparency, working with business and customer groups, and swarming together as a cross functional team.
We will look at what the common anti-patterns are on agile teams that stifle analysis, and we will look at what teams that are getting great results are doing from a customer, product and analysis perspective!
Agile analysis is connected to great results!
Learning Objectives:
- Understand what good agile analysis is
- Discover and understand the anti-patterns of agile analysis and team practices
- Discover what great product and analysis looks like at the team and organization levels
- Connect analysis to agile team results and agile values
Business agility doesn’t happen by accident. Tinkering with organizational schemes can be helpful, but will not be sufficient. Rapid software development can’t get you there either. You must engineer for change – and your solutions must be scalable and sustainable.
Challenges abound. Organizations are undergoing rapid digital transformation. Most processes and entire industries are being disrupted. How do you survive and thrive in these challenging times?
The Knowledge Age requires major adjustments. Professionals need a laser focus on the knowledge assets that make the company smart – rules, vocabularies, decisions, and strategy. Business agility can be engineered under digital transformation!
- How you should think about business agility
- Why data quality is more of a problem – and a different problem – than you think
- Policy busting – key to business transformation