Session ONE – Becoming a data-centric organisation through value extraction & developing your digital eco-system
- Building & delivering your data and analytics strategy
- Defining organisational capabilities & requirements
- Bringing harmony to your data flows
- Improving the relationship between producers & consumers
- Big data & analytics governance modelling
- Advancing ML & AI through augmentation
- Achieving trust in automated decision-making processes
Conference Chair’s Opening Address
Building & Delivering your Data and Analytics Strategy
One of the most consistent queries which occupy the thoughts of enterprise leaders is ‘’how do I build and develop our data and analytics strategy’’. One of the primary reasons for such concerns is the speed at which data and analytics is changing, which in turn complicates the way businesses seek to utilise its capabilities and presses the need for the development of both workforce upskilling and organisational restructuring.
Our opening address explores how best to build a digital strategy which fully incorporates data and analytics, how incorporation supports a holistic approach, enables transformation and delivers on key business objectives through identification and dissemination of best practice.
Defining Organisational Roles and Responsibilities
In pursuit of becoming an increasingly data centric business, key enterprise decision makers and influencers are increasingly looking to organisational roles and responsibilities to identify strengths and weaknesses within data and analytics teams. Equal to this is the need to continuously monitor and develop your teams through upskilling and performance measurement.
We address the key roles and responsibilities required for success in data and analytics, how such roles and responsibilities are situated within an enterprises organisational structure, the forces which necessitate change and the impact on skills.
Harmonising your big data flows: turning raw information into actionable insights
As organisations seek greater collaboration across departments and disciplines to collect and analyse data, they must be prepared to manage and eliminate the barriers which present themselves as they seek to provide data in a form which is usable across departmental workforces which are characterised by various capabilities and disciplines. Coupled with the seemingly exponential growth in collection points and volume, the demand for valuable insights is outstripping capabilities.
- Data engineering as a solution
- Addressing the gap in delivering data
- Creating a flow from experimentation to production & consumption
Data operationalisation: the relationship between producers & consumers
All organisations experience similar dilemmas with data which go beyond the typical issues of sourcing, recording and governing. The fragmented relationship between producers and consumers is regarded by many as the primary cause for concern which reinforces existing difficulties. Defining the how and why of data creation, what it represents and the value to be gained all lie within the space between producers and consumers.
We address the increasing significance of DataOps, its potential to resolve the disconnect and how you can begin road mapping implementation.
Effective data & analytics governance for dynamic performance
Just as all elements of the enterprise D&A landscape must ensure that they are fulfilling their roles and responsibilities, so too must your data and analytics governance model move beyond being viewed as a static, policy centric factor.
To ensure successful implementation and enforcement of the policies designed to give character to your governance model its important to allow such policies to be both flexible and context relevant.
- Adaptive governance practices
- How such practices support business initiatives
- The damage of maintaining current practices
Questions to The Panel of Speakers
Refreshment Break Served in the Exhibition Area
Deepening the relationship between ML & AI through augmentation
As a leading disruptive trend, augmented analytics has the capability to leverage machine learning automation and AI, which in turn can advance the means by which data is sanitised, how initiatives are generated and how data scientists and the models they operate within are established.
As an approach which generates insights using machine learning, augmented analytics is rightly regarded as a leading asset in the data and analytics market. As a result, enterprise leaders now need to begin adoption at a greater rate to meet the maturing capabilities of their digital platforms.
We address how you can begin to better leverage this key trend.
The anatomy of high technology decision making processes
Given the high altar upon which disruptive technology has been placed, it is easy to forget that we still require the ability to gain an insight as to how automated decision-making processes are conducted and the extent to which we can trust in the results achieved. Contained within such conclusions can be harmful biases which impact on regulatory compliance, your ability to innovate a product offering following automated consumer feedback which is misaligned with reality and inaccurate financial forecasting.
- How to build trust in your processes
- Which models to deploy in decision making
- Understanding the models at your disposal
- Knowing when to apply decisions reached
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
Networking Lunch Served in the Exhibition Area
Session TWO – Future proofing your investments with the right technology, architecture and infrastructure
- Data lake modelling
- The logical warehouse
- The disruptive role of IoT
- Big data infrastructure evolution
- The impact of digital twins on enterprise apps
- Self-service analytics for advanced insight
- Deploying continuous intelligence tools
Conference Chair’s Afternoon Address
Data lakes: addressing the lack of maturity in current models
All organisations want a data lake which supports the four key elements of big data collection. Sadly, as is becoming increasingly obvious, the environment in which many exist lack the maturity necessary to support businesses as they look to grow and secure their future big data needs.
- Why data lakes fail
- The traps to avoid
- The primary reasons behind failures
- How you can detect and make corrections
Adding logic to your data warehouse
The traditional data warehouse remains foundational both in the service it provides and the base it provides for analytics programmes. However, just as other previously popularised methods by which data is sourced, analysed, produced and consumed are being disrupted by emerging technologies, so too is the increasing demand for new types of data warehouses.
We explore the evolution of warehouse architecture, how to add new infrastructure to existing architecture and what it means to add logic to your warehouse.
Securing your big data & analytics processes against IoT
Whilst the challenge of big data, both in terms of its size and complexity is well accepted, many enterprises have yet to fully realise the challenge posed by IoT and the way in which it disrupts the collecting, storing and processing of data. Equally true is the reality that, done properly, enterprises can use such disruption as a business advantage as IoT possess the capability to gain and present insights which are out of your line of sight by generating data heavy insights.
- IoT as a driver for innovation
- How IoT establishes new business proposals
- IoT as advanced analytics
- How to address disruption positively
- Assessing critical capabilities
Preparing your big data infrastructure for the future
At the centre of any digital transformation strategy is the need to assess the current capabilities of your infrastructure, what additions will be required to develop for the future and how to best resolve legacy issues without adding complexity and impacting interoperability. Ensuring that your infrastructure supports your business operations across the global enterprise landscape is vital.
We address, the forces behind digital transformation which support advances in data infrastructure, how this informs database management, creates adaptation in existing technologies and supports the emergence of new technologies.
Questions to the Panel of Speakers
Afternoon Networking and Refreshments served in the Exhibition Area
The impact of digital twins on enterprise applications
The use of digital twins, whilst currently in a state of proliferation due to it being regarded as a recent innovation, will shortly become normalised across the enterprise network. Such a development will help deliver substantial business benefits, from predictive behaviour which positively disrupts existing investments, from infrastructure, applications and data.
- The significance & character of digital twins
- The impact it has had on business thus far
- Future projections
Creating the right environment for self service analytics
Whilst current analytics and business insight tools are deployed across many organisations, truly agile, well governed and impact minded self service tools are still out of reach for many. Many are torn between being overly centralised or too anarchic in their deployment methods.
We address, identifying best practice starting points, how to manage evolution and how best to manage necessary expansion.
The pursuit of continuous intelligence
For many enterprises, the achievement of continuous intelligence remains elusive as they seek to better revenue generation, the allocating of resources, the relationship with their customers and other important elements. For those who have managed to properly deploy, the benefits range from quantifiable benefits in revenue, dramatically improved customer relations and reliable business performance metrics.
We address, best practice for deployment, the reason behind its popularity and what makes for a successful implementation.
Questions to the Panel of Speakers
Closing Remarks from the Conference Chair
Whitehall Media reserve the right to change the programme without prior notice.