Session ONE- Data leadership, strategy and governance
- Best practices and tools for Machine Learning, exploiting emerging tools and technologies like AI and understanding how they are influencing data science
- Using data to drive innovation, implementing an enterprise-wide data strategy, cultural transformation to become data-centric
- Data engineering and architecture
- Gaining real-time actionable insights from your data
- Managing data science teams, breaking down siloes
- Ethics, security, privacy considerations
- Data design, interactivity, visualisation
- Turning data into new visibility and business intelligence
The Conference Chair's Opening Remarks
Making data analytics work for you
Organisational design in people and processes is as important, if not more important, than technological design and implementing big data architecture. Without doing so you will not realise the value in the architecture you have built, and the infrastructure designed to support the analysis of big data.
In our opening address we explore:
- Building capabilities and making your organisation data-centric
- The importance of people and processes
- Who is using big data, why and when?
- How to measure organisational performance
- Breaking down siloes and building collaborative data science teams
Creating a customer-centric culture from the top down
Increasing competitiveness within the enterprise landscape requires a refining of the systems by which the people, processes and technology within your organisation maintain a consistently engaging dialogue with your customers.
Traditional brand loyalty is dead, and your organisation will be too if you don’t develop an enterprise-wide data-centric strategy designed to keep customer value at its core.
- What data is necessary to establish a 360-degree view of the customer that supports a singular company culture
- Investments in critical IT to support data-driven insights
- A series of value-based metrics that define the success of the company’s customer-centric culture
Pivotal roles in data management: Defining your data governance model
Well thought out data governance roles and responsibilities lie at the heart of successful data governance programmes.
Before beginning a process of defining the roles and responsibilities of the data stewards within your organisation and moving towards the establishment of a model of data governance, its important to define an operating model which can be assimilated into your business structure.
- Executive, strategic, tactical, operational & support roles
- How to recognise your stewards
- How to apply roles consistently through all phases of your programme
Achieving C-suite level buy in: Why it’s critical to becoming a data driven organisation
Those who are tasked with making data analytics work, and for it to be shown to be working to senior leaders, are often asked to highlight its value as quickly as possible to justify investment in the tools necessary to fulfil the ambition of being truly data driven.
The truth is, when you finish building your big data infrastructure and you’ve established how to secure big data and analytical tools, you need to figure out who’s going to use it and how, why and when, as well as the types of analysts required. You will also need to understand the quality of the data, its cleanliness and whether the model through which it is inputted is fit for purpose; only then can you begin to achieve the appropriate level of buy in.
We discuss practical ways in which you can achieve buy-in to your big data programmes from the very top.
Data security: AI driven privacy and protection
Enterprises engaged in digital transformation journeys are required to manage an increasing volume of data for the purposes of maintaining regulatory mandated privacy and protection from incalculable threats. A more robust compliance framework requires an aggressive security posture which many organisations find challenging.
Equally, the growth in and proliferation of data across the enterprise means every department, element of the business and overarching purpose of activity is dictated by the insights gained from achieving value in data.
With the deployment of AI, we explore:
- Private and sensitive data
- Data mining
- Data movement (data in transit vs data in rest)
- Linking identities
- Analysing risk, anomalies, fraud detection
Questions To The Panel Of Speakers
Morning Networking and Refreshments served in the Exhibition Area
How data can help you to optimise every decision, process and action
To ensure that meaningful action is achieved as ever greater volumes of data are processed, enterprises should establish cross-functional programmes under which they can prioritise business process optimisation.
We address the key pillars required to achieve optimisation in data & analytics leveraging.
- Select process partners
- Establish process methodologies
- Implement process platforms
- Leverage process accelerators
- Deploy process applications
Big Data Strategic Decisions: Moving from Analysis to Action
Data is ever-present, and the implications are endless. It can help you determine who to hire, what prices to set, what supply source to focus on, and assist you in structuring your budgetary requirements.
Strategically informed big data decisions, analysis and action gives you the frameworks and tools, innovations and insights to make better decisions and compete in the age of big data.
- Data-driven decision-making essentials from conceptual frameworks and tools to design thinking, agile, and data visualisation.
- The implications of the latest developments and future of big data from machine learning to AI whilst illuminating the risks, limitations, and ethics of big data.
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
(To view topics see the seminars page)
Networking Lunch Served in the Exhibition Area
Session TWO – Architecture, innovative technology and techniques
- Securing your big data environment
- Machine learning production and deployment
- Digital ecosystem and driving digital transformation
- IoT & big data
- Making sense of data lakes
- Big data within the context of security
- Emerging models of data lakes
- Big data workload management
The Conference Chair Opens the Afternoon Session
Securing your big data environment: addressing the concerns and counter measures
How do we address the positive of breaking down silos whilst addressing the gaps in security this creates? How do you achieve value in your security architecture without resorting to a multi-platform, multi-system, and silo heavy environment?
- Securing your big data enterprise hub
- Developing strong access controls
- Strong authentication for both users and systems
- Full audit lineage
- Compliant protection through encryption
- Reducing complexity
Machine learning in production: From development to deployment
Enterprises face various challenges when developing algorithms for putting machine learning in production. This is especially true given that machine learning is an experimental and vastly exploratory process, whereas the demands of deployment are centred on providing consistent results that are secure and effectively managed.
When problems arise, many enterprises make the mistake of relying on the data team alone for resolving such issues which typically occur during the machine learning journey from development to production when in fact it should be treated as a joint exercise between data science and operations teams.
We address the value in having operations involved in the ML journey from the beginning rather than simply being the recipients of such development, how this can lead to an appreciation of each department’s roles and responsibilities and how better collaboration leads to agreed guidelines by which optimisation in ML deployment can be achieved.
IoT and big data: establishing new ventures and overcoming existing barriers
IoT spending is set to increase by 15 percent to reach $772.5 billion by the end of 2018. It is clear that the IoT is set to become even more influential within the enterprise environment through greater connectivity, greater divergence from existing business automation processes and the creation of entirely new revenue streams.
The most intriguing element of such progression is the increasing realisation across enterprises that IoT alone is not truly disruptive without combining with AI, blockchain and fog computing. By combining such technologies with IoT, enterprises will increasingly be able to realise the value in such investment and establish streams of revenue in previously unimagined contexts.
We explore how you can overcome previous barriers to adoption, security, bandwidth and data analytics through greater IoT integration with existing technology. We also look at its growing potential to drive new value propositions and enable new business models.
Big data through a security lens
How do we continue to gleam valuable information from big data processes whilst securing such data from the methods by which we store it?
- The deployment of big data in place of SIEM as a cost-effective measure
- Providing a single data store for security event management and enrichment
- Detecting and preventing advanced persistent threats through big data analysis
- Anomaly detection through feature extraction
- Consolidation and analysis of logs automatically from multiple sources rather than in isolation
Questions to the Panel of Speakers
Afternoon Networking and Refreshments served in the Exhibition Area
Emerging models of data lake storage
Given that 60% of companies admit to being unable to take full advantage of the data they hold, the need to work with big data smarter and faster is evident.
80% of data lakes are increasingly being viewed as not including effective metadata management capabilities. This has produced a momentum towards a more intelligent data lake solution which can find, prepare and protect data for analysis.
Given that many data lakes initiatives were rushed out, with the result being that many have turned into poorly performing swamps, the need to develop intelligent lakes has never been more pressing as enterprises face the challenge of modernising their architecture and big data initiatives.
We discuss how you should be governing, mastering, managing the data in your business data lakes in light of new technologies.
Using kubernetes for big data workloads
Given that big data is now a fully normalised element within the enterprise environment, organisations are increasingly seeking to address the most productive way in which they can support large database workloads on containers. Currently, a particularly popular way in which to optimise the container ecosystem is with the use of kubernetes because it enables you to launch production workloads at scale.
Shifting from a host-centric to a container-centric infrastructure makes it easier to take advantage of the portability and flexibility of containers.
We discuss ways in which your organisation should be
- Managing a broader range of workloads
- Data processing & container orchestration through a single interface
- Improve server utilisation
- Better isolating between workloads
- Allowing nodes to support batches of varying latency & sensitivity
Using Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance
Whilst the value of big data is evident, the most effective way to capture it is not. We explore:
- Choosing the right solutions and tools to access your data
- Discovering hidden connections in your data
- Giving everyone access to Big Data – democratising data ownership
- Turning your data into a story
- Monetising your data in an ethical way
Questions to the Panel of Speakers
Closing Remarks from the Conference Chair
Conference Closes, Delegates Depart
Whitehall Media reserve the right to change the programme without prior notice.