Dealing with the sheer abundance and acceleration of multi-source data is a huge challenge facing most organisations today. Departmentally there are different platforms used with diverse data sets, and an uptake of multi-cloud strategies to further complicate the matter. Many companies admit that they are still yet to overcome this. How do we tackle the issue of integrating organisational silos and ensure the different platforms communicate effectively? How do we make data more easily accessible and accountable for both employees and customers? With more focus on customer experience and personalisation, it is essential to have a data management layer that has strong analytics capabilities.

How can we achieve this?

It’s been said that the survival of a business is dependent on having an agile and data-centric architecture. Encompassing data collection, governance and security amongst other processes, data architecture addresses the current and future data management needs of an enterprise. Alongside this, the increasing number of disparate databases is generating silos and difficulties with data accessibility.

Join the discussion around producing an effective data architecture.

With the great wealth of data produced daily, the constant battle is ensuring that it is valuable, secure from hackers and compliant with ever-tightening regulations. This underpins all aspects of a data-centric business. As we have already experienced, business data is highly attractive to hackers and we desperately need to mitigate the uncertainties in order to maintain consumer trust and company reputation.

So how do we become more proactive in our approach to data privacy and regulation? How do we identify the vulnerabilities and take the necessary measures to secure these? 

Using data analytics and AI, many have already improved CX and sales by creating more real-time, personalised experiences and recommendations. Can we produce revenue-generating insights while keeping the user's privacy and rights in mind? And have you considered the social impact of commercialising personal data? Join us to discuss how best to map your customer journey to put yourself ahead of the competition. 

Data quality and governance are huge challenges currently facing organisations -- impacting decision making, commercial opportunities and user experience. With such business-critical consequences, it’s no surprise that this is a top priority. This stream will explore best practice for data quality management including how to mitigate human error in data entry, the role of automation and master data solutions. Also how organisations can integrate a watertight data governance strategy to harmonise this and drive consistent, quality data from people, processes and systems.

With a significant skills gap emerging in the industry, and lack of departmental alignment, how and why should we make data everybody’s business? It is fair to say that there is a necessity for cultural change in order to break down organisational silos and to drive a customer-first approach. This stream will explore the importance of developing and implementing a data-centric culture in order to both highlight the opportunities in big data, and accelerate the deployment of new capabilities. The increasing demand for recruiting, retaining and developing talent is essential to this process, especially given the crossover of technical and business requirements of data-focused roles today. 

This session will bring to light the latest case studies and progress in RPA, AI and ML, including the recent uptake of MLaaS. We’ll look at what organisations have done thus far to overcome common obstacles such as data quality, bias and scalability, but also at how to tackle the skills gap and why this is key to mass AI enterprise integration. 

It is not only important how you manage your data, but how and where you store it. Cloud has been at the forefront of this shift in data storage and now data platforms are increasingly being deployed in these environments. As ever, this is not without its challenges including: security, data management and integration across hybrid and multi-cloud landscapes. For data leaders, the burning question is also who will power their AI, ML and analytics strategy; is this the platforms, cloud vendors or the specific big data solution providers? Let’s discuss both the business opportunities and the challenges that cloud and data platforms create, and how to select the best option for your organisation.

Most organisations are continuing to invest heavily in analytics. With the new generation of tools, time taken to generate reports and uncover relevant business insights is already being rapidly reduced. Augmented analytics and DataOps are producing a disruptive wave, increasing demands for analysts to become more business savvy and be able to tell compelling stories with data.

Join the discussion about the evolving world of analytics, the importance of data quality and creating a targeted customer strategy.

Deep learning is highly relevant for enterprise organisations especially with vast quantities of data available for processing. These algorithms, however, are complex and require a lot of nurturing to get right. This stream will dissect the uses of deep learning such as smart assistants or augmented analytics. It will also explore the challenges of training learning models and the interoperability between neural networks.

The importance of securing data cannot be understated especially as AI-driven analytics gains evermore personal insights, and tools such as facial recognition track individual whereabouts. The first steps to legislating privacy have begun, but all businesses have an obligation to use consumer data ethically and protect against attack. So sit down with your security team and work out a plan of action.

What are your responsibilities? What legislation will impact your latest deployments and what resources you need to protect your reputation, assets and customers?

To be competitive, customer experience needs to be at the forefront of your development objectives. As seen with the introduction of smartphones, immersive experiences will soon be the next evolution seamlessly integrated into our everyday lives. For many organisations VR, AR and MR aren’t a major consideration and for those exploring, it’s often in silos. But, there is a real opportunity to get ahead, take your customer experience to the next level and educate yourself. This stream will support your CX strategy and share new developments in self-service, enhanced personalisation as well as showcasing how AI can create successful multi-channel customer experiences.

Process automation, though not AI, is an important tool for organisations especially in the finance and retail world. RPA or robotic process automation can revitalise employees by doing away with their repetitive, mundane tasks and inspire productivity to work alongside the bots. This stream will explore the opportunities for organisations including for customer onboarding, monitoring and controlling risk and identifying potential fraud as well as the new possibilities when we add a layer of intelligence and empower our employees.

For those early adopters, AI is already delivering positive ROI. The bulk of use cases have created smarter products, optimised internal operations and enhanced decision making; however organisations are faced with a skills gap to manage these new projects and business models. This stream will share the successes, challenges, types of delivery platforms and the skills needed in-house to embed AI into the very core of your organisation. Also hear where businesses are investing in AI next and whether the concerns around cyber security will hold them back.

AIOps is the next buzzword you’re scratching your head over and calculating whether it really can live up to the promise of improving IT productivity. Said to drive efficiencies and real-time insights for networking teams, containers and even cloud environments, this stream is set to expand your knowledge and awareness of deploying AI in IT operations.

What are the benefits, challenges and case study examples of the impact on digital experience?

Combined with machine learning, analytics enables organisations to get deeper and faster insights. With the growth of adoption, let’s explore the best approach and solutions for your business users and support the hard work of your data scientists.

Also what’s next for analytics with machine learning and will it impact the role of business intelligence solutions?