Big Data Streams for 2020


Following on from our first round of rigorous industry research, we are delighted to present our new streams for 2020.

If you’re interested to present your story within one of these, make sure you contact our Producer, Katya Hoile, on:

Data management and integration

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?

Data architecture and databases

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.

Analytics, DataOps and decision-making

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.

BI and data discovery

Experiencing data silos is nothing new for organisations, but the rise of machine learning and data discovery might be the solution - helping to reduce the time to sift through data and find valuable insights that can be exploited for competitive advantage. The challenges for BI, however range from ensuring we have diverse data sets to avoid bias in decision making, to helping scientists centralise their insights with actions. We will also be exploring BI platforms, self-service and embedded analytics, and using AI to find the patterns and outliers in data.

We will look at why this is important to ensure your information drives business value and improves customer experience. 

Data privacy, data security and regulation

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? 

Customer experience, engagement and targeting

Data is our most important asset. Using data analytics and AI, many companies have already been able to improve on sales and customer loyalty by creating more real-time personalised experiences and product recommendations. From measuring engagement levels to predicting market trends, big data and analytics can be exploited for accurate customer targeting, personalised marketing campaigns, and gaining a better insight into consumer buying habits.

Join the discussion on how best to map your customer journey, understand where to concentrate business efforts, and gain competitive advantage.

Data Quality and Governance

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.

Data-centric culture and talent

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. 

Commercialising data and ethical considerations

In a world where data is the new oil, it’s no surprise that leaders are looking to capitalise on this. Who are the market leaders who have turned raw operational and customer data into new streams of revenue? How can we follow in their footsteps? As great as this is, it inevitably brings rise to ethical considerations. If the personal information is removed from the data, is it then okay to monetize? With an increasing focus on customer experience across industries, how do we ensure transparency at all stages when using this data? How do we gain revenue-generating insights while keeping the user’s privacy and rights in mind?

Cloud, storage and data platforms

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.



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