Paid | Big Data Strategies

• Accessing the power of unstructured data through machine learning
• Creating business value and actionable insights from unstructured data
• How images, video, speech and text can all be better recognised and processed
• Key use cases and data sets that can benefit most from a ML approach

• Establishing evidence-based decision making as a core part of the digital workplace
• Defining a clear strategy to leverage data whenever and wherever possible to enhance business efficiency and effectiveness
• Putting big data at the heart of your business
• Closing the data literacy gap, hiring talent and getting buy in from the top.

You know the feeling. You’re in a meeting and the presenter is sharing data-driven insights. But they make no sense! It’s not the data that’s the problem, it’s the way they are presented. This is the Last Mile of Data Analytics. All your investment in people and technology means nothing if you do not effectively share the insight with others, in order to drive better decisions. Designing charts for meetings, presentations or even keynotes is a learnable skill. Andy Cotgreave, author of the Big Book of Dashboards, and Computing’s Analytics Professional of the Year, has sat through thousands of meetings, and seen many crimes committed against data. He’s even committed many himself. He’s also seen how effectively it can be done, with a little bit of effort. In this keynote, Andy will share his tips on presenting data effectively. You will leave with a bunch of easy-to-apply tricks to make your charts punch home their message in every meeting or presentation you lead.

Making data science a success is really hard with up to 85% of projects and initiatives around big data and data science failing, according to Gartner. The reasons are complex but often misunderstood. As project management begins to grapple with the opportunities presented by data science those responsible for implementation need to proceed with care.

What is so different about data that it needs new approaches? This talk will focus on the requirements for data science success and looks at a future after the hype:

• Motivation: Vanity project or aligned business strategy with senior leadership buy-in?
• Requirements and preparations: Solid foundations or duct taped data silos and constant fire fighting bad data?
• Hiring: Unicorns with the right skill sets to be a commercial data scientist or expensive mis-hires?
• Delivery: Models in production serving business needs or undocumented proof of concepts on laptops?
• Retention: Roadmap of game changing projects or abandoned team and expensive write-offs?

Data is captured and used across all functions across a company. Third-party systems built for (and usually excelling at) only one use case make it difficult for data to be used for other purposes. Sometimes, the wrong data is captured, or multiple systems capture the same data in slightly different ways and formats. Efforts such as trying to optimise your supply chain using customer interaction on your website are almost impossible. Or does changing your CRM system mean you will lose data on your customers?

Not being in control of your 1st party data limits your growth, potential and value of your company. This talk is about the importance of owning your own data and how you manage doing that.

Today, besides its technical challenges and complexities, digitalization is also very much of a big culture exercise in established globally operating companies. The business-IT alignment becomes more important than ever as with the advent of recent new technologies, the changes are often initiated and driven by IT as opposed to traditional reactive IT, that responds to business specifications. Our learning and experience suggest that, clear navigation during times of such rapid changing environment requires the architecture practice to be more prominent with its boundary spanning role both within and outside of the organization, to help setting the right vision

This talk will cover the challenges we found to start implementing a data science team both from the technical and human point of view, by going through at least one example of a model implementation at nectar.

Main points will  include:

• Technology needed (and problems/limitations with old/new technology)
• Skills involved further than Data Science
• Improvements regarding previous implementation

So what: Asking the right questions to improve business performance with data science. We know data science can deliver huge value to our businesses (otherwise we wouldn’t be here, right?). How can you translate your capability into actual results, secure support from stakeholders, and get the best value?