Analytics and Machine Learning

Data is everywhere. From transforming industries to fuelling transactions, powering cutting-edge research to underpinning vital public services, data ricochets in all directions all day, every day. But that’s not all.

When processed and analysed correctly, it will also reveal insights into how an organisation can improve efficiency, reduce costs, increase competitiveness, grow market share, and fulfil its mission.

With data volumes growing every day, so, too, are the regulations governing their use and storage. So, how can organisations identify the data that will make a difference? And how can they ensure they are abiding by regulations, as well as ethical considerations and build this insight into their day-to-day operations?

Many organisations have multiple failed data analytics experiments behind them, and find themselves overwhelmed by the many challenges that identifying and operationalising data insight bring. And that’s where BAE Systems Applied Intelligence can help.

Data, information and intelligence are at the heart of what we deliver. We specialise in helping secure government departments and high-trust sectors exploit their data using artificial intelligence, machine learning and other more traditional analytical techniques.

We have a track record of helping our clients with:

Identifying the value in data

The need:
Identifying and unlocking the value held within data, prior to major technology investments

Typical challenges include:

Garbage in, garbage out – no matter how slick your analytics tools and techniques, if your data is inaccurate or poorly prepared, you will not obtain the value you were looking for
Time to deliver value – the value of insight needs to be rapidly proved and integrated into business operations as you go. Don’t be side-tracked by shiny, new cloud data platforms; you can find ways to extract value from the data you already have
Technology constraints – new tools are entering the market every day and you need to be experimental with a versatile environment that embraces open source tools and new techniques without restrictions

Shaping ethical Artificial Intelligence solutions

The need:
Artificial intelligence, including machine learning, robotics and sensors, has the potential to have massive benefits to both an organisation and its customers, but there needs to be a sense of comfort around how it is being used. There are multiple areas of ethical concern, including privacy and surveillance, bias and discrimination and the role of human judgment. How can organisations successfully exploit artificial intelligence whilst using a structured method to consider, address and design ethical considerations into solutions?

Typical challenges include:

The scale and pace of change – ethical considerations include honesty, integrity, fairness, transparency, accountability and inclusiveness. With the breadth and rate of change, how can these all be cohesively considered and understood when developing AI solutions?
Understanding the risk of deployment – risk assessments must be carried out to understand how algorithms may lead to bias in analytics models
Transparency around how AI is being used – the ways in which the data and algorithms are being used should be clearly communicated and available

Scaling insight to production

The need:
Experimental environments can be a quick route to valuable new business insight. However, many organisations face more significant challenges when trying to productionise this initial insight to drive improvement to user engagement, or optimise large scale processes. This requires more than just insight. What’s also needed is high quality engineering to embed that insight into each user interaction, or each step in a complex workflow or supply chain.

Typical challenges include:

The breadth of skills required to operationalise insight – translating data solutions into secure, scalable and resilient productionised, engineered components covers activities ranging from strategy and architecture to data science and analytical techniques combined with software engineering disciplines.
Levels of security, risk and operational assurance required – analytical models deployed in large scale, business critical systems demand a high level of assurance
Making analytical solutions relevant to satisfy multiple levels of users – value can only be realised if data is used to drive decision-making. It is critical to make sure that insight is delivered to users in a way that suits their skills, culture and ways of working

Data, security, information and intelligence are at the heart of the critical business solutions we deliver for our clients. Handling Terabytes of data in real time every day, we deliver in-depth expertise in data management and data engineering and highly experienced data scientists and data engineers.

This mix of people, processes, culture, technology and data can generate real insight for your business and help you get the absolute best from your data.