Data and Artificial Intelligence

Every successful data related activity starts with the input data – the adage ‘rubbish in, rubbish out’ was accredited to the late Wilf Hey nearly fifty years ago when working for IBM and it remains true to this day in the context of data engineering. The engineering process starts with identifying, gathering, cleansing, standardising and transforming the data. The data then needs to be exposed. Depending on the size and complexity of the data, this may be as simple as creating a data base or require us to design and build a data warehouse or a data lake for the largest and most complex datasets. We can provide the skills and experience to define the data architecture, create the data governance rules, define the data security strategy and principles, design and run any extract, translate and load (ETL) processes and migrate the data to the final database, data warehouse or data lake.

Once the data is prepared, its value can start to be unlocked. Standard tools and methods such as data mining, business intelligence (BI) and data visualisation can be used to provide insights, identify trends and provide high level summary information. This process is relatively quick and straightforward and can provide valuable input to decision making, target setting and prioritisation. We can provide the know-how and services to deliver the complete solution or to work alongside you to assist in the process. We can create operational dashboards with intelligent KPIs, data visualisation, all available on mobile as well as desktop devices. We can also provide realtime visualisations so you can track progress of any aspect of your business. We can augment the solution by adding a chatbot interface and use natural language processing (NLP) to provide simple and intuitive access for everyone.

For some data applications, the insights delivered through data mining, business intelligence, and data visualisation may be sufficient. However, there are an increasing number of tools available to provide additional insight, including artificial intelligence (AI), machine learning, anomaly detection, computer vision and specialist forecasting and recommendation engines. Analysis can be automated, carried out in real-time, and the solution can make recommendations and perform actions which would have been previously been carried out by a human. As with the more straightforward data analysis techniques, we can augment these solutions by adding a chatbot interface and using natural language processing (NLP). Our Data Science practice has a breadth and depth of skills and experience to help you decide which of these tools are applicable for you and work with you to deploy them.