Data is at the heart of all business, increasingly relying on an ever-growing number of data sources to drive and automate business, the reliability of how data is gathered and integrated often leaves data estates in a mix of new and legacy that interact in an architecture that has developed organically over time, without being engineered to operate reliably at scale.
We have put DataOps at the core of what our data team does. Our experience is helping organisations leverage their data by raising the DataOps culture.
This means that data is truly managed as an asset, that data is registered, catalogued and data flow is monitored and controlled. Exceptions are raised and managed as the impact of bad data and unreliable data management can have balance sheet impacting consequences.
The business of business is data, it is a strategic advantage when leveraged. DataOps is the glue that makes the whole data management work, from collection, to analytics and artificial intelligence. Putting DataOps at the centre of your data strategy is how you profit from your data.
Read our latest report AI: Hype vs Reality which aims to demystify the hype surrounding scaling AI in the Enterprise.
Transformation of business models and delighting customers are top business imperatives. Constructing compelling data strategies and designing the supporting architectures needed to transform organisations using data is hard. Working closely with your business leaders we first understand your organisational goals, and then set out for you the enterprise data collection and exploitation strategies that will move the needle. Our data strategists, engineers and data science experts keep this process grounded in reality – offering expert advice through this process to identify quick wins and to avoid projects that are technically risky with little or no return.
Predicting an outcome is no longer science fiction, its data science. Putting machine and deep learning into business as usual has become the cornerstone of building the modern intelligent enterprise. Our data science and engineering team bring a wealth of experience and practical know how, and can help you deliver solutions across prediction, forecasting, optimisation and geospatial projects. We provide a full range of skills from hypothesis generation, experimental design through to feature engineering, model training and automated deployment.
Bringing your data to life with compelling visualisation making the complex simple, intuitive and driving action is critical today. Applying analytical techniques to get under the hood of the business problem and tell a comprehensive story based on facts not opinions enables trust. Our analytics and visualisation team are expert data storytellers and know how to drive value from data.
The ability to gather data to create a considered view of the enterprise information landscape becomes more complex as additional data is sourced and generated. Our knowledge graph experts work to develop ontological models of the enterprise, and select the right graph database to solve the business need. We then produce scalable, accurate and rich knowledge graphs that uncover insights never previously found.
The cloud represents the opportunity to truly leverage data at scale. We help our clients navigate the complexity of cloud options, weighing up the benefits and then help deliver the platform on which they can execute their data workloads taking advantage of streaming and batch data. In addition, we securely transform from existing data platforms on premise and deliver the best of both worlds to ensure investments are maximised. Our data engineering and dataops teams rapidly provision data platforms, start the data flowing and industrialise the solutions.
The investments made in databases are the foundation on which business is built and have become critical to business success. However, these investments degrade over time and represent a significant operational risk if they are not maintained and modernised. We accept that not all databases need migrating to modern technologies, migrating to newer versions while maintaining compatibility extends the life of the solutions and maximises value. Our experienced database administrators have years of experience migrating from complex legacy environments onto the latest versions and cloud based equivalents.
To realise the benefits of data led transformation, you must embed your successful data programmes into your production systems in a way that allows you to consistently deliver value over time. Data reliability is increasing in importance as businesses are now totally dependant on the flow of data, knowledge and predictions. The reliability challenge is hard, as it requires you to manage the feeds and flows of data that generate value, even as the quality of the data drifts over time. Our DataOps experts help you automate, instrument, and report on the health of your production data pipelines as they run day in and day out, and can provide you with tools and techniques needed to manage change effectively, whether it arises through data quality exceptions, or through planned upgrades to your system landscape.
With phishing attacks playing a major role in the attacks we’ve seen this year, and the increase of attacks playing off the pandemic, it’s more important than ever to instil the basics of cyber security in your teams, especially if they’re working from home and perhaps less in the loop with training and common phishing attempts.
Clients may trust you with some of the most important transactions in their lives, yet you may barely know them at all. These relationships are engineered by you, the business, and it’s down to you to orchestrate that relationship to provide you with timely, usable data.
COVID-19 repercussions are coming and good companies will be laser focused on the clients; great companies will do this by embracing data platforms and analytics. Director of Financial Services, Chris Mills explains how to value your client through data excellence.
Coronavirus, as a black swan event, will have invalidated many of the algorithms businesses typically use to make operational forecasts and other strategic decisions. Such AI tools are trained on previous data using year-on-year patterns, making it difficult for these algorithms to predict the effects of an unknown.
With the merits of ESG investment performance being a hot topic, the conversation should be on responsible and sustainable investments. Yet the focus is on the data requirements and standards, which are critical to the successful delivery of this rapidly increasing branch of the investment world.