Data Culture

Manufacturers - you need DataOps

DataOps also helps you build a more agile digital supply chain by enabling analytic teams to automate their processes, which in turn reduces cycle time on data analytics. Getting this data infrastructure right is critical for helping Operational Technology (OT) get the most out of real-time data to optimise processes. DataOps is part of your ability to move quickly in the digital space. It should be part of your overall approach to your developer velocity and can help staff to discover the data they need to drive insightful improvements in your organisation.

Europe's '2030 Digital Compass' and manufacturing

The 2030 Digital Compass aims to digitally transform Europe by the end of the decade. It aims to strengthen digital infrastructure and facilitate digital transformation of businesses and the public sector. At its core will be a framework that allows businesses and services to go digital in keeping with European values.

Smart Manufacturing is all about Real-Time Data Analytics

Manufacturers are spending far too much time on data entry or looking at stale data which is hindering growth. Here are seven ways real-time data can turn things around.

What is data science strategy?

Data science strategy refers to a company’s vision for how data will be used to help achieve the company’s business goals, how to build a thriving data culture, and how to address the skills and knowledge required to execute this vision.

What is AI strategy?

AI strategy refers to a company’s vision for how AI will be deployed to help achieve the company’s business goals. It should be closely linked to your data strategy and hence to your business objectives.

What is developing a centre of excellence?

Developing a centre of excellence promotes collaboration in your business and defines best practice for your specific focus area - whether it be AI, data science, analytics or business intelligence - to drive progress towards your strategic goals.

What does it mean to be AI-Ready?

Artificial Intelligence (AI) and Machine Learning (ML) have burst into the spotlight in recent years getting attention from businesses and all levels of society. Awareness of AI has drastically increased as people become more familiar with how the tech giants are using data to enhance their products and create better solutions. The market is opening up to more AI-infused products, and the public are regularly interacting with AI as it slips into their day-to-day lives through smartphones and virtual assistants.

Why do you need business intelligence?

How quickly could you answer the question, "How's your business doing?" if it was asked right now? How detailed would your answer be, and how confident would you be in your answer? If you were utilising business intelligence, your answer would be fast, comprehensive and accurate.

The real dangers of AI: Getting left behind

With AI set to expand and evolve over the coming years, with 61% of businesses already implementing some form of AI, you may be beginning to wonder where AI might fit in your company. Is it a god send to simplify daily work tasks, or is it an unsolicited evil come to displace hardworking individuals? In reality, AI can help to speed up processes and eliminate mundane tasks saving businesses precious time and money, thus it can be used to enhance jobs rather than replace staff.

Removing AI bias for better decision making

It is difficult to deny that humans make biased decisions. Unconsciously we all make choices that are based on prejudices and flawed associations. This bias that we introduce to our business decisions can trickle through entire organisations, from recruitment to market segmentation. AI, with its lack of consciousness, human experience and gut feelings, has the potential to remove bias from businesses, and yet all too often AI is found to exhibit the same biases that we do.

How can companies lay the foundations to scale up AI?

Implementing AI at scale in an organisation can yield a wealth of benefits, from improving profit margins to saving workers' valuable time. But getting value out of AI projects requires long-term planning, culture shifts and organisation-wide training.

Project management: Are you backing the right AI projects?

As an executive with an influence over whether your company implements AI and which projects it embarks on, there’s a lot of pressure on you to be successful. The future of AI within your company could rest on you on how your chosen projects perform.

Do you really need big data to start using data science?

All businesses generate data. Even the smallest business has access to hundreds, if not thousands, of interesting data points that they could explore. But it is not uncommon for business owners to think their data is small, inferior and not yet worth analysing. This is where they are wrong every time. Starting small is the best thing you can do, so we say, the time to start your first data science projects is now.