Go Arc is a manufacturing operations management platform, a SaaS platform that aggregates data from an entire manufacturing facility and breaks down conventional silos of knowledge and data by analyzing field safety reports from safe-connected workers. This includes the analysis of data from ERP systems, OT, and IoT sensors.
  1. Home
  2. Blog
  3. The Nine Critical Drivers of AI and Analytics Success: Part 01

The Nine Critical Drivers of AI and Analytics Success: Part 01

Share this article

Analytics and artificial intelligence (AI) can transform your business, redefine your operations, reshape your facility, and re-engage with your employees. Their intelligence dance through data and insights can revolutionize how you manage systems, track maintenance, leverage technologies, and ensure safety. Their functionality can give your organization the resilience and robustness it needs to thrive in uncertain times and within complex markets. But… ‘making use of data and analytics will not be done in any single bold move but through multiple coordinated actions,’ says McKinsey.


And the research firm is right.


Success within AI and analytics is not just about implementing and investing in the technology; it’s about enabling the company and its people to make the best possible use of this intelligent technology. Nine critical drivers will enable your success at scaling and leveraging your AI and analytics, and in this, part 01 of the series, we look at the first five…


Driver 01: A strong and unified management commitment

Your leadership team must be wholly aligned when it comes to your analytics and AI vision and strategy; otherwise, it will become fractured and irrelevant. Senior leadership should set the explicit goal of integrating AI and analytics throughout all operations and engage in its implementation and use. This level of commitment should start from the top to ensure it is implemented properly but must reach deep into the structure of the business to ensure it is used effectively.


Driver 02: Increased investment with a focus on the last mile

Gaining value from AI and analytics starts from the foundations. You need to establish the building blocks of effective analytics, including data, processes, technologies, and people. To extract value from your analytics, you need to focus on implementing it in the last mile and embedding it into the core of all your workflows and decision-making processes.


Driver 03: A clear data strategy with strong data governance

A data strategy is essential to driving value and enjoying success with your AI and analytics. To create a solid strategy, you need to consider the following four steps:

  1. Have a clear data ontology based on current and projected use cases.
  2. Have a corresponding master data model across key domains such as customer, product, location, or employees, and have established business ownership around how they are addressed.
  3. Implement governance plans that establish who is accountable for the quality and maintenance of each data set and that segment the data sets into hierarchical categories. It is important to understand that not all data is equal, and not all data deserves first-class treatment.
  4. Have a complete understanding of, and plan for, the technical requirements of your data environments. For example, use cases may require a dynamic environment in which data is assessed and analyzed in real-time.

Driver 04: Use sophisticated analytics methodologies

Implementing a methodology for developing analytics models, interpreting insights, and deploying new capabilities is essential. Don’t only focus on model development through relevant methodologies either; also work to continuously maintain and upgrade your models as part of a sophisticated model-management function. This is key to ensuring that your methodologies and models remain relevant.


You should also constantly test and upgrade the quality and performance of your analytics models using a challenge and test approach that pits existing data sources and algorithms against new and potentially better alternatives. It is an approach that will keep your data, AI, and analytics fresh. Finally, you should use sophisticated analysis techniques such as reinforcement learning and deep learning. These provide a substantial lift in value over using more traditional analytics approaches.


Driver 05: Ensure you have deep analytics expertise enabled by a tailored talent strategy

You need to develop deep functional expertise in the areas of data science, data engineering, data architecture, and analytics transformation. This will ask that you attract the best analytics professionals by building a workplace culture that goes beyond just compensation. Talent is hard to find and retain, making it critical for you to engage with talented individuals and organizations that can help you squeeze out every last drop of digital value from your data.


In part 02 of this series, we will take a look next week at the remaining four drivers of success in AI and analytics and unpack why it has become absolutely critical for companies to pay attention to these technologies today

Previous Article
The Value of Asset Management #3: Towards the Autonomous Factory
Next Article
The Nine Critical Drivers of AI and Analytics Success: Part 02
  • 1
  • 2
  • 3
  • 4
  • 5