Part 3: Mastt vs Power Bi. Becoming AI-ready for next generation construction dashboards

Being AI-ready for Construction Projects

In our previous blog posts, we explored the differences and limitations of Power BI vs Mastt to automate dashboard insights for capital works projects.

In Part 1, we highlighted how Power BI falls short when it comes to data capture and preparation. Construction consulting professionals and asset owners delivering capital improvement programs often rely on manual data entry and compilation from various sources, including spreadsheets, to integrate with Power Bi. This manual process not only consumes valuable time but often introduces data inconsistencies and errors, which can undermine the quality of the overall data and reliability of the dashboards.

In Part 2, we discussed the challenges of modifying existing Power BI dashboards as project, program and portfolio needs evolve. Construction professionals face the daunting task of reworking data models, collection methods, restructuring dashboard layouts, and ensuring data accuracy. Without expertise and due care, you may end up with a database of information that is disparate and unreliable. These challenges demand extensive training and effort, hindering the ability to adapt dashboards to changing requirements and often precipitates engagement of external data consultants.  

In this final Part 3 comparative blog post, we delve into why these challenges can be detrimental to AI readiness and how construction professionals need to become "AI ready".

Limitations of Manual Data Entry, Fragmentation, and Spreadsheets for AI Readiness

Manual data standardisation, normalisation & aggregation in spreadsheets or other databases present significant limitations for being ‘AI ready’ and prepared in the construction industry. There are troubles with many organisations' current process:

• Construction professionals often deal with data from various sources, and struggle to consolidate data to one unified location. While this doesn't make AI data analysis and visualisation completely impossible, it makes the task vastly more difficult and will require data experts to fix the data inputs used for AI.
  

• Manual data manipulation is prone to errors, which compromises the quality and integrity of data that would be used as the substrate to perform AI analysis or automation of work. As construction projects grow in complexity, the volume of data increases, and the process of managing this data entry becomes more time-consuming and error-prone
• Fragmented data stored in spreadsheets impede data accessibility and collaboration with AI. Accessing and sharing data becomes cumbersome, hindering effective collaboration and utilisation of AI capabilities for data analysis and visualisation.

What's the key learning? To harness the full potential of AI algorithms, a pre-requisite is storing your construction data in an accessible and properly structured database.

What does a good Dataset and Database look like?

There's an old saying in data management and analysis: Garbage in = Garbage out.

A good dataset and database that is "AI ready" exhibits characteristics such as centralising necessary cost, time, risk, user and status information in a sufficient and relevant way. The data must be high quality with proper cleaning and preprocessing with accurate annotation and labelling, balanced data distribution, robust data privacy and security measures in addition to great scalability to handle larger volumes of data that we hold in the construction industry. These qualities ensure that AI models can learn effectively, generalise well and provide reliable insights for informed decision-making.

Power BI: Co-Pilot enabled ... but it's early days

Co-Pilot, Microsoft's AI capability tool, has recently become available to Power BI users (for an extra fee). This is encouraging ... but before you get too excited, it comes with caveat. It's "early days" AI in Power BI.

The primary issue is that getting results from Power BI's AI capabilities still requires a high level of training and expertise to enable its effective use. Before you can even get started with Power BI's AI capabilities, you still need to have core Power BI expertise to design and implement an appropriate Power BI data model.

In the words of Power BI expert Avi Singh: "Power BI AI will perhaps help you put lipstick on a pig, but I'm sure that's not what you want. You can not have a crappy Power BI model and expect AI to do magic on that. There needs to be somebody who has the business expertise and the technology expertise."

If you're confident that your team are expert Power BI users, you may be in luck with Co-Pilot. You will, however, still need to adapt and maintain the initial Power BI AI dashboards whenever the asset owner's requirements change.

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Mastt: out-of-the-box AI-ready dashboards and data analysis

Mastt addresses these limitations by providing construction professionals with an AI-ready platform for dashboards and data analysis out of the box. With Mastt, manual manipulation of data is eliminated through automated data capture and integration. This ensures consistent and standardised data inputs, enhancing the reliability and integrity of AI-driven dashboards and analysis.

Mastt handles your data cleaning and standardisation just through its daily use, enabling construction professionals to work with high-quality data inputs for AI algorithms. Mastt’s clean and standardised data enhances the accuracy and reliability of inevitable AI-driven dashboards, empowering professionals to make informed decisions based on reliable insights.

Conclusion: Better AI capabilities are coming

Being AI-ready is essential for staying competitive and driving efficient project outcomes. While Power BI's new Co-Pilot AI capabilities are encouraging, the verdict from Power BI users is that AI results are mixed and it still requires significant data knowledge and set-up to ensure that the AI work is based on high-quality data. Otherwise, it is very easy to be a case of "Garbage in = Garbage out"

Mastt's comprehensive data features - including data normalisation, standardisation and collaboration capabilities - meanwhile overcome the limitations of fragmented data sources, making it an ideal platform for construction professionals and asset owners to be AI-ready straight out of the box.

With Mastt, construction professionals can seamlessly adapt to changing project needs, make data-driven decisions, and unlock valuable insights for improved program performance. By getting ‘AI-ready’ with Mastt, CIP managers and asset owners can lead the industry to leverage artificial intelligence to drive efficiency, optimise resources, and achieve successful project outcomes.

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