Breaking Down Data Siloes For Data-Driven Decision Making


The successful delivery of a project regularly relies on project managers effectively and efficiently making decisions, often relating to costs, resources and budgets.

Decision making is significantly easier when there is information available to support the process and sharing information in a meaningful way can help to improve the delivery of projects, and the likelihood of project success.

From individual knowledge and experience to historic documents, to emails and video chats, there is no shortage of data available to today’s project managers. However, up to 90% of data exists in an unstructured, inaccessible format. Commercial and procurement teams are routinely hampered by the limited visibility of information.

Additionally, they lose a large percentage of their working day searching for the information required to perform their role effectively. The challenge of accessing siloed data points is exacerbated by the diverse computing systems used by different organisations.

By harnessing the already available but currently undiscoverable data, firms will be able to deliver improvements in the productivity, procurement, and planning associated with a project. The question is, how can organisations access this data?

The adoption of AI technology in project management is in its infancy, but it represents a huge opportunity for organisations to harness their existing data in all areas of project management, from planning to procurement to productivity.


The value of information depends on the insight you can gain from analysing and evaluating it, but, as the volume and variety of data increases, so does the amount of time required to find the information that you need, reducing productivity and leaving little time for analysis and insight.

There has been a huge increase in the use of email, video chat, messenger services, and other non-traditional channels to share information and business decisions but capturing and using knowledge shared this way is challenging. Adding to the challenge, these communication tools have different search interfaces, which require multiple and repeated searches to find information.

With no mechanism for easy discovery, most current retrieval systems rely on keyword searches, which enables users to combine words and modifiers to retrieve relevant information, but these often yield irrelevant results as the search parameters are so large and there is a lack of context.

Using AI-enabled technology allows a richer input of information as a query, including dragging and dropping full documents into the interface, which means that the results are more accurate, relevant, and refined. This is particularly useful for individuals working on projects where there are potentially thousands of different results per keyword.

The context enabled by AI data discovery refines the returned results to a manageable number that can easily be reviewed. Reducing the amount of time spent searching for information offers a tangible cost-saving for teams and improves overall productivity by enabling resources to be reallocated to other areas of a project.


During the bidding process, organisations may have previously relied on individual knowledge and experience, which is valuable but may be subject to unconscious bias or affected by individual motivations. With projects of five to 10 years lead time, it is difficult to accurately predict scope, complications, and market shifts; often companies need to decide whether to bid on a project with limited, or incomplete, information.

AI tools can unearth siloed information from historical projects, enabling commercial teams to use the data to make informed decisions to balance portfolios and calculate accurate contingency levels. By enabling access to data that may not have been easily available previously, such as historic contracts, workforce capacity and average geographical expenditure, teams can uncover variables that may predict project profitability, successes, and failures, to enable more efficient and successful tendering.


The subcontractor procurement process can be a lengthy one, with multiple specialists involved and a lack of empirical evidence to inform outcomes. Adopting AI technology can reduce the time and labour-intensive process from weeks to days, allowing a rich pipeline of options to be maintained as the need to reduce the number of options for human review will be removed. Using AI technology also reduces unconscious bias during the procurement process, allowing pitch decks to be submitted anonymously and benchmarked against predetermined criteria consistently and equally.

It is clear that adopting AI technology can have a positive impact on a project from its earliest stages by improving productivity, reducing business risk, and increasing profitability.

Jon is the CEO and founder of iKVA, which develops AI-enabled knowledge management software for organisations.

Jon Horden
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