When Dashboards Aren't Enough: How Knowledge Mining Drives Better Outcomes
Construction leaders looking to leverage state-of-the-art technology including artificial intelligence and prescriptive analysis frequently consider investing in a cloud-based dashboard platform for their companies. Dashboard tools are often seen as a major initiative for a company's long term strategy. Unfortunately, however, the implementation process is rarely simple and straightforward, and dashboard-centric initiatives often result in disappointment. Without targeted efficiencies and systems in place, the project dashboard can present more problems than solutions.
This scenario is so common that it has a name: Dashboard Disasters. Team leaders can avoid this situation by addressing the data input and reporting processes to confirm they can naturally integrate within the company. If the data collection is too labor-intensive and results only in irrelevant information, it's not worth the effort. Instead, many leaders put their efforts towards knowledge mining for optimum outcomes.
Avoiding Dashboard Difficulties
The first step to successful and productive analytics is finding the right tools. Many pre-set dashboards do not measure metrics that are focused on particular industries, such as construction. It is ideal to invest in an infrastructure that already targets a particular business model, so that IT professionals do not have to spend too much time—and resources—on making a system work.
Look instead for infrastructure that is compatible with the metrics that benefit the company, such as fluctuating market costs for materials and sub-contractor bids and performance reports. Also, a dashboard should contain an interface that is easy to use and customize for proper reporting and integration within the team. If data cannot be added in an automated fashion, managers may spend more time on data than improvements.
Finally, the dashboard should be able to produce real-time results for the analytics required. Dashboards that live on the cloud, easily accessed from any web browser, also make it easier to access remotely at any time.
What is Knowledge Mining?
But just having the right dashboard isn't enough to utilize the information provided by daily business operations and industry-wide data.
Executives need to focus less on data mining and more on knowledge mining. Here's the difference: Data mining is a computerized process of collecting and learning from information. Knowledge mining is the next step. This process is when the artificial intelligence uses not only the data for determining outcomes and analytics but also the relevant background knowledge.
This knowledge may include industry and market trends, such as real estate updates or new local or regional building regulations. By linking the data with other important information, executives are able to create meaningful patterns and novel predictions for data-driven decision making. This high-level application takes large amounts of data and distills it within a context that makes sense.
With knowledge mining, it is possible to extract meaning from big sets of data. The proper dashboard technology can then present models and forward-thinking metric sets that can be used to help a company transcend to a highly competitive position within the industry on a local, regional, or global level.
Automated tools and machine learning, when used in collaboration with knowledge mining, allows companies of all sizes to overcome dashboard difficulties and instead focus on project health, success, and future growth.