How to Leverage Data Analytics in Project Management to Improve Business Revenue

With the revenue from data analytics and project management set to reach $187 billion (by 2019) and $5.81 trillion (by 2020), it is imperative that these two business processes combine their strength to boost the profits and reputations of enterprises – instead of remaining standalone implementations in themselves.

Data Analytics

How to use data analytics effectively in the internal workings of an enterprise

Hitherto, data analytics has been a post-project launch operation to analysis the market reach of the products. Big data company can benefit a lot by bringing in data analytics earlier into the picture and utilizing it end-to-end in the project architecture, to better know the results of the business processes being used in project management.

Data analytics has been widely used to identify business opportunities, reduce overhead costs, aid faster decision-making, and thus, increase customer satisfaction. Even though it may be real-time data analysis, it has been a passive process that just provides the ‘revenue-based results’ of existing products and processes.

In the near future, data analytics will also be used as widely in formulating strategic project management processes after analyzing the ‘impact results’ from ongoing business processes of pre-sales, development, and customer service. This will require enterprises to create new employee roles that act as Data Analytics Managers in individual project cycles.

In fact, some of the big data companies are already utilizing data analytics to monitor customer satisfaction, transactional insights, and digital engagement over social channels to come up with project management strategies that foresee the user requirements and include relevant features in the development stage itself.

Outcomes of using data analytics in project management

As mentioned above, using data analytics in project management is an upcoming practice that focuses on developing ‘systems of assessments’ that produce reliable data, than tracking unnecessary data streams that impart no actionable intelligence.

The success of a project highly depends on having a measurement plan (fed by data analytics channels) that maintains the fidelity and efficacy of the implementation. Fidelity is measured both in terms of staying true to the intended project features and customer loyalty to the end product. Efficacy is measured by pre-defined outcomes of product usage and market strategy that ascertain whether the customer is getting value out of the product.

Data analytics systems that tell the enterprises (from customer behavior, not direct feedback forms that might irritate the potential long time users of the product) about what they want from the product. This serves to bring in actionable intelligence on what policy changes are needed in the project initiatives, direction of development, execution mechanism, and metrics of measuring product success.

This leads to deriving important conclusions like:

  • Customer behavior can be used to know what features need to go into the product, thus enabling the project managers to incorporate them into project versions before releasing them into the market. This reduces iterative releases of features through direct customer feedback/complaints.
  • Community enthusiasts are the better option over having customer service staff, for some people-oriented products – as inter-customer networking was observed to happen more than customers calling the customer support centers.
  • Customer segmentation based on demographic and behavioral patterns can help develop customized product features/promotions targeting the particular segment.

Other than project management, data analytics also influences its aligned departments like sales, operations, marketing, accounting, human resources, etc. by helping identify the kind of projects that should be taken on, company processes that lead to better productivity, campaigns that will improve customer rapport with the product, and reduce internal costs of implementation of the projects.

Limitations that should be overcome before adopting data analytics to improve project management

Data analytics systems are comparatively dumb in the present days, as compared to the futuristic solutions that should provide direct actionable intelligence and lead to automatic redressal of tricky situations in project management. Hence, big data company should take due care and gather data from only relevant channels. Raw data is of no use. However, wrongly processed data can actually be detrimental to the project. This makes it necessary for the project managers and data analysts to combine their expertise and take the right call at the right time on whether to depend on calculated results or rely on human analysis of the data visualization provided by the analytics dashboard.


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