By Josh McAllister | Follow on Twitter!
According to IDG, “Worldwide revenues for big data and business analytics is expected to grow to more than $187 billion by 2019" and the project management industry is projected to hit $5.81 trillion by 2020. If a company, large or small, wants to seek out opportunities, reduce costs, make smarter and efficient decisions as well as increase customer satisfaction, then data analytics is the solution.
By analyzing data, the company can flag problems and recognize trends. For example, in an engineering department project managers can look at data to determine if their projects are on track and make changes to meet deadlines. Another example is when a project manager in the marketing department needs to evaluate their customer interactions and make changes as necessary.
Although previous generations might be hesitant to embrace the benefits of using digital tools to analyze big data in project management, those that take the plunge will reap the advantage. Learning how to leverage big data in project management will give companies a competitive edge.
Creating successful projects
Data mining allows project managers to sift through large quantities of data to reveal information such as trends about a subject. Finding trends allows project managers the opportunity to use data to make corporate decisions for creating successful projects.
For example, one manufacturing company wanted to find out how much time was being lost to non-essential overhead tasks. This company had multiple buildings, and transportation between buildings was provided by shuttle bus. Because management requested to know how much time of project personnel was being lost to project capacity through non-essential overhead task, the company determined that existing timesheet and project management data was not being collected at a sufficient level of detail to answer the question fully.
Over a 90-day period they adjusted their internal timesheet and project schedule systems to match and encouraged 100% participation of the staff. Through data mining, it was determined that over 16% of total resource capacity was being spent on non-essential overhead tasks, and over 10% of total resource capacity was being spent on non-essential inter-office transport.
The company initiated a series of policies that included co-locating project personnel to the maximum extent possible and requiring the fewest number of meeting attendees to travel to wherever the largest number of meeting attendees were located for project meetings. The result was a savings of well over 10% of total resource capacity or the equivalent of hiring 150 new employees for free.
Data mining and analysis can be done on any subject such as trying to understand types of internet traffic, or trying to understand physical environment data or even trying to understand an organization’s internal systems. By analyzing the narrowed scope of data, the company can make better-informed decisions leading to greater success projects and profits.
Reducing project complexity
According to a 2013 paper written by Walter Ginevri and Marco Guerini, they found after analyzing the Standish Group statistics that they were faced with a contradiction of a “significant downward trend of failed projects (from 23% in 2000 to 15% in 2004), followed by a rise, to a level worse than the starting one (24% in 2009).”
This fluctuating trend could be attributed to both the complexity and uncertainty associated with the operations that all the managers have to deal with on a daily basis. They alleged that ignorance or inadequate knowledge of information available and the relationships among the elements involved is a key complex amplifier.
Data analysis leads to reducing project complexity. Having inadequate knowledge of information to make decisions is determinant to any business. Many managers must deal with uncertainty and complex problems, but if they can uncover digital material using the right tools to comprehend the project’s problems then they can reduce the intricacy of the project.
Gathering information is vital to the success of any product or business and it is what drives business decisions. By analyzing what the content is about, how the content is presented, when the content is delivered, where the content is delivered, and who delivers the content project managers have a firm grasp on how to reduce project complexity and see the bigger picture.
The problem with data mining is that structured as well as unstructured data can be uncovered. However, any digital material that is analyzed whether structured or unstructured can help managers towards project success. The key is to make sure the data is relevant.
Big data mistakes to avoid
Any experienced project manager knows how important it is to avoid making common big data mistakes. Just because you endeavor a big-data project, it does not mean you will always come up with something of value. Here are the major big data mistakes to avoid:
- Ignoring data quality
You must have confidence in the data that is used for project management since choosing the wrong data sources may make your company come to false conclusions. One particular problem that can reduce data quality is the integration of unstructured data into data sets. Make sure you resolve any problems related to the source, age, and hygiene of the data before processing it.
- Getting into the mindset that big data is only for trained IT professionals
Although big data platforms are new, companies should not be afraid to allow different departments to access the data as needed. Project managers do not have to have great technical knowledge, but should be familiar with the advantages and disadvantages of technology. Personnel must be able to dissimilate information, find value in the information (or discard it), and then make use of the information to guide wise company decision-making.
Have a training session to teach top managers and others on how to use the data, and train the IT department on how to enable and conform the data platform. A company must beware of inexperienced personnel who may get caught up in the process of data mining. Often inexperienced people are not able to recognize that all data does not always have value.
- Collecting lots of random and irrelevant data
Because of the immense amount of information available, project managers may find it difficult to extract precise and relevant data from the information to make decisive decisions. No matter what the quantity of information is available, if sense cannot be made of it, then it serves no purpose.
Project managers must ask themselves the question, “Is the information relevant?” They must be able to filter out irrelevant information to move the company forward in a positive direction.
- Not acting on the data
When gathering data, it is important for a company to quickly act on the data. However, companies should be careful to interpret the data carefully to ensure people are not jumping to conclusions leading to bad decisions.
Be careful when dealing with information about new trends or technology because a project manager may not have enough foresight to see how the data gathered will affect the organization and then take no action on it.
To conclude, big data and data analytics is the driver of operations for a business by providing a close real-time view of a project’s status. It will, therefore, become infused in the company’s architecture from end to end allowing for strategic project management in enterprise program management offices.
Any project manager wanting to thrive in the coming years should begin to learn how to leverage big data to give their company a competitive edge. After all, companies that have the competitive edge have managers who can create successful projects by acting quickly on reliable information to make informed and wise business decisions.
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ABOUT THE AUTHOR: Josh McAllister, a freelance technology journalist with years of experience in the IT sector. He is passionate about helping small business owners understand how technology can save them time and money. Find him on Twitter @josh8mcallister.