The project management industry is projected to hit $5.81 trillion by 2020. This is not surprising at all, since project management is amazing and those who understand good project management practices are granted important benefits. Nonetheless, most organizations have a 70% project failure rate, as they either don’t meet their goals or they do but use more resources than it was initially planned.
Now, new digital technologies are disrupting traditional project management and promise to substantially bring down project failure rates. Big Data, Business Intelligence (BI) and Business Automation (BA) can have a fundamental impact on the way project managers work.
Indeed, data is everywhere and can be used in a wide range of ways to achieve many different goals. BI and BA are both based on Big data analytics. BI allows the analysis of past and real-time data to understand past performance and inform current decision-making. BA, on the other side, is more complex and is used to predict trends, discover new patterns and prescribe actions for best results. However, both really help to stop managing projects reactively and start proactively identifying what is the best path to a project’s success.
Vast amounts of data are generated in any phase of a project. Analyzing and understanding all that data helps project managers take better decision when planning and delivering projects. It also helps them managing the resources assigned to a project, the risks it involves and the quality of its processes and outputs.
In a 2013 article, scholars Ginevri and Guerini highlighted the importance of Big Data analytics in knowledge management. They found most projects fail due to “the inadequate knowledge of information available and the relationships among the elements involved”, which in turn makes projects much more complex to manage. However, they found that Big data analytics helps project managers by greatly reducing project complexity. As more information is made available, it becomes easier to see the bigger picture.
Big Data project tips: embrace smart data
Based on what was previously discussed, setting up big data projects should be a priority for any organization, independently from its size or vertical. But how do you do that?
First of all, keep in mind that investing money and energy in a big-data project doesn’t guarantee it will generate meaningful insights. This is why you need to be aware of the most typical challenges and mistakes in big data projects.
First, project managers shouldn’t let the technology behind Big Data scare them. For project managers, Big data is about business opportunities, not technology. They don’t need to have a complete understanding of how Big Data works, they just need to be able to identify valuable information and use it.
Further, some companies wait too long before acting on their data, which in turn loses relevancy or ends up never being used. Data relevancy is probably the major problem of Big Data projects. Indeed, when data quality is low or when the information it provides is irrelevant, the project usually fails producing valuable intuitions and might even lead to false conclusions.
You should therefore focus on smart data to implement a successful Big Data project. Smart Data is the part of Big Data information that is actionable and actually makes sense. The specific goals and organizational details of your Smart Data project will depend on the particular features and needs of your company. However, the steps to design an optimal, tailored Smart Data strategy are similar for every business.
The 5 steps to a greatly tailored Smart Data project
1) Define your business use case
Clearly identify your goal (e.g. cost reduction, faster decision-making, increased customer loyalty, new revenue streams...) and how you will measure success. Focus first on what really generates value for your business.
What are your business’ key requirements? How do they relate to your target groups and geographical scope? Why did previous attempts to reach those goals fail? What budget do you have/need? Who are the key actors involved and what are their skills?
2) Plan your Smart Data project
In this phase you should explore more in details the conclusions you reached while defining your business use case. Develop a Smart Data project roadmap to define the different phases of your project and what actions are required for each of those.
For instance, let's assume your objective is to increase customer loyalty. In this phase, you could decide to focus on building a better customer experience (CX) and therefore identify which success factors you should consider when improving your CX. These could include measurable changes in your corporate culture, the degree of involvement of the organization’s leaders or the level of employee buy-in.
In this phase you should also define the specific KPIs you plan to measure for each success factor.
3) Analyze the technical requirements
This is the last step in the design phase of your Smart Data project. You need to make sure since the beginning that the data you will retrieve and use is relevant.
There are three groups of data sources you need to analyze: your internal tools, systems, applications and other data sources; the external data sources used to enrich your data; the internal and external applications and systems you could integrate in your information management infrastructure. Data can be in many different forms, either structured or not. Text, sensor data, audio, video, click streams, log files are just a few examples.
It is crucial for the success of Smart Data projects that all data sources are integrated and that all the information flows to the same data lake. Otherwise, you risk creating data silos which won’t provide new insights and could even produce misleading suggestions. Ask yourself which data sources are relevant for this project, what type of knowledge you are looking for and in what measure, how you plan to extract and use your data…
At the end of this stage, you should have a clear picture of which features you need to include in your final Smart Data Application so that it serves your goals and is coherent with your business use case. This will let you determine which solution for big data storage and management fits you most. Make sure to take care of all these technical aspects before implementing your Smart Data project, as overlooking this step could doom your strategy.
4) Implement, Operate and Monitor your Smart Data project
Once you are done with the design of your Smart Data project and have assessed your technical capabilities, you can start implementing your strategy. In this phase, always keep an eye on the security, performance and compliance of your project. Further, make sure to involve the organization’s leaders, as Smart Data projects take time to scope and therefore risk failing without executive-level sponsorship.
One way to implement your Smart Data project can be to work on incremental releases. Once you have built your initial data hub, don’t stop. Always keep incorporating new data hubs, one at a time.
This approach is beneficial because it lets you incrementally adjust your operation and, therefore, better understand how to use the data in your company. However, it risks involving data paralysis if not managed with care. Make sure to create clear, repeatable process and action paths to ensure the data learnings are actually applied to improve the way the company operates or deals with certain issues.
Finally, remember to always monitor your KPIs and ROI values as this is the only way to know if what you are doing goes in the right direction. Constant monitoring enables you to continuously optimize your system and make sure you identify and solve issues before they arise.
Big Data is about constantly improving at what you do thanks to the growing amount of meaningful information you can access to. It is therefore important to keep using Big Data to identify and solve unthought weaknesses and issues and start a process of continuous improvement. And guess what? Smart Data can help you identify where and how to focus your next Smart Data project.
In conclusion, Big Data projects can be challenging, but are absolutely necessary in an increasingly complex environment like the one businesses face today. Smart Data projects are the key to success, but require you to have an accurate understanding of your company. As the future of project management depends on data, those who will master smart data analysis will reap great benefits.