Improving big data project management
Big data and project management often go hand in hand, but some project managers, or those wishing to follow a career in project management, may be unfamiliar with the term. Big data certainly has a major influence on the way companies can be restructured and improved to drive innovation and growth so it’s crucial that people involved in big data projects fully understand how to realise the benefits.
Globalisation and technology have enabled project-based businesses to grow like never before. However, more projects, more information and more resources all need to be effectively managed in order to capitalise on the potential opportunities rather than be swamped with data. The right tools, training and knowledge are also important to help people come to terms with big data.
So let’s first look at the definition of big data and how data scientists use it in conjunction with their project teams. Then we’ll look at 5 ways in which businesses can employ big data practices to streamline all that information, then extract the value to create new growth and opportunities?
What is big data?
The term “big data” refers to the large volume of a wide variety of information that inundates most businesses on a daily basis. It is often defined by its core features - commonly referred to as the 3 V’s. That is Volume, Velocity and Variety.
“Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” Gartner
Volume - With the rise of social media, e-commerce, and the Internet of Things (IoT) there is more information available to businesses than ever before. IT project managers are inundated with not only the quantity of data, but they are also presented with the problem of where to store it all. Moving to cloud storage is a cost-effective and convenient solution but does need careful consideration when the data is highly confidential.
Velocity - Digitalisation, an “always on” culture and the rise of real-time data delivery has resulted in an almost constant flow of new information. As bandwidths expand, so too does the speed at which organisations can receive their data. That’s why velocity is often just as important to monitor as the volume of data. If the velocity is so fast that the data cannot be analysed before it becomes outdated then that data will cease to have or add value.
Variety - Different datasets from multiple sources can make it challenging to index and group the information in a way that enables it to be effectively analysed and understand the patterns and trends within the data. Therefore, efficient data management systems and protocols need to be in place when working with big data. Yet traditional software, hardware, data management tools and methods struggle to provide the solution.
Big data for big returnsThe rise of big data means that many companies have outgrown their initial IT structures. This means that systems have become ineffective and are actually costing money rather than making it. According to management consulting firm McKinsey, it is estimated that the healthcare industry alone could literally save billions. That is if it could integrate its own big data with other necessary business processes.
Data science, therefore, holds the key to success. But even global giants can feel lost when it comes to turning big data into big business value. With ever increasing technology, new data streams will always be incoming. And although traditional programs still have their place – increasing numbers of project managers are being called to play their part in integrating this knowledge to a company’s advantage.
Data scientists and project teamsIt is data scientists that carry out the intricate and specialised work of taking data to form usable algorithms. But the first step in the process can be carried out by a project management team. Especially if you can integrate project teams with individuals who have a background in IT. That way they can start the process of cleaning up and categorising the data and then testing the resulting algorithms.
In this instance, a big data project could save both costs and time for organisations.
So how do companies employ big data practices?
1. Infuse project management skills into big data projects
The ideal scenario is for a skilled project manager, conversant with big data methodology, to be included in the project team. Expertise in communication and leadership skills, risk and benefits management, scheduling resources and quality control are all then embedded with an understanding of big data. Alternatively, you can employ an experienced project manager in the IT team to assist with better defining and managing big data projects, using tried and tested project methodologies and approaches, for more successful outcomes.
2. Get IT on boardCompanies with data science teams need to collaborate closely with their IT departments and project teams. Many organisations began with the initiative of standalone data science teams. Yet the data science results will still need to be incorporated into projects and the main business-as-usual processes. In order to yield the most efficient and profitable results, therefore, requires moving out of silos and using the combined skills of all departments.
Tip: Not having everyone on board is a major reason for why IT projects fail. See our infographic on some more reasons as well as some ways to prevent it from happening in the first place.
3. Develop a monitoring team
There’s no point combining big data unless it works properly! Constant maintenance (at least during the initial set up process) and feedback loops between the professionals involved in the integration is an absolute necessity for technology to work seamlessly. The ability to oversee a problem-solving team forms part of the necessary toolkit of project management skills.
4. Use an agile methodologyTraditional waterfall and iterative project management methods need to be replaced by agile methodologies to track and interpret the continuous process of big data. Data scientists already understand this necessity and an agile approach has been well-tested on very many IT projects to date and proven its worth. Although it is important that project managers learn to combine linear and iterative flows of activity with the fast-pace and early delivery approach of agile working. This will help integrate the demand of changing algorithms with regularly revised data to ensure new systems, when fully delivered, do actually meet business requirements.
5. Implement project management systems and tools
The volume, pace and variety of big data can be overwhelming so it is, of course, necessary to have data management systems in place to organise and analyse all the information efficiently. But don’t neglect established project management tools such as Gantt charts and Kanban boards. Creating a single, shared system for both your data scientists and project teams can help coordinate both departments and help to successfully manage their collaborative work.
Although big data can seem overwhelming, with proper business processes and practices in place, it’s not only the next step in digitalisation, but it’s the next step in harnessing a far richer source of information than companies have ever experienced before. A source of information which provides new insights that could lead to an explosion of previously unimagined opportunities for organisations willing to take on the challenge.
Capitalising on big data projects to drive forward innovation and growth is within the grasp of all businesses providing they are prepared to adapt their data and project management systems for a new era.
Rory Hodder is a project management consultant, with particular expertise in negotiation and facilitation skills. He has worked in this field over many years and also holds professional project management qualifications in PRINCE2 and APM.