Data analytics is the process of analysing raw data to extract valuable insights. Data analytics techniques can reveal trends and ‘dark data’ that would otherwise be lost in the mass of information. This insight can then be used to optimise processes, reduce costs, or leveraged to unlock additional revenue streams.

Research by international advisory and research company, Gartner, points to the fact that as many as 60% of big data projects started this year will fail to go beyond piloting and experimentation. The reasons for such failures are many, and vary from organisation to organisation. The software, the talent hired or the lack of a well-defined business problem all could be responsible to some degree.

One common reason for data projects failing, however, is as a result of how the analytics team is structured, with the wrong choice putting the project in a position where it cannot produce tangible benefits. The two most common team structure approaches are distributed and centralised.

When analytical teams are centralised, the data and talent to provide analytical expertise and support reside in one group. This group serves a variety of functions and business units and works on diverse projects. The centralised team is also responsible for setting the analytical direction of the entire organisation, whilst also being in a position to develop a Centre of Excellence (CoE) where best-of-breed solutions and ideas are shared amongst peers. A distributed analytics team, on the other hand, pushes the analytics experts out into the different business functions. Effectively, each unit manages and creates its own data ecosystems independent from the main hub, with analysts tasked to interpret the data from within that team.

Naturally, there are pros and cons to both approaches. However, in my experience, a centralised data and analytics function works more effectively, if governed by the appropriate processes and principles. A centralised function can leverage and share knowledge more effectively, work on a team standard of analytical ‘toolboxes’, communicate domain and business knowledge in an agile way, and collectively endeavour not to work in isolation redoing fundamental work each time. Inversely, with distributed analytics, the analysts focus only on a single product of function, often in isolation, without considering the rest of the business. Often, distributed functions are operationally bogged down, focusing on delivering on the immediate business task, whilst not taking a tactical approach towards sourcing outside insights. Although analysts are doing the same type of job, with the distributed model there is often limited data and knowledge sharing, a duplication of effort and the value of the analytical effort is diluted.

With a centralised analytics model, how methods and processes are executed can have stronger oversight from the core data analytics team outsourcing their efforts to the entire organisation. They can optimise on each skillset their team holds and utilise them in a way that is most cost-effective and efficient.

Although it can be more resource and time intensive to set up centralised data analytics in an organisation, the value of the data, and the fact that it is much easier to set up a CoE within this model, are both strong positives. A centralised approach enables efficiencies that are not easily achievable otherwise, with the added benefit of being able to reincorporate tools, insights and lessons learnt back into the team to improve future delivery.