Six success factors for building a successful data analytics organisation

By Dennis van der Linden, senior manager – data, Valcon

Being a data driven organisation is something a lot of companies aspire to. They recognise that leveraging insights from data the organisation already has will ultimately lead to better decision making. But it’s something that almost 70% of them have failed to achieve, according to statistics from a Harvard Business Review report. The study also found that 72% have yet to forge a data culture. So what’s standing in their way? 

A lot of organisations realise what they need to do and make a lot of effort to integrate data from various source systems into a data warehouse or data lake for analysis. But it often doesn’t work. One reason is that analytical efforts are often uncoordinated, which can lead to multiple versions of the truth. Another common problem is a lack of adoption by the business – this might be because the output doesn’t align with their business objectives, or they might simply misunderstand or misinterpret the insight. Another problem is that business leaders can face long development timelines for the insights they urgently need in their decision making. 

The answer to data analytics doesn’t just lie in technology solutions. It’s about creating the right culture for a data analytics organisation to be effective, which means bringing elements of the business and IT together to enable data driven decision making

Here are six key steps you need to think about towards building a successful data analytics organisation (DAO): 

  • Alignment with the business strategy: to be successful, the data analytics initiative has to align with the overall business strategy. Not only will that ensure organisational buy in, it will boost adoption – people will engage with it. Constant testing of use cases with these objectives will also help prioritise the initiatives and ensure they’re always aligned with the business goals. 
  • Organisational set-up: there is no silver bullet to what a DAO should look like. For example, analytical efforts can be organised in a centralised, de-centralised, or a federated way. But it should provide a balance in creating one trusted view on company performance, while allowing for business unit specific demand for insights. A common way to do this is to set up a centre of excellence (CoE) providing platform standards and training, with development of analytical use cases in each business unit. But an effective DAO is not an IT department – it needs to bring IT and the business together in order to incorporate data into decision making. 
  • Establish a common language: the business speaking the same language is a vital step. Take ‘customer’ – the sales department might only consider a customer to be someone who has bought something in the last year, whereas marketing might consider everyone from the target demographic to be a customer. When we want to leverage data across the organisation having clear definitions and a common language is essential. It helps to determine in which context a dataset can be used to derive accurate results. 
  • Training: providing the right training for data engineers and front-end developers to assure solid data pipelines and dashboards is key. But effective training goes further. Business stakeholders need to have the right data skills to help them adopt new ways of working and embrace new data driven decision making processes. 
  • Make data discoverable: sometimes a data analytical product doesn’t succeed because employees simply don’t know it’s there. Data has to be discoverable – in the form of a knowledge base or a catalogue – throughout the organisation. This doesn’t mean everyone should have access – we have privacy and security regulations for a reason – but they should know what is available, in which context it can be used and which processes to follow to gain access if they have a legitimate reason for using it.
  • The right data platform architecture: ok, maybe it is a little about technology as well. The data platform architecture affects which business requirements can be achieved and how teams can work together on the platform. A good example is storing raw data and making it accessible for a data analyst to provide initial insights could provide a lot of value when urgent business questions arise. So the way you get people to collaborate with data should be reflected in your data platform architecture and processes.

Becoming data driven requires more than just an investment in technology. It’s a mind set and requires a massive cultural shift. Ultimately, it’s about effective collaboration between business leaders and IT professionals through the medium of a data analytics organisation. It’s only when you get to this level can you enable your employees to leverage data in their decision-making processes. And when you do, the possibilities are endless. 

Would you like to find out how Valcon can help in getting your data analytics organisation to the next level? Contact Dennis van der Linden at Valcon: [email protected]