Data has become the lifeblood of many organisations as it is an integral tool to keep them competitive and relevant. To become more data-centric, there is a process that can be followed. We present five important steps.

 

Increasingly, executives and organisations want to have data to support or inform their decisions. However, all too often, the needed data is not readily available in the Caribbean organisations, resulting in either proxy data from other jurisdictions or industries being used, or there is a reliance on hunches and anecdotal information to make important decisions.

However, many Caribbean organisations tend to think of themselves as data-centric, since they have some targets and metrics in place that can be referenced on a regular basis. Unfortunately, that is not enough. Being data-centric suggests that there is a deep integration and reliance on data across all levels of the organisation, which is more than having it as a useful reference that is presented on the odd occasion.

Below, we outline five steps organisations ought to take to establish a data-driven culture

1.  Foster a data-driven culture

 

Although fostering a data-driven culture will be a work in progress, as a first step, there ought to be a vision for what the organisation hopes to achieve. To that end, a critical examination of the organisation’s strengths and weaknesses ought to be done, which to some degree, should be substantiated by data.  

Further, the effort to foster this paradigm should be collaborative, with the input and participation of all of the teams across the organisation. Through those discussions, it is likely that team members will begin to: identify pain points that can be improved, data sets that may already exist that are not being fully leveraged, and opportunities that could emerge if sufficient data is available. Additionally, they could share their own views and expectations of what a data-driven culture should look like, and its potential impact on them individually, their respective teams and the organisation as a whole.

 

2.  Hire or upskill workers and implement systems to collect and handle data.

Having suitably skilled individuals and well-established processes will be critical to ensuring that required and identified data for an organisation is not only regularly collected but is also processed and analysed in a timely fashion, in order to be useful.

Further, fields such as business intelligence and data analysis have become increasingly sophisticated, requiring specialist knowledge and expertise. Experienced analysts can help teams identify data that they could be generating and establish the requisite systems to collect them, and thereafter, to process it and assist in the analysis process.

 

3.  Ensure that all available data is being collected and aggregated

Following the previous step, there ought to be an emphasis on identifying all of the data that is available or can be generated, and on collating them. Typically, data will be available across the various teams. Some may be ad hoc whilst others may be regularly collected over an extended period of time. However, not all of the data might be digital or in an editable format, and all of these issues would need to be addressed at this stage.

 

4.  Get familiar with the data and what it can do

Having collated and aggregated all of the available data, the next step would be to determine all of the ways in which it can be manipulated and the types of information that can be derived. This review should be conducted across the entire organisation and should comprise department-specific data, that would help the respective teams in executing their roles and functions.

Although we tend to think the role of data is to assist the organisation in making big decisions, its true value is in the daily insights that it can provide teams through which they can better understand their work, monitor their progress in achieving targets, optimise existing processes, and innovate.

 

5.  Leverage the data for predictions and recommendations

Finally, once there is a clear understanding of the data that is available and the information that can be derived, a basis can be established for using that data to predict longer-term outcomes. To a considerable degree, building robust projections requires a comprehensive framework that draws data from a variety of sources.

Depending on the size of the datasets, the processing could be complex. However, it is emphasised that the information produced should be an input to the decision-making process. There are likely to be several variables and conditions that underpin the outputs produced, which ought to be factored into the decision-making process.

 

 

Image credit: UX Indonesia (Unsplash)