Organisations are increasingly using big data and analytics projects to serve a variety of business functions. For these projects to come to fruition and deliver enhanced results for business, they need to be managed with a consistent set of best practices.
In our data and analytics maturity framework, ‘best practice’ is defined as “The measures in place to ensure development and quality standards are met and align to predefined corporate standards”. Leaders need to ensure that these measures are in place not just for the overall data and analytics strategy but to cover each project undertaken beneath that strategic umbrella.
This guide will list some of the best practices to consider for data and analytics projects.
1. Define an approach to solving the problem
Make sure every project starts out by defining what problem it aims to solve, and how it’s going to get there. How can your team ensure that any insights are actionable, and that the actions they take are right?
There needs to be clarity around the purpose of the project, and the different phases within it which will build towards the overall insights it will aim to deliver. In addition, the entire data ‘journey’ has to be determined – once it has been collected and dashboards built to represent it, end users will need the right tools and instructions in place to action those insights.
2. Scope and define requirements
As with any business project, it is critical to fully understand exactly what the stakeholder needs and how it will provide value. Data and analytics projects will require the definition of data sources, and you’ll need to establish which KPIs and metrics you’re going to use to quantify and measure performance. Whatever domain is undertaking your project, there needs to be some way for its performance to relate to both their departmental targets and the overall business objective.
Additionally, it’s important to think about how the analytics is going to be consumed, so design your dashboards with the end users as a priority. Otherwise, they may not be able to correctly interpret data and action analytics insights.
3. Iterative & agile build process
Many analytics projects fall into the trap of the ‘waterfall approach’, whereby the build process is sequential. With ‘waterfall’ methodologies, each step must be completed before another can start, which leads to delays or an inability to change direction as the specifications of the project change.
To avoid these bottlenecks, embrace an interactive build process with agile delivery, and encourage a ‘fail fast and learn’ mindset. Learn what works best by crowdsourcing feedback from different business domains and end user groups. This will help to instil an agile project management process and allow for elaboration and additions as the project progresses.
4. Plan the roll-out initiative
Make sure every data and analytics project has a clear plan for how the end product will be rolled out to end users. Who will have access to it, and what functionality and insights should they expect to leverage? Do they know when they’ll be receiving these insights and how to make the most of it?
A lot of the success in this context boils down to training and good communication. It’s vital for any data-driven organisation to equip their teams with the skills needed to collect, interpret and action data insights effectively. For more information on how to do this, check out our guide to designing an effective data and analytics training programme for your business here.
With regards to communication, make sure all stakeholders involved in your data and analytics projects are involved in defining project goals, requirements, refinement, outcomes and success measurement.
5. Deploy the analytics
Building on the roll-out initiative, look to coordinate the technical roll-out with change management tactics. Always keep this fact in mind: analytics is an enabler of better business performance, not an end objective in itself.
As such, make sure you have the plan and the tools in place for teams or individuals to actively deploy this analytics in their respective functions. Clearly define how the analytics relate to broader business objectives and how they can be used in different domains.
Data and analytics can only be utilised effectively if your organisation is data literate. To learn how to assess your organisation’s data literacy, read our article here.
The potential of data and analytics to revolutionise business results is exciting and seemingly limitless. But organisations should establish and encourage adherence to a set of best practices for each data and analytics project, as described above, in order to ensure consistency and quality of output.
This is a crucial part of establishing a data-driven culture and equipping your teams with the skills and knowledge they need to thrive as a data mature organisation. It can help you through not just the initial transition but inform your long-term data and analytics approach and set you up for success.
This article is part of our Data-Driven SMB series. For more information, advice and resources on how to accelerate your organisation’s data and analytics maturity, click here or contact us today.
Senior Consultant With over 5 years’ experience in providing analytics business solutions for clients, primarily across Public Sector, Healthcare and FMCG organisations. Ciaran holds a MEng in Mechanical Engineering and specialises in analytics enablement using platforms such as Tableau, Power BI and Alteryx.