Five Essential Strategies To Boost Your Data Science Team’s Productivity

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A word familiar to most of us, productivity is measured in terms of the rate of output per unit of input. For the modern enterprise, this means striving to do more with less resources, a goal you cannot achieve without effective and efficient decision making. Here at Mango, one of our main aims is to enable companies to make proactive use of their data to drive better decision making allowing them to create value from insight. This alone is a pathway to improved productivity.

Why then are many companies disappointed with the return on their data science investments? To put it in perspective, around 87% of data science projects never make it into production.

The thing is, you may have the best data scientists who can program, model, visualise and wrangle data, but that is not enough. For a data science project to be successful, there needs to be more than some data science ‘unicorns’ doing their data science ‘stuff’ independently of each other and the business to support a project or use case here and there. Data science is a team sport that thrives in a company with data at its core, where there are understood methods for collaborating across both technical and business departments enabling a data science product to be maintained and utilised across its lifespan.

Here are five key areas to consider which will ensure success of your data science outputs:

1.The data itself

Data drives decision-making. If the quality of your data is poor, the outputs and resulting decisions will be poor. For data science teams to work productively and deliver effective results for the business, the starting point is with the data itself. Data that is accurate, relevant, complete, timely and consistent are the key criteria against which data quality needs to be measured.

Good data quality needs disciplined data governance, thorough management of incoming data, accurate requirements gathering, strict regression testing for change management and careful design of data pipelines. This is over and above data quality control programmes for data delivery from the outside and within.

2.Collaboration tools

Having the best quality data in the world will be useless if you do not have the tools to allow people to work together on development projects. Tools for version control and collaborative development are key to extracting value from your data. Git, RStudio and Jupyter are becoming go-to tools to enable your data scientists to manage and develop their code. The ability to provide these tools on a centralised server, accessible from anywhere and without computational constraints of a laptop, mean that you have the best chance of being successful.

In addition to these collaboration tools, you also need to cooperate on the wider project – shared platforms such as Trello, Planner or JIRA offer a great platform for sharing to do lists and help understand generally how projects are progressing.

3.Communication tools

Gone are the days where organisations can afford to operate in silos. Maximising productivity requires bringing teams together to collaborate across the business as a community that shares best practice. The adoption of effective communication tools, particularly during this period of remote working, is the only way to enable this community to thrive.

Mango relies heavily on instant messaging tools such as Microsoft Teams, which offers a great way for our team to communicate and share their own tips and tricks. We also conduct a weekly analytics club for showcasing ideas and progress of projects.

4.Stakeholder engagements

Once there is quality data, and communication and collaboration tools to support teams, it’s vital to secure buy-in and understanding from key stakeholders across the business. Data science is often accompanied by its own language, so fostering collaboration and a mutual understanding of what’s possible with data for stakeholders is vital. In the same way, by sharing with data science teams the direction in which the business wants to or needs to move, stakeholders are empowering teams with the necessary information to make sure analytical outputs support these goals.

5.Best practices that lead to long-lived business results

In order to make sure that project outputs are of an appropriate quality, and that level of quality is achievable again, processes and ways of working must follow best practice. You can aid your teams to follow best practice by developing a framework for them to work within. Standardising these approaches – take a look at Mango’s 4-step grid in the image below – ensures that everyone in your team knows their role and can generate a quality output time and time again.

The productivity of a data science team itself, and the business as a whole, relies on more than just tools, or training, or the right resources. Boosting productivity and achieving the most value relies on being a team. Data science teams will thrive in a company that has a data-driven culture, with a central platform where they can work together to efficiently produce repeatable results in harmony with the business objectives.

What’s holding you back?

If you are keen to adopt open-source data science software at scale and you need a production-ready environment that’s configured to your business but require help on where to start, Mango can help.

We can advise, install, support and train your teams on your RStudio production ready environment so you can share, develop, publish and manage data at scale – in a controlled, reproducible way. Contact us now and we’ll get you started.

 

Related content:

Podcast: Data Engineering – the key to extracting vale from your data

Blog: Future Proofing Your Data Science Team