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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 value from your data

Blog: Future Proofing Your Data Science Team 


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Here at Mango, we are often asked to come and help companies who are in a mess with their data. They have huge technical debt, they can’t link all their data sources and the number of reports they have has ballooned beyond control. Everyone has their own version of the truth and business units are involved in ‘data wars’ where their data is right and everyone else has the wrong data. How does this happen? Put quite simply, hires are focused for the ‘shiny’, interesting aspects of data science where it is easy for the business to see how they get value from that hire – business intelligence (BI), management information (MI), or Data Scientists. This ignores the more technical and less exciting but essential pillar of delivering business value: the data management and data engineering pillar which is critical to underpin any data-driven business.

The thing is, you may have the best data team who can programme, model, visualise and report with data but without well-managed, curated data, over the longer term your systems and processes will be thrown into chaos and your data will become unmanageable. This isn’t because these analytical professionals aren’t doing their job, it’s because their job is extracting value from insight, not making sure the machine behind it all is ticking over smoothly. In F1, the driver would be useless without a whole range of engineers and mechanics. If your business only has BI and MI analysts or Data Scientists, you are asking the driver to win an F1 race with a Morris Minor – you need a Data Engineer.


Turning data into wisdom – the role of the Data Engineer

Why does this happen? Quite simply, organisations often might look at the price of hiring a senior experienced head of data/data engineering or a building a data management function and decide they don’t need one and instead hire a significantly cheaper BI resource instead, expecting this person to do it all. As a role, a head of data/data engineering has changed massively since the advent of advanced analytics and now requires both specialist and strategic knowledge to build the reliable systems to collect, transform, store and provision data for analytics or other complex purposes.  The right technical infrastructure required to turn the data into wisdom in a repeatable manner bridge the gap between strategy and execution.

From assessing a proliferation of data silos to hard to maintaining “legacy” data processing systems are just common challenges and with modern platforms, data warehouses are a more collaborative affair than ever before, many of the same principles still hold. A data engineer understands data modelling techniques to build data warehouses that can be trusted, maintained, and that deliver exactly what analysts need.

It’s a false economy to overlook the critical engineering needs that a data-driven busines has. There is also cost in fiscal terms. With poorly designed systems that don’t perform, we have seen costs of transformation projects moving to the cloud double purely because of poor data management. Add to that the cost of having to constantly upgrade database servers so they can keep up with the ever increasing workload and lifetime costs get even higher. This ignores the harder to quantify opportunity cost of not being able to leverage your data, or the cultural impact of business units arguing because they have a different data-driven view of the business.

Its essential to look at the investment in an appropriate data function holistically in terms of long term gain through increased opportunities to leverage data and make better decisions, a more efficient cost base for your technology over the long term alongside an easier transformation pathway when you need to evolve as a business. Without taking that long-term view of your business, it can be hard to see how a data management function can add value. However, without one, the opportunity for improved insight and the cultural benefit of happier staff who understand how to leverage data in a way that is sustainable and beneficial to all involved will be lost.


The Key to Extracting Value from your Data

Organisations need a good data engineering function to access the right data, at the right time, and with sufficient quality to empower analytics. But what is the definition of a data engineer’s role and why is this function so crucial to bridging the gap between strategy and execution when it comes to delivering a data science project?

As data experts, we know what companies need to do to become data-driven. If you are struggling to see how a data function fits in your business or don’t know how to move to the data-driven nirvana, we can help guide you on your whole journey, from first steps through to decisions being made from a ‘data first’ mindset.

Author: Dean Wood, Principal Data Scientist

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Navigator Workbench is updated to version 9.5.1 to provide enhanced auditing and reporting functionality

Navigator Workbench 9.5.1 is the latest version to be released and is now available with enhanced auditing and reporting functionality. Navigator Workbench Streamlines PK/PD modelling for pharmaceutical and clinical research allowing organisations to make more informed decisions, faster.

These enhanced improvements to its audit reporting functionality, means that users can now view their audit trail alphabetically and includes entries for files that have been updated but also contain identical content.  As an optional reporting function, users can now optionally include items from previous reports, alongside the original reporting parameters.

Developed in collaboration with modelling teams and already used by leading pharmaceutical and clinical research organisations, Navigator Workbench allows teams to collaborate on projects, share results, manage permissions and roles as part of the Quality Control process.

Ashley Mandell-Lynn, Product and Managed Services Director said of the latest release:

We’re delighted to release this updated version of Navigator Workbench and continue to work closely with our clients in assessing and developing the Navigator Workbench platform to suit their ever-changing needs and working requirements.”

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Virtual Classrooms – bringing data science teams together

Throughout lockdown, we’ve all been encouraged to tap into virtual training opportunities – either through free online learning or professional online training courses. What isn’t being talked about is the reduction in group training – and the positive effects virtual training can have on individuals when they are able to share the experience and interact with each other as a group.

Joint training sessions have very quickly been pushed to the wayside as focus is switched to the needs of individual stuck at home.  But is this the right way to go? Surely, training as a team is an integral part of enabling individuals to feel part of a team again, bringing likeminded colleagues together to interact and socially engage for a common goal – to complete the training.

It’s all very well us undertaking personal training while at home, but it’s the exchange of knowledge and sharing of frustrations that naturally come as part of the training process that is currently being missed – if we’re stuck on a subject or training module, then we want the opportunity to voice this with our peers rather than struggle on.

Virtual training courses and workshops that are delivered live to a group by a trainer are the most impactful. Chrissy Halliday, Mango’s Customer Success Manager said:

I’m frequently engaged with our customers about education and training programs and I’ve found a common theme emerging during this period of lockdown; keeping remote data science teams connected is vital whilst striving to provide a sense of “business as usual” during these challenging times. Everyone has quickly adapted to the new virtual world we find ourselves in, and training is a wonderful way to empower teams with the latest tools & methodologies to continue to deliver value.”

Virtual team training enables individuals to talk and discuss amongst the group, allowing the trainer to advise and demonstrate in real time to their audience, ultimately providing the whole team with a platform to question, absorb and understand the subject matter.

At Mango, we believe that face-to-face training is key to delivering content flexibly to our clients. Our ‘virtual classrooms’ provide attendees with valuable access to an experienced senior Data Scientist, who is on hand to answer any queries that inevitably crop up throughout the training session – just as they would do in ‘normal times’ when delivering face-to-face training on premise to our customers.

Get in touch with us training@mango-solutions.com