Traditionally, data science teams are full of brilliant people working solo, tapping into their own data sources, running things on behalf of a department, and not the entire business. But a transition is afoot, and it can be seen in the adoption of ModelOps, driven in part by the need for data science teams to be more sophisticated and grown-up, combined with the desire for better, reusable analytics frameworks, that leverage the power of a group versus the power of an individual.

Why now? Why focus on data? Analytics has proven itself to be recession-proof. If we hark back to the economic downturn of 2008, those companies that were able to find an extra couple of points of margin or customer satisfaction, generally had a better analytics strategy they could execute on. So, they survived because they knew the combination of data analytics and the ability to act on insights gave them a survival edge. Today we have a different downward trend based on the global pandemic. So analytics is once again being used as a survival tactic, but it needs to be better managed, and that is where ModelOps comes into play. 

ModelOps as a proficiency

The notion of ModelOps is on everyone’s radar as a proficiency or a skill set needed to scale analytic practices. Companies face a challenge in that they may have the ability to build models but come undone when they need to deploy them, monitor them, test them for accuracy and performance, and move them off the workbench as a proof of concept and finally into production. 

With ModelOps you coalesce or aggregate and bring together the data science teams and the models that come with them, to better test AI and ML-driven logic, automate decision making and ultimately be more competitive. If you want to scale AI and ML into the rest of the business and infuse it into other critical applications, you need to focus on centralising a lot more of these practices. 

You cannot scale from a handful of models used in pockets of excellence to hundreds if not thousands of models without adopting an altogether different approach to modelling, design, and data science. This is where ModelOps becomes critical.

Tools, so many tools

Every data scientist has a preferred tool, whether that is R or Python, which is why it is essential to view them as data artisans or artists. These are inventive people who like to use their imagination as much as they need to apply math to a problem. It is this combination of math, algorithms, and creativity that drives analytic practices forward and propels them quicker.

It is this sheer number of tools that are driving the need to bring together data science assets and move them into an Analytic Centre of Excellence (ACE). Instead of allowing data scientists to work in silos inside of departments or far-flung organisations, we must bring together the tools and the people, so we benefit from a community approach – and it really does take a community to drive data science and analytics.

An ACE allows us to scale in ways that we couldn’t before by eliminating redundancy and standardising our approach to analytics which is key to ModelOps and scaling analytics practices. There is no sense in having multiple data scientists working cross purpose in different departments but on similar projects – you need to be able to rinse and repeat. Take the picture they have painted and let someone else use it, no matter what paintbrush they used. This way, you save time, resources and money at the end of the day.

When this is in place when it comes to deploying models it doesn’t matter what paintbrush a data scientist used, because in your Centre of Excellence built around a ModelOps model there is a centralised platform from which they are deployed and where they are managed. This negates the cumbersome siloed approach and brings efficiency to data practices.

Step one – take the leap

Building a ModelOps environment takes commitment to change, and this must come from the C-suite as much as the data scientists. Generally, an ACE should be owned by a chief analytics officer (CAO) or at the very least, the chief data officer (CDO). Someone who has a direct line into the CEO and can roll their ideas up to the very highest level of the business and are empowered to disrupt and make changes.

If you try deploying ModelOps and a Centre of Excellence without this C-level oversight, your people are going to face an uphill struggle with the business. Even smart companies fail at this because, without the authority, they bump into politics and land disputes as there are a lot of fiefdoms and kingdoms built around data and the analytics that go with it. 

Step two – reap the benefits

The next step is harnessing the collaborative aspect of ModelOps. There are always ten ways to solve a data problem, and some are more correct than others, but when people are collaborating in a Centre of Excellence, the real magic starts to happen.

I cannot stress enough how this approach to analytics helps save time and money and ensures consistency. When people are operating in defined frameworks, you get uniformity via a security framework and a data access framework where your data scientists can go to build their applications and solve data problems. Bear in mind most companies don’t battle with building models – they battle with deploying them. An atrocious number of models die on the data scientist’s workbench, simply because most companies haven’t addressed the idea of deploying, monitoring, recalibrating, and optimising their models. 

Just because a model works today does not mean it will work tomorrow – because data changes. When you are monitoring this centrally, you won’t feel the impact because a centralised tool helps interrogate the models you have in flight. The pandemic is an excellent example of how the data we knew to be true in February was entirely out of whack by May. Now when you factor in the need to scale models from a handful to thousands – this is where you will see the real benefit of ModelOps.

It takes a village

Having a good data analytics practice takes a village and a Centre of Excellence pools together data scientists, data analysts, business analysts, people from IT and the business. This gives you a much better global view of your challenges. When this village is working together, you can measure twice and cut once for the good of the whole business, not just a department.

Another benefit of ModelOps is not dictating to your data scientists what paints or brushes they use to paint their data picture, but rather centralising from where they deploy it. With this, you can see where code is, know who wrote it, access the original code, and rewrite or reuse it in a different environment. If we look where companies need to go on their analytics journey, to leverage the power of analytics truly, a lot of it must get automated and centralised. But it is a give and take from both sides; you can no longer pander to the whim of the data scientists and leave them in the basement or a closet just doing their own thing. 

ModelOps allows your business to become smarter at analytics. I always say just because you can, doesn’t mean you should. A Centre of Excellence will help you develop the knowledge to know when enough is enough, give you insight into when specific ideas have been exhausted, and more importantly allow you to focus on what is working and how you can build more of that. It also applies frameworks to data bias or even algorithmic bias with AI and ML – which is invaluable. 

At the end of the day data evolves every day. And as it changes, you need a ModelOps framework in place and software that enables it to keep pace with this constant evolution so that your models keep operating at the highest possible output and your business can embrace sustainable innovation.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.



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