3 Innovative Ways to Scale Up Analytics

After years of experience in building and deploying analytics and AI solutions, we’ve learned a thing or two about scaling up analytics. And one of them is keeping technology – not people – at the center of the analytics process.

It may seem counterintuitive, as technology is all about serving the needs of humans. But if you read on, you’ll see why we insist that tech comes first.

To successfully scale up analytics in your organization, focus on the tech, not on the people. While people are certainly an important part of data analytics (as well as the ultimate source of most of the data), the right technology will propel your business forward faster and more efficiently.

Make no mistake about it: data scientists and other data-related professions are vital to data analytics. But people are not as scalable as technology; their capacity is finite and their expertise too precious to waste on analytical grunt work. So, give them the right technical framework (as we discussed previously) and set them up for success.

Focus on Technology to Scale Analytics

As a data-focused company, we have decades of experience in implementing analytics solutions. We’ve seen that the most effective approach to analytics requires three innovative shifts: from models to APIs, from single solutions to reusable components, and from independent tools to integrated platforms

APIs

Traditionally, the technical focal point of analytics has been the model building process: making it faster, more accurate, and more efficient. Even so, there’s still considerable input coming from data scientists: refreshing the models, scaling or adapting them across brands, uses, departments, data sources, etc.

This hybrid process still isn’t fully efficient. Instead of focusing on models, why not build APIs? It would require thinking of scalability from the start of your analytics projects. And you’d have to add software development skills to the analytics team, but the final product becomes much more scalable and flexible.

Reusable Components

Analytics already has a well-defined set of standard methodologies that can be used to build different models and solutions. Typically, though, we see this applied to a custom-built solution for every use case – leading to a lot of unnecessary duplication and expense.

Why not shift to creating a generalized solution to each type of problem? Much like functions in functional programming, these solutions can be used as components over and over again.

Take customer segmentation as an example. There is an established group of techniques that we use to segment customers, and we usually do them over and over again each time we want to apply analytics to some problem. Instead, why not develop a standardized segmentation solution with editable input parameters? This would allow teams to get the same result with much less work. Over time, organizations would develop a library of these time-saving reusable components.

Integrated Platforms

Currently, most analytics solutions could be considered ‘point solutions’ – developed to be used independently on distinct use cases. But this approach doesn’t take into account that nothing in a business is truly independent; for example, a product’s price will influence its marketing and advertising. Plus, such solutions often provide less-than-optimal results.

A high-performing analytics solution should be able to model the complex, interconnected world we live in. Digital twinning is one way that we can model many factors to get a complete picture. Even for less complicated uses, integration is important; it reduces time and effort poured into developing models and solutions. More importantly, it ensures that all models work together and have access to all the data they need, regardless of what team or department ‘owns’ that data.

Are You Ready to Shift Your Analytics Approach?

Shifting to this forwarding-thinking approach does more than just make it easier to promote, scale, and adapt analytics across an organization. It cuts down on wasted time and money. Most importantly, it makes analytics more impactful and drives better user adoption.

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