To deliver strong ROI, analytics capabilities need to be able to scale. But enabling scalability across highly tailored use cases isn’t always easy.
Across the modern enterprise, every team wants something slightly different from analytics. They have their own goals, own data, and own KPIs—leading many teams to create and deploy their own analytics solutions with available resources.
Over time, that’s created a highly fragmented analytics landscape across many organisations. Siloed solutions stagnate within individual teams and lines of business, creating an environment where:
- Effort and investments are duplicated across teams, wasting vital resources that could be better spent on driving analytics innovation.
- A lack of consistency in how analytics is approached and how success is measured leads to varying recommendations from different teams.
- Solutions are poorly maintained, because there’s no centralised resource dedicated to keeping them operating efficiently or improving them over time.
- Teams can’t directly benefit from each other’s analytics innovations, or utilise existing models and solutions in new ways.
- Solutions are limited in their scope, only pulling in data from limited sources and ignoring other data that could add vital and valuable context to insights.
The missing piece in environments and organisations like these is scalability. When teams push ahead with their own siloed analytics projects, the solutions they create can’t scale—making it far harder to realise high ROI from them.
Unfortunately, there’s one big reason why that all-important piece is still missing across a lot of enterprise analytics landscapes: It’s tough to enable if you don’t have the right strategy and support.
Why has enabling analytics scalability proven so hard for so many?
There are three main challenges that limit organisations’ ability to scale their analytics solutions and investments today:
1) Disparate and inconsistent data
When an individual team builds its own analytics model, it builds around its data—designing models that make the most of the data sets it has available, whatever their type or quality may be. Models created for and driven by those data sets become incredibly tough to use in different contexts, where the same type or quality of data isn’t available. There’s no interoperability, so the models can’t be scaled elsewhere.
2) Low visibility and understanding across silos
f one team doesn’t know about an adjacent team’s existing analytics investments, they can’t leverage and customise them for their own use. Siloed creation and management of analytics capabilities creates cultures where people simply aren’t aware of where and how the organisation has already invested in analytics—leading to significant duplication of effort and increased costs for the enterprise.
3) Scalability is hard to build in retroactively
When a team identifies a new internal use case for analytics, they rarely stop to ask ‘how could other teams or markets benefit from what we’re creating?’ As a result, solutions are built with a single purpose in mind, making it difficult for other teams to utilise them across slightly different use cases. Instead of building a widely usable foundation, then customising it for each team, solutions are designed for a single team at a core level, making them tough to repurpose or apply elsewhere.
Best practices for highly-scalable, high-ROI analytics
To overcome those challenges and unlock the full ROI of highly-scalable analytics models and solutions, organisations need to fundamentally change the way they think about, design, and manage analytics capabilities.
Here are four practices helping organisations do that effectively:
Start with a standardised foundation
Each team across your organisation needs bespoke, tailored capabilities to get the most from analytics. But, that doesn’t mean they have to build their own solutions from the ground up.
By having a centralised team create a customisable, standardised foundation for analytics, teams can create exactly what they need in a consistent way that enables interoperability and sharing of models and insights across the enterprise.
With an analytics centre of excellence (CoE) for example, a centralised team can create everything each team needs for their unique use cases, and add value for that team by including insights and capabilities that adjacent teams have seen value from.
Bring data science and data engineering closer together
Even today, many still view analytics as the exclusive domain of data scientists. But, if you want to enable scalability of analytics models, data engineers need to be involved in the conversation and decision-making.
Data scientists may build the models and algorithms that generate analytical insights, but it’s data engineers that ensure there’s a consistent, interoperable data foundation to power those models. By working closely together, they can align their decisions and design choices to help scale analytical capabilities across the business.
Zoom out and get some external perspective on what’s possible
If you want analytics investments to deliver broad value across your organisation, your projects should start with a broad view of what analytics could help you achieve across multiple use cases. Practically, that means:
- Identifying high value and complementary use cases together and designing for them in tandem.
- Find the commonalities between different analytics needs that individual teams don’t recognise.
- Going to teams proactively with capabilities and insights that can improve what they do, rather than waiting for them to request specific capabilities.
- Looking at internal and external data holistically to determine where the most valuable insights may be found.
- Carefully considering how analytics can support high-level business goals, and the overall strategy of the organisation—not just individual teams within it.
Continuously learn and improve
Analytics always requires some degree of experimentation, and you can’t realistically expect every single use case to deliver high, long-term value. But, even if they’re unsuccessful, organisations should take steps to learn from each of them.
Within an enterprise, someone needs to take responsibility for learning from each use case explored. That person or team can then apply those lessons across new use cases, and use them to develop assets and modules that can be reused across geographies and domains, extending and increasing the value they deliver to the business.
Scalable analytics in action
The Smart Cube has been helping organisations across a wide range of industries create, deploy, and manage scalable analytics capabilities for many years. During that time, we’ve seen the power of that scalable approach in action—delivering solutions that grow in scope, value, ROI, and maturity over time.
Most recently, we’ve worked with a major global beverage company, supporting its marketing teams to generate and act on deep customer and market insights. It started in the US, where solutions were developed to help improve the ROI of the company’s marketing efforts and promotions. Then, once the model proved valuable, it was tweaked so that it could easily be picked up and applied by teams in Europe. Now, the global team develops models to provide support with customer understanding, deliver insight from across the distribution network, and fine-tune Marketing Mix Modelling—all built on the same foundation, but customised to deliver the most value for local teams.
In the UK, we followed the same principles when supporting a major retailer’s analytics teams. A central team developed capabilities for a small number of categories, then once they were proven, tweaked them to deliver the same insight across a wider range of categories. Its linear scalability for what can be an extremely nuanced and complex technical field.
To find out more about The Smart Cube’s analytics capabilities, and learn how we combine AI and HI to help businesses create, deploy, manage, and improve highly-scalable analytics capabilities that deliver strong ROI, visit our Commercial, Sales & Marketing solutions today.