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To deliver strong ROI, analytics capabilities need to be able to scale. But enabling scalability across highly tailored use cases isnt always easy.

Across the modern enterprise, every team wants something slightly different from analytics. They have their own goals, own data, and own KPIsleading many teams to create and deploy their own analytics solutions with available resources.

Over time, thats created a highly fragmented analytics landscape across many organizations. Siloed solutions stagnate within individual teams and lines of business, creating an environment where:

The missing piece in environments and organizations like these is scalability. When teams push ahead with their own siloed analytics projects, the solutions they create cant scalemaking it far harder to realize high ROI from them.

Unfortunately, theres one big reason why that all-important piece is still missing across many enterprise analytics landscapes: Its tough to enable if you dont have the right strategy and support.

Three main challenges limit organizations 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 datadesigning models that make the most of the available data sets, 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 isnt available. Theres no interoperability, so the models cant be scaled elsewhere.

#2) Low visibility and understanding across silos

If one team doesnt know about an adjacent teams existing analytics investments, they cant leverage and customize them for their own use. Siloed creation and management of analytics capabilities create cultures where people simply arent aware of where and how the organization has already invested in analyticsleading 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 were creating? As a result, solutions are built with a single purpose in mind, making it difficult for other teams to utilize them across slightly different use cases. Instead of building a widely usable foundation, then customizing it for each team, solutions are designed for a single team at a core level, making them tough to repurpose or apply elsewhere.

See also: Moving to the Cloud for Better Data Analytics and Business Insights

Organizations need to fundamentally change how they think about, design, and manage analytics capabilities to overcome those challenges and unlock the full ROI of highly scalable analytics models and solutions.

Here are four practices helping organizations do that effectively:

Start with a standardized foundation

Each team across your organization needs bespoke, tailored capabilities to get the most from analytics. But that doesnt mean they have to build their own solutions from the ground up.

By having a centralized team create a customizable, standardized 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 center of excellence (CoE), for example, a centralized team can create everything each team needs for their unique use cases and add value for that team by including insights and capabilities from which adjacent teams have seen value.

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 the 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 data engineers ensure 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 whats possible

Suppose you want analytics investments to deliver broad value across your organization. In that case, your projects should start with a broad view of what analytics could help you achieve across multiple use cases. Practically, that means:

Continuously learn and improve.

Analytics always requires some degree of experimentation, and you cant realistically expect every single use case to deliver high long-term value. But, even if theyre unsuccessful, organizations 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.

See original here:

Scalability: The Key to High, Long-Term Analytics ROI - RTInsights