Enterprise Analytics: Key Challenges & Strategies

How can we grow our business faster? How can we better know our customers? What new market opportunities exist? How can we better tailor our new products?

Enterprise data analytics enables businesses to answer questions like these. It empowers analysts to model scenarios, forecast change, and predict impact of real or imagined events. Having a data analytics strategy is a key to delivering answers to these questions and enabling data to drive the success of your business.

Cbus, the Australian superannuation (penion) fund for over 150,000 employers, faced these challenges as they sought to provide better services and products to their stakeholders. To illustrate the enterprise analytics journey, we’ll pull examples from the Cbus story, showing how they transformed their way of working with data.

What is Enterprise Data Analytics?

One may define enterprise data analytics as the ability to find, understand, analyze, and trust data to drive strategy and decision-making.

Enterprise data analytics integrates data, business, and analytics disciplines, including:

• Data management

• Business strategy

• Data engineering

• Analytics forecasting

• Linear programming

• DataOps

• … and more!

The field has modernized rapidly in recent years. In the past, businesses would collect data, run analytics, and extract insights, which would inform strategy and decision-making. One step followed the other.

Nowadays, machine learning, AI, and augmented reality analytics are speeding up this process, so that collection and analysis are always on. This enables companies to adapt more quickly to change. Folks can work faster, and with more agility, unearthing insights from their data instantly to stay competitive.

Yet the explosion of data collection and volume presents new challenges. In enterprises especially, which typically collect vast amounts of data, analysts often struggle to find, understand, and trust data for analytics reporting. Immense volume leads to data silos, and a holistic view of the business becomes more difficult to achieve.

For these reasons, self-service analytics has become a popular solution for enterprises of all industries. This solution empowers enterprises to scale both analytics and fact-based reporting with timely access to data.

What are the Challenges to Developing an Enterprise Analytics Strategy?

Like many, the team at Cbus wanted to use data to more effectively drive the business. “Finding the right data was a real challenge,” recalls John Gilbert, Data Governance Manager. “There was no single source of reference, there was no catalog to leverage, and it was unclear who to ask or seek assistance from.”

Gilbert further breaks down the data challenges Cbus faced, saying:

• “ challenge was around understanding the data. We might have found some data but what does it mean? There was a lack of documentation and a very heavy reliance on IT and business SMEs.

• The third challenge was around trusting the data. If you don’t understand it then how can you trust it? There are inconsistent definitions and inconsistent metrics, and a lack of trust in the data used in the metrics.

• The fourth challenge was around using the data. If I don’t trust it, should I use it? There was a real lack of confidence in using the data and the risk of using the wrong data. Also, there were no guidelines on how to handle data, particularly sensitive information.

• The fifth challenge was around reusing the data: Now that I’ve used it, how can I share my insights? There was no mechanism for collaborating and sharing insights. Users couldn’t endorse it or encourage others to use it. Neither could they warn others against using it.

These challenges prevented us from enabling and encouraging self-service analytics,” Gilbert concludes.

Cbus is not alone. While many data leaders are under pressure to use data, analysis, and modeling to drive business strategies, putting these concepts into action is tremendously challenging.

On top of the challenges Gilbert mentions, analytics leaders commonly struggle with:

• Inability to use data

• Time spent managing data

• Risk vs. reward of lots of data

• Data not available to everyone

• Business needs do not always line up with data needs

Indeed, an analytics strategy requires a solid foundation. Data needs to be organized, accessible and visible, as well as protected and governed. To achieve the end goal, organizations need a sound enterprise analytics strategy that addresses the many challenges of making data actionable.

Why Do You Need an Enterprise Analytics Strategy?

Data leaders cite an analytics strategy as a key driver for success. Indeed a Microstrategy survey of business intelligence and data analytics professionals, The 2020 Global State of Enterprise Analytics, found that the most important foundational factor that executives at successful data-strategy enterprises cited was “the creation of an analytics strategy”.

This foundation is critical. As Cbus found firsthand, integrating a data catalog into their analytics strategy has empowered them to become more data-driven and insights-led. According to John Gilbert, this supports three core goals:

• Democratizing data: making it available to the right people at the right time

• Encouraging and enabling self-service analytics

• Implementing adaptive, active data governance

These three functions empower the business to decentralize the process for data-driven decisions, so they can more quickly and effectively.

What Are the Benefits of an Enterprise Analytics Strategy?

A strong data analytics strategy should involve the entire business. It should be clear and easy to follow, with regular updates, and reflect overarching goals.

A data catalog supports a robust analytics strategy, as it unifies disparate data sources into a single system of reference for all data users across an organization.

According to SAS Insights, enterprises need a data analytics strategy to:

• Develop a big-picture view of an organization’s data landscape (which can help reduce processing and storage costs)

• Set expectations for usage based on role and data source

• Spotlight friction areas and bottlenecks for data consumers (and build a solution)

• Create a blueprint of data architecture to find inconsistent definitions

• Build a roadmap for future data and analytics projects, like cloud computing

• Evaluate and monitor data quality

• Assess data risk and craft plans to mitigate that risk

• Collaborate on data analysis across departments and roles

• Set consistent data policies

Alation Data Analytic Services

The Alation Data Catalog streamlines data collection and analysis, creating a shared learning & collaboration platform for all data citizens within an enterprise.

Indeed, the catalog supports a wide range of use cases, including analytics. As John Gilbert of Cbus shares, “Essentially the catalog sits between the data and the use cases for data, whether it be an analytics or data science use case, a data asset management use case, a data governance use case, or a risk and compliance use case.”

“Alation fits within our data landscape,” Gilbert concludes. “It’s all-encompassing and plays a key role in supporting our data management activities and objectives.”

See for yourself how Alation can play a key role in your analytics strategy. Sign up for a custom demo today.