Laying the Foundation for an Enterprise Data and Analytics Roadmap at Data Summit Connect 2020

The starting point in developing and launching an enterprise data and analytics strategy is to understand the interrelationships that are necessary to deliver analytics capabilities.

These relationships also account for skills and roles of everyone who works with data, from business executives and business analysts to data scientists.

To measure and drive success, an actionable road map, with each phase focused on being lean with a business impact, is required.

John O'Brien, principal advisor and CEO, Radiant Advisors, held a workshop titled, “Build An Actionable Roadmap For Enterprise Data And Analytics” during Data Summit Connect.

The annual Data Summit conference is going digital this year with Data Summit Connect, from June 8 –June 11 due to the ongoing COVID-19 pandemic.

“We help companies quite often figure out what their needs are from a data strategy perspective,” O’Brien said.

To embark on building a roadmap, companies must look at the data culture they are looking to foster. The different types of cultures include the goal of empowering people with self-service data analytics that empowers all employees to create reports. Another is setting analytics as a strategic priority, O’Brien explained. This is driven by the executive team to achieve business goals through analytics.

Another goal is to optimize the analytics lifecycle. This type of data culture examines how people can be most efficient working with data and reporting. Adopting intuitive tools is another area where companies can focus on using modern data analytics tools to easily adopt, use, and publish reports.

“The ability to build fast, be agile, and create data pipelines on the fly is a part of adopting intuitive tools,” O’Brien said.

The architecture for enterprise analytics is comprised of a combination of a modern data strategy and modern analytics strategy.

“We want to go from data power users to data workers,” O’Brien said.

Data needs to be monitored in real-time so it can be acted upon quickly. Real-time combined with predictive analytics can tell companies where things are heading in a more accurate way.

“The business value increases by being more reactive in real-time and making better predictions,” O’Brien said. “We want to prepare the organization for that.”

There are 5 points that are important for building an analytics framework. This includes:

  • Simplifying finding data
  • Simplifying access to data
  • Data governance and Security
  • Business self-service data
  • Value of near real-time data

In an actionable roadmap the business needs to understand customer behavior, understand product usage, increase operational efficiency, and look to ways to innovate the business model to ultimately comprehend these things, O’Brien said. Enterprise self-service and BI development can decouple business urgency from analysis and design.

Building a roadmap with business impact includes overlooking several areas such as future business priorities, analytic needs and capabilities, acknowledging the technologies in progress, and examining current pain points within the organization.

“With the growing amount of self-service analytics and emphasis on data governance there’s an increased demand for reporting management,” O’Brien said.

The data warehouse must be reimagined. Organizations should challenge traditional best practices with data management principles. Companies must understand data design versus performance design, according to O’Brien.

Foundational components for the enterprise include modernizing the data architecture, data integration updates, cloud architecture, data technologies, data management, and data science platforms.

With so many components in play, balancing architecture with delivery is key to moving forward with new ideas, O’Brien said.

Architecture strategy is delivery oriented, he explained. Organizations need to have a vision-state architecture that is holistic and high level, each project is funded for business value and delivery, and data architecture projects should be able to demonstrate ROI.

Enterprise data architecture is an environment that continually improves to enable better, faster, confident business decisions, O’Brien said.

“In order to do actionable roadmaps, you really have to think like a business stakeholder,” O’Brien said.

What does that mean? It means becoming goal driven, working in tandem with overall business objectives. Business leaders typically have budget and timelines for their goals.

Webcast replays of Data Summit Connect presentations are available on the DBTA website at