How To Build An Effective Data & Analytics Roadmap

By Sonia Johnson on Jan 13, 2020

How To Build An Effective Data & Analytics Roadmap

The idea of creating a data and analytics roadmap for the enterprise can be a daunting task, especially with the expansion of new data sources, the proliferation of new analytics systems and tools, and the constant demand from the business for faster insights.

But without a roadmap, data and analytics efforts may underdeliver, increase complexity, or worse, cost you more time and money.  If no common vision and roadmap for analytics exists, each function in the organisation tends to create their own set of initiatives to solve their own immediate needs - this usually occurs in a siloed way making the overall analytics portfolio ineffective. IT organsiations that develop an enterprise data and analytics roadmap or plan, create visibility into the transformation required to underpin how data and analytics can align to the overall strategic priorities of the business and appropriately engage the right stakeholders.

We work with IT leaders who are either doing a lot with their data but need help to assess the effectiveness of this or others who are under pressure from the business to make better use of their data, but don’t have specialist resource to put a data and analytics roadmap or plan in place.  Here are a few steps to consider when mapping out your enterprise data and analytics plan:

1. Involve Stakeholders Early

Ensuring a clear understanding of stakeholder needs and the vision for data and analytics in the organisation early is critical.  This helps to avoid any roadblocks on the way. Engage stakeholders to understand their roles, objectives and the ways in which they work to determine what’s important and how data can best support them.

2. Align To Business Priorities

Try to avoid starting with a technology-led approach, which is a mistake many make. Companies can feel obliged to invest in the latest analytics software tools or trends like artificial intelligence (AI) and machine learning, without first determining how this aligns with the goals of the business.  If you’re looking to create an effective data and analytics roadmap, the starting point should be business objectives, such as top-line growth, cost reduction etc, along with business strategies identified and then fleshing out the role data and analytics plays in achieving these.  This should culminate in a clear data vision of where value can be delivered. 

3. Assess Your Current State

Conduct an audit of all existing data and analytics technology in place and then how they map to the organisation’s analytics needs and goals. This should involve identifying key challenges and developing a maturity model to measure proficiency across key measures of success and capabilities for the platform(s).  A review of the data and analytics environment and architecture should encompass three core areas:

  • Infrastructure (source systems, hardware & software, performance/scalability/growth & migration to the cloud)
  • Data (data integration, data warehouse & data models & data catalogs)
  • Insights (reporting & analytics, user access & security & advanced analytics)

4. Articulate Your Desired Future State

Now that you’ve done your audit, recommendations should be made as to what the desired future state is of your data and analytics environment across your infrastructure, data landscape & insights environments which should indicate appropriate tools and technologies that should be considered once mapped back to enterprise objectives and strategies.

Identify and prioritise the key capabilities that will enable the organisation to accelerate its analytics capabilities in a way that will deliver benefits quickly.

5. People, Processes & Technology Need To Work Together  

The last part in developing the data roadmap should focus on team structure and processes, as people, processes and technology should all work together. It will be of primary importance to ensure that any technical solution proposed has process frameworks built around it that the team are comfortable with and that are both efficient and robust. Taking into consideration team skills mix, data governance frameworks and processes, proposing a delivery model that will best fit the company.  Processes that drive user adoption are often over-looked yet critical in deriving value from data & analytics. Defining an approach for innovation should also be an output. 

6. Using Expert Specialist Resource

Creating an effective data and analytics roadmap for an organisation should assesses the current state of your data & analytics capabilities, define where you want to be going forward and create a plan for how to get there and how this can evolve on an ongoing basis. Even if you have data and analytics teams inhouse it's often worth engaging a data and analytics specialist partner to review what's been done and ensure strategic alignment of plans and roadmaps.  This also brings a fresh perspective and experience from other client engagements.  Outputs should include a detailed report of the Data Roadmap along with architecture and technical documentation as well as a high level scope and cost estimates to deliver priority projects.

As data and analytics specialists, Inside Info has been designing, building and maintaining agile data, analytics and business intelligence solutions for medium to large enterprises for over 16 years, having delivered over 500 projects, specialising in the Qlik platform. If you’d like to learn more about our approach and framework in developing a Data and Analytics Strategy and Roadmap, contact us to receive an overview. 

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Written by

Sonia Johnson

Sonia Johnson heads Inside Info's Marketing team, as an experienced B2B marketer, having launched and built the Qlik brand in the Australian market. Sonia has 20 years' experience working within the IT and telco industries, having worked for IBM and Vodafone, the last ten years have been focused within the business intelligence and corporate performance management sectors.

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