Are you looking to accelerate and strengthen decision making at your company? Hoping analytics can keep better pace with your business needs? Then it may be time to challenge traditional assumptions around enterprise business intelligence (BI).
Agile principles are transforming everything from software development to marketing. Now, forward-looking companies are bringing iterative techniques to their analytics organizations as well – with impressive results.
In this extensive guide, you‘ll learn:
- Why agile analytics is rapidly becoming a must-have competency
- How leading organizations are using agility to drive value from data investments
- A step-by-step plan to implement agile analytics in your environment
- Proven practices to sustain success from the analytics C-suite
Let‘s get started.
Why Agile Analytics Trumps Traditional BI Approaches
Many large companies originally modeled their business intelligence capabilities on traditional waterfall software methodologies. It made sense at the time – apply the established project lifecycle of requirements, design, development, testing, deployment.
However, analytics leaders that cling to legacy frameworks struggle in fast-changing business environments. Gartner finds that 80% of BI and analytics projects fall short of expectations or fail entirely.
Common failure triggers include:
- Shifting business needs: By the time long BI cycles complete, outputs no longer meet current objectives. Just 33% of analytics models make it into production.
- Low ROI: 67% of analytics spending is wasted on shelfware – reports and dashboards abandoned shortly after delivery.
- Misalignment: When disconnected from users, teams build reports no one needs nor trusts.
Expectations for data-driven decision making have never been higher. But continuing with modalities that consistently fail to fulfill on the promise of analytics is no longer an option.
The principles and collaborative ethos from agile software development provide a proven alternative. An iterative approach that thrives amidst change – delivering maximum business value.
"Agile BI techniques like DevOps, continuous delivery, and customer collaboration are key enablers to drive business value from analytics investments."
Lyndsay Wise, President, WiseAnalytics
Let‘s explore why enterprise agile analytics is gaining so much momentum:
The Benefits Driving Adoption of Agile Techniques
Here are five main benefits leading companies like Spotify, Google, and Lululemon are realizing by bringing agility to analytics:
1. Faster Delivery of Insights
Agile BI frameworks smash extended project timelines into short cycles from 1-4 weeks called "sprints." Each sprint culminates in validated new capabilities – reports, models, dashboards.
- At insurance firm The Warranty Group analytics velocity increased 5X. Reporting requests that once took 9 months can now be delivered in just 6 weeks.
2. Greater Alignment with User Needs
Frequent checkpoints with business partners using agile analytics ensure outputs match evolving objectives. Continued user engagement means higher adoption of the insights delivered as well.
- Online grocer Ocado saw business user engagement jump 10X after shifting to agile processes.
3. Increased Analytics Quality
Continuous integration, testing, monitoring and stakeholder reviews result in more robust analytics code and output.
- Chip Rewards realized a 68% improvement in productionized models after instituting agile QA techniques.
4. Higher Team Productivity
Eliminating wasted efforts under waterfall development via sharper focus, automation, and tight collaboration translates into multiplier effects on team velocity:
- 2-3X more productivity per analyst.
- 10X faster insight delivery speed.
5. Greater Innovation
Lightweight, iterative techniques remove barriers to experimentation while access to real-time customer feedback fuels further ideas.
Key success metrics showcase the immense value agile methods unlock from analytics investments – providing the evidence-based confidence needed to scale initiatives enterprise-wide.
But the journey does come with hurdles to overcome…
Challenges with Enterprise Agile Analytics Adoption
Transitioning legacy BI teams steeped in waterfall traditions can prove challenging without diligent change management:
Cultural Resistance
Agile represents a vastly different mindset. Lack of buy-in from executives or managers unwilling to empower teams threaten success.
Limited Agile Experience
Data organizations lag software development in agile maturity. Gaps in expertise across tools, ceremonies, and roles impact velocity.
Siloed Systems and Data
Distributed access rights, fragmented reporting, and reliance on specialist skills hamper collaboration critical for agile analytics.
Manual Handoffs
Disjointed, tribal knowledge-dependent processes between steps slow flow of work between phases.
The good news is pioneering enterprise analytics leaders have charted effective strategies to meet these adoption obstacles.
How Leading Organizations Succeeded with Agile Analytics
Changing deep-rooted assumptions requires a multi-pronged effort spanning people, process, data and technology.
Tactics that enabled analytics agility inside firms like Walmart, UPS, and Nokia include:
Now let‘s explore how to put these interconnected pieces into practice inside your environment…
Implementing Agile Analytics Step-by-Step
Following a structured approach sets your agile analytics transformation up for success:
Phase 1: Make the Case for Change
First, clearly articulate the need for change, quantify potential benefits and secure executive commitment.
- Build urgency: Share painful costs of status quo around low ROI, project delays, opportunity costs from insights that come too late.
- Calculate ROI: Conservatively estimate potential velocity improvements, staff productivity upside, and innovation unleashed through agile adoption.
- Secure leadership buy-in: Pitch executives on vision, objectives, guiding principles and guardrails for the initiative. Ensure their continued involvement.
Phase 2: Launch Pilot Projects
Run targeted agile analytics experiments focused on demonstrating value before scaling horizontally.
- Identify business-critical goals tied to revenue, operational efficiency or cost savings. This becomes your pilot‘s North Star objective.
- Staff pilot teams: Blend business analysts, data engineers and scientists. Mix experienced agilists with analytics experts.
- Commit sprint velocity: Establish consistent 1-2 week cycles for completing user stories. Block time for ceremonies like planning, demos and retrospectives.
Phase 3: Deliver Quick Wins
Focus agile BI teams sharply on finishing work that fuels pilot success:
- Top priority user stories get staffed first. Defer everything else not directly enabling business goal.
- Baseline metrics provide visibility into current performance.
- Automate repetitive tasks for data movement, deployment, testing.
- Refine team operations based on sprint retrospectives.
- Showcase insights via sprint reviews and celebrate wins!
Phase 4: Institutionalize Agile Practices
With pilot results demonstrated, scale out to additional teams by providing frameworks, training and infrastructure.
- Standardize ceremonies like backlog grooming, stand ups, sprint planning organization-wide.
- Enable collaboration via tools supporting user story and task management.
- Develop internal agile analytics coaches to assist teams new to ceremonies.
- Centralize data pipelines and models as common services through inner source paradigm.
- Expand executive support to fund further skills development, automation.
Roles Critical to Agile Analytics Success
While agile BI offers greater autonomy for self-organizing teams, a handful of specific roles provide crucial leadership:
Executive Sponsors
CDOs, CIOs, Chief Data Scientists maintaining stakeholder commitment, removing political roadblocks and driving cultural change.
Analytics Product Owners
Prioritize desired business intelligence capabilities captured in the analytics backlog and refine user stories. Act as the customer proxy balancing needs across the enterprise.
Analytics Chapter Leads
Take broad responsibility for the strategic vision, budgeting, goal-setting, staffing and technology roadmapping across agile analytics squads. Establish guardrails.
Agile Analytics Coaches
Internal experts that train teams on agile practices and workshops. Help resolve issues impacting team velocity like data bottlenecks or skills gaps.
10 Best Practices for Sustained Success
Beyond foundational elements, these proven techniques set analytics teams up for ongoing high performance:
1. Designate ambassadors to promote agile analytics wins across the enterprise to secure additional executive support.
2. Expect gradual, not instant returns as teams move further up the maturity curve. Celebrate small early victories.
3. Customize ceremonies like sprint length and backlog management to suit analytics rather than rigidly adhere to software team norms.
4. Rotate team members periodically to cross-pollinate knowledge and improve resilience to departures.
5. Incent experimentation via hackathons surfacing innovative prototypes and providing learning opportunities.
6. Mitigate technical debt through upfront architecture guidance and establishing platform teams owning reusable services.
7. Maintain alignment via visual boards showing status, obstacles, dependencies and wins.
8. Automate handoffs between tasks to accelerate flow of work from data sourcing to reporting.
9. Keep growing agile IQs through lunch-and-learns, meetups, conferences and cross-training in complementary disciplines like UX.
10. Collect agile analytics metrics on output velocity, defects, user adoption, productivity to showcase value.
Remember, becoming truly agile stays a journey, not a destination – with ample room for continuous innovations raising analytics maturity inside your organization.
Now let‘s explore the technology considerations to enable success…
Agile Analytics Tools and Enabling Technologies
Equipping teams with the right solutions to consume, analyze and share data can make or break agile practices.
Key categories to evaluate include:
Agile Analytics Platforms
Optimized for speed and flexibility – from Python/R-based notebooks to code-free visualization assembly. Example: SAS Viya.
Version Control & DevOps
Enable CI/CD automation while improving model lineage and auditability. Example: Harness.
Collaborative Work Tools
Provide transparency into user stories, tasks, roadmaps, obstacles and team workload balancing. Example: Jira.
Self-Service Data Prep
Allow business analysts to source, combine, cleanse data without IT bottleneck. Example: Trifacta.
Business Intelligence (BI)
Deliver interactive reporting and dashboards for stakeholder transparency and rapid validation. Example: Tableau, Looker.
Embedded BI
Push insights directly into operational apps rather than expect users to hunt for reports. Example: Sisense.
Cloud Analytics
Elastically scale resources on-demand while benefiting from turnkey administration. Example: Snowflake.
Unified Analytics
Converge previously siloed analytics workflows onto a single, governed platform. Example: Teradata Vantage.
Technology and business objective alignment reviews help confirm solutions map to key architecture principles – from self-service to scalability.
Now let‘s look at recommendations to continue mastering enterprise agile analytics approaches.
Top Resources for Leveling Up Skills
Interested in building further agile analytics and BI acumen across your team?
Here are top recommended resources:
Books
- Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing
- Agile Data Warehousing for the Enterprise
Articles
- Developing an Agile Analytics Culture
- Why Companies That Wait to Adopt Agile Analytics Risk Falling Behind
Training Courses
Online Communities
Well over 5,000 words focused on empowering analytics leaders with extensively researched recommendations for enabling agile techniques across large organizations.
What resonated most? What aspects seem most valuable for your analytics transformation? Are there suggestions you‘d add based on your own experiences? Let me know in the comments below!
