Starting Strong in Data: 
90-Day Success Guide for New Data Leaders

Chapter #1: Understanding things

1.1 Identify blindspots

Don't assume your past wins guarantee future success. Every company/industry has its own data culture, tools, & challenges.

For example, a retail data leader may find their customer segmentation skills don't directly apply to patient cohort analysis in healthcare.

Beware of these blindspots for a smooth transition & growth.

“People rush it knowing what they've already done previously. If I've done this in previous places, this is what I'm going to do here. It doesn't always work.”

Ust Oldfield

Head of Analytics, Advancing Analytics

Exercise time

Personal blindspots

• Open self: Existing data skills (e.g., finance for banking)

• Hidden self: Talk to colleagues for feedback & embrace lifelong learning

• Blind self: Seek feedback from manager & peers

• Unknown self: Explore industry trends & new data technologies

Job-related blindspots

• Industry specs: Industry-specific tools & tech (e.g., HIPAA for healthcare)

• Decision flow: Understand the approval process & who makes key data decisions

• Data culture: Is data shared openly or siloed? How does it drive decisions across departments?

1.2 Understand the broader business goals

Align yourself with your organization's broader goals - vision, mission, & how data plays a role in it.

“Understand the business, then figure out how data can help. Know what you're doing and what the business wants to achieve before guiding them.”

Ust Oldfield

Head of Analytics, Advancing Analytics

“Ask questions, then show them how data helps”


Ust Oldfield

Head of Analytics, Advancing Analytics

“Align with company & department strategies. Identify data gaps to bridge.”

Vladimir Langutinskiy

Head of Data, Formel Skin

1.3 Identify & meet key stakeholders

Pin down the stakeholders crucial for your projects’ success. Building healthy relationships with them gets you buy-in for your ideas.

Chapter #2: Developing a foolproof data strategy

2.1 Assess the data landscape

Get a feel of the current data scene:

• Existing data team

• Data infrastructure

• Data governance practices

• Data literacy across teams

This helps identify gaps, challenges, & areas for improvement.

Next, assess the resource needs:

• Tools & tech

• Human resources (team size, skill sets)

Discuss team needs with your manager during your KPI meeting.

Talk to stakeholders

A few questions to get the conversation started:

Review documentation & technical artifacts

Beyond conversations, check out these resources:

Data governance policies

• Go through the data management, privacy, & compliance procedures

• Look for data classification standards, data stewardship roles, & data retention policies

• Evaluate the governance framework & its implementation. Identify gaps in policy enforcement or adherence.

IT architecture diagrams

• Study the IT setup to understand data flow & integration points. Includes data pipelines, storage solutions, processing workflows, & system dependencies.

• Assess architecture for scalability, fault tolerance, & performance. Spot potential failure points or inefficiencies

Existing data reports and dashboards

• Evaluate the quality and relevance of current reports & dashboards

• Focus on accuracy, completeness, timeliness, & relevance of data. Identify recurring issues or gaps in reporting

• Analyze data models & query performance. Ensure data sources are well-integrated and reports are generated accurately and on-time

Assess data quality

High-quality data is crucial for reliable insights. Here's how to assess it:

Data profiling

• Use tools (e.g., Talend Data Quality, OpenRefine) to analyze datasets for accuracy, completeness, consistency, & timeliness

• Implement automated profiling for regular monitoring & create visualization dashboards

Data quality metrics

• Define & measure key metrics based on your industry (e.g., error rates, missing values, duplicates)

  • Healthcare: Complete & accurate patient records

  • Retail: Verified customer data for personalization

• Develop validation rules to enforce data quality standards within ETL processes

Root cause analysis

• Investigate the root causes of data quality issues (e.g., poor data entry, system integration failures)

• Use techniques like Pareto charts & fishbone diagrams to identify & address root causes systematically

Deliverables

• Current data infrastructure & limitations

• Gaps in data governance & data literacy

• Specific data quality issues & business impact

• Recommendations for immediate & long-term improvements

2.2 Align data initiatives with business objectives

Data is powerful, but it needs direction. Tie them back to broader business goals.

Best practices

Understand leadership priorities

• By this point, you must know what the leadership wants to achieve with data.

• Focus on initiatives that provide visibility into P0 & P1 company goals.

Identify data's impact

Pinpoint areas where data can be a game-changer:

• Streamlining processes

• Freeing up resources

• Automating tasks

• Enhancing decision-making

Balance internal & external focus

Address both external goals (customer-facing) & internal needs (team efficiency, collaboration, tools).

"Include internal data team initiatives - changing dev process, infra, tooling, or improving collab with other teams"

Vladimir L,

Head of Data, FORMEL SKIN

Prioritization

• Low-hanging fruit: Identify easy wins for momentum & stakeholder buy-in.

• C-level input: Collaborate with executives to prioritize initiatives

• Re-prioritize as a team: Align everyone on the most critical tasks

• Focus on alignment, not just idea collection.

2.3 Negotiate success with your boss

Talk to your boss about how to measure your wins in the first 90 days.

Get to the "break-even" point (meaning your work is as valuable as your role costs) faster.

Best practices

Set KPIs

View your 90-day plan as a contract with your manager. Set SMART goals based on those metrics. Think CLV, churn rate,  efficiency metrics, & innovation rate.

"Success is defined differently. Ask how they measure success."

Ust Oldfield

Head of Analytics, Advancing Analytics

Track & review

Under-promise & over-deliver, always.  Check progress & update the tracker regularly.

2.4 Secure early wins

Early wins aren't about hitting core success metrics right away — they're about building momentum & trust.

Your teammates will make an impression of you in the first 30 days (sometimes as early as a week). What impression you’re going to leave? Answer these to find out:

Q1. Do you have the same values as the team you joined?

Q2. Do you have the right kind of energy?

Q3. Do you expect high performance from yourself?

Best practices

Ask thoughtful questions

Know about the past, present, and future of the team's work. Get to know your colleagues on a personal level.

Set achievable targets

Align with the metrics agreed upon with your manager.

"Focus on quick wins that deliver business value, not big technical projects. Build trust by tackling pain points & creating a roadmap for initiatives."

Vladimir L

Head of Data, FORMEL SKIN

2.5 Establish data governance frameworks

Define the policies, processes, & roles responsible for managing data assets. This helps keep data accurate, secure, and compliant with regulations.

Amidst all this, don’t overlook one facet of data governance.

“There's a lot of conversation about how to set up the policies, right roles, right processes, what tools should be used. But the main purpose of data governance is to solve problems. It could be efficiency, revenue, or risk problems.”

Tiankai Feng

Data Strategy & Governance Lead, Thoughtworks

Best practices

Ditch the jargon

Talk about the business's biggest pain points. Propose data governance as a solution, not a fancy concept.

“Don’t walk around mumbling things like “I did ALS (Alternating least squares)”.

Susan Shu Chang

Principal Data Scientist, Elastic

Define roles clearly

Data Stewards: To maintain data quality 
& integrity.

Data Owners: To ensure data accuracy 
& usage policies within their domain.

Data Custodians: Manage technical aspects of data storage & protection.

Improve data quality

Identify & fix data errors (duplicates, missing values, inaccuracies). Use data quality tools or custom scripts to automate cleaning. Set up validation rules to ensure data meets standards before loading. e.g., a standard date format for all data entries.

Ensure compliance

Follow regulations like GDPR, CCPA, HIPAA. Develop policies for data privacy, retention, and user consent. Manage data subject requests (access, deletion).

Implement security  measures

Encrypt data at rest and in transit. Use role-based access controls (RBAC) to restrict access. Set up monitoring and auditing to detect suspicious activity.

Chapter #3: Building trust & credibility

3.1 Communicate data strategy with right people

Keep the most important people in your org. informed about your data plan.

Best practices

Get people onboard

• Focus on benefits, not costs: Don’t talk data costs to leadership. Highlight potential losses without it instead. It could be missed revenue, lower efficiency, or regulatory risks.

• Involve other departments: Data is only valuable when used by everyone. Talk to other teams about their needs & goals. Show how data can help them  and tie it back to the bigger picture.

Define roles clearly

• Regular updates: Publish a monthly newsletter featuring stories of how data helped different departments. Share team progress & plans.

• Data in business terms: Link data initiatives to revenue, cost savings, or customer happiness. Talk about ROI and customer satisfaction. Show how better data reduces churn through accurate forecasting.

• Address concerns proactively

  • Predict leadership's concerns & have data-backed answers ready. Show a cost-benefit analysis with savings & revenue gains.

  • Highlight potential risks and your solutions. Explain how data security measures will protect data & ensure compliance.

"Data teams win trust by working with others. Data is valuable when used, 
so involve stakeholders to showcase its benefits and secure leadership buy-in."

Tiankai Feng

Data Strategy & Governance Lead, Thoughtworks

3.2 Build a data-driven culture

Building a data-driven culture is a marathon, not a sprint. But here's how you can start:

Best practices

Encourage critical thinking

Ask follow-up questions to spark critical thinking about data requests.

"Challenge data requests! Ask 'Why?' and 'Value?' This ensures data aligns with business goals, not tradition.”

Ust Oldfield

Head of Analytics,
Advancing Analytics

Break down silos

Move away from a ticket-based system for data requests. Encourage open communication and "chat" about data needs, not just forms

Lead by example

Highlight data-driven decisions in meetings. Celebrate successes and identify what’s worked & what’s not. Encourage using data with intuition for decision-making.

Make data accessible

Replace clunky data systems with user-friendly tools. Create a central data hub where everyone can access & analyze data easily.

3.3 Get quick wins

Quick wins are crucial! They build trust, enthusiasm, & showcase your value to the team.

90-day stories

Customer feedback analysis

A retail company launched a quick-win project to analyze customer feedback using text analytics tools. Within a month, they identified common complaints & refined it, resulting in a 15% improvement in customer satisfaction scores.

Sales performance dashboard

A quick project to create a real-time sales performance dashboard for the sales team allowed them to track daily targets & adjust strategies, leading to a 20% increase in monthly sales.

Inventory optimization

An initial project to optimize inventory levels based on historical sales data reduced excess inventory by 25%, leading to significant cost savings within a few weeks.

3.4 Accelerate your learning

You've had some wins, now it's time to focus on structured learning.

Chapter #4: Scaling & empowering the data team

As your data initiatives gain traction, you should think about growth.

4.1  Identify skill gaps

4.2 Upskill & train your team

Empower your team on areas they are lagging in.

4.3 Encourage collaboration & cross-functional partnerships

A strong data team thrives on collaboration. Here’s how to do it:

Chapter #5: Overcoming challenges & pitfalls

You've made significant progress! Let’s explore common pitfalls & strategies to overcome them.

5.1 Challenges new data leaders face

Align yourself with your organization's broader goals - the company’s vision & mission and how data plays a role in this.

• Resistance to change: Some organizations may resist adopting new data-driven approaches due to traditional practices & cultures.

• Data quality issues: Inconsistent or poor-quality data can make it difficult to use data effectively for decision-making.

• Resource constraints: Limited budgets, staffing, or access to necessary technology can pose challenges for executing projects.

• Delayed data strategy: It’s easy for new data hires to totally ignore the data strategy & go into the BAU mode.

"Focus on value, not just new tech! Don't get caught up in replacing old systems (like Oracle or SSIS) with new ones (Snowflake or Databricks) without a clear strategy. While these upgrades can improve efficiency, they don't guarantee value.  Define the value your data initiatives will deliver before making changes."

Ust Oldfield

Head of Analytics, Advancing Analytics

• Lack of data governance: Absence of clear data governance frameworks can lead to data silos, privacy concerns, & compliance issues.

• Overcoming skepticism: Convincing stakeholders of the data initiatives’ value amid skepticism or misconceptions about data's impact

Head-on solutions

Data literacy empowers your team to leverage data the best way.

Build alliances:

• Find colleagues who share your vision for data-driven approaches.

• Collaborate to overcome resistance & promote data's value.

Showcase success:

• Share success stories & quantifiable wins to win over skeptics.

• Show the real-world impact of data initiatives.

Clean up your data:

• Implement data validation tools & data quality checks.

• Foster a culture of data ownership & responsibility across the org.

Prioritize projects:

• Focus on high-impact, resource-feasible projects first.

• Use cost-effective solutions like open-source tools & cloud services.

Focus on strategy:

• Don't delay data strategy development.

• Communicate it clearly to leadership to secure resources & agree on timelines.

Data governance:

• Establish clear data ownership, access controls, & security protocols.

• Collaborate across teams to ensure data integrity & compliance.

Educate stakeholders:

• Conduct workshops & share success stories to showcase the benefits of data-driven decisions.

• Demonstrate how data outperforms traditional methods to achieve success.

5.2 Navigate organizational politics & resistance

Understanding office politics helps reason why some people resist your ideas. You can win over stakeholders & get their support for data initiatives by doing so.

5.3 Adapt to changing business needs & emerging tech

Best practices

Prioritize, don't panic

Don't chase every tech trend. Evaluate new tools based on their fit with your business needs & resources.

"Focus on core data principles, not passing trends like data mesh. Adapt to new tools & methods while maintaining a strong foundation."

Christophe Blefari

Staff Data Engineer, Blef.fr

Encourage KT & innovation

• Implement mentorship programs to transfer knowledge and skills between experienced and junior team members.

• Motivate the team to pick small pilot projects. These allow for quick testing, learning, and adaptation before scaling up.

Reward & plan ahead

• Acknowledge & reward their contributions.

• Equip your team with advanced tools, but avoid overwhelming them. Focus on value, not just the latest trends.

Chapter #6: Measuring success

Measuring success is often a big headache for most teams, including data. Here’s how you can get started:

6.1 Revisit KPIs

Evaluate and adjust the metrics to track the impact of data initiatives. This ensures that KPIs remain relevant & aligned with evolving business priorities.

6.2 Report progress & successes to stakeholders

6.3 Embrace an agile mindset