5 steps to implement a successful data and analytics strategy

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Discover how successful companies implement data and analytics strategies to drive differentiation and growth in an increasingly competitive marketplace.


Why a data-driven and analytics-driven strategy is necessary
Today, business success and digital initiatives are driven by data and analytics strategies that are tailored to each company’s business ambitions.

According to Gartner experts, “ The need for greater contextual awareness enabled by capabilities to plan scenarios, optimize, prioritize and focus investments has become a priority .”

Agility in data analysis is essential to build capabilities to luxembourg phone number lead detect and respond to new demands or crises that may arise in the market or the company. Those who achieve this goal become leading organizations with unprecedented innovation that adapts to changes and meets new requirements.

Gartner has released a new report that, through research and interaction with thousands of companies from various industries, identified 5 steps to implement a successful data and analytics strategy .

This is an IT roadmap that provides an understanding of the key stages, resources, and personnel needed to plan and execute an effective data and analytics initiative .



Key steps for a successful data and analytics strategy
Based on best practices from companies that have successfully implemented data and analytics initiatives in their organizations, Gartner proposes this IT Roadmap:

1. Vision and strategy
At this stage, stakeholders are brought on board for the data and analysis program.

Actions to be developed include:

Understand key business priorities and how data and analytics align with delivering business value.
Establish a baseline of the current state of the company that triggers a search for continuous improvement.
Identify opportunities to monetize and exploit data assets
Design an agile data and analytics strategy that responds to a changing landscape of business and technology opportunities.
2. Establish operational framework
At this stage, the development of a balanced operating model with improved operational efficiencies becomes essential .

Actions to be developed include:

Identify roles and competencies, targeting the operating model needed to create a data-driven organization.
Create a two-tier organizational model: centralized teamwork with decentralized teams.
Design the architectural framework for the data and analytics platform.
Create different management bodies to oversee strategies.
3. Establish governance
At this stage, the implementation of governance standards and procedures comes into play to establish a risk mitigation structure.

Actions to be developed include:

Establish consistent data definition standards and define governance policies.
Develop a data management and integration framework to meet emerging challenges.
Measure the competitiveness, relevance and timeliness of data.
Develop a framework to resist data grabbing, preventing data hijacking and enhancing privacy.
4. Continuous intelligence
This stage is about achieving quick wins from data-driven insights .

Actions to be developed include:

Integrate data and analytics capabilities across digital business platforms and ecosystems to support business growth, speed, and agility.
Equip business users with analytical models to understand present realities and predict future states.
Automate the process of data visualization and analysis using augmented analytics.
5. Improvement and progress
At this stage , continuous improvement of data maturity and analysis must be considered for successful results.