Data Analytics Strategy

Data and analytics roadmap for business transformation

Home / What we do / Data strategy

Extract maximum business value from your data

Enterprises benefit from increased efficiency and productivity that comes from thoroughly managed data engineering and data science initiatives that support business goals. Sigmoid has partnered with many Fortune 500 companies to successfully develop and implement data-driven frameworks that addresses technology, processes and changes to the organization, to optimize and improve business. Whether it's developing a new data strategy framework, enhancing the existing one, or executing it, our data consultants provide the right perspectives and data analytics tools that are aligned with short and long term business initiatives.

Driving an enterprise-wide data management strategy that fuels innovation

Business Transformation icon

Business Transformation

Embed D&A into business strategy and connect data to business value with data maturity assessment and a roadmap to data monetization.

Data Modernization icon

Data Modernization

Build a resilient and scalable data mesh architecture that leverages new technologies and increases business agility, enabling effective data democratization and empowering domain specific teams.

AI and Analytics Consulting icon

AI and Analytics Consulting

Assess the readiness of data to prioritize complex AI use cases, expedite ML model deployment, and ensure seamless integration of enterprise data analytics solutions for enhanced insights.

On Premise to Cloud Migration icon

Cloud Optimization Service

Create strategies and robust data pipelines for migrating from on-premise to the cloud and develop the right cloud infrastructure and ecosystem partners.

Podcast

Data analytics strategy roadmap to successful data modernization for CPG

Listen to The CPG Guys podcast as they interview Sigmoid’s CTO, Mayur Rustagi, and Mondelēz’s SVP of Supply Chain – Strategy and Transformation, Frank Cervi on what makes for a successful data management strategy for CPG companies.

Listen to podcast
CPG Podcast Sigmoid

Assess data maturity & strategy framework for analytics success

Build an effective data foundation for all your data practices to enable better customer engagement, reduce operations costs, and empower your business decisions.

Customer success stories

Explore our other data and analytics offerings

Data engineering

Drive higher business outcomes by operationalizing AI platforms and improving the efficiency of data pipelines on cloud.

Data science

Get faster actionable business insights using data science, visualization, and AI for a high success rate on your analytics initiatives.

Accelerators

Leverage our pre-built analytics assets and proprietary frameworks to accelerate data-to-value for your business.

Insights and perspectives

Sign up for a complimentary data strategy session!

Talk to an expert about your goals and challenges, and get advice on immediate steps you can take to get more value from your data.

FAQs

A robust data analytics strategy ensures that the right data is captured, processed, and made accessible to relevant stakeholders in a timely manner. It aligns business requirements with specific data use cases to address each organization's unique data analytics needs. By helping decision-makers identify trends, patterns, and correlations, a strong data analytics strategy aids in mitigating risks and optimizing resource allocation, ultimately driving improved decision-making.

Data governance establishes guidelines, policies, and procedures to ensure data quality, integrity, consistency, and security throughout a comprehensive data and analytics strategy. It helps define roles and responsibilities for data management, including data stewardship and data ownership, ensuring accountability and clear decision-making authority. By establishing data governance practices, organizations can enhance data trustworthiness, promote data sharing and collaboration, enable compliance with regulations, and support the alignment of data initiatives with business objectives.

A clear data analytics strategy roadmap enhances operational efficiency by providing a structured approach to data management and utilization. It ensures that data is collected, stored, and processed efficiently, eliminating redundant or unnecessary data silos. A well-defined data strategy enables automation and integration of data workflows, minimizing manual efforts and increasing operational efficiency across various functions and departments within the organization.

An enterprise-wide data analytics strategy provides a shared framework and roadmap for data-driven decision-making across business functions. By establishing standardized data definitions, metrics, and reporting mechanisms, different business functions can align their understanding and interpretation of data. It breaks down data silos and promotes a culture of data-driven collaboration, leading to informed decision-making and more cohesive business strategies.

To create a robust data analytics strategy, follow these key steps:

  • Define objectives: Clearly outline the goals and objectives you aim to achieve with data analytics.
  • Assess current state: Evaluate existing data infrastructure, analytics tools, and team capabilities.
  • Identify data sources: Determine the sources of data needed to support your analytics initiatives.
  • Data quality assurance: Ensure data integrity, accuracy, and consistency through proper quality assurance processes.
  • Choose analytics techniques: Select appropriate analytics techniques such as descriptive, diagnostic, predictive, or prescriptive analytics based on business needs.
  • Build infrastructure: Develop or enhance data infrastructure to support analytics processes effectively.
  • Implement tools and technologies: Choose and deploy analytics tools and technologies that align with your strategy and infrastructure.
  • Establish governance: Define data governance policies to ensure compliance, security, and privacy standards are met.
  • Skill development: Provide training and development opportunities for the team to enhance their analytical skills.
  • Monitor and iterate: Continuously monitor performance, gather feedback, and iterate on the strategy to adapt to changing business needs and technological advancements."

Having a data analytics strategy can offer a wide range of benefits for businesses, including:

  • Informed Decision Making: Data analytics can provide valuable insights that help businesses make informed decisions based on evidence rather than intuition or guesswork.
  • Improved Efficiency: By analyzing data, businesses can identify areas where they can improve efficiency, streamline processes, and reduce waste.
  • Better Customer Understanding: Data analytics enables businesses to better understand their customers' behaviors, preferences, and needs, allowing them to tailor products, services, and marketing efforts more effectively.
  • Competitive Advantage: Businesses that effectively leverage data analytics can gain a competitive edge by identifying market trends early, spotting opportunities, and responding quickly to changes in the market.
  • Risk Management: Data analytics can help businesses identify and mitigate risks by predicting potential problems, detecting anomalies, and improving overall risk management strategies.
  • Cost Reduction: By optimizing operations and resource allocation based on data insights, businesses can reduce costs and improve profitability.
  • Innovation: Data analytics can fuel innovation by uncovering new opportunities, facilitating experimentation, and supporting the development of new products, services, or business models.
  • Personalization: With data analytics, businesses can personalize their offerings and customer experiences, leading to higher customer satisfaction and loyalty.
  • Performance Monitoring: Data analytics allows businesses to track key performance indicators (KPIs) and measure the effectiveness of their strategies, enabling them to make adjustments as needed to achieve their goals.
  • Regulatory Compliance: In industries subject to regulations, data analytics can help businesses ensure compliance by tracking and managing relevant data and processes."

Looking to revamp the data and analytics
roadmap for your enterprise?