Building A Big Data Team and Strategy

- Overview

Building a Big Data Team and Strategy refers to the process of creating a dedicated team within an organization specifically focused on managing and analyzing large volumes of data (big data), alongside a comprehensive plan outlining how that data will be collected, stored, processed, and utilized to achieve specific business goals. 

In reality, a data scientist is a group of people who act in unison. Data science teams often come together to analyze situations, business or scientific cases that cannot be solved individually. The solution has many moving parts. But ultimately, all these pieces should come together to provide actionable insights based on big data. Being able to use evidence-based insights in business decisions is now more important than ever. Data scientists combine technical, business and soft skills to achieve this. 

When building a big data strategy, it is important to align big data analytics with business goals. Communicate goals and provide organizational support for analysis projects. Build a diverse talent team and establish team spirit. Remove barriers to data access and integration. Ultimately, these activities need to be iterative in response to new business goals and technological advancements. 

Often, in large enterprises, most of their data used to run in silos. Keeping data in disparate systems forces their teams to make siloed decisions. While this approach is a common result of organic growth over time, connecting the pieces and optimizing the entire data asset can be difficult. In turn, applying advanced analytics and machine learning has become more difficult, and deeper insights remain out of reach. 

However, it is no longer necessary to group data into business groups and use it individually for internal business applications. Instead, the modern data age requires a well-curated strategic infrastructure to deliver on the promise of deep, transformative insights.  

- Key Components of a Big Data Team and Strategy

Modernizing data assets isn't always easy. It involves introducing new processes, using new tools, and people who support cultural change.

Key components of a Big Data Team and Strategy include:

• Team Structure: Data Engineers: Design and build data pipelines to collect, store, and transform data from diverse sources.
• Data Scientists: Analyze data using statistical methods and machine learning algorithms to extract insights.
• Data Analysts: Visualize and interpret data to generate actionable reports for business decision-making.
• Data Governance Specialist: Ensure data quality, compliance, and security by defining data access controls and policies.
• Strategy Development: Identifying Business Needs: Defining key business questions and objectives that can be addressed through data analysis.
• Data Source Evaluation: Assessing the available data sources, including internal systems, external APIs, and social media.
• Technology Selection: Choosing appropriate big data tools and platforms for data storage (like Hadoop, data lakes), processing (Spark), and visualization (Tableau).
• Data Governance Framework: Establishing data quality standards, ownership, and access controls to ensure data reliability.

[More to come ...]