At Origin – it’s our belief that in 2024 and beyond every company needs to be a Data company.
But don’t just take our word for it. According to Forbes, Data-driven companies are 23 times more likely to top their competitors in customer acquisition, about 19 times more likely to stay profitable and nearly 7 times more likely to retain customers.
In nearly every industry, we’ve seen companies that leverage Data as a core part of their business model, outperform those that don’t – and in some cases completely transform their industry.
In automotive, Tesla has leveraged data analytics to enhance the driver’s experience through predictive maintenance, safety, navigation, and are on their way to the holy grail of fully autonomous vehicles. They’ve raised the expectations we all have of the vehicles we get into.
We’ve seen Zara excel in the Retail industry in their obsession with customer data and insights that’s enabled them to stay on the cutting edge of trends and consumer preferences, and effectively manage their supply chain and operations to enhance profitability.
In sports – the popular book and movie, ‘Moneyball’, chronicled how the Oakland Athletics leveraged advanced Data methods to make the playoffs two years in a row with one of the lowest budgets in the MLB. Since their success, nearly every Sports franchise now invests heavily in Data & Analytics to inform the players they choose to have on their team, better scout opponents, optimize fan engagement, and enhance revenue.
At Origin, we are working with companies in both regulated and traditional industries like Healthcare, Financial Services, and Manufacturing to leverage their data to build new products and services for customers and employees.
With all that said, what exactly does it mean to be a Data company?
Data companies prioritize data and analytics throughout their entire value chain including:
- Customer Experience (Personalization & Targeted Marketing, Customer Service, Loyalty)
- Employee Experience (Improved Training & Workflows)
- Business Processes (Optimized Efficiency & Decision-Making)
- Product Development (Data-Driven Innovation & Feature Creation)
- Operations (Enhancing efficiency and Supply Chains)
- Business Model Innovation (Leveraging data to build new products and services that can be monetized)
The 3 Pillars of Becoming a Data Company
The journey to becoming a data company requires focus on three key areas: Culture, Process, and Technology.
- Culture: Building the Foundation
- Leadership Buy-In: C-suite commitment is crucial for investment in the right team, partners, and tools.
- Data Strategy: Establish a clear roadmap for leveraging data as a strategic asset (decision-making, process optimization, innovation).
- Data Ownership & Governance: Define data sources, ownership, collection, quality, security, and compliance for high-quality data access.
- Data Talent: Assemble a team of data scientists, strategists, and engineers. Consider a blend of in-house expertise and a consulting partner.
- Process: Turning Strategy into Action
- Agile Execution: We recommend two- to three-week sprints for efficient data projects. Core activities include:
- Business Goal Definition: Collaborate with business units to understand their goals and how data can help achieve them.
- Data Discovery and Collection: Identify relevant data sources and ensure data quality.
- Data Analysis and Modeling: Extract insights and develop models using statistical analysis, machine learning, or other techniques.
- Model Evaluation and Validation: Measure and test model effectiveness.
- Implementation and Integration: Integrate validated models into organizational systems.
- Monitoring and Maintenance: Continuously monitor and maintain models for optimal performance.
- Communication and Reporting: Regularly share findings and recommendations with stakeholders.
- Continuous Improvement: Always strive to refine processes, models, and methodologies based on new data and feedback.
- Agile Execution: We recommend two- to three-week sprints for efficient data projects. Core activities include:
- Technology: The Tools to Empower
- Cloud Infrastructure: Modernize on-premises systems and applications in the cloud (Azure, AWS, Google Cloud).
- Data Management: Consolidate data from various sources into a central location using a data management platform (consider data Lakehouse architecture like Databricks).
- Data Governance: Implement data governance tools to manage the data lifecycle (Databricks, Collibra, or Alation).
- Data Preparation: Utilize tools to streamline data cleaning and organization for analysis.
- Data Visualization and BI: Employ data visualization tools to transform complex data into user-friendly visuals.
- Data Science: Leverage data science tools for data wrangling, analysis, and interpretation (explore platforms like Databricks with open-source LLMs for custom LLM creation).
The Future of Data: Data Intelligence Platforms (DIPs)
DIPs combine functionalities into a single platform, eliminating the need for multiple tools and integrations. Databricks is a frontrunner, offering a comprehensive solution for cloud, data management, data governance, data science, and generative AI.
Becoming a data company is a journey, but the rewards are substantial. Origin can help you navigate this path and unlock the power of data to dominate your market.
.svg)