In our latest edition of The Credera Brief video series, Credera Chief Data Scientist Vincent Yates discusses how to turn your data into money by walking through the first pillar of Credera's Monetization Methodology.
Improving Internal Efficiencies: Pillar #1 in Credera’s Monetization Methodology
Every organization operating in the modern world has to address the question: How do I make the best decisions to drive optimal outcomes?
Think of the last time you had to answer a difficult question. Whether it’s, “how much money should I invest in my marketing budget?”, “how many spare parts do I need to have on hand?”, or “which of my employees is doing an extraordinary job?”. How did you answer that question and gather relevant data? Did you have the necessary time to get all the data you wanted?
Most organizations struggle to get the right data to the right person at the right time. Most organizations don't truly treat their data as their most valuable asset. It's an afterthought at best or often entirely ignored until the moment somebody must answer one of these difficult questions.
By spending the energy up front to identify the moments in time you use data to make decisions, you can unlock extraordinary value for your organization. Start by systematically identifying those moments and streamline the process.
This is not about automating jobs, rather, it’s about helping your team make consistently better decisions with fewer mistakes and less time wasted searching for data in emails, portals, dashboards, or CSVs.
To learn more about turning your data into money, watch part one here.
The Credera Brief
At Credera, we believe the toughest business challenges are best solved by a team—a team who works together to share knowledge to accomplish their goals.
We created The Credera Brief series to distill trends and ideas from Credera leaders and experts. These short-form videos share different insights and perspectives across the topic spectrum from MarTech and innovation to strategy and technology.
- Data & Analytics
- Data Integration
- Data Integrity
- Data Privacy
- Data Quality
- Data Science
- Data Storage
- Data Governance
- Data Collection