Digital Transformation is the business imperative for success for nearly all organizations in competitive markets, and that transformation is driven by a need to:
- Respond quickly to business disruptions
- Identify new business opportunities
- Find ways to be more efficient
Let me explain:
1. Responding Quickly to Business Disruptions
Responding quickly to business disruptions has been behind a massive shift towards agile methodologies in manufacturing, in engineering and software development, and increasingly, in other parts of the business as well. But, what is the link between being agile and using data? It’s actually quite simple: To be agile, you need to run iterative experiments, collect data, and then iterate again based on what the data tells you about the last iteration. This is called “continuous improvement”.
2. Identifying New Business Opportunities
Identifying new business opportunities is even more directly about data. New business opportunities are often found through personalization, customer experience, or predicting future needs. The core technology to identify any of these opportunities come from analytics and more specifically, machine learning. These technologies depend on large datasets of time-based data.
3. Find Ways to Be More Efficient
The same technologies are employed to find ways to be more efficient. The only difference is the source of the data. For example, efficiencies are found through analyzing product, customer, purchase, and manufacturing data.
A Data-Driven Transformation
So, it shouldn’t be surprising that data and analytics are critical to digital transformation. A simple way to view the transformation is as follows:
- Data allows us to ask new questions
- Analytics allow us to identify opportunities
- More data is the basis for competitive advantage
- More data and agile methodologies enable us to find cost efficiencies
What it Means for Your Business
What is the take-away for every organization that is interested in digital transformation? Keep all of your data and invest heavily in data scientists who understand analytics.