Data is everywhere these days. Day in and day out, companies generate enormous amounts of data. But what should you do with all that data? Data Science may be the answer. What exactly is Data Science? And what challenges do you face when developing a data strategy? You can read it in this blog.
What is Data Science?
Data Science is the analysis and interpretation of large amounts of data, with the goal of turning it into valuable information. It involves using scientific methods to answer questions, discover patterns and find coherence in data.
To find the right answers, a data science team must perform a number of operations on this data:
- Transform: aggregation, enrichment, processing
- Learning: regression, clustering, classification
- Prediction: simulation, optimization
What is the purpose of Data Science?
The main goal of Data Science is to transform data into valuable information that is of use to organizations and/or society. Data Science consists of a combination of three fundamental skills: domain expertise, mathematics and computer technology. Thus, for adequate implementation, you need a team that has experience in all of these areas.
Domain expertise
The team must be able to formulate the problem statement by asking the right questions.
For example: Does better accessibility to shopping cart data lead to higher returns? Or: Are there certain product attributes that differentiate us from the competition?
Math
Data Science becomes truly valuable when high-level mathematical constructs are applied. This requires experts in statistical modeling, signal processing, probability modeling, pattern recognition, predictive analytics, and a host of other specialties.
Computer Technology
Being able to handle the huge amount of data and deliver the desired results in a timely manner requires not only the right architecture, but also special storage and networking capabilities.
Challenges in creating a data strategy
When you want to set up a data strategy, you face a number of challenges:
- It requires leadership teams that can build comprehensive strategies and plan technology and engineering effectively.
- People with the appropriate combination of skills are hard to find: data scientists must have technical skills, mathematical, analytical and industry knowledge, combined with business acumen and “soft skills” to turn data into valuable information.
- You need the right technologies to efficiently process large amounts of data in a short period of time.
- You must have the right infrastructure in place.
Would you like to know how we can get the best out of your data, which tools we use to do so and how we can help you set up a data strategy? Then contact cimt, the data management experts.
Also read our previous blog here