Seven steps to start using data science in your business
October 25, 2019 10:33
3 min read738 views so far!
Data science has become a well-known concept in academia and has also become popular in the business world. But what is the basic definition of this promising and emerging area?
Data science is an interdisciplinary area focused on data study and analysis, which extracts information to aid decision-making, including complex predictions and analysis. Knowledge in Big Data and Machine Learning is elementary for this area, which also brings together mathematical techniques, statistics and other subfields of computing.
Below are the seven tips that need cautious and efficient forecasting in your business.
1. Check your business needs and the feasibility:
know and define the problems that are to be solved by data analysis. Evaluate the cost-effectiveness of deploying the solution and be aware that a large amount of data is required to analyse. Analytical techniques need big data, Machine Learning algorithms and need enough information to define metrics and compare results.
2. Search for a data scientist:
A data science professional needs to have a piece of accurate mathematical knowledge, as well as expertise in specific tools for applying algorithms. Besides, this professional should have a critical and analytical view of the business and the data generated. Seek a qualified professional or train someone with an appropriate profile within your team.
3. Keep in mind the expected results:
It is important to know what to expect after this hard work, what impact it will have on your business, what you want to improve by applying this solution. Select the key KPIs - key performance indicators - that will be influenced.
4. Define which data will be analysed:
Data collection is an extremely important step. Start small, narrow down, apply to departments that need a big positive impact. Have, in an organized manner, all feasible information to perform the analysis.
5. catalogue the information:
It is important to organize all the necessary data available to us, so that later the algorithms can act intelligently. The structuring of the initial dataset directly implies the results to be obtained.
6. Use artificial intelligence to your advantage:
Through Data mining we can analyze the collected information through predictions. At this stage, it will be possible to identify metrics, projections, and predict forecasts on the data obtained.
7. Transform the Results:
Technically understanding what happens during the data analysis process is as important as knowing how to translate the results generated through the algorithms into business language.
A good understanding of bridging the business and the technical side is crucial to achieving the project-defined goal. By following the above -mentioned methods, you could see your business blooming in unexpected ways laying the big platform for much bigger things that awaits you.