Big data and data science become mature and it’s the great time to take your corporation as a data-driven organization. There are lots of fruitful facts for a business using data analytics as an ordinary operation and management tool.
- Improve the Brand image
- Attract audience by picking the right content (product / service)
- Simplify the meaning
- Leverage the power and reach of social media
Entrepreneur Magazine (original article)
HMV is a failure example of traditional company and the streaming service is now about 34% of the total earning of the Music industry. There are still lots of people getting their living in the Music industry in alternative ways. With the Internet, all business is facing competition globally. It is important for businesses to improve themselves with the help of data.
As 2019 is just started, it is time to share different experts’ viewpoints on the trends in data science.
It’s very interesting that they are saying something “not too surprised” and many of them are running in reality. For example, large corporation management like Oracle is still talking about the Artificial Intelligence (AI) and Machine Learning (ML) with the interview with technopedia.com.
Article by Technopdiea.com
However, there is another article trying to consolidate different sources to see any common ground about the data science trends in 2019.
Article by DataVersity.net
In this article, more different areas are being covered such as Virtual Reality and Information Security.
To sum up, it is more mature to have more solutions by the support of data science. We are moving from data analytics to intelligent automation.
Another day, there is another sharing of an article worth to read. In this week, we would like to highlight the importance of privacy when doing data collections. Even with GDPR is now applied to EU countries, but there is still room of improvement for handling data privacy. There are lots of data analysts and data scientists’ collecting too much details including unnecessary personal data for their projects.
Article from ITPRO (itpro.co.uk)
They are sharing the bad example of Microsoft for collecting personal information from Office 365.
As experienced data team, we are putting “ethical data science” as the first priority. Masking personal information and anonymous data should be enough for most cases of data analytics. Therefore, institutions and governments should always refine guidelines and rules on data collection to strengthen personal data protection aligned to the technology development.
With the raise in AI and machine learning, companies in APAC region are investing large sums of money in Big data and analytics. It is now a new source for pushing the economy.
However, as a data science consulting company, we do suggest – “understand before invest”. We have participated in 5 projects in last 3 years to “rescue” clients getting wrong with other consultants. It is the right direction to invest in Big data for insights, decision support and automation. Nevertheless, the positive result is only based on whether you’re getting the implementation running at the right track.
We do have some suggestions:
- Don’t take IT company as “Big Data” consultants
- Don’t take the team focused on hardware / software selling
- Do take the team with Project Management and Analytic methodologies well presented to the business users before the project start
- Do take the team with Business Analysts or consultants
- Do take the team with tracking records
- Do start the project small and transform your business with iterations
Finally, it’s really important for business to have proper analytics to support the survival and continuous growth. I do recommend every business manager should study things like “data management” and “data driven business”, etc.
We would like to share a new by insidebigdata.com again. There are lots of articles / news flooding around the internet but most of them are related to advertisement or sponsored organization. We filter the valuable information for our audience only. For the article shared, it is showing some clues on analytics at the retail industry by Aaron Hoffer, Lead Data Scientist for Alloy.
He discusses common problems in forecasting processes, and the importance of focusing on “true demand” by his experiences. He suggests from micro scale by shops demand rather than sales forecast for the whole organization to avoid the boost of inventory with wrong forecast results.
Please find out more clues below – worth to read for both data scientists and managers:
The above article is highly recommended by our professional service team.
There is an interesting conversation published by TechRepublic. Big data is now an economic topic rather than just technology. For applied science, it is far important to have real world applications rather than just on the paper or in the lab. Please watch it and take Big Data into actions:
[Remarks:] Smart Data Institute Limited is a learning company sharing news and knowledge with the public for the promotion of value in technology applications. Also, we do organize different training courses to different organizations and institutes.
There are lots of misunderstanding on the field – Data Science. Unfortunately, there are lots of immoral businessmen stepping in and claims themselves data scientists. It’s better to view the articles of Harvard Business Review with the sharing of data scientists worldwide.
For SDI team, we are focused in applying our data science experiences to help customer solving their business pain-points by insights. In order to have the best-fit solutions, we are always working on industrial experts as a team like Financial Analyst, Accountant and Business Management Consultant.
Data Science is an applied science and its succeed is based on implementation. Let’s take a look on an article by insidebigdata.com. They are sharing their viewpoints as “Data science is practice, not a particular skill set”. In the opinion of the data science team in SDI, data science is a combination of skills to be applied in the real world for solving business pain-points or social problems rather than pure statistics or computing projects.