In this week, we would like to suggest reading an article published in “AnalyticsInsight.Net” about Big Data Trends in 2019. To be honest, most of them are happening in 2018 and some of them are already running in our data science lab by Smart Data Institute Limited and Clear Data Science Limited.
Top 7 Big Data Analytics Trends For 2019
The trending things are listed with our additional notes / views:
- Fast Growing IoT Devices – it is becoming hot from 2017 and keeping it up
- Predictive Analytics – this topic is not new and most people are not doing well in the past
- Dark Data – only a lower percentage of data is being analyzed. So, there is still a big room to grow.
- CDOs in Demand – not only CDOs, but all roles in data science is facing a big demand
- Quantum Computing – it sounds nice for Quantum Computing for solving any complex problems but there are still getting to improve for the integration of tools
- Open Source – SMBs should start here if without any analytics before
- Edge Computing – it is closely related to IoT (in our opinions, not the writer of the article) with computing power close to end-user for sharing workload from centralized computing power. It could leverage the network and server power (also cloud) by sharing workload to local computers near users. Thus, it is aligned with the IoT characteristics.
Finally, we would like to say “not just read other people’s ideas, turn them into actions for your own experience”. This is always the believe of our learning team and committing team members.
Inventory Management is extremely critical for retail, wholesale and manufacturing. Customer Satisfaction, cash-flow and profitability are highly related to the inventory management. There is another area to be considered – logistic management.
Thus, there are lots of people trying to minimize the costs with prediction on damage stock, healthy stock level and best-selling channels / retailers, etc.
We would like to share an article with this vital topic for many businesses:
If the prediction is being accurate, the fruitful results should be reflected immediately. Previously, SDi team members have participated a project with a battery manufacturer to provide alerts for expiry and prediction on battery demands by retailers. Their revenue is being improved by >20%. Another real example is a luxury retailer to improve their fashion inventory by prediction of sales. The stock level in different stores are better managed by the monitoring and prediction systems. They have saved >10% of wastage (disposal) within their chains operating in China. These are real cases leading by our Data Science Evangelist – Samuel Sum.
In this week, we would like to share an article by a British magazine. This article is giving concise definition and explanation across jargons of data mining. There are still many so-called data experts not able to understand the right situation for classification or regression. Please get it right by understanding more…
In this week, we would like to share an article discussing the basic concepts of AI with the role and importance of training. Training absolutely crucial that everyone involved in the development of your model understands how it works.
Original Article: https://insidebigdata.com/2018/10/08/ai-training-work/
The Arthur for the article uses an example for a basket of songs in English and Spanish for the machine to categorize the song according to the language. He points out a number of common problems, such as:
- Data quality for training
- Overfitting of Sample
- Testing considerations
Personally, he is discussing machine learning – a subset of AI rather than the huge picture of AI. Nevertheless, he has illustrated the fundamentals of the “machine learning” processing steps and concerns. It is still an article worth to read for beginners.
Businesses are taking more serious concerns in investing data management like data warehouse, data lake, etc. However, business owners should put the expected return on the investment in the first priority. In our team experiences, the value being created by mainly cost cutting, improving work-flow existed, helping generation of new sales, etc.
In the article below by InsideBigData, Alika Cooper shares her viewpoints where business value can be increased.
How Data Management is an Opportunity to Create Business Value?