IBM - Modern Data Warehouse Example

Let’s see top management viewpoint on modern data warehouse

Lots of technologists are promoting replacement of data warehouse by data lake.  In the view point of our data science team, a modernized data warehouse should carry its own value for the analytics nowadays.

There is an article by sharing the viewpoint of CEO of Yellowbrick – Neil Carson what vital technology and tools being used.

Original Article

His opinion is aligning with our data science evangelist – Samuel Sum on Hadoop.  It is a large-scale data store but it is not easy to access and manage in many situations.  It is always better to have a structured data store like data warehouse for easy user access.  Also, a key-point of a successful data analytic environment is the storage speed storing the data.  With SSD (flash memory), the data warehouse (both ETL and access) are now several times faster than before.

Finally, the editor of this webpage suggests read an article of Samuel Sum – talking about the Data Lake (Data Lake VS Data Warehouse).

IBM - Modern Data Warehouse Example

Digital-Data Driven

Prepare your Organization to be Data-Driven now

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.

  1. Improve the Brand image
  2. Attract audience by picking the right content (product / service)
  3. Simplify the meaning
  4. 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.

Digital-Data Driven

data warehouse

Data Lake VS Data Warehouse

Our team leader / founder – Samuel Sum has written an article on his blog about Data Lake and Data Warehouse.  There are lots of people trying to drop their data warehouse.  However, Samuel is providing his viewpoint on the value of the data warehouse.  Also, his suggestion on data lake architecture is being discussed by the real-world experiences with our professional service team.

Big Data Career Path

Lead your Career in Big Data Field

In our team, most of the team members are working hard to grow themselves as top data scientists.  There is an article to discuss how-to have a career in Big Data.

Original Article by

To be honest, there are tons of things to learn for becoming a leading data scientist.  Our team leader has reviewed this article and suggested all team members to read it once.

Big Data Career Path

Megadeals VS Data Analytics

Suggested Article by McKinsey about Megadeals and Data & Analytics

We are always visiting our clients to discuss the value of data analytics.  One of the area is being argued about data analytic failed to provide insights for exceptional big deal in the B2B business.   The real case situation is being applied in a consulting business like our team with 1 to 3 mega deals in millions scale contributing up to 30% to 40% revenue in total.

Most people think that data analytics should only be good in “regular” deals rather than “mega-deal(s)” due to the data availability.   Nevertheless, we have won some projects by lots of research and development based on our own KPI knowledgebase of KPI, open data and data from the Census and Statistics Department.

Original Viewpoint from McKinsey:

The article of McKinsey shares their viewpoint for high quality “small” data could be the key of making Megadeal.


Data Science & Personal Data Protection

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 (

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.


Big Data Trends in 2019

Top 7 Big Data Trends in 2019

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.

Original Article:

Top 7 Big Data Analytics Trends For 2019

The trending things are listed with our additional notes / views:

  1. Fast Growing IoT Devices – it is becoming hot from 2017 and keeping it up
  2. Predictive Analytics – this topic is not new and most people are not doing well in the past
  3. Dark Data – only a lower percentage of data is being analyzed. So, there is still a big room to grow.
  4. CDOs in Demand – not only CDOs, but all roles in data science is facing a big demand
  5. 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
  6. Open Source – SMBs should start here if without any analytics before
  7. 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 by Intelligence

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.

Inventory Management

A Concise Introduction about Data Mining

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…

Recommended Article:

Data Mining Example