Data Driven Company

Recommended Article: Five Characteristics of a Data-Driven Company

We would like to share an article in the TDWI website by Irina Peregud.  It is talking about the 5 characteristics of a Data-driven company.  With the digital age nowadays, data analytics play a more vital role on the business development.

(Original Article in English)

https://tdwi.org/Articles/2018/09/26/PPM-ALL-Five-Characteristics-Data-Driven-Company.aspx?Page=1

For the 5 characteristics mentioned, they are:

  1. Creative executives who run their businesses with passion and curiosity
  2. Data democratization – related to the level of disclosure of data
  3. Data literacy – employees’ ability to read, work, analyze, and argue with data
  4. Automation of data management workloads – lots of manual work will kill the possibility of “data-driven”.
  5. A companywide, data-driven culture – education plays a very important role for the culture

We would like to borrow the ideas for different experts to help clients working out a real data-driven organization.  With SDI team experience, data-driven culture is the most critical characteristic (especially the mind-set of top management)

Data Driven Company

Big Data & Analytics

Invest in Big Data Smartly

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.

https://www.entrepreneur.com/article/319984

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:

  1. Don’t take IT company as “Big Data” consultants
  2. Don’t take the team focused on hardware / software selling
  3. Do take the team with Project Management and Analytic methodologies well presented to the business users before the project start
  4. Do take the team with Business Analysts or consultants
  5. Do take the team with tracking records
  6. 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.

Big Data & Analytics

An Article by McKinsey – worth to read for data driven business

In this week, I have found that lots of people sharing the article “Why data culture matters”.
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/why-data-culture-matters  (English)

The writers from McKinsey telling truth about data culture related to company decision making. To be honest, we are also working on data science and serving many clients without proper direction on data management and their operational plan together.  This is an area that we have raised different recommendations to our corporate clients for continuous improvement.

We are always getting more chances to educate businesses to have a proper mind-set when dealing data. There is no myth for getting instantly results by just picking up analytic tools. It is more important to have everyone in the organization leading the proper way to deal with data and their own role & position along with data analytics and applications.

Retail Dashboard - by IBM Cognos

Demand Forecasting Mistakes in the Retail Industry

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:

https://insidebigdata.com/2018/09/03/demand-forecasting-mistakes-retail-industry/

The above article is highly recommended by our professional service team.

Retail Dashboard - by IBM Cognos