Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
No Result
View All Result

Here is how IBM’s Data Scientists look at the Data-Driven Future

by Kim Deen
November 24, 2019
in Artificial Intelligence, BI & Analytics, Data Natives, Data Science
Home Topics Data Science Artificial Intelligence
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsAppShare on e-mail

An aspiration to create a data-driven future has resulted in massive data lakes, where even the most experienced data scientists can drown in. Today, it’s all about what you do with that data that determines your success. And IBM has the recipe for this. Read on. 

“Without data, you simply can’t compete in today’s market”, tells Dr. Susara van den Heever, Executive Decision Scientist and Program Director of the IBM Data Science Elite team for Europe, Middle-East, and Africa. Her team is supporting companies in their journey towards data-driven decision making and business strategy. “I can’t think of a company today that doesn’t want to be data-driven. If we are not data-driven, we can become extremely biased in everything we do.” 

Why be data-driven?

Still, a lot of companies have difficulties making the transition. Chan Naseeb is a Lead Data Scientist at IBM’s Data Science Elite team and sees various reasons for why companies are holding back. “Some have a short term focus: let’s finish this project and start with AI afterwards,” he tells. “Others have a narrow focus: we serve our clients the way we used to and we will continue to serve them this way.” Some just have a lack of skilled resources.

Another issue is a focus on the return of investment. If companies want to become data-driven, they should be willing to freewheel for a while. “In the beginning, you may not gain a lot as it is a journey and not just a one-off effort. You see the technology working and you can solve a business problem,” Naseeb tells. Some companies are just not aware of what is to come: “they don’t have a clear picture of what it would mean to them if they base their decisions on data more”.

“Most organisations will have to tackle challenges, rather sooner than later,” says Stephan Lobinger, the Lead Solution Architect for Data Science & Business Analytics with IBM Cloud. “Companies who are data-driven have competitive advantages. Hence, their likelihood of outperforming or even replacing the ones who aren’t is simply higher.” 


Join the Partisia Blockchain Hackathon, design the future, gain new skills, and win!


Why? Data-driven companies have a better picture of the market and the customer. That is especially important as companies have increasingly less direct contact with their customers. “When is the last time you went to your bank? As banks interact less directly with their customers they need to leverage what they have”, tells Lobinger. “Interactions via online banking are therefore valuable. You can use this data to improve your services for the customer.”

Where and how to start your journey to be data-driven?

The right ingredients for a smooth transition, says Lobinger, are small steps, allowing some freedom and creativity, and ultimately: learning from mistakes. “Don’t shoot for the moon. It’s good to start with small projects from which you can learn”, he tells. “You don’t aim for 100%, you aim for 80%, but assure you learn from mistakes.” 

IBM helps clients via their ‘AI ladder’. Following their motto “no Artificial Intelligence without Information Architecture”, the first step is sorting out the data information architecture. During this stage companies determine their use cases and the data helps them to gain insights. When this is up and running, IBM works in collaboration with the client on their data analytics. “Here we are looking backwards”, tells Lobinger. “We get to the underlying conclusions: What are the correlations, what are the drivers?” The next step is looking into the future: Machine Learning and finally even AI. 

In the long run, a cultural change within the company is needed. During many projects, Naseeb faced resistance from different stakeholders and different departments, who weren’t convinced a data-driven project was worthwhile. Getting everyone on board can take a while, but he always sees this happening over the course of a project. “We keep all other business units updated and show them what we have developed,” he tells. “At a previous project we started with one use case, but ended up working on twenty, for different departments.”

For business leaders, it’s important just to begin, with a clear data strategy in mind. “A mistake many companies make is thinking they need to have all the data at hand,” says Dr. Susara van den Heever. “You first need to think about what you are trying to achieve. Are you trying to improve the lives of employees? Are you improving your manufacturing plan? It’s all thinking about the use cases for the next two to three years that you want to achieve, and getting a data strategy in place.”

Revolutionary are the ‘aha moments’ she sees her clients having from time to time. “That’s what I find most exciting, that aha moment when they realize what they can do with technology what they couldn’t do before,” she tells. “When there is truly a big change in terms of saving time, money and ecological footprint.” 

Whatever it is a company is facing, IBM has developed strategies and products to support data-driven transformation on all levels – from cultural to technical. At Data Natives 2019, IBM host the Data Science and Developer track, where Susara van den Heever, Dr. Chan Naseeb, Stephan Lobinger, and more leading experts will show exactly how companies can overcome obstacles. Meet them at the conference !

Related Posts

AI Text Classifier: OpenAI's ChatGPT detector can distinguishes AI-generated text

AI Text Classifier: OpenAI’s ChatGPT detector indicates AI-generated text

February 1, 2023
BuzzFeed ChatGPT integration: Buzzfeed stock surges in enthusiasm over OpenAI

BuzzFeed ChatGPT integration: Buzzfeed stock surges after the OpenAI deal

January 31, 2023
Adversarial machine learning 101: A new frontier in cybersecurity

Adversarial machine learning 101: A new cybersecurity frontier

January 31, 2023
What is the Nvidia Eye Contact AI feature? Learn how to get and use the new Nvidia Broadcast feature. Zoom meetings and streams get easier.

Nvidia Eye Contact AI can be the savior of your online meetings

January 30, 2023
How did ChatGPT passed an MBA exam

How did ChatGPT passed an MBA exam?

January 27, 2023
What is AI prompt engineering? Learn how to write a prompt with examples. ChatGPT prompt engineering and more explained in this article.

AI prompt engineering is the key to limitless worlds

January 27, 2023

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

LATEST ARTICLES

AI Text Classifier: OpenAI’s ChatGPT detector indicates AI-generated text

A journey worth taking: Shifting from BPM to DPA

BuzzFeed ChatGPT integration: Buzzfeed stock surges after the OpenAI deal

Adversarial machine learning 101: A new cybersecurity frontier

Fostering a culture of innovation through digital maturity

Nvidia Eye Contact AI can be the savior of your online meetings

Dataconomy

COPYRIGHT © DATACONOMY MEDIA GMBH, ALL RIGHTS RESERVED.

  • About
  • Imprint
  • Contact
  • Legal & Privacy
  • Partnership
  • Writers wanted

Follow Us

  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
No Result
View All Result
Subscribe

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy Policy.