Generic

3 machine learning benefits for IT managers

Share this post:

Data science and machine learning have become a vital part of business and profit growth. Many organizations now depend on it, leaving team managers with several challenges: managing data science teams, improving ROI on projects, choosing the right solution, to name just a few. There are of course several IT platforms that facilitate machine learning. But how can they aid in meeting those challenges?

Better team collaboration
A platform like IBM’s DSX (Data Science Experience) is all about team building. It integrates several older services like SPSS and unifies these into a single toolkit that also contains new solutions. Data scientists, data engineers, data analysts and application developers all use the same DSX environment, sharing tools and data easily and communicating directly without having to setup connectivity first. In this environment team members, can work together in a far more efficient way than before.

Management involvement
Preparing the data is something that usually takes up 80% of a data scientist’s time. So it’s obvious that even small improvements in this process will have big overall efficiency benefits. Here’s where IBM’s DSK can make a vast difference. It allows a data scientist to drag & drop data from different sources into the whole data set, which is quite a bit faster and more efficient than the ‘old’ way of having to program the entire model. Programming is only required to make small improvements – i.e. tuning the algorithm. And less programming again leads to a higher level of management involvement, now that a manager can easily understand what his team members are doing.

Data transparency
Since drag & drop is transparent, it offers a clear and complete view, allowing a manager to understand what his team is doing and how the project is coming along, whereas programming used to be a very specialist job. But, from a manager’s point of view, there are more benefits than efficiency alone. DSK is all about transforming information to insights, so it tells a manager the exact meaning of the data. Which might be key in achieving GDPR compliance since the EU’s upcoming privacy regulation has transparency at heart, requiring organisations to give people extensive information and control over the data they collect and how they use it.

So whenever a machine learning platform starts to sound like an expensive necessity, remember that both tangible management benefits as well as an attractive ROI are possible.

Take a look at IBM Data Science Experience.

Technical Sales - Data Science at IBM

More stories

Is regulation enabling or hindering innovation in the financial services industry?

Anne Leslie, Cloud Risk & Controls Leader Europe, IBM Cloud for Financial Services Europe’s financial services sector is in the throes of wide scale digital transformation – a transition being accelerated by the growing adoption of digital solutions and services to help keep up with the demands of digitally savvy consumers. While there can be […]

Continue reading

The Digital Operational Resilience Act for Financial Services: Harmonised rules, broader scope of application

The Digital Operational Resilience Act – what and why As part of the European Commission’s Digital Finance Package, the new Digital Operational Resilience Act, or in short DORA, will come into force in the coming period. The aim of DORA is to establish uniform requirements across the EU that improve the cybersecurity and operational resilience […]

Continue reading

Banking on empathy

Suppose you’re owning a small boutique wine shop and have gone through two difficult years because of the Covid-19 pandemic. As the pandemic seems to be on its way back, it is time to revitalize the shop. And this causes direct a huge challenge: the wine stock needs to be replenished but you have used […]

Continue reading