Three Ways of IBM Cloud Private for Data can accelerate your AI

The last two weeks have been really exciting to be part of Big Data and Analytics at IBM. We have just launched IBM Cloud Private for Data, an integrated data science, data engineering and app building platform. Our broader offering, IBM Cloud Private is helping our clients and partners to harness everything cloud has to offer, but off of the public cloud, securely and behind their firewalls. It seems like CEOs all over the world are realizing that to be competitive, they have to embrace technologies like private cloud and artificial intelligence (AI).…

IBM Recognized as Hortonworks Partner of the Year

Working with Hortonworks over the past year has been an inspiring experience, and one that I know is constantly driving towards greater value for those looking to get the most out of Hadoop. That’s because IBM and Hortonworks are both committed to creating solutions that are built on an open foundation and capable of supporting even the largest enterprises. The results speak for themselves in the feedback we receive from our clients. 1. Why embrace open source? There’s a lot to love about open source technology. Open source promises continuous…

Learn to deliver fast ROI with data science

Data science is a high-impact discipline that can truly move the needle in any enterprise and industry. But as the practice evolves, data science leaders and their teams can encounter a multitude of challenges: demonstrating value, evolving organizational practices and transforming outdated data management to fit into a world ready for artificial intelligence. IBM Data Science Experience was designed to kick-start and rapidly scale data science projects, fitting any deployment needs in a multicloud environment. In this video , Tim Bohn from the IBM Data Science Elite team, and data science…

How Data Science for the insurance industry

Data science is a priority for most businesses today, and data science teams are under more pressure than ever to deliver return on investment (ROI). That’s especially true, given the expense of building and maintaining data science teams. If models don’t translate to measurable business impact, efforts can be undermined. What does it look like to deliver business value to an insurance company, where there’s a delicate balance between reducing expenses and giving customers a positive claims experience? In this Q&A, IBM Financial Services Solution Architect Irina Saburova discusses an…

4 Steps for running machine learning pilot project

Running a machine learning pilot project is a great early step on the road to full adoption. To get started, you’ll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in school. Here’s how it works: Evaluate your results. Next, you’ll want to analyze the results of your experiment. What patterns do you see? Is your current business strategy out of touch with the data? If it is, that’s good. It means…

Overwhelmed by complex data?

Businesses have never had more access to data – and that can sometimes bring enormous problems as well as benefits. The more data you have, the more difficult it can be to organise, analyse and make use of. Fortunately, there’s technology available to help. Managing big, data-heavy projects can be daunting. To give just two examples, we’ve worked recently with a company that needed to review its data for thousands of counterparties for compliance with anti-money laundering legislation. The company was given six months to complete the review, which would…

Using data and technology to pick out the bad apples

In our first blog we talked how organisations can protect themselves against financial crime, for the second blog in our series we’ll look into more detail about how to use data and technology to understand your customers. How would you spot a criminal when you’re out and about? Criminals – particularly those involved in the money laundering industry – tend to look and on the surface, behave just like everyone else. Which often makes them very difficult to spot. Banks and other financial institutions know that some customers, whether business or personal,…

What are Semantic Layers and Why Should Product Managers Care?

If you’re a product owner, you’re likely looking to integrate embedded analytics into your data product in a way that makes it usable by everyone in an organization. For those who are , you should be thinking about semantic layers. In my role as Senior Technical Product Marketing Manager, they’re something I’ve been thinking about a lot lately, and I’m not alone. For those unfamiliar with semantic layers, think of them as a shield—or a layer, which is where the name comes from—between the user and the sheer volume of data that’s…

5 CAREERS TO CONSIDER FOR DATA ENTHUSIASTS

Data Scientist There are some compelling reasons to become a data scientist. First, it’s a field that is growing at an astonishing rate – IBM predicts that by 2020, the demand for data scientists will grow by 28%. That’s good news for job-seekers, especially since that kind of demand can bump up salaries—which are already into six figures. What do data scientists use? They harness large amounts of data to help businesses make strategic decisions. Data scientists work by starting with trying to solve a problem. This problem could be…

In which Danny Kaplan discovers statistics

My background has been in science, not in math or statistics. I did physics and biomedical engineering, especially in cardiology. I used to work in a physiology department at McGill Medical School, but I came back to the US because I was interested in teaching, and at a small liberal arts college. It happened that, while I was here for my interview, they asked whether or not I could teach statistics. They said, “We always need someone to teach statistics. Could you teach it?” For no other reason than that,…