With all the data that’s created each day and the possibilities for making data-driven decisions in business, every company must have a way to present data to business users in a user-friendly and informative way. In my experience, a lot of organizations get this wrong. In the pursuit of making
Data is the lifeblood of our economy today. It helps businesses in every single industry provide better, more personalized experiences for their customers. And with the IoT, we are creating data at unprecedented rates. However, there are a few challenges to IoT data storage and consumption that many large enterprises
Data storytelling can yield significant benefits in informational analysis, but it requires skill and expertise. Learn some tips from data experts to get the most out of the experience. The age-old problem with data analysis is making the best use out of the information obtained by carefully parsing it for
Marketing teams have been struggling for years to adjust to their changing roles amid digital transformation. This year, they’ve added a global pandemic, layoffs, and budget cuts to the ever-changing marketplace. Whereas just a few months ago, many were arguing whether CMOs are even relevant post-digital transformation, marketing teams are
Way too often, we’re guilty of relying on data to make decisions for us. And who can you blame us? Data is sexy. It’s tangible. It makes it easy to support our argument. But without context, we can’t replicate the successes or fully understand the shortcomings that the data conveys.
Today’s HR teams potentially have access to huge amounts of data, and this can bring great rewards for those who use that data intelligently. But, data also brings its own unique challenges. Therefore, before implementing any data-driven HR approach, it’s important to consider the potential pitfalls that surround employee-related data, particularly when it comes to their personal data.
In the last few years, we’ve seen incredible advances in data, analytics, and artificial intelligence (AI). While there is understandable concern about what this means for human jobs, I still believe this is an exciting time for businesses – and the people who work in them.
Data science is a major area of investment for banks due to its proven impact on operations such as fraud protection, risk mitigation, customer relationship management, and more. But while investments in AI are growing, banks are often finding that their existing analytics and business intelligence technology and talent aren’t capable of meeting their current and expanding needs. Challenges in resources, technology infrastructure, and the ability to operationalize models quickly and efficiently can prevent financial institutions from fully leveraging AI and data science to drive business impact.
These challenges, paired with the need to remain competitive in a quickly evolving market, compelled the Sumitomo Mitsui Banking Corporation (SMBC) to seek out innovative solutions to help it maximize its AI and machine learning (ML) investments.
The big data technology’s adoption has shown lots of promises and is very popular among end-use industries. As big data adoption continues to spread, and integration with artificial intelligence and cloud becomes more streamlined, further growth is projected. The global big data technology and services market is poised to reach a valuation of over $118.52 billion by 2022 growing at a CAGR of 26.0% from 2015 to 2022.
Despite the many benefits that data offers, cultivating a data-driven culture hasn’t been easy. Recently, NewVantage Partners published a study that showed companies are failing in their efforts to become more data-driven.