Instead of relying on inaccurate, messy and low-quality data, business leaders can instead drive innovation forward by turning to automated data management, creating a clean and steady data flow.

Businesses like yours need to connect applications and data sources to better serve customers, automate processes, and improve decision-making.

Recently, I published an article on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

As technology advances and digitization takes over, there is an expectation that our lives will be more simple. ‘Self-service’ capabilities like Self-Service BI are the manifestation of this expectation within many technologies. For most, ease of use is no longer enough. Now tools must be simple to use, and flexible enough to cater to a wide range of skills and intricacy of analysis.

Amidst growing inflation, big and small businesses are looking to cut costs to stay competitive. According to Gartner, reducing IT costs is a top priority for 40% of CFOs. Cost-saving initiatives usually focus on firing people or getting rid of un(der)used technology. This might work in the short term. For example, the IT cost reduction strategy of renegotiating service level agreements and software licenses with your service providers can impact the bottom line.

The value of embedded analytics is unmistakable. Application teams that embed dashboards and reports drive revenue, reduce customer churn, and differentiate their software from the competition. While embedded dashboards create real value, they can also come with real costs. These costs are not always visible when companies plan for their analytics offering but can significantly impact production, scale, and the speed of bringing analytics to market.

Over the past handful of years, systems architecture has evolved from monolithic approaches to applications and platforms that leverage containers, schedulers, lambda functions, and more across heterogeneous infrastructures. Cloudera Data Platform (CDP) is no different: it’s a hybrid data platform that meets organizations’ needs to get to grips with complex data anywhere, turning it into actionable insight quickly and easily.