Data, insights, cloud, agile, analytics. These are all terms that get thrown around a lot in technology these days. But the truth is that unless you can combine some or all of these concepts, the bottom line benefit to your business will likely not as great as you may expect.
This is the first in a sequence of blogs that looks at how Planning Analytics and Decision Optimization can help organizations go from a plan to the right plan by leveraging optimization throughout the planning process.
The data lake can be considered the consolidation point for all of the data which is of value for use across different aspects of the enterprise. There is a significant range of the different types of potential data repositories that are likely to be part of a typical data lake.
It’s easy to be blinded (and impressed) with the rapid innovation and evolution in the arena of big data. Today’s most technically sophisticated companies have the opportunity to exploit big data tools to address mind-numbingly cool use cases and produce very enticing results. However, so many companies today still depend on the ideals and workflow (not to mention the sunken investment) of the traditional data warehouse.
Most of us are privileged enough to live in societies where clean water is a given. When our physicians and the media advise us to stay hydrated, it’s a simple health consideration. But in many countries, access to water is a huge issue – approximately one third of the world’s population does not have access to safe drinking water.
Line-of-business (LoB) stakeholders want to know that their analytics investment will help them make better, faster, and smarter decisions, with measurable business results. But for many, measuring success from applying Machine Learning and Decision Optimization is not obvious. Learn the top 3 factors to look at when evaluating these powerful technologies for your business decision-making process.
This is the fourth in a series of blogs on analytics and the cloud. Read our introduction to the series. This blog concerns itself with the rise of open source software and how it is used for a whole host of analytical purposes. However, as will be seen in this blog, there are significant gaps in open source capability in this space and may always will be.