Data is the raw material that fuels artificial intelligence and machine learning initiatives, but it actually can’t be that raw. It needs to be as accurate, timely and well-vetted as possible — or else AI will deliver erroneous or biased results. At this stage, most enterprises haven’t quite locked down the viability of the data employed within their AI efforts. Read More
Tag Archives: Data Lake
How to Create a Successful Data Strategy
With rapid advances in AI and data science, data has become an essential asset to every enterprise. Setting up a data strategy, therefore, has become every enterprise’s mission, particularly in the C Suite and at Executive levels. What is a data strategy and how do we create the right data strategy?
… There are 6 areas that we should focus on to create a successful data strategy: Alignment, Architecture, Process, Organization, People, and Long-term planning. Read More
The Virtual Analytics Hub
All analytics assets on a single pane of glass!
We are excited to announce the launch of Neebo, the Virtual Analytics Hub that enables analytics teams to connect to, find, combine and collaborate on trusted data assets in hybrid cloud landscapes, and provides a unified access point where they can more effectively leverage all their analytics assets and knowledge.
With Neebo, you can initiate projects in minutes and promptly answer complex business questions, which drives greater agility in data-driven decision making. Neebo is a SaaS solution that allows you to get started in less than an hour, and requires no IT support. Read More
What is Data-as-a-Service?
Data-as-a-Service (DaaS) is an open source software solution or cloud service that provides critical capabilities for a wide range of data sources for analytical workloads through a unified set of APIs and data model. Data-as-a-Service platforms address key needs in terms of simplifying access, accelerating analytical processing, securing and masking data, curating datasets, and providing a unified catalog of data across all sources. Read More
Data is Not Oil. It is Land.
It has become common to talk about data being the new oil. But a recent piece from WIRED magazine points out problems with this analogy. Primarily, you must extract oil for it to be valuable and that is the hard part. Framing data as oil is not illuminating for executives trying to value their data assets. Oil is valuable, marketable, and tradable. Without significant effort, data is not. Data has more in common with land that may contain oil deposits than it does with oil.
Framing data as a real asset may help executives understand its value. Read More
Is Your Data Ready for AI?
Companies are champing at the bit to introduce any solution that promises Artificial Intelligence and Machine Learning. But hasty adoption is leaving one important question unanswered.
Is your data ready for AI?
For most companies, the answer is no.
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5 Reasons You Need a Better Data Management Solution
Congratulations.It took time, but you’re data-driven. You’ve got data scientists to crunch numbers, a fully stocked data lake, and analysts to make it all make sense. But even with the infrastructure in place, benefiting from data isn’t as easy as flipping a switch. These days, businesses have yet to properly manage their data.
And so, they ask themselves a new question:How do you turn a lake of data into streams of insight?
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Use Data Lakes to Bet on the Future of Artificial Intelligence
Artificial intelligence has moved far beyond the stuff of science fiction. And, for all the benefits AI provides today, we can only guess at what the future of artificial intelligence holds. To help ensure that they will be able to take advantage of any and all AI advancements, many companies are making use of data lakes.
Indeed, one of the single largest tech trends of the last five years has undoubtably been the mainstream adoption of artificial intelligence. Within just a few years time, artificial intelligence has gone from being relatively obscure to being used almost everywhere. In many ways, it reminds me of the way that cloud services suddenly gained mainstream acceptance a decade ago. All at once, software vendors collectively felt the need to rebrand their products to reflect cloud readiness. Today, the same thing is happening with AI.
As with cloud services, there are countless use cases for artificial intelligence. One of the main use cases that is driving adoption (at least, in a generic sense) is that artificial intelligence engines can sometimes be used to spot trends and derive meaningful insight from an organization’s existing data. The flip side to that idea, however, is that for the artificial intelligence engine to do its job, it needs access to raw data. There are obviously a number of different ways of making this data available for analysis, but one of the best options may be to create a data lake. Read More
What is DataOps and Why It’s Critical to the Data Monetization Value Chain
In my previous blog “How DevOps Drives Analytics Operationalization and Monetization”, I discussed the critical and complementary role of DevOps to operationalize and monetize the analytics that came out of the Data Science development process. While the combination of Design Thinking and Data Science accelerate the creation of more effective, more predictive analytic modules (where analytic modules are packaged, reusable and extensible analytic modules), it’s the combination of Data Science and DevOps that drives analytic model operationalization and monetization. Read More
Can Data Lakes Solve Machine Learning Workload Challenges?
Year after year, the field of ML is progressing at break-neck speed, and new algorithms and techniques are entering the space at a high frequency. Also, machine learning workloads are becoming increasingly more prevalent. However, there are significant challenges in democratizing machine learning and reliably scaling and deploying ML workloads. Read More
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