Logical Data Warehouses in 2020: Guide to Data Virtualization

Data virtualization enable organizations to increase analytics effectiveness and reduce analytics costs by creating a virtual layer that aggregates data from multiple sources. This enables companies to access data from multiple sources without setting up a costly data warehouse or spending time on data preparation. It is also called Logical data warehouses – LDW, data federation, virtual databases, and decentralized data warehouses.

A traditional data warehouse relies heavily on ETL that needs a significant programming effort with special tools and scripting languages. A logical data warehouse creates a virtual layer that handles the ETL. Read more

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Augmented Analytics in 2020: Democratization of Analytics

 AI capabilities such as machine learning, natural language processing and computer vision and to some degree other technologies like AR and VR are poised to augment analytics activities like preparing data and identifying insights. Augmented analytics will enable companies to run more efficient and effective analytics departments and internalize data-driven decision making and enable employees to become citizen data scientists.

What is augmented analytics?

Cognitive or AI-driven or augmented analytics all mean modern analytics: analytics that leverages the latest advances in AI algorithms such as deep learning. When you google these terms, you may find slightly different explanations  Read more

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Cognitive Computing = AI. Let’s not create another fancy term

Cognitive computing is another term for AI and cognitive analytics is analytics that leverages the latest advances in AI.

Wikipedia agrees with that. Wikipedia states that:

At present, there is no widely agreed upon definition for cognitive computing in either academia or industry.

And then goes on to explain that cognitive computing includes AI. We couldn’t agree more.

Though there is some verbiage around how cognitive differs from AI, we have not seen any rational  description of that difference. Vendors and some industry analysts and pundits make ill-defined distinctions between the 2 technologies but as a computer scientist and AI industry analyst, I can’t tell the difference. So unless someone comes along with a clear definition of how cognitive computing is different from AI, we act as though they are the same. Read more

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Cloud analytics in 2020: Guide to cost-effective analytics

Businesses from various industries are producing a vast amount of data they don’t benefit from. Data is only valuable if enterprises can benefit from the insights derived from it. Technical expertise and the cost of analytics investments can be a barrier to achieve this. Cloud analytics services bring the value of analytics to businesses quickly without extensive setup times.

AaaS also replaces on-premise analytics with a cloud-based system. This enables companies to replace investment with operating expenses. Given the highly evolving nature of modern analytics, this enables companies to try various analytics approaches before making a large investment. Read more

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Demand forecasting in the age of AI & machine learning [2020]

Businesses face different inventory challenges when they are dealing with supply chains. Demand forecasting helps businesses reduce supply chain costs and bring significant improvements in financial planning, capacity planning, profit margins and risk assessment decisions.

Machine learning algorithms improve forecasting methods in accuracy and optimize replenishment processes. With these advances, companies are minimizing the cost of cash-in-stock and out-of-stock scenarios.

What is demand forecasting?

Demand forecasting is a field of predictive analytics and, as its name refers, it is the process of estimating the forecast of customer demand by analyzing historical data. Organizations use demand forecasting methods to avoid inefficiencies caused by the misalignment of supply and demand across the business operations. Read more

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