68 percent of surveyed businesses recently responded that they use machine learning (ML) or plan to do so in the next three years. AI technologies rapidly are becoming how businesses distinguish themselves from competitors. But choosing the best way to implement AI isn’t always a straightforward
To accelerate its journey to AI, a data-driven organization needs a trusted data foundation that empowers information stakeholders. Stakeholders need the ability to discover, understand, integrate, analyze, govern and self-serve structured and unstructured data — on premises, on cloud, and hybrid
Most sales-driven organizations have needed a customer retention model at some point or another. The request is fairly straightforward: identify the customers that a business might lose. But the process can create a nightmare.
There’s a general need for next-gen executives to not only understand corporate regulations, but be able to adhere to and follow them using metadata solutions like data governance. As the business world’s top asset becomes data, data governance will ensure that data and information being handled is
No matter what site you search, it’s pretty clear that self service data is a top trend in the data market today. The knowledge and insight that we can obtain from data is truly a secret weapon. But the challenge is making the data available while keeping it trusted and governed.
Perhaps one the single most significant changes to the analytics landscape in recent years had been the emergence of the data scientist. This role is continuing to evolve, with many organizations still in the process of establishing how best to incorporate this relatively new discipline into their
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.
In the past, the relationship between the different models that might be used in defining a data warehouse was a very linear one. There may have been different model artifacts used as the team responsible for developing the data warehouse progressed through the usually waterfall-type set of
The Academy Awards provided a great example of the challenges of data integration. The business output of the data integration processes in the award ceremony is the announcement of a winner in a specific category.
The need for information is paramount to our need to excel and succeed. Businesses rely on information for strategic planning and driving growth. Individuals rely on information to make decisions and gain understanding of things. All information is driven by knowledge, and for knowledge to be an
Data models for developing data warehouses need to evolve for managing and defining data lakes. This first installment of a blog series on charting the data lake introduces the potential role of data models in data lake environments and how they need to take an active role in defining and managing
How can we ensure that metadata about all types of data is accurate, available, ubiquitous and universally accessible? Standards are certainly necessary, but we also need a new way to think about how metadata is created, managed and maintained.
As the data used by an enterprise grows in size, variety and importance, it is no longer acceptable that the gathering and maintenance of metadata remains an under-funded and neglected afterthought for data-driven organizations. Metadata management needs to become a key focus of an organization's
Metadata and governance might not have a long history of setting hearts ablaze, but those who are recognizing their importance to self-service are looking to metadata to help organizations fully leverage a wide range of assets across the business. In this InsightOut podcast, IBM Analytics CTO Tim