It’s no secret that most successful businesses today depend on data—and that’s more true of technology companies and start-ups than any other segment. The ability to analyze information, derive insight quickly, and make confident, timely decisions can often help a software company break into a new
Upon reading his own obituary in the newspaper, famed author Mark Twain is said to have remarked that reports of his death were greatly exaggerated. I can only imagine that if the data warehouse appliance were a 19th century American novelist, it might say the same thing. For a while now,
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.
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
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
Data transformation doesn’t have to mean moving data away from its source. Rather, modern approaches to data transformation offer a range of advantages that can help enhance performance while heightening access to data.
The inability of lines of business to not serve requests because they have to wait for IT provisioning can lead to a proliferation of analytics silos that can cause a loss of control of data. See how the next big stage of analytics with integrated Apache Spark helps organizations understand the
IBM Insight at World of Watson 2016, 24–27 October 2016, at Mandalay Bay in Las Vegas, Nevada, is the only place to be for people who work with data. Take a look at this list of top-ten reasons you wont’ want to miss out on one of the most intriguing and innovative events of the year.
Maybe classifying data as structured or unstructured isn’t so simple. What is structured to some may not be structured to others and vice versa. When it comes to the business value of data, consider another way to look at data—whether it is repetitive data or non-repetitive data.
The IBM dashDB Local warehouse solution combines a full range of data warehousing capabilities while delivering the levels of flexibility and control associated with the cloud. Begin your own free trial of dashDB Local today to discover how it can help you bring new levels of capability to your
The choice to flex a data warehouse on a private cloud is a personal one. It offers benefits in three key areas: enhanced control over data and apps, better management and monitoring, and custom tailoring that is built to address specific user requirements and self-service applications.
A world that grows increasingly complex calls for disruptive innovation in an open, collaborative environment. See how open data science provides an ecosystem of expertise, skill sets and advanced open source data science tools that fuels collaborative creativity in the development and deployment
Understanding data and data relationships is particularly vital in the energy and utilities industry. Discover how industry data models serve as blueprints for defining structures that provide a broad, in-depth view of business, and how they helped one energy and utilities organization extract data
As a foundation for data lakes and refineries, NoSQL databases provide access, processing and storage to structured and unstructured data for high-performance statistical modeling and exploration. Take a look at the multitude of advantages of NoSQL databases and opportunities to bridge them to open