Business decision making is undergoing a data-infused renaissance.
Organizations are tired of the limitations of spreadsheets and
dealing with long IT business intelligence (BI) development cycles
just to gain access to the data they need now. Fortunately, with
the advent of visual analytics and discovery tools (many offered
in the cloud), the journey to data insight is getting simpler and
faster. Rather than trying to divine meaning from a group of
predefined reports or simple static dashboards, visual analytics
helps users gain insights from data more quickly using intuitive data
visualization. Increasingly, visual analytics tools provide easy-touse
data preparation features for better data access. They support
collaboration, mashups, and storytelling.
TDWI Research sees growing interest in applying more modern,
up-to-date tools for working with data.
The report argues top-down and bottom-up BI are flip sides of same coin that needs an harmony. This also describes the rise of data discovery tools as a bottom-up reaction to heavy handed BI and have crushed the top-down camp's monopoly of BI, that has unleashed a bevy of data silos.
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for
the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and
discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the
right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
Published By: Datawatch
Published Date: Mar 21, 2014
Big Data is not a new problem. Companies have always stored large amounts of data—structured like databases, unstructured like documents—in multiple repositories across the enterprise. The most important aspect of big data is not how big it is, or where it should be stored, or how it should be accessed. It’s the efficacy of business intelligence tools to plumb its depths for patterns and trends, to derive insight from it that will give companies competitive advantage in an increasingly challenging business climate. Visualization allows companies to analyze big data in real-time across a variety of sources in order to make better business decisions.
This new research — based on data collected in May 2013 from 29 organizations using visual data discovery and 95 that were not — reinforces those findings. In addition, Aberdeen's research demonstrates that visual data discovery can help to usher in a different approach to analytics, an approach that is far more user driven.