There is a growing need for a method to aggregate data and impose business strategy onto emerging technologies. Big Data is bulky and it lacks the precision needed for many important financial decisions. Smart data gets to the core of the information, allowing executives to zero in on important issues rather than waste time on extraneous or distracting information. Aggregation of that data is what the industry needs, and thats where Smart Data delivers.
Full Article: http://entradasoft.com/blogs/how-can-smart-data-improve-search-based-analytics
What is Search Analytics
Search analytics takes this approach to the next level by offering an interactive environment wherein business users can obtain rapid, accurate results. These tools use natural language processing (NLP) to simplify the input and output so that users can ask questions and receive answers without programming or analytical knowledge, thereby enhancing user adoption and the clarity and usefulness of the analysis and reports the enterprise produces.
The whole point is to solve business problems large and small. Information that doesn’t contribute to this goal can be sidelined. Since Big Data does not focus on any particular subset of information, Smart Data usage translates into focus on quality instead of volume. Qualitative data analysis opens up opportunities for firms to speed up the data delivery process, which allows for more time to develop creative solutions.
What are the Challenges of Data Discovery?
Successful data discovery relies on complete, accurate, manageable, and consistent data. Therefore, the major challenges in data discovery come from the collection, storage, and management of data.
Volume
Volume describes the enormous quantity of data created and stored, which can hamper analyses and introduce bias. Data discovery must overcome this challenge with strong data governance and capable technology.
Variety
As the number of data sources continue to soar, the increasing variety of formats presents a challenge in presenting data consistently. Successful data discovery requires strong technical skills to gather and clean data so it’s ready to be analyzed and consumed.
Data Velocity
Velocity is the speed at which data is created. Data discovery becomes a challenge as the rate of data creation grows by the day. New data must be continuously and correctly added to the repository to ensure timely insights.
Consistency
Data must remain consistent across an organization so everyone within it is on the same page. Inconsistencies can result in poor decisions based on invalid or out-of-date data. It’s critical there be a single version of the truth as data is edited, pulled, and analyzed on a regular basis.
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