How Can Smart Data Improve Search Based Analytics

How Can Smart Data Improve Search Based Analytics

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.

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 does not 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 is 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 is critical there be a single version of the truth as data is edited, pulled, and analyzed on a regular basis.

Data Management

Mismanaged data introduces several hurtles into the data discovery process. Data collected and stored inaccurately, illogically, or inappropriately can introduce errors into an analysis without the users knowledge. While issues of data management are often created far before analysis takes place, they pose serious hurdles within the data discovery process.

What are the Benefits of Data Discovery?

Data discovery provides a framework for firms to unlock and act upon the insights contained within their data. It transforms messy and unstructured data to facilitate and enhance its analysis. Data discovery allows firms to:

Gather Actionable Insights

From KPIs to trends and distributions, the data discovery process instantly unlocks essential information within unstructured data. Data discovery takes complex data and structures it in ways which allow users to visualize and comprehend the information within it.

Save Time

While analytical tools require data to follow a specific format, data is rarely stored to match this requirement. Data discovery aggregates and formats data from various sources and different structures to facilitate its analysis. This process provides analysts with the right data in the right format.

Scale Data Across Teams

Data is versatile and often contains information that can be used in several different analyses. Departments or users can leverage the same data in different ways to create unique insights. Data discovery facilitates this process and provides all users with a single version of the truth.

Clean and Reuse Data

Data analysis is a continuous process. As new data is collected, current data needs to be cleaned, stored, and made available for future use. Data discovery leverages both new and past data so it can be reliably reused at scale.

Steps of Data Discovery

Collect Data

The first step in the data discovery process is to gather the right data in one place. Data, scattered across many sources, must be placed in a single area where analysis can take place

Cleanse and prepare data

Raw data imported from different sources can rarely be analyzed as-is. Data needs to be cleaned and structured in ways that facilitate reliable and robust analysis.

Share data

With data constructed and free from redundant or unneeded information, it must be shared with others in the organization. While a statistician and data scientist will analyze different aspects of data, they will each provide their own interpretation and analysis of it.

Analyze and generate insights

Individuals can read, analyze, and create value from validated data when there is a single distributed version of the data. Common tools include distributional analysis, predictive models, and market basket analysis. It is important to understand the type of insights generated by different analytical tools.

Visualize Insights

Insights need to be communicated once they are found, and visualizations allow users to easily do this.

Trends in Data Discovery

Big Data Discovery

Big Data Discovery is the creation of business insights through the combination of methods used in big data, data discovery, and data science. This new method employs advanced analytics from data science, technology, and big data to generate insights autonomously and continually

Smart Data Discovery

Like big data discovery, smart data discovery relies on machine learning and artificial intelligence to run analyses. However, smart data discovery is more human-controlled. This difference can be thought of as who asks and answers questions.

Warpping up

While the scale and frequency of data creation present significant data management challenges, it also provides opportunities to develop insightful analytics. Smart data discovery methods provide highly contextualized results for the most tailored use cases. Smart data discovery is the beginning of a revolution in Business Intelligence. The current data discovery and visualization tools will get smarter, requiring minimal analytical expertise to help move the business user quickly from questions to insights.

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