While the digital age has brought an onslaught of data to businesses and organizations, the abundance of data is useless unless it can deliver insights and lead to better business decisions. Good data discovery tools are key to delivering those insights. Business intelligence applications provide functionality in multiple areas, but the data discovery process might require separate data preparation tools, visual data analysis, and advanced analytics. These tools come in the form of applications within business intelligence software. Data discovery enables organizations to search for patterns or specific items within consolidated data sets from various sources. Often, the data visualizations are presented in dashboards, reports, charts, and tables. Business intelligence and data analysis allow users to access massive amounts of data and quickly draw conclusions to create advanced information presentations. Let’s take a look at some key data discovery tools.
Depending on the data type used for business analytics, one of the most common tools in the data discovery processes is search-based. Today, to find answers online quickly, users input a term into a search box and receive information almost instantly. The process is practically effortless and makes researching information on the Internet both user-friendly and effective. Search-based data discovery is similar to this and involves the construction of data views through text-search terms. This method functions as a simple search engine to help users identify data points and guide them to relevant data. Both structured and unstructured data can be identified with search-based tools. Search-based data discovery tools have three key features: a structure to store and model data, built-in performance layers to provide summaries and pre-calculations, and an intuitive interface that allows users to explore data. Search-powered data discovery can transform the way businesses operate by empowering users with data. It allows technical analysts to look at more complex questions and inquiries while giving non-technical users the ability to quickly derive insights from company data.
The process of visual analysis often involves going between data preparation and visual inspection. It is similar to putting together a puzzle with one piece until you complete the full picture. When you try to fit a piece that doesn’t quite work, you throw it back and try another. Once your data’s visual picture becomes complete, you can run predictive models and advanced analytics against it. Visual data discovery tools do most of that work for you and provide users with quality checked visual pictures in real-time. These tools use a variety of presentation types to speed up the process of uncovering relevant data. Users can examine data through dashboards, reports, charts, and tables. Visual data tools allow users of all levels to create a more advanced and descriptive analysis than search-based tools. However, it is restricted in that these tools provide a more limited scope by focusing exclusively on quantitative data. This data discovery process does provide a way for more experienced users to explore beyond what’s included in standard charts and graphs within traditional BI.
Along with the benefits of data discovery tools also come several challenges that organizations will need to consider. The capabilities of many of these tools, while leading to greater efficiencies, can also create risk. IT involvement and intervention will be needed for quality data governance. Data quality, security, and privacy are all issues that are related to data governance and the use of data discovery. Organizations need to be sure that data is protected and being used responsibly. This is especially true when business users have such broad latitude with data discovery tools. This is of particular interest today, considering the recent increase of data breaches and the amount of personally identifiable information that companies are gathering. A good data discovery tool can easily and quickly aid businesses by processing large data volumes from various sources. As data discovery has become integral to many companies’ success, the use of data discovery tools is now among firms’ best practices.