Have you ever played Clue? It’s a board game where you must make sense of a seemingly random myriad of clues to determine a larger conclusion: Who is the murderer and how did they do it? Similar to Clue, data discovery leads you to larger conclusions about your business (though they’re hopefully less gruesome).
Although businesses are swimming in information, the inability to pull meaningful insights leaves many holding a candlestick in the drawing room with Colonel Mustard without any context. What’s worse, technical and business users are also left in the dark with no way to make sense of that mass of information.
Data discovery involves the collection and evaluation of data extracted from multiple sources to understand the bigger trends. The point of data discovery is to bring disparate data sources and siloed information together to draw meaningful conclusions.
The intelligence gained from data discovery is the true benefit here. Teams can use visual analysis and advanced analytics to detect patterns, gain insight into what those patterns mean, and ultimately utilize those insights towards improving business processes.
To get started with data discovery, you must first clean, sort, and consolidate your data, correcting data errors and filling in the blanks on what’s missing. From there, the visual analysis and advanced analytics can come into play. It’s important to note that data discovery is a subset of business intelligence (BI). BI software will give you the resources expand your data discovery, manipulating large volumes of data to enact the advanced analytics you seek. But, more on that in the next section…
The Link Between Business Intelligence & Data Discovery
Because BI can be expensive and difficult to use, most organizations hire a specialized team to implement it. This leaves much of the company out of the conversation when it comes to data discovery.
Data discovery tools sprouted up as a solution that would make it easier and faster for everyday business users to engage with data trends and uncover patterns. But that doesn’t mean it’s a catch-all solution that eliminates the need for BI. These two data science approaches should be used together to extract data from multiple sources, cleanse and consolidate that data, visually analyze and run analytics against it, and disseminate the results throughout the organization. Both approaches have their own strengths and benefits.
When to Use Data Discovery vs. BI
Data discovery tools are ideal for one-time queries where users must quickly analyze insights. They look for patterns using search-based or visualization-driven analysis so users can extract the exact information they need for specific data investigations. That’s perhaps the biggest strength of data discovery tools: speed and focused analysis.
Business Intelligence, on the other hand, builds on data discovery with an ability to scale and analyze massive volumes of data. BI focuses more on long term, historical/current/future data sets to garner a deeper view into processes and strategies. Its uses are more ideal for complex predictive and operational analysis.
Data discovery can be considered a subset of the broader business intelligence definition. Together, they unite both structured and unstructured data to help businesses tackle issues around productivity, forecasting, operational efficiency, and more.
Using Data Discovery & BI to Solve for X
At the end of the day, the question we need to answer here is how we can apply data discovery and broader BI analysis to solve business problems. When data discovery and BI processes come together, the resulting analytics reveal trends, but also present some new questions. For example, if your analytics show you have high customer churn after the first year, the next question would be why? Where is this problem stemming from?
Through the use of BI software, teams uncover the underlying causes. Data discovery and BI can help solve business problems related to all sorts of things: customer churn, excessive discounting, lost market share, data governance oversight, slow product and service development, operational gaps, etc.
Data discovery tools allow business users to explore data, and BI software takes that to a more complex, predictive level. Getting the right mix of these solutions for your business depends on your needs, but at the end of the day, they both act as a powerful asset towards intelligent, data-driven, and proactive business decisions.