The process of navigating or applying advanced analytics to data in order to detect informative patterns that would not have been discovered otherwise is known as data discovery. Data discovery, like a golfer stepping back from the ball to assess the terrain before a putt, allows businesses to step back from individual data points, combine data from multiple sources—including external third-party data—and see the big picture, leading to better decision-making and business strategy. So, when performing data discovery, you might not always know exactly what you’re looking for — you might just be looking for patterns and outliers to help you better understand your data.
Importantly, data discovery does not necessitate the development of complex models by business users. Most businesses that use data discovery do so as part of their business intelligence (BI) software, which provides a comprehensive view of their operations in a simple dashboard or visual format.
The process of discovering data consists of five steps. It is also an iterative process, which means that companies can use their results and feedback from business stakeholders to continue collecting, analysing, and refining their data discovery approach over time.
Step 1: Determine your requirements: Effective data discovery starts with a clear goal in mind, such as resolving a pain point. This entails thinking about what kinds of data would be useful to know while remaining open to unexpected insights along the way. For example, a fast-moving consumer goods (FMCG) distributor may decide to re-examine its logistics data in order to reduce food waste during shipment by 10%.Alternatively, a retail bank may analyse its web data in order to reduce bounce rates for new prospects.
Step 2: Gather data from various sources.Because no single data stream can tell the entire story, it is critical to combine and integrate data from multiple sources for effective data discovery. This procedure is also known as data crunching.
Step 3: Cleanse and prepare the data. This is the laborious part of data discovery—and a critical component of its value. Cleaning and preparing data for analysis assists organisations in reducing “noise” in their data and obtaining clearer direction from their data analyses.
Step 4: Analyze the data: Business leaders can gain a complete view of their operations and solve operational riddles by combining information from multiple departments, integrating it with external data, and cleansing it for analysis.
Step 5: Document and iterate on your findings: Data discovery is a continuous improvement process, not a one-time event. Malcolm Gladwell, author of the best-selling book Outliers, stated that it takes 10,000 hours of practise to master a particular skill—and the same is true for businesses learning to master their data. They must approach data discovery as a way of life, with the goal of continuously improving and running more efficiently.
Business intelligence includes data discovery. It refers to the process of collecting and consolidating data from multiple databases into a single source so that patterns can be investigated and detected more easily. The following are five advantages of data discovery for businesses today.
Data discovery gives businesses a big-picture view of their organization’s many data streams, allowing them to combine these streams in their analyses and develop well-rounded solutions to their challenges or customer needs. A retail bank, for example, can combine customer data from its website, mobile app, social platforms, and ATMs to get a more accurate picture of each person it serves and better understand their behaviour.
IT and data expertise should not be required to gain business insight. Data discovery makes data analysis understandable for all stakeholders in the organisation, regardless of data literacy. Sales teams, for example, can see how their strategies drive or stop leads throughout the sales funnel; finance teams can identify and cut excess fat from their organisations’ operating expenses; and marketers can connect data from various customer touchpoints to see how their activities align with sales success. In short, data discovery has nearly limitless applications to meet the needs of various business teams.
As data volumes increase and governments invest more in data security, risk management and compliance have risen to the top of corporate agendas. Data discovery enables businesses to identify outliers and potential threats in their data and manage them more proactively. Companies can also stress-test their data management practises to ensure compliance with regulations such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) (GDPR).
Every day, businesses collect more data from more sources and in new formats. Data discovery enables accurate classification of all of this information based on the channel, conditions, and context in which it is collected. Retailers, for example, can distinguish between customer data collected by their marketing, sales, and service teams in order to assess their entire customer experience rather than a single point in time.
Using predefined controls or contextual factors, businesses can apply specific actions to the data they collect in real time, ensuring proper storage and analysis, as well as secure and compliant data practises. Data discovery is critical to achieving this level of control.
Businesses collect massive amounts of data about their customers and suppliers, as well as information about their own operations. Furthermore, they must combine data streams from a variety of online and traditional systems, as well as from various channels such as mobile phones and tablets, as well as platforms such as Facebook and other social networks.
Organizations can use data discovery processes to connect data from all of these sources, prepare it for analysis, share it among internal teams, and support critical decision-making with valuable data-driven insight. Artificial intelligence (AI) has now added a new level of sophistication to data discovery.
The process generally consists of five steps—with continuous iteration—whether using manual or advanced data discovery techniques.
We are as flexible as you require. It is our responsibility to ensure that you are satisfied with your product and the development process.