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“Data is the new business oil.”
This phrase is meant to communicate the idea that your business data can be extremely valuable if utilized the right way. Data has the potential to fuel an entire organization only if it’s organized and studied every single day.
A study states that by 2025, the total amount of data created, captured, copied, and consumed globally is expected to project over 180 zettabytes. This makes it extremely difficult to imagine the data silos generated by a single organization. In this data-driven world where IoT (Internet of Things) and BI (Business Intelligence) are the priority of the day, over 60% of organizations are struggling with unstructured data.
Therefore it’s crucial for enterprises to look for data solutions that allow them to seek out the value of their data from information, metrics, and insights. One such solution is data discovery.
Data discovery is a process that involves analyzing and collecting data from multiple sources to discover outliers, trends, and patterns within the data.
Data discovery covers both structured and unstructured data and helps generate data visibility throughout the organization.
Now, what do you get out of data discovery?
You can leverage the insights gained to enhance your business process and stand out in an already saturated digital ecosystem. Almost every business collects massive data from vendors, suppliers, customers, operations, and production processes, both from traditional and digital transactions.
Not taking advantage of this data can keep you drowned with useless information. This is when you need data discovery to analyze the information, automate the management and help you visualize your business in a big picture.
Now that we are on this topic, you’d be surprised to know that the global data discovery market size is projected to reach USD 14.4 billion by 2025, growing at a CAGR of 15.6% compared to 2020. This growth is due to certain market dynamics such as :
Previously, the enterprises have been practicing data discovery manually with the help of excel sheets, carrying out documentation and analysis all by themselves. Experts called it an inefficient process that took a lot of time and effort. The insights gained were also highly dependent on the individual’s understanding of data and were subjected to human errors.
Manual data discovery included manual data mapping, categorizing metadata, documenting rules, and gaining insights using critical thinking.
With the introduction of Artificial Intelligence (AI), Business Intelligence (BI), and Machine Learning (ML), smart data discovery has become the go-to process for every modern data-driven enterprise. With the help of this automated data system, enterprises can easily conceptualize and present the data insights on an integrated platform.
Smart data discovery includes automated data preparation, integration and presentation of hidden patterns, and visualization of trends and information for smart decision making.
This seamless data translation process empowers businesses with real-time actionable changes that directly impact growth and profitability.
Smart data discovery process comes in multiple forms, combining analysis, visual outputs, and modeling. To attain maximum value from this process, you first need to understand the format of how data discovery works. Below are the three categories of data discovery that can help you gain a bigger picture of data operations.
Data preparation is the most vital step that comes before any discovery and analysis. This step involves cleaning the data (structured and unstructured), reformatting, and merging that data from all sources to be studied in a consistent format. The better you prepare your business data, the effective insights it provides. If you ask how the data prepares itself?
The data might deduplicate, detect outliers, delete the null values, and format itself to high quality for better analysis.
Data visualization is another effective way to fully comprehend business insights. Here the data is visualized in the form of dashboards, flow diagrams, charts, and other formats. This is basically the result of predictive analytics and machine learning. This comes in handy for non-technical department teams to understand the relationship among various data streams.
For instance, your finance team can analyze the cost vs. revenue and pinpoint the areas of improvement for every department.
In the same way, your design team can monitor the entire customer lifecycle using the same data source and function accordingly.
This is where the description and visualization part is merged to get a complete picture of the company’s business data. This advanced analytics and reporting system organizes, summarizes, and breaks the complex data into simple, intuitive reports for future decision making.
So far, we have discussed several applications of smart data discovery. However, these applications and perks are not just limited to smart and insightful decision-making. Below are the top five benefits of smart data discovery for modern enterprises.
Enterprises collect more data from thousands of sources every day in new formats. Through a smart data discovery system, you can classify all of this information precisely based on the condition, channel, and context in which it is collected.
For example, retailers and manufacturers can differentiate between the consumer data collected from the sales, marketing, and service teams to merge and assess the entire customer experience. Without data discovery, they would have to go through every single piece of data separately.
You can apply specific actions to your data using different data discovery techniques, such as predefined control over data. This gives you actionable full-time control over your company’s data. For example, you can easily compare your current year’s profit margin to the previous year’s profit and gain insights into future profit probabilities.
As the data volumes grow and the consumers get more invested in data protection and security, compliance and risk management are on the top of every enterprise’s agenda. As mentioned above, smart data discovery spots the potential threats and outliers in the data so you can manage them proactively.
With the trending smart data discovery features, you can also stress test your data management practices to ensure that your business complies with the General Data Protection Regulation (GDPR).
Data should not be only understandable to IT experts and data analysts. With a smart data discovery system, data insights are easily accessible to nontechnical departments such as sales and HR along with clients and stakeholders. In short, data discovery can be an all-in-one solution for every team’s needs.
For instance, the sales team can put figures to strategize how to stop lead expenses. Data discovery can help visualize the same figures to the marketing team to analyze the customer’s call to action points. This way, one data source can be used for unique analysis and decision-making.
This benefit cannot be stretched enough that data discovery plays a crucial role in providing future insights. For example, a retail chain can combine consumer data from its application, website, social media, and ATMs to gain a detailed view of every customer it serves. This helps understand consumer behavior and its convenience for future growth.
Apart from this, the new and historical data is constantly prepared and recycled for future accessibility.
Any data discovery tool or solution that you wish to employ in your enterprise should be able to go through all the three categories of data discovery discussed above. To implement a successful smart data discovery process, you must seek reliable data analytics solutions.
While you are looking for data discovery solutions, here are some key attributes that every modern data discovery tool must offer.
This is a fast-paced data environment where time and data can be capitalized quickly. You cannot afford to wait for analytics and BI and let your business be affected by delayed insights. Hence, your data discovery tool should be able to integrate the vast sets of data from multiple points, filter them and deliver real-time insights.
A quality data discovery tool should offer advanced visualization and reporting features to conduct quality analysis and derive maximum value out of insights. Your data discovery tool should be able to merge multiple charts and provide advanced comparisons. Also, seek out features like formatting capabilities for underlying trends and color-coded indicators.
Professional analysts and data experts should not be the only ones to understand the tool and its navigation features. Data discovery allows everyone to access the gained insights; therefore, the data discovery tool must be familiar to every department (technical and non-technical). This will allow a free flow of information within your enterprise.
Professional data discovery tools allow users to customize their data fields according to their business requirements. These custom fields can be accessible to all to collaborate and unify the business decisions.
Discovering, cataloging and profiling your business data is one of the most complex yet critical steps towards comprehensive data strategy. Allow us to help you with improving data quality and maintaining data compliance. We can generate deep, actionable insights for your business growth and scalability. Connect with us to derive great value from your data.
A. Below are the general steps to perform smart data discovery that ensure your data is ingrained the right way.
A. Smart data discovery has many applications, from enhancing backend operations to improving customer experience. Data discovery can be utilized in:
A. Data discovery software and tools help in collecting and combining data from various sources and points to identify unknown patterns and trends. Data visualization, data monitoring, data analysis, data report and advanced statistical analysis are some of the primary functions of data discovery software.