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You’ll be hard-pressed to find an organization or CEO who doesn’t understand the role of data in their business operations. Instead, the difficulty lies in figuring out where the data is, the right kind of data to focus on, and the insights needed to grow.
These issues can be resolved with adequate knowledge of data discovery and discovery tools for organizations of any size.
We’ve put together this introductory guide on data discovery and the important things to know about it, like:
- Categories of data discovery
- Benefits of data discovery
- Types of data discovery
- Data discovery tools
What is data discovery?
Data discovery is the process of searching, uncovering, and combining data points and patterns to generate insights and the starting point of the data acquisition process. Data discovery is not just haphazard data collection; it’s intentional, strategic data collection with the goal to generate insights from the right data.
This involves looking for hidden values that reveal patterns and trends, connecting the dots between unrelated data sets, and delving deeper into existing data items. It’s a process used by business analysts and data scientists to drive company growth.
Data discovery is not limited to data sourcing and sorting; it’s deep work involving data classification, data catalog tools, presentation, and analysis. That’s why visual display and interactive storytelling are helpful in the data discovery process.
Data Discovery and Data Governance
Data discovery and governance are two complementary concepts that ensure data quality and compliance within an organization. Data governance sets expectations around data input, availability, collection, usage, security, and integrity.
Since data discovery is an organization-wide process involving collaboration between technical and non-technical teams, a clear framework for accessing and handling data must exist. This ensures that non-technical teams can source data from the IT department and make sense of it.
For instance, if you want to determine the monthly delivery volume, different facets of this information will come from different sources like the Sales, Logistics, and Finance departments. But each area has different ways of defining and recording delivery information. Sales may record deliveries as the number of products a customer ordered, whereas Logistics logs it as the number of goods shipped out from the dock. These numbers will not be the same. Who, then, do you rely on for accurate data?
Outlining and enforcing a consistent standard for retrieving such data falls under data governance. Without data governance in place, you’ll end up with inaccurate data and poor insights that don’t match the reality of your business operations.
Data governance enables data discovery by defining:
- Reliable data sources
- Data logging instructions
- Data purpose and value
- What kinds of data should be logged and accounted for
- Procedures for naming conventions, data sharing, and more
- Parties involved in data handling and access (i.e., data ownership and stewardship)
- When to access or delete data assets to avoid backlog and decrease storage costs
Data governance establishes a central source of truth across the organization and enables successful data discovery.
A strong data governance model also ensures that as organizations acquire and analyze data, they do not violate regulatory standards or engage in non-compliant activities. Failure to adhere to standards like GDPR, CCPA, HIPAA, and others can lead to reputational damage, revenue loss, and legal fines or penalties.
Data Discovery Categories
The data discovery process is divided into three categories: data preparation, data visualization, and advanced analytics.
Data preparation is the process of acquiring, cleaning, structuring, and transforming data into a usable format. This is the pre-processing stage, where you extract the needed information from data assets.
This stage lays the foundation for visualization and analytics. Activities at this stage are focused on making data easier to work with by:
- Eliminating duplicate data
- Identifying and removing outliers
- Sorting data items into their right sets
- Removing inconsistencies in data sets
These processes take up the bulk of time spent on data discovery but they are needed to increase the chances of accurate and quality data insights.
Data visualization refers to the graphical representation or display of data. It is the process of presenting data and information through visual elements like graphs, charts, maps, diagrams, infographics, plots, animations, and other visualization tools like Tableau and Power BI. With data visualization, it’s easier to:
- Help data users identify trends and patterns
- Communicate data insights in a relatable way
- Provide more context and meaning to data sets
- Create customized reports for data presentation
Advanced analytics is a data processing methodology that uses predictive modeling, deep learning, Natural Language Processing (NLP), process automation, and machine learning algorithms to make predictions, forecast market direction, and make sense of incomplete data. Advanced analytics work across data in any format, including complex data sets, unstructured data, and historical data.
Discovering data and making it presentable doesn’t complete the data discovery journey — it’s deriving value from the data that drives the process.
Benefits of Data Discovery
Data discovery provides insight into what goes on within your organization and your industry. The benefits span operations, marketing, sales, customers, and employees. These benefits include:
- Visibility into business data
- Informed decision-making
- Insightful market predictions and business growth
- Customer and employee insights
- Unlocked competitive advantage
Business Data Visibility
Data discovery involves a thorough investigation of your data assets and folders to reveal what you have and make sense of it. This may be as straightforward as retrieving data where it’s stored. Or it could be a more complex approach to identifying patterns and building blocks of the data you’re after i.e., metadata.
Business decisions are most effective when backed by facts and numbers (i.e., data). Data provides the right context to make the decisions that will most impact the business’s current needs.
Forecast Market Predictions and Business Growth
Sometimes, data discovery will have you digging into company archives to unearth historical data for a more accurate understanding of your company’s progress. That can help you arrive at a near-accurate estimate on your most vibrant sales period. Or determine the brand-building activities that yielded the best results in previous years and can still be replicated.
Customer and Employee Insights
Sorting, segmenting, and analyzing data can reveal specific details about customers and employees. You can drill deeper into your company data to understand business users’ behavior and preferences and build a targeted marketing campaign built on those insights. Or you can monitor employee engagement and productivity through surveys to benchmark and track their performance and sentiments.
Unlock Competitive Advantage
A well-built internal workflow and feature-packed product are not enough to overtake a competitor in the industry. Data discovery that reveals information about your competitor’s business model, market trends, and customer behavior will help you unlock competitive advantage. These insights can be used to create improved products, create a better unique selling proposition, and improve customer experience.
Benefits of Data Discovery Tools
Data discovery tools collect, integrate, and analyze different data sets within an organization. Beyond these functions, data discovery tools have become a necessity for achieving data-driven growth. This is because, on average, businesses have a mean of 400 data sources. This is excluding the intensive process of arriving at quality data insights.
Data discovery tools shield your business from the impact of poor data quality by offering the following benefits:
As with most technological tools, data discovery solutions help businesses save time. Through data automation, your employees can spend less time finding, sorting, cleaning, and classifying large amounts of data. Users can work with large, complex data sets, customize reports, and replicate previous discovery processes to generate insights in near-real time.
Agility and Specificity
Most data discovery tools work with advanced analytics technologies like machine learning, natural language processing, and text analytics. This helps them adapt to complex data problems, solve them, and develop algorithms for better output in the future. They can also drill down to specific data sets, identify patterns and produce granular insights that can be acted upon.
Data discovery tools can be set up to guide how data should be presented, worked on, and stored. Having this feature in-built means businesses will comply with the data governance regulations they have been mandated to follow in line with the industry’s best practices.
How to Select a Data discovery tool
The data discovery market is full of discovery solutions, but not all will fit your business needs. Knowing how to identify the best solution is important if data quality is your goal. So, here are important criteria to look for in data discovery tools:
Since data discovery involves technical and non-technical teams, the tool should be easy enough to be understood by different parties. User-friendly software is intuitive, easy to navigate, and easy to implement. Anything short of this will affect your productivity level and the quality of data insights.
Machine Learning and Artificial Intelligence Capabilities
The best data discovery tools make it possible to work with big data and carry out in-depth data analytics during data discovery. You’ll be able to pull out insights, narrow your search, and train the tool to improve the quality of information it produces based on your business needs.
Data, especially big data, can be intimidating, which is why visualization helps when working with it. A good data discovery tool should have options for creating images, graphs, charts, and display patterns that make data presentation fun and easy to handle.
A part of the goal of data discovery is gathering data points from different sources to arrive at a single source of truth. Collaboration enables users to achieve this in an organized and stress-free manner.
Does the tool grow with your business needs, or will you need to move to a new solution, or worse, suffer data loss or poor user experience when the tool gets out of capacity? Evaluating your options on this basis will save costs in the future that would have been used in transitioning to a higher tool. While being scalable, the tool should also be built to accommodate multiple users when the need for that arises, else it will become overwhelming for one person to use as the business expands.
Integration with other tools and systems
A data discovery platform doesn’t have to be an all-in-one solution to your business needs. Sometimes, pairing them with additional tools will give your business the best results, but the software has to be built to make that possible, and you have to be on the lookout for such features.
Security and data privacy
Your preferred data discovery tools should keep your data assets safe from breaches and other cyber threats. An inbuilt encryption and access control feature on the tool will make this possible if your organization deals with sensitive data like financial information or customers’ addresses. The best sensitive discovery tool also helps your business stay compliant by automatically running it through regulatory checklists and procedures you set up for your organization.
Vendor support and community
Most software has an efficient support system and thriving user community as a form of customer support and community building. Opting for a data discovery tool with this feature means a shorter learning curve with the software and a shorter time to value.
Unlock the power of data discovery for your organization
Combining the best hands, business models, and techniques can help your organization move forward. But it starts with uncovering good business insights after making your data discoverable. Data discovery makes this possible by leading you to the right information and keeping you informed on business issues that would have otherwise skipped your knowledge.