A Guide to Data Discovery: Definition, Tools, and Benefits

Data discovery is a dynamic, constantly evolving discipline. The global data discovery market is set to grow 15.6% every year and hit $14.4 billion by 2025. To justify this growth rate, there must be a significant competitive advantage for companies that adopt data discovery. 

To understand the value of data discovery, we must first understand how data science has changed over the last couple of decades. In the process of converting raw data into actionable insights, the main limiting factor used to be hardware. In the last 20 years—especially since the advent of cloud computing—storage and processing power have become limitless for most purposes.

Today, the limiting factor is having too much data from too many sources. Continuously extracting value from all that data is a massive challenge. Consider a social media company that gains business intelligence insights from customer behavior and market research done by a third-party company. Now imagine there are billions of data points in both categories, hundreds of demographic groups, market research data is biased, and customer behavior seems to contradict market research in 40% of instances.

Data discovery can untangle the mess.

What is data discovery?

Data discovery involves collecting, integrating, and analyzing data from multiple sources to uncover hidden insights and aid business decision-making. Our hypothetical social media company might find that certain demographics respond to incentives that other demographics ignore.

When you arrive at such insights, you convert them into a format (usually visual) your organization can easily understand. Then you validate the conclusions. Finally, you map them onto your business processes, risk management, and forecasting.

In short, data discovery is the process by which we turn data into actions.

The importance of data discovery

Data tends to grow exponentially. It’s challenging for businesses to track these datasets and combine them into meaningful insights.

Businesses often opt to automate as much of this process as possible. That’s where data discovery comes in. Implementing data discovery in your organization can help you track of crucial key performance indicators (KPIs), control risk, and make informed forecasts.

Data discovery process

The process of data discovery typically unfolds in three stages:

1. Data ingestion: Extracting data from a source
2. Data preparation: Making the extracted data usable
3. Data exploration: Reviewing the prepared data for hidden insights

Data ingestion

Data discovery begins with collecting and storing relevant data. Data can come from a wide range of sources: APIs, scraping, form inputs, surveys, customer feedback, and third-party data sets. Because there’s so much variety, data comes in various levels of structure (e.g. structured, semi-structured, and unstructured). Advanced data discovery tools also track the overlap and movement of data across different categories.

Data preparation

Data prep begins with normalizing the structure of all collected data. Normalization involves restructuring and deduplicating various types of data to maximize its usability for systems and humans. Examples of different types of data that may need to be normalized include JSON data from an API, SQL data from user inputs, and CSV data acquired from a vendor.

Preparation also involves assessing data for incompleteness, corruption, and quality. Typically, the bigger the dataset, the greater the number of quality issues. These can range from harmless inaccuracies to deep misconceptions that can mislead your company in dangerous ways. For example:

• You might misunderstand customer behavior due to data from an unreliable tracking mechanism (relatively harmless)
• You might invest billions of dollars into an outdated technology based on faulty or incomplete market research (dangerously misleading)

Data exploration

Once data quality is assured, it’s ready for exploration. Data exploration involves extracting and summarizing key statistical features of the data. Some common data exploration techniques are:

1. Descriptive statistics: Summarize the main features of a dataset to provide a helpful overview of the data
2. Visualization: Graphically represent the data
3. Pattern recognition: Automatically recognize trends and regularities in data

Data discovery tools and techniques

The data discovery toolkit is diverse and constantly evolving. Generally speaking, tools can be classified into two categories:

1. Traditional tools: Commonplace apps and services that have been in use for many years
2. AI/ML tools: Modern apps and services with built-in intelligence and automated pattern detection

Traditional tools

Traditional data discovery tools include Excel, relational SQL databases, and business intelligence tools such as Tableau and Power BI.

Then there are open-source tools, such as powerful Python libraries Pandas and NumPy. These libraries are popular among data scientists because of their robustness and raw computing capabilities. They are particularly powerful for iterating over large datasets.

AI/ML tools

Artificial intelligence is changing data science dramatically. Approaches such as clustering, regression analysis, and anomaly detection extract insights that are simply invisible to traditional tools. Unsupervised learning techniques allow artificial intelligence to discover hidden patterns without any need for human intervention or input.

Traditional rules can detect patterns, but unsupervised learning is a different beast. There are technically no limits to the depth of analysis based on a machine learning model. Self-driving cars are a great example—they are able to analyze situations of unlimited complexity and make decisions at the speed of light.

Automation doesn’t make humans obsolete. It can be particularly lacking in terms of data classification and applying real-world context to results. Sometimes, a human may decide to disregard certain data because they know the source to be unreliable. Other times, the most efficient solution proposed by AI may not be ethical or legal.

Use cases and benefits of data discovery

How you integrate data discovery into your business depends on your goals, processes, and employees’ skills. Broadly speaking, there are three kinds of integrations:

1. Enhancing business intelligence: Identify KPIs, optimize decisions, and gain insights from data
2. Compliance and risk management: Reduce risk, maintain regulatory compliance, and simplify data processes
3. Driving innovation and growth: Use your data to improve your business and customer experiences

Enhancing business intelligence

Data discovery can provide invaluable insights into customer behavior, market trends, and the interplay between the two. It can identify the KPIs for your organization and optimize your decision-making specifically for those KPIs.

The data discovery process can analyze historical data, as well as make forecasts. If you can anticipate trends using these insights, your business can outperform and outpace the competition.

Compliance and risk management

Have you tried keeping tabs on constantly changing regulations? How about the new dangers arising daily from the dark corners of the internet? Data discovery can help identify potential risks and maintain data privacy, all while tip-toeing through regulatory minefields.

When your company operates in the global market, legal requirements and risks become very complex. There are simply too many moving parts for any single human to keep track of.

Having a data discovery process that automatically factors in all these changes allows you to focus on deriving actionable insights from the data.

Driving innovation and growth

Data discovery can inform your product development process. Most products fail, but you can swing the odds in your favor by digging up new insights in competitive market data to get ahead of your competition. 

For example, Netflix closely tracks viewing patterns, engagement metrics, and user feedback. This careful data gathering helps them produce original content that appeals to their audience and remain highly competitive in the streaming industry. In analyzing their viewer data, Netflix found that their audience was very interested in political shows and enjoyed films that featured Kevin Spacey. What did that yield? “House of Cards,” one of their first original productions and an huge critical and commercial success.

Challenges and best practices

All the high-profile data leaks of recent years demonstrate that data security cannot be taken lightly. If a major email provider can leak billions of user passwords, nobody is safe. While we strongly suggest you consult a security expert on these matters, here are some proven best practices to adhere to.

Data governance and privacy

If you don’t have strict data governance principles in your organization, you’re asking for trouble. It only takes one malicious or careless employee with access to the wrong data to make private or sensitive data public.

The rules of data governance should be crystal clear. At a minimum, apply these three rules:

1. Every employee should know exactly what data they have access to
2. Every employee should know who to go to when they need access to a certain type of data, or an aggregation of data
3. Every manager should know which of their workers have access to which datasets

Your data rules should be easy to understand and communicate. Remembering complex compliance and governance rules simply is not top of mind for most employees, especially when it’s not a core part of their job.

Data literacy and employee training

According to research from Stanford University, around 88% of data leaks happen due to employee errors. It’s not normally a problem of malicious behavior, but rather of data literacy.

Employees who struggle with data literacy are more likely to:

• Corrupt data unwittingly: Import data that didn’t pass quality control, allow customers to submit data directly to the dataset
• Be careless about privacy: Log in from insecure connections, store their password unsafely, or break data governance policies
• Adopt false positives: Draw conclusions from incomplete or ambiguous data
• Ignore relevant data: Fail to feed relevant datasets into the data discovery process

By training your employees in the basics of data science, security, and algorithms you can prevent most problems from the list above.

Monitoring and troubleshooting

Regardless of your processes, problems will happen.

When it comes to data, issues tend to avalanche out of control before you know it. That’s why it’s paramount that you implement monitoring tools and processes to detect problems early. Once detected, a problem should be immediately forwarded to the relevant teams for troubleshooting.

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