Data discovery: how to find value in data

Having a vast amount of data is just the first step for companies looking to make more strategic decisions; the second and also greater challenge lies in how to transform that data into actionable insights that truly make decisions more accurate. Without an effective data discovery approach, data becomes just a meaningless pile of information, incapable of providing any real value. 

Data discovery, the process of exploring and analyzing data to identify patterns, trends, and insights that aid in informed decision-making, is the bet of many companies. The data discovery market is projected to grow by 15.6% per year, reaching $14.4 billion by 2025, as indicated by a study from Markets. Another study, this time from McKinsey, revealed that companies investing in data and analytics are three times more likely to report that these initiatives contribute at least 20% to EBIT, a key financial indicator used by large companies.

In this article, we explore the best data discovery techniques, how this process aids in decision-making, how marketing, sales, and operations benefit from it, how to deal with the challenges of data security and protection, as well as the role of data discovery professionals. Happy reading! 

What is data discovery?

Data discovery is the process of identifying, collecting, and analyzing data within a company to generate valuable insights. This process involves locating relevant data from various sources (both internal and external), integrating, analyzing, and visualizing it to understand trends, patterns, or anomalies. The objective is to facilitate informed decision-making and the efficient resolution of business problems.

What techniques are used in the data discovery process?

The data discovery process uses a variety of techniques; the choice and prioritization depend on the analysis objective and the results obtained in preliminary tests. Each technique plays a role in different phases of the process, from data collection and preparation to analysis and visualization of results. Here are the main techniques used:

Exploratory Data Analysis (EDA)

EDA is used to explore data and obtain an initial understanding of variables and their relationships before applying any formal models. Techniques involve both graphical methods (such as histograms, boxplots, scatter plots) and descriptive statistics (such as mean, median, and variance). This technique helps identify patterns, anomalies, and formulate hypotheses for further analysis.

Data Mining

Data mining applies algorithms to identify hidden patterns and relationships in the data. Mining techniques such as clustering, association rules, and classification are often used to discover trends and correlations that may not be apparent through conventional methods. This is particularly useful in large datasets.

Data Visualization

Visualization is an essential part of the data discovery process, as it allows insights to be easily understood and communicated. Visualization tools transform data into graphs, interactive dashboards, heatmaps, and other types of visual representations, facilitating pattern identification and decision-making.

Statistical Modeling

Techniques such as linear and logistic regression are used to model the relationship between variables and make predictions based on historical data. Additionally, time series models are commonly applied to forecast future trends, such as product demand or market fluctuations.

Machine Learning

Machine learning uses algorithms to identify complex patterns and automate predictions. Supervised and unsupervised models are employed to analyze large volumes of data, with classification, regression, and clustering algorithms being the most commonly used in data discovery to improve the accuracy of analyses and predict future behaviors.

How does data discovery assist in decision-making?

In 2022, McKinsey forecasted that by 2025, most employees in large companies would be using data to optimize nearly every aspect of their work, and we are close to this.

This scenario is due to changes in competitiveness; in the past, being data-driven meant merely collecting and using information in a basic way. Today, with advancements in technologies and the greater capacity to process large volumes of information, companies and professionals not only analyze data more quickly but also generate strategic insights from it. Furthermore, the increase in data literacy within companies enables more employees, beyond just IT specialists, to understand, use, and make data-driven decisions, promoting a more data-driven culture across all areas of the organization.

Here are some ways data discovery aids in this process:

Identifying Patterns and Opportunities

Data discovery enables decision-makers to identify hidden patterns and trends in the data that may not be evident through traditional analyses. This includes detecting changes in customer behaviors, market fluctuations, and emerging opportunities that can be leveraged to improve products or services.

Data-Driven Decisions Based on Reliable Information

Through the preparation, cleaning, and integration of data from various sources, data discovery ensures that the data used for decisions is accurate and of high quality. This process helps avoid making decisions based on incorrect or inconsistent data, thereby improving trust in the information.

Predictive Analysis to Anticipate Outcomes

The use of machine learning techniques and predictive analysis in data discovery allows for predicting future scenarios, such as market demand or operational risks. This ability to anticipate outcomes based on historical data and identified patterns assists in preparing for future challenges and defining more accurate strategies.

Risk Management and Compliance

Data discovery helps identify anomalies and outlier behaviors, allowing companies to detect potential risks and regulatory compliance issues before they become critical. In this way, organizations can take preventive action and avoid negative consequences.

What are the challenges in implementing data discovery?

Although the value of data is widely recognized, the challenge lies in implementing the strategy or process. The rush to explore data and automate processes is creating a divide among companies: on one side are those that can transform data into a competitive advantage, and on the other are those that, lacking the appropriate infrastructure or strategy, fall behind. 

Here are the three main challenges faced by companies:

Cultural Change and Training

Adopting a data-driven culture requires employees at all levels to be able to understand and use data for decision-making. However, many companies struggle to create this culture, especially when employees lack technical training or do not yet recognize the value of data in their daily activities.

Technological Infrastructure

Many companies struggle to integrate the tools and systems needed to implement data discovery efficiently. Inadequate infrastructures, such as a lack of automation tools or cloud architectures to support real-time data, can limit companies’ ability to explore the full potential of their data.

Interpreting Results

Although data visualization tools are important, correctly interpreting the results can be complicated. There is always the risk of over-interpretation or misinterpretation of the data, leading to incorrect conclusions. This issue can be exacerbated by a lack of a clear understanding of the limitations of statistical models or machine learning.

In light of these challenges, careful planning and a minimum infrastructure are necessary when implementing data discovery. Therefore, do not neglect to invest in scalable tools, train employees in analytical skills, ensure strong data governance, and adopt a security-centered approach.

What is the relationship between data discovery and data protection and privacy?

Data protection and privacy are important aspects of the data discovery process since the data discovery process involves the collection, integration, analysis, and visualization of large volumes of information, much of which consists of sensitive data. 

This implies the need for strict privacy and security measures, ensuring that practices comply with data protection laws and that individuals’ rights are preserved. Below are some of the main aspects of this relationship:

Compliance with Regulations

Companies must ensure compliance with data protection legislations such as GDPR and LGPD, which establish strict guidelines for processing personal data. This includes obtaining proper consent, ensuring that collected data is limited to what is necessary for the specific purpose, and ensuring that data is handled securely.

Data Minimization and Anonymization

Data minimization must be applied to ensure that only the necessary information is used for a specific analysis. Additionally, anonymization or pseudonymization techniques are often used to protect individuals’ privacy, enabling data to be analyzed without directly revealing personally identifiable information.

Security in Accessing and Processing Data

Implement strong access control mechanisms to ensure that only authorized users can access and manipulate sensitive data. Furthermore, it is necessary to ensure that data is protected throughout the process, using encryption and other security measures to prevent data breaches.

Risk Management

A key objective of incorporating strong data protection practices into data discovery is to mitigate risks associated with data breaches and ensure organizations can identify potential issues related to compliance or security during data analysis. Identifying anomalies or dealing with sensitive personal data requires robust controls to prevent misuse.

How can data discovery be applied in marketing, sales, and operations?

The most strategic areas of a company tend to be those that directly impact its ability to achieve long-term goals and maintain competitiveness. For many organizations, these are the areas of marketing, sales, and operations. Data discovery can be applied in these sectors to drive efficiency, improve decision-making, and identify new opportunities. 

Marketing

Customer Segmentation: Marketing teams can explore large volumes of data to identify consumer patterns and behaviors, allowing for more precise segmentation, which helps create more personalized and relevant campaigns focused on specific niches or customer preferences.

Sentiment and Trend Analysis: Using data mining and text analysis, data discovery helps identify how customers are reacting to products or brands on social media and other digital channels, enabling marketing to adjust its messages and strategies in real-time.

Insights: By exploring data from various channels, such as email campaigns, social media, and paid advertising, data discovery provides insights into the impact of each channel on conversions, allowing for the optimization of marketing efforts and resources.

Sales

Sales Forecasting: Data discovery can be used to create predictive models that help forecast future demand based on historical sales data, consumer behavior, and market conditions, enabling sales teams to adjust their strategies and goals more effectively.

Optimizing the Sales Cycle: By analyzing sales performance and the steps in the sales cycle, data discovery can identify bottlenecks and areas for improvement in the sales process, helping to optimize each step and improve conversion rates.

Operations

Improving Operational Efficiency: By exploring data on production, logistics, and supply chain, companies can identify areas where they can reduce costs or improve workflow.

Inventory Management: By analyzing sales data, market demand, and delivery times, data discovery helps optimize inventory management, preventing excess stock or shortages.

Risk Management and Problem Prevention: Predicting potential failures in processes or machinery, allowing for predictive maintenance and mitigating the risk of unexpected downtimes.

What are the trends in data discovery?

The trends in data discovery are linked to the evolution of data analysis technologies, machine learning, and visualization, alongside the growing demand for more agile and data-driven decisions. Here are the main trends:

Automation and Artificial Intelligence (AI)

The use of AI and machine learning is growing increasingly, AI-assisted tools are capable of automating processes such as data cleansing, pattern detection, and even suggesting insights and correlations without the need for human intervention. This accelerates the analysis process and reduces the workload of analysts.

Automated data discovery tools, also known as AutoML, allow even non-technical users to explore and interpret data with little or no supervision, facilitating the democratization of data access within companies.

Self-Service Analytics

The trend of self-service analytics allows business users to explore data directly, without relying on IT teams. Data discovery tools are becoming more intuitive and user-friendly, enabling marketing, sales, and operations professionals to extract insights autonomously, without the need for advanced data science skills.

Integration with Big Data and Cloud

With the increase of large volumes of data (big data), data discovery is increasingly integrated with cloud computing platforms and big data solutions, such as Snowflake and Google BigQuery. This allows for large-scale analysis, where massive data is processed in real-time, providing quick insights at scale.

What is the role of data discovery professionals?

Data discovery professionals play a role that goes beyond simple data analysis; they are responsible for exploring, analyzing, and visualizing large datasets to identify valuable patterns and insights that aid in decision-making. The depth and breadth of these professionals’ responsibilities vary according to the level of seniority, and their functions can be divided into three main categories:

Junior Data Professionals

At the junior level, professionals are often tasked with initial data exploration and cleaning tasks, using basic data analysis and ETL tools to ensure that the data is ready for use. Their primary responsibility is to ensure that the data is accurate and consistent by eliminating errors, duplicates, and missing values. 

They may also assist in creating reports and simple charts, based on EDA techniques to help the team understand the characteristics of the dataset. They generally work with tools such as Excel, SQL, and begin to familiarize themselves with visualization software like Tableau or Power BI.

Mid-Level Data Professionals

At the mid-level, data professionals take on a more active role in analysis, using more advanced techniques such as data mining, predictive modeling, and machine learning algorithms. They are responsible for identifying complex trends, correlating data from multiple sources, and discovering strategic information that can impact the company’s performance. In addition to data analysis, they also develop interactive dashboards and detailed reports for different stakeholders. 

Another responsibility is to ensure data quality and governance, meaning ensuring that the data used in the analyses complies with the organization’s security and privacy standards, avoiding risks and misinterpretation errors.

Senior Data Professionals

At the senior level, data professionals not only analyze data but also define data strategies for the entire company. They work directly with executives and business leaders to understand the company’s strategic challenges and align data analyses with long-term goals.

They often lead the application of complex artificial intelligence and machine learning algorithms, automating the process of discovering insights and predictions. They are responsible for implementing predictive models that can anticipate market trends, customer behavior, or optimize operations. Furthermore, they lead data governance initiatives to ensure that analyses comply with privacy regulations such as GDPR and LGPD. 

Success Case

An example of success is Standard Chartered Bank, one of the leading banks in the world, which implemented data discovery to improve data security and ensure regulatory compliance. With a robust data discovery solution, the bank was able to identify and manage its sensitive data spread across multiple systems and environments. This allowed the institution not only to meet compliance requirements such as GDPR but also to optimize the use of its data to enhance operational efficiency and reduce cybersecurity risks.

The implementation of data discovery also enabled the bank to map its data assets, improving control over where sensitive data was stored and ensuring it was adequately protected. This initiative led to a significant reduction in data breach risks and helped the institution maintain customer trust while optimizing data storage costs by identifying and eliminating redundant data.

Why hire a partner in the data discovery process?

Becoming a data-driven company is a challenge that requires not only the implementation of new technologies but also a deep cultural transformation. In this context, hiring a partner maximizes the chances of a successful transition.

Objective has expertise as a reference in data and cloud migration, which many companies do not possess internally; we identify the best data discovery, data integration, and predictive analysis tools. Furthermore, we assist you with regulatory compliance and data security, which are essential in regulated sectors. Contact our specialists right now.