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Data Solutions

What is data discovery & why it matters?

date: 18 June 2024
reading time: 8 min

We live in the golden age of data: we are surrounded by it, we get it from a wealth of sources, and we can use it in business to make important decisions. Yet to use it for this very purpose, we need to be able to analyse it and identify its patterns, trends and all insights. This is what we call data discovery. Today we look at it in more detail and check why it matters.


What is data discovery?

Let’s start with getting a good understanding of what smart data discovery is.

We can say that data discovery is like going on a treasure hunt for information in a vast sea of data. It’s the process of exploring and uncovering valuable insights from various sources, such as databases, documents, or even social media. Another good comparison is digging through a mountain to find hidden gems.

In data discovery, analysts use tools and techniques to sift through this mountain of data, looking for patterns, trends, or anomalies that can provide valuable knowledge.

It’s all about turning raw data into meaningful and actionable insights that can help businesses make better decisions, understand their customers, or solve complex problems. Just like a detective solves a mystery, data discovery helps us uncover the stories hidden within the numbers.

Data discovery definition
Data discovery definition


What are the goals of data discovery?

Let’s now look at data discovery goals.

Firstly, data discovery aims to uncover hidden insights buried within the data, revealing patterns, trends, and correlations that might otherwise go unnoticed.

Secondly, it seeks to empower decision-makers by providing them with actionable insights derived from the data, enabling them to make informed choices and strategies.

Thirdly, data discovery strives to enhance understanding by shedding light on complex relationships and phenomena, helping organisations gain deeper insights into their operations, customers, and markets.

Ultimately, the overarching goal of data discovery is to turn raw data into valuable knowledge that drives innovation, efficiency, and success.


How is data discovered? Data discovery tools and methods

Data discovery is accomplished through a variety of tools and methods designed to sift through and extract valuable insights from large volumes of data. Common tools and methods used in data discovery include:

  1. Data visualisation tools, such as Tableau, Power BI, or QlikView, which allow users to create visual representations of data such as charts, graphs, and dashboards. Visual data discovery helps to identify patterns, trends, and outliers within the data.
  2. Data mining techniques such as clustering, classification, and association rule mining help uncover hidden insights in the data.
  3. Statistical analysis such as regression analysis, hypothesis testing, and correlation analysis are employed to analyse the relationships between variables and identify significant trends or correlations.
  4. Natural Language Processing (NLP) tools, such as sentiment analysis or topic modelling, can analyse unstructured data such as text documents or social media posts to extract valuable information.
  5. Machine Learning and Artificial Intelligence algorithms can be trained to discover patterns and make predictions from data. Techniques like supervised learning, unsupervised learning, and reinforcement learning are applied to uncover insights and patterns within datasets.
  6. Exploratory Data Analysis (EDA) involves visually exploring and summarising datasets to understand their structure, distribution, and relationships. Techniques such as histograms, scatter plots, and box plots are used to gain insights into the data before further analysis.
  7. Data catalogs and metadata management tools help organisations inventory and document their data assets. These tools facilitate data discovery by providing information about the available datasets, their structure, and how they are related.
  8. Data quality assessment helps identify inconsistencies, errors, or missing values in the data, ensuring that only high-quality data is used for analysis.

By leveraging these tools and methods, organisations can effectively discover, analyse, and derive actionable insights from their data, driving informed decision-making and business success.

Data discovery tools and methods
Data discovery tools and methods


The data discovery process: 4 main phases of data discovery

As a systematic journey through the vast landscape of information, aiming to unveil hidden insights and drive informed decision-making, data discovery comprises four main phases, each playing a crucial role in transforming raw data into actionable knowledge.


Data Preparation

The first phase allows to rearrange the data in order for visualisation and analysis to go smoothly and quickly. Without it, data would not be cleaned and wouldn’t be so useful.

Data preparation phase includes:

  • data collection, meaning gathering data from various sources such as databases, spreadsheets or APIs,
  • data cleaning, which encompasses removing inconsistencies, error and duplicated to ensure accuracy and reliability,
  • data integration, meaning combining multiple datasets into a unified format for analysis,
  • data transformation, which means converting raw data into a structured format suitable for analysis.


Data Visualisation

Data visualisation, also known as data mapping, starts when all data have been already prepared and transformed. It allows to display data in visual, understandable forms, such as charts or graphs.

Data visualisation is comprised of:

  • charting – creating visual representations of data using charts, graphs and dashboards,
  • exploratory visualisation – interactively exploring data to identify patterns, trends and outliers,
  • interactive dashboards, meaning building interactive dashboards to provide stakeholders with real-time insights and actionable information,
  • storytelling, meaning communicating insights effectively through compelling visual narratives.


Data Analysis

Data analysis is all about analysing data in order to summarise it and organise it into a necessary format.

Data analysis can be divided into:

  • descriptive analysis, meaning summarising and describing the main features of the data,
  • inferential analysis, meaning drawing conclusions and making predictions based on statistical inference,
  • predictive modeling, meaning building machine learning models to forecast future trends or outcomes,
  • advanced analytics, meaning applying advanced analytical techniques such as clustering, segmentation or sentiment analysis to uncover deeper insights.


Repeat

Data discovery is a process that is repeatable, meaning it involves revisiting previous phases based on new insights or changing requirements.

This phase consists of:

  • incorporating feedback from stakeholder to refine analysis and improve the quality of insights,
  • continuous improvement achieved by refining techniques, updating data sources and adopting emerging technologies,
  • adaptation to evolving business needs and challenges by iteratively refining the data discovery process.


Data discovery process: tips and best practices

Although data discovery can be a very complex process, following certain tips and best practices can greatly enhance its effectiveness. To navigate you through the data discovery journey we prepared some tips which may prove useful:


Define clear objectives

Clearly define the goals and objectives of your data discovery initiative. Decide what insights are you looking to uncover and what business questions are you trying to answer.


Start with high-quality data

Ensure that you start with clean, accurate, and relevant data. Invest time in data cleaning, normalisation, and validation to improve data quality, as it will have an impact on the outcome of your data discovery process.


Understand your data sources

Gain a thorough understanding of the data sources you’re working with: check where the data comes from, how it’s collected, and any limitations or biases inherent in the data. This will help you interpret insights accurately.


Use a variety of tools and techniques

Employ a diverse set of tools and techniques for data preparation, visualisation, and analysis. Experiment with different tools and techniques to uncover insights that may not be apparent with one method alone.


Iterate and refine

As we already mentioned, data discovery is an iterative process. Don’t expect to uncover all insights in one go. Iterate on your analysis, refine your approach, and revisit previous steps as needed based on new insights or feedback.


Collaborate across teams

Foster collaboration between data scientists, analysts, domain experts, and business stakeholders. Each stakeholder brings a unique perspective to the data discovery process, leading to more comprehensive insights and informed decision-making.


Focus on interpretability

Ensure that your insights are interpretable and actionable. Don’t just focus on finding patterns or correlations – strive to understand the underlying reasons behind them and their implications for the business.


Document your process

Document your data discovery process, including data sources, methodologies, and assumptions. Proper documentation ensures transparency, reproducibility, and accountability in your analysis.


Stay ethical and compliant

Adhere to ethical standards and data privacy regulations throughout the data discovery process. Respect user privacy, anonymise sensitive data, and ensure compliance with relevant regulations such as GDPR or HIPAA.


Continuously learn and improve

Stay abreast of new tools, techniques, and best practices in data discovery. Embrace a culture of continuous learning and improvement to stay ahead in the rapidly evolving field of data analytics.


How Future Processing can help your company with data discovery

If data discovery process sounds a bit daunting or you don’t feel you have the right team to do it, think about getting an external partner who can help you through the process.

At Future Processing we are highly experienced at making the most of our clients assets, applying innovative and advanced data discovery solutions and taking our clients’ and their business processes to the next level. Our data solutions consulting will hep you make the most of what data is offering you – just get in touch and check how we can help!

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