Data Insight Discovery: How to Go From Data to Insights
Every business industry, from manufacturing to SAAS, benefits tremendously from digital and technical infrastructure advances. With more sophisticated sensors on everyday devices and a better-connected global data grid, the data flow keeps steadily rising.
Deriving strategically valuable insights through data discovery will open new possibilities, enhance decision-making, and help you comply with data regulations. In our increasingly data-rich world, we can use models from the data we receive to provide insights into the past and present to tailor our business services for the future.
At Captain Compliance, our mission is to help businesses stay on top of any data handling regulations and constant changes in legislation. This article will give you an insight into the journey from data to insights and explore methods and tools to help your business traverse this process. It's about handling data safely and knowing where to look to interpret it.
- Data insights are valuable in long-term strategic decision-making, converting raw data to actionable knowledge.
- Compliance is paramount in the data insight discovery, so you can withhold ethical standards and maintain consumer trust.
- Efficient data handling is about identifying patterns, formulating hypotheses, and contextualizing findings to extract key insights.
Navigating the Data Landscape
Businesses today have an astonishing array of data types and sources. Before crafting a data insight discovery process, it's vital to understand the types and qualities of certain data.
Defining Data and its Types
Data comes in many shapes and forms, types, and formats, but ultimately, it can be categorized into three fundamental types: unstructured, structured, and semi-structured data.
Let’s define and explore each data type briefly:
- Structured data is highly organized, easy to analyze, and mapped in a database following strict naming and metadata conventions.
- Unstructured data comes in a form like an audio, video, or text file that lacks any predefined structure. For example, email feedback you receive from consumers can be challenging to categorize and structure, as the feedback is often nuanced and not binary.
- Semi-structured data combines the elements of both while using tags or identifiers to define unstructured data better.
The Abundance of Data Sources
The rise of what is known as big data was due to the rise of vast and complex datasets generated daily and has revolutionized how we handle data. Here is a short overview of the three primary data sources businesses deal with daily:
- External data encompasses any source of information outside your business and is derived from primary and secondary market research, social media, and public data repositories.
- Internal data generated within the organization, customer records, and operational data are a few of the widely utilized data types.
- Big data’s impact reaches into every aspect of business operations; even if all your business has is a web page, the moment it goes live, you are partaking in a globalized information grid and data flow system.
Dealing with the unbound amounts of data bouncing around can sound intimidating, but unlocking it will give you a greater technical breadth of insights to optimize your decision-making and operations.
Knowing how to safely handle external or internal sensitive data is paramount. Read more on our outsourced compliance services at Captain Compliance.
The Art of Data Collection
Data collection methods are the tools you use to capture raw data before moving on to categorize and analyze the information to draw insights. Each data collection method available is developed to address a particular requirement and conclude a specific research objective.
Common Data Collection Methods and Their Purpose
Data collection methods aim to generate external data flow to your business to address unknowns in the scientific method and reinforce the research question and hypothesis through data gathering.
Here are some of the ways to receive novel information before you can proceed to analyze it:
- Surveys and questionnaires are popular choices as they can translate unstructured data in the form of questions and then structure the responses to analyze trends and draw conclusions.
- Web Scraping, or the automated extraction of data from online pages and repositories, provides a bulk of unstructured data for analysis. Learn more about the process of data discovery scanning.
- The Internet of Things (IoT) gathers real-time input-generated data from devices, wearable technology, vehicles, and many others to generate data enabling rapid processing of consumer behavior.
These are some of the ways we can gauge human behavior and determine consumers' preferences when interacting with a product.
When talking about its application, we come to realize how much power data holds. This is also why ethically handling consumer information is paramount to shaping a free society.
Today, we have multiple governing entities that regulate how your business can handle consumer data:
- Privacy and Consent are the core pillars, with regulations like GDPR and CPRA governing how personal data is collected and used.
- Data Security and Compliance measures are required to protect valuable information and maintain the trust of customers and stakeholders.
By now, we understand the difference in data types and formats, ways to collect external data, and also that the entire process is regulated and certain safety standards must be met.
Data Preprocessing and Cleaning
Data processing and cleaning are required steps in getting your business data ready and optimized before it can be used for analysis. The data processing method aims to meet certain pre-defined quality, readability, and reliability standards and best practices.
Data cleaning and transformation
Data cleaning and transformation refer to the process of detecting outliers in your repositories and addressing them promptly. This increases statistical power and lessens bias by trimming and transforming your business data.
Outlier data points, significantly different from the majority, can distort analysis results. A study published by Deloitte on outlier data emphasizes the importance of outlier detection in fraud detection and data quality assurance. Advanced statistical methods and visualization techniques help identify and deal with outliers effectively.
Missing Data Skewing Results
When data is missing from within a more significant subset, it can hinder decision-making. Missing data can lead to biased estimates and reduced statistical power. Furthermore, a publication by IBM noted that you can estimate and impute missing data only for its quantitative variables.
In the world of big data, if a tiny subset of less than 5% of the total consumer generated is missing, this can cause a ripple effect and skew any other data set related to or dependent on the missing information. Luckily, with the help of advanced machine learning and prediction software, we can more accurately compensate for such data flaws.
To remedy missing data, we have an in-depth guide on the tools your business can utilize to map and discover dark data sectors.
Data integration and normalization
Data integration and normalization are further ways to prepare your business information to be ready for analysis or data visualization. Data integration is responsible for bringing together or combining data from multiple sources, systems, or databases and aggregating it into a single comprehensive data repository.
- Data consistency refers to the uniformity and reliability of your business data from multiple formers and spanning different formats. Its primary goal is to ensure that data from differing systems and databases aligns in structure, naming convention, and metadata descriptions.
- Standardization of data formats simply refers to utilizing the same data formats (PDF, JSON, CSV, etc..) when handling the same data elements. This helps with data encoding and ensuring any software you use can interface with the data formats it's presented for compatibility and speed.
Data normalization, in its essence, serves the purpose of ensuring data interoperability by facilitating a harmonized information chain. The data integration and normalization process at its core helps to 1) Extract, 2) Transform, and 3) Load the data into more complex information integration platforms or tools.
Uncovering the Hidden Gems: Data Analysis
Data analysis techniques serve to find, locate, and reintegrate missing or dark sectors of your business repository data. Left out in the dark, such data can be ground zero for non-compliance or mishandling when an external audit occurs.
The main goal of data mapping is to ensure all sensitive PII information is accounted for and has built-in defense countermeasures.
The four primary techniques to guide you in data analysis are as follows:
- Descriptive Analytics: This technique aims to summarize, describe, and showcase historical data to understand better what has occurred. Descriptive analysis answers vital questions on past events by combining them with present data trends to produce trend predictions via data relationships.
- Diagnostic Analytics: Moving further along after descriptive analytics, diagnostics focus on the “why” or cause behind observed past and present trends. Analyzing consumer data can reveal why a particular trend occurred, giving you room to iterate and brainstorm a solution.
- Predictive Analytics: After understanding why an event occurred or what caused it in the first place, your business can create a prediction model to better react in the future. By utilizing machine learning algorithms, your business can anticipate outcomes by congregating past data and charting future possible trends.
- Prescriptive Analytics: This is the most advanced form or type of data analysis as it doesn't just predicate future outcomes but also recommends actions to optimize these outcomes.
In summary, businesses that address all of the most critical what-if scenarios are the ones that will stay on top in any shifting market condition.
Tools and technologies for data analysis
Data analysis tools, machine learning, and data visualization are all indispensable parts of the standard modern data-driven decision-making process.
Data visualization tools
Data visualization tools refer to the software and methods you utilize to display the findings or to visualize data analysis results. Humans are much more apt to recognize trends and better grasp a concept when it is drawn out in a pie chart, trend line, or kinetic 3D sculpture.
Data mapping visualization is a fine art form, and there are a plethora of tools to use to empower analysts via compelling variations. Read more on our dedicated article about data visualization.
Machine learning algorithms
Machine learning is a subset of data analysis that is focused on creating predictive models and using them to discover or obtain even further insights. Machine learning works by crafting and training algorithms to discern patterns.
Simpler linear regression algorithms to advanced deep learning and AI function by automatically learning based on patterns to make future classifications and predictions. The more they are trained, with a greater variety in outcomes, the more holistic their responses are.
Machine learning is applied across multiple business domains, ranging from healthcare preventative measures and risk assessment to finance and even the defense sector.
Data storytelling and visualization
Data visualization, being more of an art form, should also craft compelling story narratives that take you through discoveries in a palatable form for both technical and non-technical stakeholders and other people of interest.
Well-organized data visualization allows you to make decisions faster and with more confidence, as it can highlight the most critical information you uncover through data mapping.
Data visualization coupled with machine learning algorithms is a staple in helping your business make decisions in today's data-centric world. Efficient data storytelling helps you turn non-descript data into a visual representation that can be more easily interpreted.
Cultivating Insights for Strategic Thinking
So far, we have discussed how Identifying patterns and trends is the first step in insight generation. This process involves data analysis techniques to uncover an anomaly and help you react to a forming trend. For instance, if your retail shops suddenly drop sales during the cold season, this could signal a future trend in your market ecosystem.
Knowing when an anomaly is statically significant holds the same value as spotting the change in the first place.
Formulating hypotheses happens after you first spot any pattern or anomaly form. This step involves your business organization making the most well-educated guess possible about the reasons behind the observed trend.
Interpreting data insights
Contextualizing findings refers to understanding the implications of broader circumstances revolving around your data. Before jumping to conclusions, other external factors that provide more context should be examined.
If a sales drop happened in a given area, was it also affected by a significant event outside your control? In this circumstance, reading too far into the sales drop and adjusting your entire retail strategy could be overcorrecting based on non-contextual data.
Identifying actionable takeaways occurs only after gathering insight, cultivating a hypothesis, and contextualizing the findings.
Actionable takeaways are any steps you take to correct or address the results from your data insight discovery process.
When these actionable takeaways are backed by data that is properly categorized, formatted, and accurate, you set your business up for more accurate decision-making.
As we close this article on data insights and their role in strategic thinking, you might ask what comes next. The journey of data discovery is not a single instance but an ongoing process of constant improvement, iteration, and adjustment to adhere to evolving regulations.
Investing in the right data analysis tools and enhancing your business data collection, processing, and handling techniques can foster a data-driven culture within your employees.
This is where Captain Compliance plays a vital role - we serve as your trusted partner in ensuring any data you collect for analysis adheres to the highest ethical and regulatory standards.
Contact us to discuss how you can safely turn your data into actionable insights.
What is the discovery of data insight?
Data discovery is the process of uncovering valuable information, patterns, or trends within a given dataset. It includes the steps of exploration and analysis to reach meaningful insight that aids in your business's decision-making process.
What is an example of a data insight?
An example could be if you notice a steep decline in your website visitors during specific times throughout the week. This could help you adjust the marketing or product offers to rotate during set periods of time to target and increase user conversion more optimally.
What does insight mean in data?
In the content of data, insight is the actionable knowledge you receive from analyzing the data. Understanding patterns and relationships between data sets can give you a greater technical depth of understanding to make a decision.
Why is compliance important in data discovery?
Ensuring consumer data is safely handled is the hallmark of regulation standards like the GDPR or HIPPA. Non-compliance can lead to severe monetary fines and legal action. Outsourcing compliance is a great solution for staying on top of such regulations.