What is a data journey?

Introduction

The digital world is now well over a decade into being in the so-called “Zettabyte Era”, when data first needed to be recorded with a digit followed by 21 zeros. The volume of global IP traffic and generated data is growing at such an extraordinary pace, that the General Conference on Weights and Measures introduced four new numerical prefixes in 2022.

With this vast volume of information generated by users, come new opportunities for businesses. Yet, there is a lot of truth in the saying that “data is the new oil”. Just like petroleum, if data isn’t refined, i.e., turned into insights, it isn’t of much use.

To become valuable, it needs to undergo a process called the “data journey”.

In this guide, we explore what it is, what stages it entails, and what elements and procedures it cannot do without. You’ll learn how to ensure high data quality, integrity, and security, which help turn your business into a data-mature organization.

Let’s begin by looking at the definition of “data journey” and a brief review of its four steps. 

What is a data journey and what does it involve?

A data journey is the path that an organization’s data proceeds through from the moment the data is collected, all the way to how it’s visualized and used for decision-making. It involves crucial elements like ensuring high data quality, reliability, and security from unauthorized access. A properly architected data journey has the power to drive your organization’s data literacy, bring evidence into the decision-making process, and transform you into a data-mature organization. All of these concepts are covered in detail in this guide.

A data journey can be broken down into four steps.

Step 1: Defining, collecting, and selecting data

The first step begins before you start collecting data. Namely, you must know exactly which types of data you’re looking for given your business objectives. By understanding what tasks and decisions it will facilitate, you can decide on the best sources and data collection methods. 

Data governance plays an important role in finding data and should inform all your decisions. It outlines the standards and rules for data collection, making sure that your data is relevant, accurate, and legal to access given local and international legislation. 

Since data governance specifies who owns, classifies, and manages data, it should act as your organization’s North Star in all the steps that follow.

Step 2: Processing and cleaning data

Collected data is stored in repositories in unrefined formats. You need to process and clean it to make it useful for your business. An important element of this step is standardizing data, i.e., transforming it into a specific format. You can turn to the DataOps approach, which uses automation and continuous integration, to accelerate this process. 

Standardization prepares your data for the next stage.

Step 3: Modeling and analyzing data

Data modeling helps you ‘unpack’ the data you store. It lets you uncover the relationships between different parts of your organization’s data. 

It’s connected to data analysis, which looks at what your data holds and, among others, uncovers root causes and recommended courses of action.

Once your data is modeled correctly, your organization will be more equipped to run thorough data analysis. 

Step 4: Interpreting and visualizing data

Data visualization is about turning data into information everyone within the business can understand. All the anomalies or trends that you’ve uncovered in the previous stages are now given a visual format.

That said, the ability to present data comprehensively might be difficult, especially if you demonstrate relationships between multiple findings. That’s where using BI tools instead of generic dashboards will have an enormous effect on proper data interpretation.

Let’s take a closer look at each of these stages below.

Find, collect, and select relevant data

How insightful your data discoveries are depends on your ability to collect and choose the right data. Here are some considerations for this early stage of the journey.

Understand what types of data is at your disposal

Data comes in various formats. Some of it is easily quantifiable, while others might be descriptive and not as simple to analyze in bulk. Some of the most common data types include:

• Structured data, which is presented in a numeric, table format. Since it has a ‘structure’, it’s easy to search and run large-scale analyses.
• Unstructured data, which can take on the form of long strings of text as well as videos and photos. Data needs to be ‘pulled’ from these data types, for instance, by using image-to-text technologies like computer vision.
• Semi-structured data, where a part of the data is in the right format or might include certain tags. 

Knowing what type of data your organization handles lets you decide on the right collection and analysis techniques.

Prioritize data reliability

Consider first-hand data and data from trusted third-party channels. The more trustworthy the data your organization uses, the higher the accuracy of your data-driven decisions further in the data journey. This translates directly into lowering the risks of making judgments based on inaccurate records.

Keep your business objectives in mind

Select data that will help you inform your organization’s objectives; ask yourself whether the data you select will help make better decisions, or introduce unnecessary complexity and disrupt your analysis.

Once you’ve established what type of data you’d like to gather, you must also decide on the right collection methods. These commonly include:

• Running data queries, which let you quickly find and extract data from a structured database.
• Data warehousing, where you bring together data from multiple sources and can run analysis for its entirety.
• API integrations, where you can collect data from apps developed by a third party, for example, SaaS platforms that are part of your company’s tool stack.

Read our dedicated article to learn more about data selection and collection methods.

Process and clean your selected data

Before you can start analyzing what your data holds, you need to make sure your data is free of any erroneous information and that it’s in the right format. This is where you engage in data cleaning and processing.

Data cleaning is a procedure, in which you check your data for any flaws or discrepancies like duplicated records or inconsistent labeling. These often happen when you merge data from multiple channels. 

By running data cleaning, you can spot structural issues in your data. These can be caused by human errors like typos as well as due to the lack of data formatting, labeling, and naming standards. For example, let’s assume that a manufacturing company has branches in the United States and the UK. The terms ‘lorry’ and ‘truck’ both appear in their data set. Due to the absence of naming conventions, they might be treated as two separate items. This could lead to inaccurate results if the company wanted to run an analysis of its fleet performance at an international level.

Cleaning data also lets you remove any irrelevant data, i.e., such that it does not relate to your business objectives or hypotheses. For instance, if you wanted to analyze your quality assurance team performance in a specific location, then, for the sake of this particular analysis, you’d be able to filter out all unrelated locations.

Once you’ve cleaned your data, you can proceed with processing it.

Data processing takes place when you upload the cleansed data set into a data warehouse, where it’s transformed into a format comprehensible for the system. How it’s handled will depend on the types and number of data sources (database, data lake, IoT devices, etc.). 

Bear in mind that, as your organization collects data on an ongoing basis, you should also process it cyclically. To make this as efficient as possible, it’s worth turning to DataOps practices.

DataOps will ensure that your data is changed into the desired format without losing the informational value. Not to mention, it allows you to continuously power your business with the latest insights.

Read our piece on data cleaning and processing techniques to learn more.

Analyze and model data to start deriving insights

The third step is where you begin turning data into information. It entails two processes known as data analysis and data modeling. 

Data analysis lets you take all of your consolidated, cleaned, and processed data and see what it is telling you about the condition of various business disciplines. It lets you not only check what happened during a particular timeframe, but also helps spot trends and correlations between different data, and seek fitting solutions.

There are several data analysis techniques to choose from – which ones are best depends on your specific business case. These are:

• Descriptive analysis, where you focus on analyzing historical data only and identifying “what” happened. 
• Diagnostic analysis, which seeks out any correlations and patterns to help you not only understand what, but “why it happened”.
• Predictive analysis, where you use your findings from past events to create simulations of “what will happen”. It requires a higher level of statistical and analytical knowledge than the first two types.
• Prescriptive analysis, where predictive analyses are paired up with a recommended, data-driven course of action. It tells the story of “what will happen, and what we should do about it”. It’s the most advanced form of analysis in the data journey.

Meanwhile, data modeling is an approach that supports effective data management. Data is standardized and organized into a visual format which displays how each data is connected to others in the system. This makes it easier for all stakeholders to understand the relationships between various data types. It also reduces the risk of database errors, boosts data consistency, and, ultimately, grants you access to accurate insights.

There are many data modeling types, but three common ones include:

• Hierarchical model, structured in a simple top-to-bottom, tree-like manner, according to hierarchy. It’s suitable for databases that have clear parent-child elements. Since the structure isn’t flexible, it’s difficult to apply changes.
• Relational model, where data is organized into a table with columns and rows. Relationships between various elements are formed through “keys”. These demonstrate the different relationships between data, even those stored in other tables. This model is much more flexible, letting you create dynamic links between various tables.
• Network model, in which relationships between objects are demonstrated on a graph, where an arc shows how the data is connected, and nodes indicate the object types. What makes this model stand out is that it doesn’t function in a hierarchy of elements, but instead uses visuals to indicate relationships between data.

Learn more about each of the above techniques in our piece on data modeling and analytics.

Visualize your findings to enable data-backed decisions

The final step in the data journey is about turning your findings into a visual format to enable proper interpretation of data.

Visualization techniques like dashboards help you create a narrative, where you bring together data from numerous sources and combine it into a story everyone within the business can understand. This democratizes access to insights and powers data-driven decision-making.

Here are some considerations you must be aware of at this stage of the data journey.

Define the purpose of the visualization

Before researching the types of visuals, write down all types of data you want to demonstrate. Why have you selected each type of data for the visual? What new information will it reveal, and what types of decisions do you believe it will help drive?

Make sure that the data you display will have informative value and will align with your business objectives.

Balance complexity and simplicity

Good visuals allow teams to get a bird’s eye view of the insights, but also to take a deeper dive into data if need be. Your dashboard should act as a single source of truth, and demonstrate all the connected findings.

Consider the best visual for the target group 

The more complex or extensive the data you want to present, the more important it is to choose a visualization method that prioritizes clarity. Who will see and use the visual? Can you be sure they’re all tech-savvy or not? Remember that the dashboard you create will be used for decisions, so you need to make sure everyone understands the data correctly. This leads to the next point.

Data visualization decisions should be a collaborative effort 

Dashboards act as a visual summary for the entire data journey, which is why the decisions shouldn’t be made autonomously. If you’re unsure which type of visual to use, consider testing it out with a small focus group. Ask them to interpret the chart. Write down their feedback and apply the necessary changes to avoid the risk of data misinterpretation. 

For a more comprehensive overview of this stage of the data journey, read our dedicated data visualization article.

Now that we’ve covered each of the steps, let’s look at some of the key elements that contribute to proper data journey implementations, starting with two areas – data quality and data integrity.

As mentioned throughout this guide, making decisions on erroneous, duplicated, or obsolete records derails the entire data journey. Since data enters your organization via multiple channels, it needs to undergo data quality and data integrity procedures before it’s greenlighted for analysis. Both of these processes are crucial components of the data journey.

‘Data quality’ refers to the criteria that define if the data is ready for use. These include its completion, consistency, and relevance. While it’s connected to data integrity, these terms aren’t synonymous.

Data integrity is a much broader discipline, where the primary purpose is to be sure that all data entering or leaving the organization is in its original form, protected from unauthorized access. Data integrity assesses data through three criteria – dependability, consistency, and accuracy. 

Companies can use various methods to make sure their data is in good shape and isn’t tampered with. Here are a few.

Implement data documentation and standardization

Documentation allows you to stay on top of what happens to your data, how you collect it, and what processes it undergoes. Meanwhile, standardization is about deciding on a set of cohesive rules and formats everyone must follow. These can include time data structures, naming conventions, and units. For instance, a global manufacturer might decide that everyone should use ‘milliliters’ when referring to the liquid volume of the produced product.

Maintaining thorough documentation and standardization translates into better performance, as everything is kept in a cohesive, readable format. 

Run data profiling to spot existing weaknesses

This will allow you to examine your current data and check for any anomalies like duplicates, incomplete records, and errors, among others. This will give you an understanding of how much of your data currently doesn’t meet criteria either due to formatting or its low quality. 

There are plenty of data profiling tools on the market, many of which are open-sourced. After inspecting your data, you’ll be able to craft a plan to improve the data’s quality and boost integrity in the system.

Educate your team on the significance of data integrity

Explain to your team how important it is to work together towards data integrity. Underline that everyone in the organization can benefit from trustworthy data, knowing that it wasn’t manipulated anywhere throughout the journey. Also, educate your staff on how to spot any events threatening your company data and what preventative measures they must take to keep it away from unauthorized parties.

To learn more about each of these data journey elements, read our dedicated article on data quality and integrity.

The link between the data journey and a company’s data maturity

Companies are called ‘data mature’ when they are proficient in finding, analyzing, and using data in their decision-making. Each decision, across all departments, is always verified and backed by data. Reaching a high degree of data maturity is impossible without ensuring a sound and secure data journey, where data quality and integrity are a top priority.

Data maturity is measured on a scale from 1 to 4, where ‘1’ refers to the least data-literate organization, while ‘4’ marks the highest level of data literacy.

Here are some of the characteristics of organizations at each data maturity level:

Level 1: Explorer

• Understand that data is an invaluable asset, but don’t know how to use it effectively
• Still make decisions based primarily on their intuition as opposed to what data tells them data
• Lack clear data governance policies

Level 2: Picking up the pace

• There’s an agreement across management that the organization should analyze data to improve operations and boost its market position
• Departments research relevant data analytics software, while employees begin understanding how access to these tools could help validate business hypotheses
• Teams begin looking into data and optimizing their work by referring to it

Level 3: Data comes forward in decision-making

• Everyone at the organization, regardless of their tech-savviness, knows where to find the data they need
• Employees can ‘read’ dashboards and properly interpret data
• Data informs all of the company projects

Level 4: Data mastery

• Each business decision is backed by data
• The organization has strict data governance policies
• There is a single source of truth for driving decisions

Read our dedicated piece on data maturity to learn more about each stage and the importance of running data maturity assessments.

Ensuring data privacy and security throughout the data journey

When you build your data journey, you must also remember to safeguard the data across all steps.

Your organization must meet the highest data security and data privacy standards. Bear in mind that, while connected, these are separate concepts that require individual attention.

Data privacy focuses on keeping personal information from unauthorized viewing, disclosure, and use. The two main objectives are making sure it’s accessed by a bare minimum of individuals, and that the people whose data is kept control how their data can be used, stored, and shared. Organizations that collect data must stay compliant with global and local privacy regulations, like Europe’s GDPR and California’s Consumer Privacy Act.

Meanwhile, data security is about taking measures to prevent data breaches and ensure data integrity. On top of internal security guidelines, it also involves the use of software such as firewalls and encryption.

There are several things organizations can do to minimize the risk of data falling into the wrong hands. Some of the recommended practices include:

Creating a strict data usage policy

95% of all data breaches are traced back to human errors – that’s why each member of your organization must be aware of the permitted and prohibited uses of data. The policy should also state who can access which types of data and what the procedures are if someone violates the data usage policy.

Running regular risk assessments

Your organization constantly exchanges data and integrates with internal and external systems. One change in your existing setup or a missing software update could threaten your data’s safety. That’s why it’s important to run regular risk analyses to potentially nip any threats in the bud. Measure the potential impact and likelihood of each identified task and take preventative steps.

Understand all the privacy regulations that apply to your business

Some regulations apply to most global companies, with Europe’s GDPR being a good example. However, there might also be industry-related and local regulations in place. Are there any federal or state privacy laws that you need to comply with? Which markets do your leads and clients reside in? 

Knowing what each of the privacy regulations requires on your end can help you decide whether you should become compliant with a market, or block access to your website or product from it as it isn’t financially viable.

Enable multi-factor authentication (MFA)

Introducing MFA is one of the most effective ways of keeping data intact. Whenever anyone from your organization wants to log into the system, they will have to confirm their identity either through fingerprint scans, voice recognition, or other secondary method. Without completing the second authentication stage, potential hackers will not be able to log on and compromise your data.

These are just a few of the tips we’ve gathered – to read about all nine practices, refer to our article on data security and privacy.

Implementing a data journey – getting started

The data journey helps transform the data you collect into a valuable asset for your organization. This empowers you to put evidence at the core of your decision-making and gives you the tools you need to begin your path toward being a data-mature organization. 

Remember that it’s not a one-time, linear process – new data enters your organization every day, potentially making yesterday’s or last month’s analysis obsolete. Data privacy regulations and security protocols are also constantly refined, which means you must stay on top and adapt.

Introducing a DataOps approach can help continuously improve and automate a lot of processes within your data journey, making it easier to stay aligned with your business objectives.
C&F can help you start looking past raw data and become a data-driven organization. Reach out to our experts to learn where you are on your path towards data maturity, and how you can begin or refine your data journey.