What is data?

Data is the foundation of most modern business transformation programes. Through data, organizations gain valuable insights that can be applied to drive improved outcomes.

There are various types of data that organizations collect, which generally fall into two main groups: qualitative and quantitative.

More specifically, big data is used to achieve digital transformation across industries, ranging from healthcare to supply chain. Businesses use big data analytics tools to gain deeper insights into customer behavior and boost how well they operate. The main types of big data include transactional data (like sales records), machine data (from sensors and IoT devices), social data (from social media) and text data (from emails and documents).

The different types of data

Raw data comes in many forms, and a single data point can fall under more than one category. 

Qualitative data

Qualitative data describes things, not numbers. It focuses on specific qualities or features that help us put things into categories. In business, this could be customer feedback from surveys, interview notes or social media comments. This gives organizations rich context and understanding.

Quantitative data

Quantitative data concerns numbers and information that you can measure or count. This is the data used for sales figures, website traffic, inventory levels or sensor readings from factory equipment in a business setting. It's the foundation for statistical analysis and drawing conclusions based on measurable information.

Structured data

Structured data is like a perfectly organized spreadsheet. Every piece of information fits neatly into specific rows and columns, making it standardized, clearly defined and easy to search. This data usually lives in relational databases and is often quantitative.

Examples of structured data include customer names, addresses, credit card numbers or numbers in an Excel file.

Unstructured data

Unstructured data, on the other hand, is the complete opposite. It's like a messy drawer full of random items, without any predefined organization and it's stored in its raw form. This type makes up most (often 80 percent or more) of all business data and it's growing fast. Examples include emails, videos, audio files, social media posts and customer reviews.

Unstructured data is usually qualitative and very hard to process and analyze with traditional tools. It often needs advanced techniques like data mining and machine learning. The explosion of unstructured data creates a huge analytical challenge and drives the need for cutting-edge technologies.

Big data

Big data is used for powerful applications like predictive analytics, which forecasts future events and sentiment analysis, which monitors public opinion about products or services.

Businesses use big data analytics tools to gain deeper insights into customer behavior and boost how well they operate. Key types of big data include transactional data (like sales records), machine data (from sensors and IoT devices), social data (from social media) and text data (from emails and documents).

How can data impact digital transformation?

The importance of data shows up in several critical ways for organizations. First, data significantly improves decision-making accuracy. Data allows them to make decisions based on facts, numbers and clear patterns, leading to more accurate outcomes. For example, if data shows a dip in afternoon sales, a business can launch a "happy hour" promotion to attract customers during those slower times, boosting overall sales, as suggested by business analytics examples.

Second, data helps create cost-effective operations. Analysis helps businesses pinpoint inefficiencies, bottlenecks and areas where they can improve. For example, the transportation industry heavily relies on data-driven decisions to optimize routes, improve delivery times and reduce costs, using real-time data from GPS trackers and weather forecasts to make smart choices about vehicle routing and load optimization, as demonstrated by industry examples.

Third, data provides a substantial competitive advantage. Data-driven organizations are better positioned to identify trends, anticipate customer demands and stay ahead of the curve. 

Real world business examples of how data was used to achieve transformation 

How Netflix used data to achieve personalization and drive retention 

Netflix, the world's biggest streaming service, boasts an impressive 93 percent subscriber retention rate. The company meticulously analyzes vast amounts of viewing data, including what users watch, when and for how long, as detailed in case studies. These insights then power personalized recommendations, generate AI-driven trailers and even guide the creation of original content that perfectly matches audience tastes.

Consistently delivering highly relevant content tailored to each person, Netflix has significantly reduced how many subscribers leave and built deep user loyalty, cementing its leadership in the market, as noted in their success story.

As written in Netflix’s company blog, “Full ownership often means building new data pipelines, navigating complex schemas and large data sets, developing or improving metrics for business performance, and creating intuitive visualizations and dashboards – always with an eye towards actionable insights.”

Click here to read our deep-dive into how advanced data and analytics help Netflix generate billions.

Amazon: Optimizing the E-commerce giant

As the largest E-commerce business globally, Amazon credits a remarkable 35 percent of its sales to personalized recommendations. The company's data analytics capabilities allow it to analyze extensive user behavior data, such as items viewed, products added to carts and purchase history. This is used to generate highly tailored product suggestions.

Beyond personalization, Amazon uses dynamic pricing, adjusting product prices up to 2.5 million times daily based on real-time shopping patterns, competitor prices and product demand. What's more, Amazon's success is deeply tied to its ability to predict demand and manage inventory with exceptional operational efficiency, as highlighted in its case study. This data-driven approach streamlines its supply chain, minimizes stockouts and reduces excess inventory, ultimately making customers happier and more loyal.

How Zara achieved a responsible supply-chain through advanced analytics 

Zara stands out for its incredibly responsive supply chain, which is a direct result of its advanced analytics. By constantly analyzing real-time sales data and customer feedback, Zara can quickly adjust its production plans and inventory distribution to match emerging fashion trends.

This data-driven agility extends to logistics and distribution, where analytics optimize shipping routes and warehouse operations. This careful optimization significantly cuts down the time it takes from design to products hitting store shelves, allowing Zara to quickly jump on market shifts and maintain its competitive edge, as highlighted in their business model.

Data governance

Data governance sets procedures and standards that dictate how data is used by a company, and is becoming increasingly more important as data is being collected and used more than ever before.

"Organizations have a tendency to look at data governance or data management as an impediment or a level of overhead," says JD Donnelly, senior director, professional services at Precisely. "In reality, it's a catalyst for organizations to go faster."

There are various laws and regulations setting out industry standards. For example, the General Data Protection Regulation (GDPR) in Europe sets out principles such as lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitations, integrity, confidentiality and accountability. 

Similarly, the California Consumer Privacy Act (CCPA) in the US gives individuals significant rights over their personal information, including the right to know, delete and opt-out of its sale or sharing and freedom from discrimination for exercising these rights.

However, businesses, not only governments, have a responsibility to ensure that data governance is a priority, because etter data governance also allows for better artificial intelligence (AI) to be built.

“Think of it this way: good data governance helps companies build better AI, which can then help them do amazing things like develop new medicines or create more efficient energy sources. If the data is bad, the AI-generated output will be bad. That could lead to serious problems,” says Ramnath Natarajan, director of intelligent automation and enterprise integration center of excellence at Johnson Controls in a PEX Network report. 

According to the PEX Network report, there are 8 key data governance checks, metrics and KPIs that organizations should consider:

1.       Data quality

2.       Data fairness and bias

3.       Data security

4.       Data availability

5.       Data stewardship

6.       Data usage and adoption

7.       Data compliance

8.       Data governance training and awareness

Data and AI

AI is currently transforming business structures and enabling digital transformation projects to happen at scale. However, without high-quality data, AI tools will fail to achieve their desired purpose. Data is the fuel by which AI tools run off and rely on to learn and deliver.

For example, large language models (LLMs) are trained on vast sets of data. They are trained to identify certain patterns of which they make predictions and actions based on. Without data, these AI models would have nothing to run off. “It is a fact that the performance of generative AI systems hinges entirely on their training data quality,” says Rahul Zende, principal data scientist at Navy Federal Credit Union.

From predictive analytics in finance and healthcare, to natural language processing and computer vision, AI depends on high-quality data that is accurate, consistent, and representative. The better the data, the better the results. When data is fragmented, biased, or incomplete, AI can produce unreliable, discriminatory, or misleading outcomes.

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