What is big data?
The term “big data” refers to large data sets, usually measured in terabytes or petabytes, that are analyzed to provide business insights. It’s defined by its variety (the different types of formats of data), velocity (the speed at which data becomes available), and volume (the amount of data collected). Big data can include structured data, unstructured data, and semi-structured data, although fully structured data is rare when dealing with big data.
Companies can no longer afford to collect data without immediately gaining insights for timely and relevant action. Whether it’s informing data-driven decision making, enhanced operational efficiency, or risk management, big data can give a big competitive edge.
Want to learn about the inner workings of big data and how it could help your company? We get into all of that and more below.
- What are the origins of big data?
- What are the “Vs” of big data?
- Why is big data important?
- How does big data work?
- What are the benefits and challenges of big data?
- What are some common big data use cases?
- What are some important big data best practices?
What are the origins of big data?
Big data originates from database management. Though data has been around for millennia, the term big data became necessary to convey large amounts of data once data’s volume and velocity blew past human capability. When a flood of digital information started coming in, companies needed to create tools to ensure successful data storage and to find value in the data.
Many organizations in the IT space, especially those in Silicon Valley, have focused on creating frameworks to deal with big data. These frameworks were created to deal with scenarios where there is so much data it can’t possibly be processed by a small number of machines.
Today, there are three common types of data: structured, unstructured, and semi-structured. Structured refers to data displayed in well-defined tables, unstructured, which includes data points like logins, website clicks, page views, or video views, and semi-structured, data that contains a mix of structured and unstructured.
Next, we’ll talk about the six Vs of big data.
What are the “Vs” of big data?
The three main characteristics of big data are handily known as the three Vs: variety, velocity, and volume.
- Variety means the various composition of data sets. Structured, unstructured, and semi-structured data are examples of variety within data.
- Velocity describes how quickly data becomes available to the organization collecting it. Adobe, for example, collects over 250 trillion transactions a year, which comes out to around 475 million transactions a minute.
- Volume refers to the pure amount of data collected. If YouTube subscribers upload 380,000 hours of data an hour, that is a high volume of data. If an organization is dealing with 380,000 emails an hour, the volume of the data is significantly less, but the velocity is still high.
Over time, these three Vs have expanded to six to include variability, veracity, and value. Those of us who appreciate mnemonic devices thank whoever came up with six descriptive words all starting with the letter V.
- Variability deals with establishing context and comprehending how data is constantly changing. If the same process constantly gives a different result, that’s variability.
- Veracity refers to accuracy. Untrustworthy data is useless data.
- Value is the culmination of the previous five Vs. It’s the profit your company sees from the data.
Now that we’ve explored what big data is, let’s dig into why it’s such a big deal.
Why is big data important?
Nowadays, companies must harness the power of big data to understand the big picture of what their customers think and where their business is headed. The more data an organization has, the more well-informed decisions they can make. Companies want to understand how their customers are interacting with their brand, and for organizations with enormous global audiences, that requires large volumes of data.
One increasingly important use of big data is understanding customer needs better. Providing a premier customer experience and evolving to meet the needs of the customers is no simple or easy task. Organizations need to understand where their customers come from, what they do on the website, how much time they spend on the website, and how often they complete a transaction or convert.
Behavioral data is collected from customer behavior on websites and other channels such as mobile, email, etc. Transactional and personal information may also be collected. Understanding this data can give you important insights about how to improve sales velocity and how to optimize different digital interactions. Many decisions around optimization boil down to the amount of data available and the insights that can be pulled from that data.
In the next section you’ll learn how big data works.
How does big data work?
The data life cycle starts with information collection from data sources and ends with pulling insights from the collected data. Establishing a strategy around your big data is crucial when getting started so you understand your goals at each phase.
Big data integration
The first step, data collection, involves creating an infrastructure for collecting all the data points coming in. The infrastructure will depend on the type of data, but the raw data always persists somewhere so that further analysis can happen as needed.
During this step, you’ll need to integrate the collection of data from various sources and applications. This requires collecting, processing, and formatting the data correctly so your data analysts can get to work.
Big data management
The next question is how to store and organize the data. Determining where the data should live and how to catalog it so other systems know it exists is as important as it gets. Data is only as useful as the metadata that describes it. If an organization has large volumes of data, but no way of discovering that data or informing someone what that data is about, the data has no benefit. As far as storage, typically big data is stored in the cloud, although servers are another popular route.
Big data analytics
After data is stored and managed, it can then be analyzed for insights and patterns. The insights derived from big data analytics can then be visualized to inform stakeholders of the findings and make recommendations for the organization’s next steps.
This is where the technology landscape of big data processing comes into the picture. This includes analytics engines like Apache Spark or Databricks, which make it easier to manage large amounts of stored data, as well as big data technologies built around messaging, like Kafka, which specializes in processing streaming data that is continuously generated. An organization may also choose to build and manage their own custom framework.
What are the benefits and challenges of big data?
It’s important to consider the challenges when deciding to implement a strategy around big data at your company. Below we’ll explain some of the most common pros and cons so you know what to expect.
Big data challenges
Collecting data is not enough. Organizations must be able to access, analyze, and shape the data. Unstructured and semi-structured data is often difficult to work with. Without proper management, the data can eat into costs without really providing any value. The right set of technologies can help companies make sense of their data and can help confirm or refute initial instincts about taking a course of action.
Better big data management comes with maturity. If an organization is starting to explore data for the first time, they may want to slow down and make sure they are asking the right questions. There can also be biases or anomalies in the data, which may not be apparent when first using big data.
Companies also must be careful about how they use the data they collect. For example, they may collect personally identifiable information (PII) like credit card numbers or email addresses, but they may not want to or have permission to use that information for certain marketing actions or make it available via unsecure locations.
Having a proper framework for data governance will help prevent mistakes of improper data access and use, and maintain compliance with regulations, by ensuring that data is properly labeled for its intended use.
Big data benefits
Conversely, there are many positives to harnessing the power of big data at your company. For one, you can look forward to improved operations. With the right data analysis tools, you can optimize business processes, streamline resources, and reduce costs.
Sometimes the key to protecting your company from waste or fraud is right there in the data. Big data can show you patterns and insights that may otherwise have gone undetected. With data analytics, you can be proactive and mitigate risk.
These days it seems the quickest way to your customers’ hearts is through personalization across all platforms. With big data, companies can learn more about their customers’ behaviors and appropriately personalize products in their marketing campaigns.
Last but not least, big data gives you a competitive advantage over other companies in your industry. You’ll have insider knowledge of market trends and insights, keeping you nimble to adapt quickly to changing customer demands.
Up next, we’ll review some use cases for big data.
What are some common big data use cases?
There’s no way around it — leaders must understand the big-picture strategy for how big data can influence each department and your overall product roadmap.
Some of these use cases include:
- Operations — You can use big data to optimize your supply chain through demand forecasting, real-time inventory management, and predictive maintenance.
- Machine learning — Big data can train machine learning models for predictive analysis. The more data it has access to, the more accurate the predictions.
- Security — Detect threats to confidential intelligence and fraud of any sort by leveraging big data and applying machine learning algorithms.
- Product development — You can use big data to inform how your products evolve. Analytics such as test market, focus groups, and social media can be extremely useful for understanding — and emphasizing with — your customers’ pain points.
Now that you’re more aware of big data’s far-reaching use cases, let’s go over some best practices for creating the best big data strategy possible .
What are some important big data best practices?
Above all else, it’s important to strategically approach big data so that your company is using it to its maximum potential.
Best practices to consider may include :
- Slow down and ask the right questions. Better big data management comes with maturity. If an organization is starting to explore data for the first time, they may want to slow down and make sure they’re asking the right questions.
- Optimize data quality management. Collecting data is not enough. Organizations must be able to access, analyze, and shape the data. Unstructured and semi-structured data is often difficult to work with. Without proper management, the data can eat into costs without really providing any value.
- Put controls in place to make sure you follow regulatory compliance. Companies generally have contractual obligations that specify how long they can hold on to data. These contractual obligations will vary quite a bit and are governed by regulations in different geographic locations. Customers may also ask for their data to be purged. When these requests come in, companies must make sure they scrub their system of any data that might be related to that customer so that they can stay in compliance with privacy regulations.
For the last section of this article, we’ll peer into the not-so-distant future of big data.
How will the use of big data continue to evolve?
One trend for the use of big data is the increased speed at which insights can be available, decisions made, and action taken.
Companies need to react to customer behavior in real time. Years ago, organizations may have been able to sit on the data they collected for 24 or 48 hours, but organizations must now respond in an instant, which requires the technology to run queries against large amounts of data as it becomes available.
There is a significant shift happening in the world of big data as it moves from a batch-oriented way of thinking about things to something that takes place in real time. Machine learning and artificial intelligence (AI) are instrumental in increasing the speed of big data analysis.
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