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Have you ever wondered what big data is?
Here’s a guide to help you make sense of this concept, how machine learning can be used effectively alongside analytics and more!
Technology has become a part of our daily lives like never before. From smartphones to laptops and tablets, our device usage is at an all-time high, showing no signs of slowing. But, have you ever wondered how much data is generated when making a call, sending a message or browsing through your social media feed?
Based on statistics from 2020, each smartphone user creates 1.7 MB of data every second, yes, SECOND. Over the day, that amounts to a lot of data, and that’s just from one person. It is estimated that by 2025, 463 exabytes (one exabyte is one billion gigabytes) of data will be generated each day. Now that’s some big data, right there! (Please excuse the cheeky pun :))
This gives us the perfect segue into the core topic of this article, Big Data. Big Data has become the next big thing (no pun intended this time) in the data science and analytics world, but why is that so? What makes this data set so unique and significant?
We’ll cover all that and more in this article and provide you with all the information you need to consider how adopting a data-driven strategy and decision-making could be the secret sauce to taking your business forward.
As the name suggests, big data is an extensive data set composed of structured, semi-structured, and unstructured data collected by companies for machine learning and data-driven decision-making aided by predictive modelling and complex analytics.
In simple terms, our daily usage of technology has created so much data every second that they had to call it “big data” because that’s just how much of it there is.
A famous phrase is, “Big data is for machines; small data is for people.” But what does it mean, you may ask? We’re confident that you’ll be able to answer this question by the end of this article.
Managing big data effectively is no easy feat. Organisations usually pair up sophisticated systems that process and store big data alongside tools that support analytics to form their data management infrastructure.
Big data is often characterised by the three V’s:
Although big data doesn’t translate to any specific or exact volume of data, big data deployments often involve terabytes, petabytes, and even exabytes of data created and collected over time. That’s a lot even for calculators to process!
In this internet age, billions of users connect daily and share information, upload content (audio, video, images), and communicate online worldwide. This rising big data is not an oversight anymore but a highly potent tool in a business’s toolbox if you can harness it properly. Many companies use big data to implement data-driven strategies, achieve growth, and defeat competitors.
In the next section, we’ll go over how big data is used across organisations and different types of industries.
In the business world, companies use big data in their systems to understand consumer behaviour, trends and patterns to improve operations, provide better customer experience, and create highly catered and targeted marketing campaigns. Ultimately, these actions lead to an increase in revenue and profits and help the business grow.
Businesses that use tactical data-driven decision-making effectively hold a significant competitive edge over those that don’t or are struggling to do the same. Such companies can make faster and more informed business decisions backed by real-time data and analysis.
The use of big data is compelling in marketing and advertising. As much of traditional media has been replaced by digital mediums, using big data provides valuable insights into customer behaviour, demographics, psychographics and more.
Companies can use this information to refine their marketing, advertising and promotions to increase customer engagement across each step of the buyer’s journey and influence conversion action.
You must have seen ads before playing a video on YouTube or come across sponsored posts while scrolling through your Instagram feed. These are some excellent examples of Big Data used in marketing and advertising.
Companies use a combination of historical and real-time data paired with supporting analytics to understand the ever-changing consumer behaviours and become more responsive and attentive to the exact needs of their customers.
All in all, with the vast amount of data generated daily through social media, for example, it is safe to say that big data has become an absolute goldmine for businesses to gain invaluable insights to tap into the hearts and minds of their ideal buyers.
But that is not the entirety of the scope of big data usage. In fact, we’ve only scratched the surface. Big data is used in a whole host of different industries.
Take oil and gas companies, for instance; they use big data to identify potential drilling locations and monitor pipeline operations. Utility companies use big data analytics and metrics to track electrical grid systems.
Big data is also an integral part of the finance industry, with many firms relying on big data for risk management, identifying investment opportunities and trends and analysing real-time market data.
Big data is used mainly for supply chain management and freight operations in transportation and manufacturing.
And finally, governmental organisations in many countries use big data for crime prevention, intelligent city planning and urban development forecasting, among other critical national and international growth initiatives.
Well, now that we understand what big data is and how it is used across different business sectors, let’s look at how big data is generated and where it comes from.
Attributing big data generation to a single source from a myriad of sources is challenging. However, we can typically categorise these sources as user-generated and machine-generated data.
Let’s look at an example to solidify our understanding.
Think of advertisements on social media once again; every time you click or don’t click on that sponsored post, data about your advertisement preferences is stored somewhere (more on this later). Now, think of all the users doing the same thing as you in different parts of the world, and think of all the data being created. Crazy, right?
But social media is just one part of the puzzle because user-generated data can also come from other sources such as retail point-of-sale systems, customer databases, emails, medical records, internet clicks, and mobile apps.
Machine-generated data can include network and server log files, data from sensors on manufacturing machines, industrial equipment and internet of things devices.
Big data is not restricted to internal systems but often involves external environments, such as public government data, data from search engines such as Google, financial markets, weather, traffic conditions, geographic information, scientific research and more.
And lastly, big data also comes in images, videos, and audio files, with social media significantly contributing to this particular form of data sets.
Next, we will go through the processes and platforms involved in collecting big data in a continuum.
Big data is often stored in what we call “data lakes”. Data lakes start by collecting data from different sources through a common data ingestion framework focused on standardising the vast array of data sets into a centralised format and storing it in a standard storage repository.
Data lakes can support various data types and are typically based on cloud storage services, NoSQL databases or other big data supporting platforms.
While data lakes are the central common theme of a big data storage environment, there can also be multiple storage systems. For example, a primary data lake might be integrated with other big data storage platforms, such as relational databases or a data warehouse.
The data stored in data storage systems can be in its raw source form and later processed for analytics. Ultimately, the end goal of this storage process is to make the data readily available for analysis by end-users such as business analysts, data scientists, and data-driven business decision-makers and executives.
Storing the vast amount of generated data is one thing, but processing it continuously is another challenge. For starters, big data processing places heavy demands on the underlying computing infrastructure.
The necessary processing capabilities are often facilitated by a distributed cluster system comprising hundreds or thousands of servers using big data technologies such as Hadoop.
The central concept of big data processing involves dividing large data sets and computations into smaller, more manageable chunks handled by multiple computers or servers, which produce the resulting database that can be used for analysis and reporting by end-users. Hadoop is one such technology that embraces this concept.
That said, while the theory may sound straightforward, getting that kind of processing capacity cost-effectively and efficiently is challenging. This is why the cloud has become a popular environment for big data processing, as it tends to be relatively inexpensive.
This is mainly because of the flexibility that organisations have to choose from. They can deploy their own cloud-based big data systems or use pre-built big-data-as-a-service offerings from cloud providers.
The scalability offered is also a key point to note, as businesses can invest up to just the exact number of computational servers they need to perform competent big data processing and analytics. You’ll have to pay for what you need and can always scale up quickly if required.
We’ve thrown the term “big data analytics” several times in this article, and it’s for a good reason. Big data analytics is a big part of how businesses can utilise the vast amount of information generated from the internet and establish a competitive edge.
Think about it for a second; if the collected data is not used for analysis, it will be a massive waste of an invaluable resource.
With that being said, to get insightful and impactful results from analysing big data, data scientists and data analysts must have a thorough understanding of the available data and a keen sense of exactly what they’re looking for.
This is why data preparation becomes essential to the data analytics process. Data preparation involves profiling, cleansing, validating and transforming generated data sets.
Once the data has been gathered and analysed, various data science and advanced analytics principles can be used to run different applications, using tools that provide big data analytics features and functionalities.
These principles include but are not limited to machine learning, predictive modelling, marketing analytics, data mining, statistical analysis, streaming analytics, text mining and more.
If we take customer data as an example, the different types of analytics that can be conducted with this big data set are:
We’ve already established that big data is a powerful resource with which most businesses ought to do more, but what challenges with big data technology should you be aware of?
As we discussed before, processing capabilities are large and costly, at least during the setup phase, and designing a big data architecture is not an easy feat.
While both of those issues can be eased by using a managed cloud service, your IT managers and developers will still need to keep a close eye on cloud usage to ensure expenses don’t get out of hand.
Furthermore, we also have to think of transferring on-premises data sets, such as those stored locally on computers and servers and the accompanying processing workloads, to the cloud, which can often be quite complex.
Lastly, the accessibility of data is another challenge that needs to be considered. Especially in distributed environments comprising multiple platforms and data sets, making the data accessible to data scientists and analysts can become challenging due to the constantly generated data’s variety and velocity.
Having the right team and the correct infrastructure to handle the storage and computation of big data are obvious prerequisites but are not enough to manage big data and use it effectively for business success.
Establishing a big data strategy is just as if not more important. Here are some take-home steps you can think of implementing to build a practical big data implementation blueprint.
What do you intend to achieve with big data, and how do you plan to use it to complete your overarching business objectives? All businesses are unique and special and have different goals and objectives, so ensuring your big data strategy aligns with what your company wants to achieve is vital.
The next step is identifying data generation sources, current business processes, technologies used, and data assets.
Based on your findings from the previous step, conduct a gap analysis to find the latest techniques and devices that can be used to overcome the identified shortcomings and possible improvement areas.
It is essential to start small here, as this is the first time you are implementing this in your business. You don’t want to take up more than you can handle at this stage.
This step aims to finalise a few big data applications specific to your business to get you closer to achieving your business goals and objectives.
Once you have a solid foundation of overall business objectives, a clear understanding of data sources specific to your business, and appropriate use cases, it is time to map out the outline of deployment.
A helpful exercise is to picture the end goal and work backwards from there to the ground level.
Lastly, see if your team is competent enough to implement an endeavour of this scale and complexity and upskill or enhance your workforce as necessary.
So, suppose you want to join the big data revolution and ride the wave of success that businesses are experiencing as they become more data-driven. In that case, assessing your team’s capabilities is essential.
Do you have the right people to manage and analyse this data? And do you have the tools and infrastructure to support big data projects? If not, don’t worry – plenty of options are available for less technical users who want to get started with predictive analytics or deploy a big data platform.
Contact our consultants today for a free discovery chat – we’d be happy to help guide you on your journey to becoming a big-data powerhouse.