The world has seen the rise of devices from smart health devices to IoT devices used in measuring pressure in the oil and gas industry. What do these devices do? They create data, measure it, store it, and provide insights per business requirements.
So, how much data is created by these smart devices? In 2025, the total data volume from all IoT devices is projected to be 79.4 zettabytes.
What is the expected amount of data produced in the future? In 2022, the planet will produce and consume 94 zettabytes. The proliferation of the Internet of Things connected devices will only add more bandwidth to this number!
How will big data influence the future?
I feel that the value of big data in digital transformation comes from an organization’s ability to combine both in their efforts to enable the digitization and automation of business operations.
It enables organizations to be more efficient and innovative and create new business models through digitization and automation.
By 2022, IDC projects worldwide revenues from big data and business analytics (BDA) solutions to top $260 billion.
The future of big data could see organizations using BDA to combine analyses from the digital world to create real-world solutions. This would allow for a greater understanding of how people interact with technology and enable organizations to provide more efficient and effective services.
Why is big data so important to businesses?
In my opinion, big data implementation in business operations can allow organizations to boost productivity, deliver superior customer service, create customized marketing campaigns, and take other measures to increase revenue and profits.
What are the benefits of big data for businesses?
Big data is an excellent resource for driving intelligent business decisions and changes. Here are eight examples of how big data revolutionizes how companies operate.
1. An agile supply chain management
Whether it’s a toilet paper shortage or a slew of other items, the disruption caused by Brexit, a pandemic, a ship trapped in the Suez Canal, or other factors, modern supply chains are surprisingly fragile.
Surprising because, typically, we are unaware of the supply chain’s capability to be fragile until a significant problem occurs.
Global supply chain management requires massive amounts of data that help avoid delays and ensure smoother operations. This can be further enhanced by big data that allows predictive analytics in near real-time basis so that the worldwide network of demand, production, and distribution can function smoothly for the most part.
With such a large amount of data available, big data systems may integrate consumer trends from e-commerce sites and retail apps with supplier information, real-time pricing, and even shipping and weather conditions to provide more information than ever before.
Even tiny e-commerce firms may profit from these insights, as they can be used to optimize business decisions like stock levels and risk reduction, or even temporary or seasonal staff hiring.
2. Insight into customers
When a modern business turns to data to understand its customers- individually or in categories- it has a wide range of sources to choose from. The big data sources that shed light on customers include the following:
- purchases and support calls
- financial transactions and credit reports
- social media activity
- data from internal and external surveys
- caches and cookies
In our modern, digital world, clickstream analysis is more useful than ever to help e-commerce businesses understand how their customers move about on their websites as they search for products and services.
By looking at what items customers add to their carts and remove, companies can get clues about what those customers might want to buy in the future – even if they don’t make a purchase immediately.
Online stores and brick-and-mortar locations can also glean a valuable understanding of their customers, often by analyzing videos to learn how visitors navigate through a physical store compared with their website navigation.
3. Enhanced market insight
Big data provides a level of depth to the understanding of customer shopping behavior as well as market dynamics that were unavailable before.
Social media is a frequent source of market intelligence for product categories ranging from breakfast cereal to holiday packages.
People love sharing photos of their daily lives with friends and family, whether they’re wearing, where they’re going, or even what they’re eating. And this tendency isn’t lost on marketers who see the value in these shared opinions.
As mentioned above, big data can also aid product development by prioritizing various client demands.
Not only does big data provide modern market intelligence, but in reality, all market intelligence for e-commerce and online markets is based on ever-changing data.
4. Smarter audience targeting
Previously, association rules, which identified everyday items bought together, were once the only predictive analysis for recommendation engines. You can still expect to find this as a feature on e-commerce websites telling us that customers who bought widgets also bought fidgets.
These newer systems aim to provide a more precise identification of prospects based on their online behavior. These new recommendation systems are considerably smarter than the ones we’ve discussed, and they’re capable of being more sensitive to demographics and client behavior as a result.
Not only are these systems used for e-commerce, but they can be found in other places.
For example, a waiter’s friendliness may come from data collected and processed by a point-of-sale system. This system would consider factors like stock levels, popular combinations of food items, high net profit foods, and even social media trends.
So whenever you post a picture of the meal you’re eating on social media, unknowingly, there is more input being given to enhance these engines further. Streaming content providers employ sophisticated methods.
They may not even inquire about what consumers want to see next; instead, the following suggestion fades in as soon as the current film, program, or song has ended, keeping viewers binge-watching by incorporating their preferences with a lot of big data analysis acquired from other users and social media.
5. Data-driven innovation
More than being inspired, innovation demands practical work to identify subject areas that show potential for further exploration. Big data analytics may help R&D lead to the creation of new goods and services.
It’s crucial to remember that while there are significant benefits associated with publishing open data, it isn’t always feasible or cost-effective.
Sometimes, the data — cleaned, prepared, and governed for sharing — becomes a product in its own right. The London Stock Exchange now generates more income from selling data and analysis than it does from stock trading.
Even with state-of-the-art big data tools, machines cannot interpret data the same way humans can. We need people trained in data analysis to use their imagination and understanding to produce new insights from raw information.
Often, when data is stored in multiple isolated places, it’s challenging to understand any resulting trends accurately. But because big data is vast and lives in one central place (like a Hadoop cluster), teams can glean new understandings from it more easily.
6. Diverse dataset use cases
I’ve experienced firsthand how data that’s created for one specific use may not fit another business circumstance.
For example, a credit card company’s marketing staff wished to discover how clients utilized the different cards in their wallets. The difficulty of the analysis was increased by frequently failed swipes and canceled transactions, often due to payment terminal connection problems or card-reader flaws.
As a result, the data set proved ideal for the initial marketing campaign. But because the fraud prevention team wanted to see fraudulent card transactions that might provide hints about unlawful activity, they couldn’t use it. Not only that, but the deleted information was being saved on tape storage and was therefore difficult to access.
In an age where data is rampant, we can store all of it in what is called a “data lake.” This refers to unstructured or raw data that has not been filtered for specific use. Only when we need to use the data for particular analytics applications do we apply data models to it.
Data pipelines can be specifically designed for each use case, or ad hoc queries can be run as needed to populate the analytics processes with information. By doing this, various types and numbers of applications become available that were otherwise impossible due to rigidity.
7. Improved business operations
Big data has the potential to improve nearly every aspect of a business. By optimizing various processes, big data can lead to cost savings, increased productivity, and even higher customer satisfaction rates.
Additionally, big data can make hiring and HR management more efficient. Improved fraud detection, risk management, and cybersecurity planning assist organizations in lessening financial losses and help avoid possible business threats.
Applying big data analytics to physical operations can provide valuable benefits, such as reducing the need for costly repairs and downtime for essential equipment. By combining big data with data science, it is possible to develop predictive maintenance schedules to keep systems and equipment running smoothly.
8. Future-proofing data and analytics platforms
Data analytics is quickly evolving. The fundamental norms of reporting, BI, and self-service analytics already put significant pressure on IT departments. Machine learning, predictive modeling, and AI technologies are becoming standard features for big businesses.
With each new generation of technology, the forms of data collected, kept and evaluated grow more complicated.
Presently, this diversity and data volume is a challenge. However, data is continuously becoming more complex and demanding as time goes on, with no foreseeable end.
As needs for data analytics rise, who knows what we’ll be up against soon? Do you want to build a platform that can last without rapidly fading into irrelevance? The key lies in big data’s flexibility and scale.
How is big data used in digital transformation?
The value of big data in digital transformation comes from an organization’s ability to combine both in their efforts to enable the digitization and automation of business operations.
It enables organizations to be more efficient and innovative and create new business models through digitization and automation.
What is the future of digital transformation for businesses?
Digital transformation is using digital technologies to develop new or alter existing business processes, culture, and customer experiences to fit changing business and market demands. This rethinking of companies in the digital era is known as digitization.
Automating critical areas like payroll used to be a tedious process, but digital transformation allows businesses to revamp these core functions. Automation can free up time so leaders can explore other business areas.
What is the objective of digital transformation for businesses?
By digitizing business practices, companies set themselves up for success in an increasingly digital world. The goal of these changes should be to make processes more efficient, give the company a competitive advantage, promote employee productivity and collaboration, and make the business model adaptive.
The skills needed to lead a digital transformation.
As people become increasingly interested in having digital experiences—like buying things online, video conference calls with salespeople, or easy customer support on their mobile phones—it’s crucial for the leaders of this transformation to know what technology will offer the best service and value. And they should be interested in technology themselves, so they can stay up-to-date with all the newest trends.
Here are some other abilities digital transformation leaders use to help their organizations adapt to the tech-first environment.
Mediating & considering various elements to strike a balance
A lot goes into selecting a new software platform and implementing it. An excellent digital transformation leader knows how to consider all of their options, weigh the importance of each platform and its potential usefulness, and then help the team learn to adopt it.
They have to consider IT skills, price, features, security, and a whole list of other issues, and then be willing to make compromises and adjustments to find a balance that pleases everyone.
Knows data interpretation
If you want to succeed in digital transformation, you need to understand and document your customers’ online experiences. Most salespeople are already good at this because they’re used to reviewing sales reports and customer data analytics.
This helps them figure out what’s happening with their customers and how they can improve their selling strategy.
Can communicate needs
Digital transformation throws traditional methods out of whack, which may be stressful. It’s critical for digital transformation leaders to communicate the new course your firm is taking, explain new organizational and policy changes, and seek feedback from staff on what’s working and what isn’t.
Knows their company inside and out
When leading a digital transformation, you must constantly assess your company’s pulse and ask yourself what it requires. Every new technological tool on the market won’t necessarily be the next breakthrough for your organization.
A digital transformation leader, however, can be prepared to identify a brand-new digital opportunity whenever it appears by keeping an eye on the company’s actual demands and procedures.
More and more businesses are digitizing their essential processes because they’re overwhelmed with data. It doesn’t matter if this data is structured or unstructured. Leaders must focus on how they can use it to add value to their organization.
With the implementation of big data analytics, companies can make quicker and more informed decisions. Having access to the correct data at all times allows for better business planning and, as a result, improved business performance.
Although it is still trending, using data effectively is a decisive task. Businesses need critical insights for faster decision-making and real-time execution. Analytic models can be built on data to unleash business insights and achieve strategic goals.
Integrating big data analytics can change an organization’s business model and create enterprise-wide transformation.