How Can You Use Big Data To Drive Business Growth?
We can define “big data” as a goal related to technological developments and changes the way data is analyzed. Since the 19th century, the sampling method has been used for large and complex data, which is natural in a period when information was scarce and digital technology was not widespread.
However, technological developments provide the opportunity to use the entire data (population) and find details that cannot be obtained with a limited amount of data; Big Data analysis allows subcategories to be expressed much more clearly.
On the other hand, the assumption of linearity, which has come to the fore with the changes in science, criticizes methods such as starting with hypotheses and reaching the theory, and considering outliers as unimportant.
Compared to hypotheses, the issue that “it would be more accurate to start with data and find appropriate patterns and correlations with this data” comes to the fore.
Big Data analysis is essentially about predictions and can also be defined as a part of machine learning algorithms within the AI research of computer science. However, instead of teaching computers to think like humans, mathematical applications to extract probabilities from large amounts of data come to the fore.
What Is Big Data And How Does It Work?
The focus of Big Data studies should be on how to create benefits with data. Big Data analytics enables the use of data. For this, businesses use AI and VM techniques (such as text analytics, machine learning algorithms, natural language processing, and predictive analytics) to obtain new information and insights with previously used or unused data.
Technological tools maximize algorithmic accuracy and computational power in Big Data analysis. Until recently, businesses performing Big Data analysis were generally ICT businesses.
However, as the technical difficulties in storing and processing Big Data come to an end and ICT businesses start to offer Big Data analysis as a service, competitive advantage for all sectors will be achieved, better decision-making, new product/service development, increasing quality and efficiency, customer and market dynamics. Applications that create benefits such as better understanding have increased.
How Can You Collect And Store Big Data Effectively?
The concept of “big data”, which does not have a precise definition, was created by astronomy and genomics, which experienced an information explosion in the mid-2000s. There are different ideas about how large data should be to put it in the “big data” category, and the perception of data size varies from sector to sector.
Davenport, one of the important writers on information management, defines “big data” as “data that is too large to fit on a single server (on a scale larger than 100 terabytes), is not structured in the form of rows and columns, or is constantly flowing so that it cannot fit into a stagnant data warehouse.” The definition of “3 V” made by Gartner company and used in many sources to describe big data is as follows:
“Having the characteristics of large volume, large velocity, and/or large variety; “They are information assets that require new forms of information processing that will increase our decision-making abilities and improve insight and process optimization.”
Big data is built on society’s ability to leverage information in new ways to produce useful insights or value-creating goods and services. Much of the confusion around big data starts with its definition.
In the research commissioned by IBM to Schroeck et al., each participant was asked to choose a maximum of two features of big data. As a result of the elections, no single feature emerged that was dominant.
Respondents were divided on how best to define big data, with three different definitions: “today’s high volumes of data, new data types and analysis, and the emergence of the need for more real-time information analysis.”
When we look at the data used personally today, it is possible to talk about much larger scales than the data of years ago. When we consider this issue from the perspective of businesses, we encounter huge amounts of data.
Many private sector and public organizations store data faster than they can analyze it. 90% of the world’s data has been produced in the last two years. Some of the sources of this big data include sensors used to collect information about climates, social media sites, digital photos and videos, purchase transaction records, and mobile phone GPS signals. 2.5 quintillion bytes of data are produced every day, and this is big data.
What Are The Best Tools For Big Data Analytics?
It’s a simple fact that data-driven decisions are better decisions. Big data results in managers basing their decisions on evidence rather than insight. This means the potential for radical change in management.
Simply put, big data gives the manager the ability to measure, thus providing complete information about his business. Being able to measure and have information makes decisions accurate and improves performance.
Big data heralds that a different type of decision-making may emerge. By leveraging controlled experience, companies can test assumptions and analyze results to guide investment decisions and operational changes.
Experience can help managers distinguish causality from pure correlation and thereby reduce variability in outcomes, improving financial and product performance.
Thanks to big data, companies will have more information about their business environments and will therefore have the opportunity to make more data-based decisions. Often companies have the data they need to solve business problems, but managers may not know exactly how to use that data.
Lower-level managers may not realize the value of the hourly and daily factory and customer data they have. Improvement in decision-making occurs by adding new data sources to explanatory models.
For example, if we have a model that can predict customers’ “second best choices” based on their purchasing history and demographic characteristics, this model can be contributed by adding customers’ comments and likes on social media.
Decisions can be made better and more timely by using big data. Complex mathematical analysis could significantly improve and even replace human decision-making.
Systems have already proven to be superior to humans in the fast-moving, complex world of commerce, and this is just the beginning. Real-time analysis of entire data sets collected from customers, employees, and even sensors will enable faster and better decisions in many areas, from inventory planning to medical diagnosis.
How Can You Use Big Data To Understand Customer Behavior?
Simply put, thanks to big data, managers will be able to measure and therefore have complete information about their business and use this information to make decisions and improve performance.
Research shows that making data-driven decisions instead of intuitive decisions is 5% more efficient and 6% more profitable than competitors in companies that are among the top three in their sectors. It is not meant here that managers should throw away their insights.
“Intuition” has also been added to the data-information and knowledge hierarchy. Discovering the relationship that sheds light on the solution of a problem, identifying the sources of emotional difficulties, and understanding the motivating force behind a person’s behavior can all be considered as “intuition”.
The power of big data can never eliminate the need for human intuition. The important point is this: Although the relationship between big data and the path to intuition is clear, this relationship is not yet fully clear in our minds. This will necessitate a correction in ideas regarding management, decision-making, human resources, and training.
History shows that managers can continue to make instinctive decisions even when little data is available. It is also a fact that power, politics, and politics in companies will not disappear anytime soon.
Still, working with big data is a matter of vision. Therefore, the political factors, power, and politics that companies take into consideration to achieve any success cannot be ignored in decisions to be made with big data, but this is not an obstacle to us benefiting from big data.
What Is The Role Of Big Data In Marketing?
New management approaches are needed in the field of marketing for the uses of big data related to internal decisions. Analytical applications that support internal decisions are taken from a database and the results obtained from the analyzed data are used to support the decision maker.
However, big data has a variable structure like a constantly flowing river. Therefore, more continuous approaches to sampling and analysis are needed. For example, social media and production data change all the time.
Big data is a revolutionary issue that is relevant to all aspects of our lives, from businesses to consumers and from science to government. Characterized as a new starting point, big data has become one of the most interesting topics in today’s academic and data world.
Today, businesses have started to adapt themselves to this concept. Big data sources come from social media flow, digital images, bank and transaction records, sensors, GPS signals and countless other sources, and this flow is rapidly increasing, 90 percent of the data in today’s world has been created in the last two years and according to the 2011 McKinsey Global Report, it will be 44 times more by 2020.
How Can You Improve Business Operations With Big Data?
Big data can open new ways for your business operations. It is believed that over time, big data can also become a new type of company asset that goes beyond business units, functioning like a strong brand and becoming the key to competition.
If this is true, companies need to start seriously considering whether they are organized to harness the potential of big data and overcome the dangers it may pose. Success will require not only new skills but also new perspectives on how the big data era will evolve.
For example, new business categories are emerging that embrace knowledge-based business models. Many of these businesses play an intermediary role in the value chain, finding themselves creating valuable “exhaust data” produced in business processes.
For example, a transportation company realized that it was collecting a large amount of information about universal product transportation in the course of doing business. Realizing the opportunity, it created a unit that sold the data to additional businesses and economic forecasts.
What Are Some Case Studies Of Businesses Using Big Data For Growth?
When considering the cases that implement the most successful strategies in the field of big data, Amazon may be at the top of the list. Amazon executives spend significant resources and energy on big data technologies to make customer experiences user-friendly, thus making more sales and increasing efficiency in all supply chains.
As an example of big data usage, we can appreciate the recommendation systems at Amazon. Amazon offers personalized web pages and interfaces for customers thanks to big data analysis. This data includes information such as customers’ past reactions and search history.
At the same time, other technical problems such as inventory management problems and fair pricing strategies are solved quickly and efficiently within an automated system thanks to big data technologies.
One of the important keys to Amazon being one of the most successful e-commerce platforms in the world today is its successful applications in the field of big data.
How Can You Ensure Data Privacy And Security In Big Data?
Big data will be a new source of economic value and innovation. For companies and managers to benefit from big data, they must first determine their goals.
The three values we mentioned above are; cost savings, improvement in decision making – faster and better decisions – which of the product and service innovations they would prefer first; They must determine what values will be consistent with the company’s current strategy. Among these values, the most ambitious thing that can be done with big data is to develop product and service offers.
After companies determine their big data goals, they will do two basic activities related to big data analysis. The first of these is “discovery”, that is, learning what the data we have and how it will benefit our organization.
The second is “production”, meaning the introduction of discovery ideas into production processes. It is also necessary to determine whether the data to be explored belongs to your company or should be obtained from external sources.
For example, if the data produced by machines in a manufacturing factory has not yet been used to improve the work, this internal data can be evaluated. However, if there is a process related to social media, which we have given as an example before, the data we need is out there.
What Are The Challenges In Implementing Big Data Solutions?
The companies of the future will be companies that turn data into products. About the future to make the right choices, learning organizations that use data to inform must be able to convert. For example, the LinkedIn company used big data to develop the “people you may know” application and thus gained millions of new customers and managed to retain them.
The issue of where data can be collected for “person you may know” offers (school, work, hometown, visited locations) falls within the field of big data. Big companies’ work on big data is directly focused on products, services, and customers. This situation gives clues about the place of big data in the organization and the speed of product and service development.
Cost savings may be a secondary goal following other goals. A decision may be made to investigate a cheaper way to design new products and services, which is the first goal. If the goal is to make faster and better decisions, it is necessary to focus on the use of external data.
Managers need a lot of data when making decisions, such as measuring supply chain risks, financial reliability of suppliers, weather-related risks, and political risks. Leading companies have come to monitor only their suppliers and their suppliers.
What Are The Future Trends In Big Data?
The volume and development speed of big data listed above affect almost all areas of life. It is seen that it is widely used in many areas from taxi services in Singapore to health, sports and innovation.
Businesses have also started to realize this transformation, and according to a study conducted on 600 global businesses in 2012, three-quarters of these businesses classified themselves as data-oriented and as the fourth factor after production, labor and capital with a score of nine out of ten.
In addition, decision-making has become more sophisticated in this environment that can be characterized by hundreds of billions of mass data. With this structure, it is necessary to reveal what can be done with big data rather than how voluminous it is.
In recent years, many publications have been made on how this concept and application will change our lives, how powerful and important it is. The reflections of the subject in marketing and especially in the retail sector are at a level that can lead to a paradigm shift. It is possible to see traces of big data in almost all areas of marketing.
For example, big data, in addition to making personalized marketing plans possible through digital sophisticated assistants, has also made decision-making under such a large data flow critical.
This article examined big data conceptually and consists of two parts. While the first part covers basic topics such as the concept of big data, its characteristics, basic defining elements and its operation, the second part of the study focuses on the applications and application potential of big data in the field of marketing.
See you in the next post,
Anil UZUN