Now present in all sectors of activity, Big Data had its glory years in the 2010s, giving way to Smart Data in recent years. The explosion of data collected through the web and other digital media has prompted companies to find solutions to exploit all this information and generate value.
The global Big Data market is expected to reach nearly $103 billion by 2027, which illustrates its importance, to the point of becoming a constant in corporate strategies. In this regard, one out of two companies believes that Big Data will impact and revolutionize the way they do business. (Source Accenture)
Yet, did you know that nearly 63% of employees have difficulties exploiting data (Sigma Consulting) and 2/3 of this data is not used for analysis, because of the lack of tools in place (Forrester)?
Eminence explains all the key concepts related to Smart Data, which are now driving corporate strategies, especially in the financial and banking sector, where data is at the heart of all processes and decision-making. Let’s find out what the applications, interests, and major challenges are.
What is Big Data?
The term Big Data was coined in 1997 to designate the large data sets, the “megadata”, that has emerged with the advent of digital technology. The information available has multiplied exponentially, requiring a rethinking of the methods of storage and structuring, searching, networking, and sharing of data to be able to exploit and analyze it in real-time.
Where does “Big Data” come from?
Big Data comes from all the interactions users have with the web and other digital technologies: emails sent, files downloaded, videos viewed, geolocation, online shopping, search engines, social networks, iOt (Internet of Things), AI (Artificial Intelligence), etc.
Major web players, such as GAFA – Google, Amazon, Facebook, and Apple – or Yahoo were the first to use this type of technology, which some people consider to be a new industrial revolution.
How to store data in the enterprise?
Storing data for analysis requires the design of a Big Data architecture, with an interface adapted to the different user teams: IT specialists, analysts, salespeople, or marketers. This is referred to as a centralized system.
The implementation of a cloud computing service is a beneficial option for cataloging data according to usage, both for IT and for commercial activity.
What are the limitations of Big Data?
With the exponential growth of data available (humans create nearly 2.5 quintillion bytes of data per day), two main issues have emerged:
- How to sort the data and keep only relevant content?
- How to ensure the reliability of the collected data?
→ What is Big Data?
Smart Data: the logical and necessary evolution of Big Data
“Too much information kills information”: this adage applies perfectly to Big Data; even more so when you consider that it would take 181 million years to download all the data on the Internet!
What is Smart Data?
Smart Data means “intelligent data” or how to extract and process relevant data quickly from the vast amount of information collected.
Smart Data is often linked to iOt and the sensors of connected objects (smart things).
The evolution from 3V to 5V
Originally, the concept of Big Data was based on the 3Vs, proposed by Gartner, an American consulting and research firm in the field of advanced technology:
- Volume: an infinite and abundant amount of data to process, collected every second
- Velocity: the speed of its creation, diffusion, collection, and sharing
- Variety: a heterogeneous origin of data sources and types, of which only 20% are stored and structured
We can now add 2 new dimensions to the foundations of Big Data, generated by the technological evolution and the new field of possibilities:
- Veracity: the authenticity of the data to ensure the reliability of the data collected
- Value: the ultimate goal of Big Data is to make the data profitable, to create value, or to monetize all information
How does Smart Data work?
The special feature of Smart Data is to select the data at the source, instantaneously, among the most relevant and correlated variables via statistical models. This helps to avoid obsolescence and allows immediate reactivity of the company, thanks to the information processing time reduced to the bare minimum. It is now about streaming analysis.
Note that all this data must comply with the GDPR standards.
Marketing and business interests of Smart Data
Thanks to the exploitation and cross-referencing of data, Smart Data allows it to segment and profile its customers or prospects, then assign them a score of relevance. Therefore, advertising campaigns can be better targeted, with the right message at the right time, like marketing automation, with machine learning, coupled with CRM.
Customer personalization is thus enhanced by demographic and behavioral data while optimizing acquisition or loyalty costs. A winning strategy at all levels and on which the foundation of Data-Driven marketing is built upon.
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The importance of Smart Data in the banking sector
The banking sector is the largest in terms of investment in Big Data, with 14% of global spending (Source: IDC), closely followed by manufacturing, process manufacturing, professional services, and government.
The dematerialization of services, the growth of financial flows, globalization, the risks of fraud and piracy, increased competition, etc. have all contributed to the rapid growth of smart data in the banking and financial sector.
What are the possible applications of Smart Data in the banking sector?
The banking sector has many fields of application, in which Big Data and Smart Data are now essential:
- Analysis and improvement of the customer journey experience
- The digital strategy to be implemented, in a sector that is very often dematerialized and whose digital practices are changing rapidly: campaigns, profiling, CRM, and marketing automation, thanks to data-driven marketing
- Offering practical, customized, and secure solutions to customers
- Provision of the necessary data in real-time to all departments of the company according to their defined needs
- Assessment and management of opportunities and risks: legislation, competition, economic situation, political, financial crises, IMF, investments, loans, etc.
- Development of forecasts on the development of the stock and money markets
- Security, detection, and prevention of fraud
What are the advantages of Smart Data in the banking sector?
The benefits are multiple and affect all stakeholders, businesses in the sector as well as their customers.
Gain new customers and better retention of existing customers
Data-driven companies with good customer knowledge are 23 times more likely to win new customers. They are also 6 times more likely to retain their acquired clients. (Source: McKinsey)
Easier and increased profitability
Companies that use data are also 19 times more likely to achieve and maintain a profitable status than companies that do not use data. (Source: McKinsey)
Productivity and profitability are enhanced through the automation of manual processes, analysis of customer payment behaviors, and continuous improvement of management tools. Companies that adopt big data analysis could thus increase their operating margins by up to 60% and consequently their profits (Source: McKinsey).
Obvious advantages in the internal organisation
Users favor better strategic decision-making (69%), resulting from a better understanding of the internal and external environment of the company through key insights and better-controlled business processes (54%). (Source: Barc)
Better understanding your customers drives sales growth
52% say they have gained an understanding of their customer’s needs and expectations. This also makes it possible to offer products and services better adapted to the audience, while at the same time collecting inputs for the creation of new services and offers. (Source: Barc)
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The challenges of Big Data and Smart Data in the banking sector
In the face of growing data flows, companies must not only be ready to regularly challenge their storage (data warehouse) and processing systems set up but also to rethink their internal organization to align with technological and behavioral changes.
- The banking and financial regulations must be taken into account as it evolves in the systems and allows for reporting used for analysis. Start-ups in RegTech have also been created in this promising segment.
- Securing data in the face of growing hacks, suspicious activities, and viruses to limit risks and guarantee the protection and confidentiality of customer accounts.
- The qualitative exploitation of data, is made possible by their accuracy and security, across all systems, in real-time.
- Data silos involve the implementation of integration tools to accommodate all media and sources of information in the company (emails, documents, applications, etc.) and facilitate their storage and dissemination in the organization.
→ Tackling cybercrime in business through cybersecurity and data protection
Companies that have taken up the challenge of digital transformation before others should benefit from a certain competitive advantage and create new market opportunities while improving their profitability.
Your agency Eminence accompanies you in the process of structuring your data, but also in complementary activities, such as tracking or profiling, helping you to determine your marketing strategy and improve your ROI. Contact us to learn more.