Big data is a major asset for businesses that can access its insights. Making this happen, though, is a complicated job that needs the right tools. Enter data enrichment.
Understanding how it works and its impact on current industries is a great way to get to know what data enrichment can do for your organization. How it benefits the use of big data will become clearer, too.
What Is Data Enrichment?
Data enrichment is the process of identifying and adding information from different datasets, open or closed, to your primary data. Sources can be anything from a third-party database to online magazines or a social network’s records.
People and organizations use data enrichment to gather legitimate intel on specific things, like a customer, product, or list of competitors. And they can start with just their names or email addresses.
As a result, the original data becomes richer in information and more useful. You can find education trends, profitable news, evidence of fraud, or just a deeper understanding of users. This helps improve your conversion rate, customer relations, cybersecurity, and more.
The most popular method of making all this a reality is specialized software. Their algorithms vary in strengths and weaknesses, as SEON’s review of data enrichment tools shows. They can target human resources, underwriting, fraud, criminal investigations, and more. However, the goal is the same: to support the way we work and give us better insights.
Data Enrichment and Big Data: What Statistics Say
Data enrichment is a good answer to the problem of big data, which often sees masses of disorganized and sometimes inaccurate information that often needs cleaning, maintenance, and coordination.
Creating a data-driven culture within organizations
Despite the benefits of smart data management and major investments already in place, only 24% of firms have become data-driven, down from 37.8%. Also, only 29.2% of transformed businesses are reaching set outcomes.
What this shows is that, yes, big data is difficult to deal with but not impossible. It takes good planning and dedication to get it right.
There are several promising big data statistics on FinancesOnline. For starters, thanks to big data, businesses have seen their profits increase by 8-10%, while some brands using IoT saved $1 trillion by 2020.
Also, the four biggest benefits of data analytics are:
Faster innovation
Greater efficiency
More effective research and development
Better products and services
These achievements are taken further with data enrichment, which adds value to a company’s datasets, not just more information to help with decision-making.
How Does Data Enrichment Help Different Industries?
The positive impact of constructively managing data is clear in existing fields that thrive because of data enrichment and other techniques. Here are some examples.
Fraud Prevention
Data enrichment helps businesses avoid falling victim to fraudsters. It does this by gathering and presenting to fraud analysts plenty of information to identify genuine people and transactions.
For example, you can build a clear picture of a potential customer or partner based on information linked to their email address and phone number. Do they have any social media profiles? Are they registered on a paid or free domain? Have they been involved in data leaks in previous years? How old are those?
It’s then easier to make informed decisions because we know much more about how legitimate a user looks.
Banking services, from J.P. Morgan to PayPal, benefit from such intensive data analytics, as do brands in the fields of ecommerce, fintech, payments, online gaming, and more.
But so do online communities, where people create profiles and interact with others. For example, fake accounts are always a problem on LinkedIn, mainly countered through careful tracking of user activity. Data enrichment can help weed out suspicious users in such communities, keeping everyone else safe.
Marketing
Data enrichment in marketing tracks people’s activities and preferences through cookies, subscription forms, and other sources. To be exact, V12’s report on data-driven marketing reveals Adobe’s survey findings regarding what data is most valuable to marketers.
48% prefer CRM data
40% real-time data from analytics
38% analytics data from integrated channels
Companies collect this data and enrich it to create a more personalized experience for customers in terms of interactions, discounts, ads, etc. Additionally, brands can produce services and products tailored to people’s tastes.
HR
The more information your human resources department has, the better it’s able to recruit and deal with staff members. Data enrichment is a great way to build strong teams and keep them happy.
Starting from the hiring stage, data enrichment can use applicants’ primary data, available on their CVs, and grab additional details from other sources. Apart from filling in any blanks, you can flag suspicious applicants for further investigation or outright rejection.
As for team management, data enrichment can give you an idea of people’s performance, strengths, weaknesses, hobbies, and more. You can then help them improve or organize an event everyone will enjoy.
Summing Up
As we saw in these examples, data enrichment already contributes to the corporate world in different ways, both subtle and grand.
With the right knowledge and tools, we can tap into this wealth of information even further, allowing it to make a real difference in how we work and what we know, rather than simply amassing amorphous and vast amounts of data.
Learn more about data enrichment by exploring our articles on data analytics.
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About the Author
Gergo Varga has been fighting online fraud since 2009 at various companies – even co-founding his own anti-fraud startup. He’s the author of the Fraud Prevention Guide for Dummies – SEON Special edition. He currently works as the Senior Content Manager / Evangelist at SEON, using his industry knowledge to keep marketing sharp and communicating between the different departments to understand what’s happening on the frontlines of fraud detection. He lives in Budapest, Hungary, and is an avid reader of philosophy and history.
“The world’s most valuable resource is no longer oil, but data.”
That statement from The Economist in 2017 cannot be overstated. Businesses in all shapes and sizes must realize that adapting to an already data-driven world is the only way to survive, connect, and thrive.
Artificial Intelligence (AI) was introduced in the 1950s by a computer researcher named John McCarthy. He defined AI as “the science and engineering of making intelligent machines.”
Nowadays, innovation pioneers like Microsoft, Google, and IBM have made strides in AI advancement to back cloud analytics, client engagement, and more. AI has become a program outlined to complete tasks that would regularly require human capabilities or input. AI is considered an innovation that takes after or mirrors human insights and actions, including speech, reviewing pictures, or making a conversation. To a great extent, AI can do those things by recognizing designs inside the information and reacting based on pre-defined rationale.
On the other hand, big data is an extensive, fast, and diverse information resource that requires advanced forms of processing to improve decision making, knowledge generation, and process optimization.
Big data describes sets of information created in different formats and through different sources, such as software applications, IoT sensors, customer feedback surveys, videos, and images..
Big datasets are developed by collecting large amounts of information from real-time data streams, established databases, or legacy datasets. As the environment constantly changes and grows, we need powerful software to protect, classify, and explain information for both short-term and long-term use.
Organizations often use a combination of cloud-based applications and data warehousing tools to develop analytic architectures that collect, organize, and visualize data. AI-powered tools are central to tailoring many of these moving parts to consistent insights that support decision-making.
Linking Up Big Data and AI for Business
Implementing big data with AI has already been vital for many businesses that aim to have a competitive edge. It doesn’t really matter whether it is a new company or an established leader in the market. They use data-driven strategies to turn information into perceptible value. It is common to find big data in almost every industry, from IT and banking to agriculture and healthcare.
Business experts acknowledge that big data and AI can create new ideas for growth and expansion. There is even a possibility that a new type of business will become popular soon: data analysis and aggregation companies for particular industries. The purpose of those organizations is to process enormous flows of data and generate insights. Before this happens, businesses should empower their big data capabilities intensively. In the past, estimations were made based on the retroactive point of view. Leveraging real-time analysis, big data can empower predictions and allow strategists to test assumptions and theories faster.
Data and AI are typically applied to analytics and automation, helping businesses transform their operations in the process.
Analytics tools like Microsoft, Azure, and Synapse help organizations predict or identify trends that inform decision-making around product development, service delivery, workflows, and more. Additionally, your data will be organized into dashboard visualizations, reports, charts, and graphs for readability.
Big data and AI in Health
The global market for AI-driven health care is expected to register a CAGR of 40 percent through 2021 and toup from USD 600 million in 2014. Further advances in AI and big data provide developing countries with opportunities to solve existing challenges in the health care access of their populations. AI combined with robotics and IoMT could also help developing countries address healthcare problems and meet SDG 3 on good health and well-being. AI can be deployed in health training, keeping well, early disease detection, diagnosis, decision-making, treatment, end-of-life care, and health research. For instance, AI can outperform radiologists in cancer screening, particularly in patients with lung cancer. Results suggest that the use of AI can cut false positives by 11 percent.
Big data and AI in Agriculture
Today’s global population of 7.6 billion is expected to rise to 9.8 billion by 2050, with half of the world’s population growth concentrated in nine countries, such as India, Nigeria, the Democratic Republic of the Congo, Pakistan, Ethiopia, the United Republic of Tanzania, the United States of America, Uganda, and Indonesia.
The growing demand for food will put massive pressure on the use of water and soil. All of this will be exacerbated by climate change and global warming.
Big data and AI in Education
AI can reshape high-quality education and learning through precisely targeted and individually customized human capital investments. Incorporating AI into online courses enhances access to affordable education and improves learning and employment in emerging markets. Also, AI technologies can ensure equitable and inclusive access to education, providing marginalized people and communities, such as persons with disabilities, refugees, and those out of school or living in isolated communities, with access to appropriate learning opportunities.
Expected Economic Gains from AI Worldwide
AI could contribute up to USD 15.7 trillion to the global economy in 2030, more than the current GDP of China and India combined. Of this, USD 6.6 trillion will be derived from increased productivity and USD 9.1 trillion from the knock-on effects of consumption. The total projected impact for Africa and Asia-Pacific markets would be USD 1.2 trillion. For comparison, the combined 2019 GDP for all countries in sub-Saharan Africa was USD 1.8 trillion. Thus, the successful deployment of AI and big data would open up a world of opportunities for developing countries.
The big data market is expected to grow tremendously over the projected years. One of the important reasons is the rapid increase in the amount of structured and unstructured data. Factors include the increasing penetration of technology and the proliferation of smartphones in all areas of life. This leads to a large amount of data.
Other industries such as healthcare, utilities, and banking make extensive use of online platforms to provide enhanced services to their customers.
Intelligent use of big data in day-to-day operations enables you to make data-driven decisions and respond quickly to market trends that have a direct impact on business performance.
If you would like to learn more about the best practices for analyzing data, sign up for The KPI Institute’s Data Analysis Certification.
The value of Big Data has found its way to the core of many organizations.NewVantage Partners’ 2021 executive survey showed that 99.0% of the companies they surveyed are investing in data initiatives while 96.0% attest that Big Data and AI efforts were generating results.
However, working with Big Data is not easy for all companies. The survey revealed that 92.2% of leading companies consider culture (people, process, organization, and change management) as the top reason why becoming a data-driven organization remains challenging.
Organizations should recognize that integrating Big Data into performance management would allow them to further improve their performance , make strategic decisions, and achieve higher efficiency in many areas of business.
How does that happen? First, it is important to know what Big Data is and what it is not.
Big Data is not about having a higher volume of data. IBM defines Big Data as “a way of harvesting raw data from multiple, disparate data sources, storing the data for use by analytics programs, and using the raw data to derive value (meaning) from the data in a whole new way.”
Mayer-Schönberger and Cukier, authors of “Big Data: A Revolution That Will Transform How We Live, Work, and Think,” wrote that Big Data can generate new insights and develop new forms of value in a manner that changes how people live.
The reason is that Big Data can reveal trends and patterns. In an ever-changing business landscape, organizations working with Big Data would allow them to make decisions based on facts. This echoes what Geoffrey Moore, a famous American organizational theorist & author of “Crossing the Chasm,” was quoted saying: “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”
Big Data’s Role in Performance Management and Measurement
The value of Big Data lies in improving the performance and processes of an organization.
For instance, Big Data can provide insights into customer preferences. Understanding customer preferences and using them as a basis for strategies can lead to increased sales. With better forecasting, Big Data can guide companies in determining where they need to invest. A manufacturing company would be able to accurately identify the equipment that needs replacing. Moreover, the automation of high-level business processes can make organizations more effective and efficient.
In the conference paper, “Is Big Data the Next Big Thing in Performance Measurement Systems?” the authors concluded that the presence of a variety of data could expand the horizons of PMSs due to the application of different kinds of metrics. The applications of Big Data in PMS are in planning, controlling, and improving business performance as well as in strategic planning, controlling operations, and processes improvement.
The authors found the reasons for using Big Data and PMSs similar, and they revolve around decision-making and action-taking. “PMS supports decision-making [by] providing meaningful and appropriate data [developed] through a series of activities, such as analyzing and interpreting data from past actions to influence the future performance.”
Big Data in Action
The success ofNetflix, a streaming service company, is attributed to their usage of Big Data. For content development, their objective is to determine what their audience would want to watch next. To analyze the behavior and preferences of their over 140 million subscribers, Netflix used metrics, such as “What day you watch content,” “Searches on the platform,” “User location data,” “When you leave content,” “The ratings given by the users,” and even “Browsing and scrolling behavior.”
Netflix also uses Big Data in addressing challenges in production planning, such as determining shoot locations and arranging a shoot schedule. With prediction models, Netflix can minimize their efforts and reduce their expenses.
Xerox, the world’s largest provider of digital document solutions, once faced a problem with its workforce and needed to cut employee training costs and lower the premature attrition of its employee pool. With the help of Big Data, the company executed a predictive recruiting program in order to assess and filter applicants. Big Data and Big Data analytics helped them recruit people who have more technical skills and are more likely to stay longer with them. This means lower cost of training. The reduced attrition successfully helped the enhancement of Xerox’s bottom line.
Big data is a new source of competitive edge for any organization as it permits them to provide faster and more intelligent decisions, makes information more transparent, generates unprecedented insights into market situations and customer behavior, and optimizes business performance.
If you would like to discover new knowledge and the practical application of best practices used in analyzing statistical data, sign up for The KPI Institute’s Data Analysis Certification.
Given that we live in a time of data and information overload, we really need to have better mechanisms to make sense of it all. Data visualization supports the transfer of information to knowledge by illustrating hidden issues and opportunities in big data sets.
Big data is creating unrivaled opportunities for businesses. It aids them to achieve faster and deeper insights that can strengthen the decision making process and improve customer experience. It can also accelerate the speed of innovation and gain a competitive advantage.
A significant amount of the human brain is dedicated to visual processing. This results in our sight having a sharpness of perception far surpassing our other senses. Effective data visualization shifts the balance between perception and cognition. The visual cortex is used at a higher capacity and the viewer understands the presented information much quicker, better and is able to make a superior decision based on the findings.
Businesses are increasingly turning to data visualization to discover the overwhelming amount and variety of data cascading into their operations, and to eliminate the struggle of just storing the data and focus on how to analyze, interpret and present it in a meaningful way. The trend towards data visualization is worth delving into by any business seeking to derive more value from big data.
Tackling big data focuses usually involves the four V’s: volume, velocity, variety and veracity. However, it does not emphasize enough another “V” that requires attention, namely visualization. Even with the use of business intelligence tools and the incredible exponential increase in computer power, the need to consume information in a meaningful manner exceeds the ability to process it.
Visualization plays a key role starting from the raw input of the big data, where structures and underlying patterns that may be held within the data can be observed or formed, to the end result of a visual representation that presents valuable key insights in a fast, efficient and clever manner.
Crafting a visualization is more than simply translating a table of data in a visual display. Data visualization ought to communicate information in the most effective way, with the prime purpose of truly revealing data in a quick, accurate, powerful and long-lasting manner.
The main problem with big data involves complexity. Information and data is growing exponentially with time, as an increasing amount of data is made available on the internet. Furthermore, the number of insights, opportunities or hypotheses hidden in a dataset is exponential to the size of the datasets.
In achieving efficiency and ensuring the comprehensibility of visual representation resulted from big data, key performance indicators (KPIs) can be used, as to attain the goal of graphical excellence and to add value to the end result. Big data visualization requires skills that are not intuitive and the entire process relies on principles that must be learned. Each big data visualization created should follow a clear path to success, namely: attain, define, structure, extract, load, display, refine the data and interact with it.
KPIs add value to the entire process by ensuring clarity in developing the strategy of the project, focus on what matters and requires attention, as well as improvement by monitoring the progress towards the desired state.
When developing a project of big data visualization, the process should follow a cursive and pre-defined flow in accordance with the project needs and the requirements of the end-user. These recommended stages are:
1.Acquiring the data: this is usually how the process starts, unrelated with the platform which provides the data. In the process of big data collection, there is also the issue of data selection. Instead of “just throwing it all in”, one should focus on selecting high quality data, which is relevant to the project’s objective and does not add noise to the end result.
The noisier the data is, the more problematic it will be to see the important trends. It is suggested to have a clear strategy for the data sources required, as well as for the subsets of data relevant to the questions the project wants to answer to.
2.Structuring: The next phase in the process refers to structuring of the acquired data. This includes the process of organizing the data to align it to one standard. The data store might be comprised of a combination of structured, semi-structured and unstructured data.
At this stage, it is easier to identify the common aspects in each sets of data and to find relationships between the data at hand. This includes translating system specific data coding to meaningful and usable data (the platform where the data will be aggregated does not know that the set of data labeled “Customer No.” is the same as “# Customer” or “ID-Customer”).
3.Loading and visual mode selection: After cleaning the data, filtering through enormous amounts of data and replicating the application logic to make the data self-describing, the process continues with loading the data in the preferred platform and choosing the visual mode of representation.
In this stage, it can be noted if the background data is very noisy or high quality, as the emerging visual representation will be either hard to read or irrelevant to the strategic objective of the project, or clear and visually engaging.
By implementing KPIs along the project and linking them to the project objectives, the increased value will be added in the form of:
Better quality of the visual representations
Fewer project delays
Less rework along the way
Improved productivity
Greater contribution to the visuals’ value
Enhanced growth and innovation of the visual representation
