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.
Image source: DKosig from Getty Images Signature via Canva
In today’s data-driven world, organizations are constantly grappling with an abundance of data coming from various sources and in different formats. Data integration has emerged as a critical process that enables businesses to connect these disparate data sources by consolidating them into repositories called data silos, creating a comprehensive and unified view of their information. This single source of truth empowers organizations to make more informed decisions and derive valuable insights for better business intelligence.
These disparate data sources can vary in type, structure, and format. Successful data integration finds a way to connect these sources, either by building relationships between them where they reside or by periodically extracting, transforming, and loading data (a process known as ETL) from these sources into one big database dubbed a data warehouse.
For example, when sales data is combined with customer data, the organization can gain a deeper understanding of customer behavior and preferences, which would allow personalized marketing efforts and improved customer satisfaction.
Data integration can be challenging as there is no one technical way of implementing it. Rather, the process depends on the needs and resources of each organization. Organizations with no technical capabilities would need to seek a third-party service provider.
Despite the variance across organizations, one thing remains consistent—every data integration process should be approached systematically by taking into consideration the following key strategic steps:
Defining integration goals: Organizations need to clearly outline the objectives and outcomes they want to achieve through data integration.
Assessment of data sources: This includes identifying all the data sources within the organization and understanding the structure, format, and quality of the data coming from each source.
Data mapping and transformation: This entails defining how different sources will be mapped to a common format. This may involve cleaning and preparing data silos in the first place.
Defining technique and tools: Based on the previous steps, a technical decision should be made on how to do the integration and the degree with which manual labor and automation will be utilized.
Building integration processes: This answers the question, “How will future data be integrated as well?” It involves defining workflows and processes that should be scalable, reliable, and capable of handling future data growth.
Testing and monitoring: As data integration is a continuous process, organizations should always test and monitor the integrated data thoroughly to ensure accuracy, consistency, and reliability. Validating the integration results should be done against predefined criteria, along with making necessary adjustments if discrepancies are found or to adapt to changing data sources and business needs.
In conclusion, data integration plays a crucial role in enabling organizations to harness the full potential of their data. By connecting disparate data sources and creating a single source of truth, organizations can unlock valuable insights, improve decision-making, and enhance operational efficiency. Following a systematic approach and leveraging appropriate integration tools lets organizations achieve successful data integration and gain a competitive edge in today’s data-driven landscape.
Get more insights on data integration and management practices by exploring our articles on data analytics.
The field of data analytics is very important nowadays, considering how the business environment is going through continuous developments in terms of technology, innovation, globalization and sustainability. The field also faces various economic struggles and unexpected challenges. For these reasons, managers and executives must remain up-to-date with information and data to make the best decisions for their organizations and maintain their competitive advantage in the market by creating value for clients.
To do so, I recommend managers and executives join different professional groups on LinkedIn where they can ask questions and discuss any challenges they are facing. They should also have subscriptions to various research journals and business magazines. It also helps to attend conferences where they can meet researchers and professionals from both the academic and business worlds.
Furthermore, following business blogs, watching podcasts, and reading books are valuable methods to gather new data to make informed decisions. By being part of professional groups on social media and attending conferences, managers and executives can find out in real-time the challenges other leaders face, discuss them, and take on new ideas for implementation as early as the next day. These communities of managers and executives are valuable assets in today’s challenging business environment.
Islam Salahuddin is a data analyst with a strong focus on storytelling and data visualization, growing statistical knowledge, and developing a set of technical skills and tools. As an expert in data analysis at The KPI Institute, Islam leads the generation of research on the domain of data analytics and the development of business analytics toolkits.
Jino Noel is a data science and technology leader with extensive experience in building data teams and practices across different organizations. His experience ranges from working in startups to large conglomerates across both Australia and the Philippines. At the time of this interview, he was the Chief Data Officer at Data Analytics Ventures, Inc. (DAVI). Currently, he is the Chief Data Officer at Angkas.
What are the key skills that a Chief Data Officer should possess nowadays?
A Chief Data Officer should have both data-related technical expertise as well as people leadership skills. Leading will always be part of the job, particularly for highly specialized technical people such as data engineers and data scientists. To be able to lead them properly, I believe it is better to be a technical person myself, so I can discuss technical matters fluently, which helps me gain their trust.
What data-related challenges have you faced as the Chief Data Officer of DAVI? How did you overcome these challenges?
Our data-related challenges are the same as any company. Being able to trust our data, cleaning up data from our sources, data latencies, and other related issues. DAVI overcame these by investing in people—hiring high-quality experts in our data engineering, data governance, and analytics teams to help us make sense of the data coming in—and building robust data pipelines that have increased the standard of quality of the data in our data lake.
How does DAVI make use of advancements in artificial intelligence (AI) and machine learning to help its clients understand their customers’ needs and buying patterns?
DAVI has recently started using machine learning to model our users’ propensity to buy certain products. This helps us create more accurate target audiences for our precision marketing campaigns. We are also moving forward with a recommendation engine project, with the goal of improving user engagement with our retail partners and with our promos and campaigns. On top of this, we are improving our machine learning operations expertise to make our model deployments repeatable and robust.
In the digital marketplace, data analytics acts as a guiding compass for app developers, enabling the creation of personalized, high-performing applications that align with user preferences. By leveraging data, developers can understand nuanced user behaviors and preferences, allowing them to tailor apps to meet specific user needs and aspirations.
Dive deeper into these discussions by reading Jino Noel’s full interview with The KPI Institute. Download the free digital copy of PERFORMANCE Magazine Issue No. 26, 2023 – Data Analytics on the TKI Marketplace. You can also purchase a physical copy via Amazon.
Islam Salahuddin is a data analyst with a strong focus on storytelling and data visualization, growing statistical knowledge, and developing a set of technical skills and tools. As an expert in data analysis at The KPI Institute, Islam leads the generation of research on the domain of data analytics and the development of business analytics toolkits.
Jino Noel