The business landscape has evolved into a complex and dynamic realm due to the unprecedented access to data. Data undoubtedly has an impact on every business, regardless of the field or size they operate in. In order to understand the nature of interactions and relationships between customers and businesses, organizations must use their tangible data to measure, analyze, and make decisions that drive consumer satisfaction.
As emerging technologies create more data silos for consumer data collection and analytics, businesses may struggle to derive customer insights from myriad digital touchpoints. Today, businesses use dozens of platforms to conduct campaigns and reach segmented customers. New and emerging technologies are introduced to the product mix every day, creating data silos for each individual platform used by businesses. For this reason, it is essential that companies understand how to consolidate their data sources in order to avoid creating disparate customer experiences and poor data-backed marketing strategies.
All in all, having access to comprehensive and real-time information serves as an invaluable strategy for businesses today. However, there is no end-all method to successfully integrate data - various data onboarding tools are available to assist organizations based on their size, industry and business goals.
Data onboarding is the process of collecting customer data from various offline and siloed sources (such as obtaining Excel and CSV files from various departments) to digital marketing applications and CRM platforms.
There are four key steps involved in the Data Onboarding Process:
The process first starts with importing offline customer data files, which includes information such as customer names, email addresses, phone numbers, geolocation, IP addresses, CRM and sales transaction data.
The onboarding systems then remove and anonymize Personally Identifiable Information (PII) within the customer data to protect individual privacy.
Next, the customer data components are matched to online profiles or anonymous digital IDs for campaign and ad segmentation.
Lastly, the matched data is distributed to technology platforms sources such as content providers and social media vendors for marketing purposes.
Although data onboarding is not a new topic, many of its concepts are not yet understood and applied by digital marketing experts and entrepreneurs.
Data onboarding is primarily used to provide data-driven marketers with a complete view of customers and prospects. When data silos are connected across marketing platforms, new marketing use cases become available. These actionable insights are leveraged to help create a clearly defined strategy to reach, target and segment customers with more relevant and personalized multichannel marketing campaigns.
Effective data onboarding doesn't simply occur when customer data is collected from a variety of cross-channel sources into a central platform. It occurs when marketers are able to utilize the data to gain a complete view of buyer journeys and optimize future ad strategies and media spend. If the practice of onboarding data is executed effectively, the benefits are limitless.
Here are a few reasons why data onboarding is advantageous for companies:
Offline data exists everywhere. In retail businesses, 90 percent of business transactions still occur offline. [https://www.entrepreneur.com/article/280013] Within consulting firms and agencies, the majority of customer interactions occur offline, such as prospective client meetings, phone calls, and more. Onboarding offline data adds underlying context, creates a consistent and simplified customer experience, and helps marketers maximize their ROI and marketing efforts.
Speed is a critical marketing advantage in order to deliver positive customer experiences in the digital, hyper-connected world we live in. Marketers are now investing in proactive technology, automation and data tools to mitigate the risk of missed opportunities in their marketing efforts. Data onboarding allows businesses to analyze real-time customer data instantaneously, re-configure marketing actions and adjust existing campaigns accordingly.
Onboarding your offline data sources and matching it to online devices or profiles creates a holistic cross-channel to better understand and segment your customers. For instance, an ad that has historically targetted a specific offline customer can now be used to target the same customer profiles online. This helps marketers optimize ad strategies and media spend by matching targetted marketing content with specific customer attributes. Through data onboarding, campaigns are more effective, personalized, and measurable.
Fully connected customer data provides marketers with a competitive edge, but the potential is still largely untapped due to its tedious and time-consuming nature. Among its shortcomings include data fragmentation, scale, and lack of first- and third-party data access. As a result, consolidating data sources into a cohesive dataset is challenging.
Here are six common pain points when onboarding offline data:
Data typically lives in silos that are managed and owned by different teams (eg. sales, marketing, finance, engineering, etc.). These data silos create discrepancies in the accuracy and completeness of data, making it difficult to obtain the fragmented and incomplete data entries during the onboarding process.
Marketers usually come across entry duplicates when onboarding offline data. This is because the same data can be used across several departments within an organization, causing datasets to overlap. Data entry duplicates make it challenging to consolidate and organize data during the cleaning process.
Each siloed dataset uses different standards and formats to organize and interpret the respective department insights. For example, the way a marketing department analyzes a particular set of information would vary from how a data science department would utilize it. For this reason, data lives in multiple platforms and must be made consistent before centralizing the various sources.
As datasets become larger, the means to catch costly inconsistencies and errors, such as an invalid email address or zip code, becomes more difficult. If these outliers are not detected promptly, the accuracy of insights used for marketing purposes is compromised.
The extent of available data magnifies as organizations grow and scale. With growing data comes the need to manage, track, and process that data to derive valuable insights. As mentioned above, disparate datasets are not guaranteed to be formatted with a consistent data structure. Scalability is critical in driving business growth, however challenging to manage when centralizing large data volumes.
Many organizations lack the expertise to derive the most valuable insights from their existing data. Not only is data fragmented across first-party data, but the access to first-party data alone is not sufficient enough to gather a comprehensive cross-channel outlook. In order to leverage strategic insights from offline data, companies must also incorporate third-party data to fill in the gap of internal data. However, third-party data is withheld within its data structures as an external source, making it less flexible and more difficult to manipulate.
When selecting a data onboarding platform, it is important to note that there is no superior solution. The set of tools you choose to use depends on your specific needs, resources and goals. However, marketers should keep in mind various key performance factors that are essential to creating a successful onboarding experience.
Using a data onboarding platform to drive marketing success requires the right tools. To avoid tool overload, first look for solutions that integrate with the platforms that you’re already using. Consolidating your audience data with onboarding tools helps to tie your media exposure back to a common platform, create a comprehensive customer experience, reach more high-value audiences and keep your switching costs low.
Many marketers process data in batch files, which take five to seven days on average. This process involves importing offline customer data, matching to online profiles or anonymous digital IDs and distributing the matched data to technology platforms such as content providers. While the data is uploading, any new data is not captured and updated into the system, losing a week's worth of valuable information. Onboarders should keep in mind the processing speed as it directly affects their ability to accurately capture, track, and measure results in real-time.
A common metric onboarding vendors use as a selling point is their match rates, which refers to the percent of users from a file that an onboarder is able to find and anonymously tag with data. The match rate measures the accuracy of your first-party data compared to a demand-side platform (DSP), which is a critical metric for understanding the size of your addressable online audience. However, many vendors tend to use statistical modelling to create higher match rates upfront, leading to false positives (expired, duplicated, incomplete digital IDs).
Onboarders should look beyond match rates and consider an onboarding vendor's data addressability capabilities. Addressability is a form of personalization at scale that connects your product or content with people who would be most engaged with it. This tailor-made customer experience optimization tool creates more opportunities for marketers to drive performance through leads, sales, and revenue.
Another common practice onboarding vendors perform is directly connecting your offline data to online cookie pools, typically with your customers' email addresses. This email-to-cookie linkage tends to generate a 30-50% match rate, which is relatively low. In order to boost your match rates, onboarding vendors use hypothetical data as mentioned above.
Rather than relying on false positives to target a broader audience, onboarders should consider partnering with an onboarding vendor that uses the authoritative identity approach. The process involves using deterministic and authenticated data to ground your offline data before attempting to match it to cookie pools or media vendors. This approach allows marketers to use any identifiers in their CRM, increases true match rates and eliminates the need for hypothetical data and false positives.
Onboarding vendors host your offline CRM data within their platform, allowing marketers to extract insightful and valuable information using the vendor's services. However, having segment-level data contained within the platform makes it difficult or impossible to connect the data with the rest of your marketing efforts. For example, once a campaign is completed, those consumer segments and insights cannot be replicated for future use. Onboarders should look for a solution that allows for full ownership and control of data in order to optimize ad strategies and track measurable results.
There are three methods to perform your data onboarding needs. Each method depends on your business goals, expertise and budget.
The simplest data onboarding approach is to manually compile the siloed data sources. This includes reaching out to customers to discuss various customer data components, requesting data sources from your customers and repeatedly following up to obtain it, receiving the data sources from customers in various formats via email, manually cleaning the data before uploading it, and verifying the data source during the validation phase.
However, this solution is neither optimal nor sustainable for larger businesses. Manually performing data onboarding is extremely costly and timely, and other solutions should be considered as an organization begins to scale and grow.
Having your data engineering team build in-house tools can allow various business functions to perform data onboarding and migration tasks. This can become a very efficient way to mitigate the training risks that come with external platforms, as the built-in tool is created by those within the organization.
However, this solution is very costly in terms of resources and time. Engineers are often swamped with customer-facing development needs, placing internal projects at a lower priority. The custom tools may also not be as secure, code-friendly and collaborative as data onboarding tools.
The manual process is too tedious, the built-in codes are too technical. Self-service data onboarding tools can help resolve this issue. These tools are secure, no-code and collaborative, backed with real-time customer support and an abundance of online resources.
Explore these six popular data onboarding tools:
Data migration is the process of selecting, preparing, extracting, and transforming data and permanently transferring it from one computer storage system to another.
There are three key phases involved in the Data Migration Process. Each of these phases consists of its own steps:
The planning phase begins with analyzing the business, project, and technical requirements and dependencies of the migrated data. Next, the hardware and bandwidth requirements are analyzed. Migration scenarios and its associated tests, automation scripts, mappings and procedures are developed and tested as well. Lastly, a migration implementation schedule is created, the necessary software licenses are obtained and the migration architecture is selected.
Once the planning strategy is established, hardware and software requirements are validated, and migration procedures are customized, the second phase begins. Prior to conducting the migration, certain pre-validation procedures may be tested to ensure that all functions are working as expected. The migration phase consists of two main steps: data extraction and data loading. First, disparate types of data are retrieved (extracted) from an old system. Next, the data is copied and loaded into a decision support database where users can access it. After the migration is complete, additional verification steps are conducted in order to ensure that the migration plan is successfully enacted.
After data migration, the database undergoes a final data verification test to evaluate the accuracy of the migrated data sources. During verification, data is tested against the production environment to identify areas of data loss. Any disparities are documented and the verification repeats until the new system is considered to be fully validated and deployed. Once the new environment is running smoothly, the legacy system is shut down.
There are four major data migration categories:
Storage migration involves moving data from one storage system to another, such as a hard disk or the cloud, for more efficient storage technologies. These technology upgrades enable enhanced performance, improved data management features and cost-effective scaling.
Database migration involves moving from one database vendor to another, moving between platforms, such as on-premise or to the cloud, or upgrading the current database software being used.
Application migration involves moving data within an application, such as shifting from on-premises MS Office to Office 365 in the cloud, or moving from one application vendor to another, such as a new CRM or ERP platform.
Business process migration directly relates to a company's business practices, often operated by business process management tools. When these tools need to be replaced or updated, they require movement of data from one environment to another. Migration drivers include mergers and acquisitions, business optimization and reorganization.
Data migration is most commonly used by organizations looking to scale and accommodate the growing needs of their business datasets. However, other situations that prompt a data migration project can include:
According to the International Data Corporation (IDC), data migration represents 60% of all large enterprise IT projects, with only 60% completed on time. Evidently, creating a well-planned data migration strategy is critical in enhancing day-to-day efficiencies and business operations for an organization. Organizations will ultimately reap boundless benefits once investing in the success of a data transfer.
Here are a few reasons why data migration is advantageous for companies:
When a company grows faster than its data storage capacity, it can lead to decreased operational efficiencies across the organization. For this reason, companies look for enterprise data migration platforms to help increase both business and environmental efficiencies. Data migration reconciles databases for better use, with improved data consistency and responsiveness across systems, processes, and the organization. The process of upgrading also eliminates wasteful data and resolves anomalies and errors in the current system. In terms of environmental efficiencies, data migration cuts down on energy usage through the reduction in media and storage costs.
Data servers, PCs and other data storage devices are costly for any business. These costs not only include the initial implementation, but also accounts for associated costs such as maintenance, rent and utility bills. By migrating data to the cloud, companies can eliminate their IT infrastructure costs, store larger amounts of data and reap cost-saving benefits in the long-term.
Security measures are critical for all data-intensive companies. Migrating data to an updated or modernized data system is the best way to improve the security of valuable data and ensure that the most up-to-date security measures are in place.
Although data migration is a critical step in driving scalability and business growth, it also comes with some hurdles. A data migration project is undoubtedly challenging and involves high risks, so understanding these factors before diving into the process is completely vital.
Here are six common pain points when migrating data:
The fundamental nature of migrating data is time-consuming, requiring meticulous planning and input from different departments within the organization. Data migration is a continual process of tedious data cleaning processes, unexpected disruptions, and repetition until a data server system reaches obsolescence. Tasks become especially tedious if poor knowledge of source data exists, such as duplicates, missing information, lack of structured formats, and misspellings.
Unanticipated costs are a byproduct of poor and improper migration planning. On top of the initial financial investment of the software migration platform, organizations must also be prepared to reap the costs during every step of the data migration process. These costs include:
The data migration process typically involves consolidating data from a disparate set of data sources with separate technologies. This approach is prone to human error if no integrated system or collaborative tool is established for these data silos, which can lead to data loss or data corruption during the migration phase. If an organization finds themselves encountering such errors, it can easily ramp up additional costs due to the failure of transferring data.
Online transfer times are often restricted by network bottlenecks such as connection speeds, system hardware limitations and other technical issues. Due to this reason, application downtime is considered a major challenge to data migration, which results in many organizations conducting migrations on a weekly or monthly basis. To minimize overall risk and downtime issues, organizations also conduct weekend migrations (though it tends to lead to costly overtime).
When transferring from one system to another, there is an inevitable risk of comprised data security. For example, cloud providers tend to always guarantee the latest data security systems. However, unforeseen circumstances such as hacking, data breach, etc. can lead to leakage of the confidential data stored on the cloud. To mitigate these risks, ensure that all data is securely encrypted before migration.
Although data migration addresses the obsolescence of a current data server system, it fails to address the notion that data storages degrade with time as technologies advance. Certain destination environments are eventually be abandoned due to the lack of advanced performance capabilities, such as the inability to handle the amount of data and applications being migrated.
To successfully transfer data with a data migration vendor, enterprise IT executives should leverage a solution that touches upon key features and capabilities that fit the organization's needs, resources and business goals.
The optimal data migration solution is project-dependent. Factors including the amount of data you need to move, how quickly you wish to accomplish the migration, the types of workloads involved, and your security requirements are all taken into account.
Here are some basic features that you should consider when selecting a data migration solution:
To successfully execute a migration project, data mapping tasks must be conducted between source fields and their related target fields. This essential step helps explain the attributes and rules that govern the data stored in a particular system during the data migration process. In order to facilitate collaboration between business professionals and data engineers, companies should look for data migration vendors that provide graphical, drag-drop nodes to carry out deployment with no coding needed.
When approaching data silos during the data migration process, it is cumbersome to manually select, prepare, extract, and transform all those datasets. Having sophisticated transformation capabilities built-in to a data migration solution helps reconcile all your data and automatically document business rules. When choosing a data migration tool, aim to search for solutions ultimately help you gain the maximum value from your data.
As mentioned previously, there are many disparate sources involved in the data migration process. If your existing tool library consists of various CRM/ERP applications and databases, consider data migration vendors that provide a seamless source to destination experience through pre-built connectors.
Automated data migration software systems can help you streamline data processes through features such as job scheduling, legacy code integration, API connections and workflow orchestration. These features become more convenient as companies begin to migrate larger datasets, helping cut implementation time, project costs and improved ROI.
To ensure that your data migration journey is successful, it is beneficial to use proven solutions and methods. These data migration solutions are divided into three types:
On-premise: Resources are deployed in-house to allow all data to sit within an enterprise's IT infrastructure.
Open-source: Community-developed tools that are openly published and available free of charge.
Cloud-based: Resources are hosted by a third-party provider where organizations can access their data on the cloud.
And there you have it! A deep dive into the processes of data onboarding and data migration. Both practices are integral steps in promoting business agility for any organization. Implementing effective and efficient data onboarding and data migration processes will help organizations eliminate the time-consuming measures of any new business initiative.
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