(Mathias Rosenthal/Shutterstock)
Doing integrations in the storage layer is a bit like asking the first person you ever date to marry you. It’s not necessarily a bad idea, but it is risky and it requires a big upfront commitment. Like a hasty proposal, when it comes to integrating data, moving and copying data in order to integrate may well work out, but it involves risk and requires a big upfront obligation. If anything changes—and something always changes—then you have to rerun all the jobs, make new copies, and move data all over again.
Because you’ve committed so early to a particular viewpoint and then consolidated it at the storage layer, you’ve also aggressively excluded other possibilities. Early binding and tight coupling are all fun and games until things don’t work out and then where are we?
While enterprise data set might seem like objective fact–like a hard, fixed, immutable thing that represents the world exactly as it is–in reality, it’s more instrumental than all that. Enterprise data represents something; it re-presents some part of the world and we manipulate the data largely to manipulate the world. Which means data is full of subjective human choices and human values. Data is really comprised of answers to very human questions: Which data should I collect? What data needs to be transformed? Which data needs to be summarized or aggregated and what data counts? What matters and what are we trying to accomplish? Every one of these choices becomes or influences a modeling decision or transformation or an invariant or business rule. The technical apparatus of data integration and analytics is shot through with human values.
The result is two facts: first, that integrating data at the storage layer excludes possibilities; and, second, that human data is a function of human choices. What follows is that when an analyst has a new idea about how to organize and understand data or a strategic initiative is mooted by a regulatory ruling or a competitor zigs instead of zags, organizations may have to throw it all away and start over from scratch.
Integrating data at the file system level is a risky proposition, the author says (Miha Creative/Shutterstock)
Suddenly data teams have to recreate a new version of the data and form a new data set. Which means they have to go through the process of remodeling, transforming, and summarizing the data all over again, including re-running the weeks-long ELT jobs and blowing up schedules, budgets, bandwidth and storage.
But what if they didn’t have to?
Leveraging Knowledge Graphs to Accelerate Insight
Whether called data sprawl or data silos, data resides in lots of places. In its natural state, data is disconnected, both from other data it should be connected to and from the business context which makes it meaningful. The natural disconnectedness of enterprise data presents a challenge for organizations that need to drive business transformation with data. Which is just to say that it’s a challenge for everyone. Data management practices that limit range of motion and increase inflexibility, including integrating data solely at the storage layer, hamper everything from app development, data science and analytics, process automation, and even reporting and compliance.
However, there is an alternative to integrating data eagerly at the storage layer; namely, connecting data lazily at the compute layer. Late binding and loose coupling in data architectures increases flexibility and range of motion. Enterprise are increasingly adopting new data management techniques including data fabrics and knowledge graphs (KG) to unify. Knowledge graphs offer a flexible, reusable data layer that enables organizations to answer complex queries across data sources and offers unprecedented connectedness with contextualized data, represented and organized in the form of smart graphs.
Built to capture the ever-changing nature of information, knowledge graphs accept new data, definitions, and requirements fluidly, easily, and in a way that promotes radical reuse of data across large orgs. This means as the enterprise evolves and greater volumes of data, sources and use cases emerge, they can be absorbed without manageability and
Knowledge graphs offer a powerful abstraction for data integration challenges, the author writes
accessibility loss, while fully representing the current expanse of what the enterprise knows.
Dissecting the Components of a Knowledge Graph
Enterprise Knowledge Graph is a technology that combines the capabilities of a graph database with a knowledge toolkit, including AI, ML, data quality, and reusable smart graph models, for the purpose of large scale data unification. Put simply, KGs know everything the business knows because it can re-present data sprawl and silos in connected data fabric.
Because knowledge graphs are built on a graph database technologies, they natively represent and store data as entities (aka nodes) with relationships called edges to other entities. Like traditional graph databases, knowledge graphs quickly navigate chains of these edges to find relationships between various pieces of data. Following numerous chains of edges at the same time, they can identify many-to-many interrelationships at multiple levels of granularity, from summary rollups to a record’s smallest details, so relevant data can be retrieved through a single query. Unlike plain graph databases, knowledge graph platforms query connected data using data virtualization and query federation techniques, moving data integration from the storage layer to the compute layer.
As data and queries become more complex, the benefits of knowledge graph’s smart data model increase, as it can connect data silos into facts that constitute contextualized knowledge. A knowledge graph also contains tools that allow enterprises to add a layer of richer semantics to support knowledge representation in the graph and strengthen machine understanding, which is something that plain graph databases does not.
For instance, where a plain graph database knows there is an interrelationship between a person node in silo A and an organization node in silo B, a knowledge graph also understands the nature of that interrelationship and it can query that relationship without first moving or copying data from siloes A and B into silo C (i.e., a plain graph database).
Combatting Big Data’s 3Vs: How KGs Unveil Hidden Insights
Stepping back and looking at the bigger picture of the modern data analytics stack, there are plenty of tools and techniques for addressing the volume and velocity challenges of big data. The cloud means, for example, never running out of storage again and it makes distributed systems easier to operate, even if they’re still really hard to build and maintain.
But the big data challenge of variety has mostly been ignored until recently. Perhaps knowledge graphs biggest contribution is to solve the variety problem by providing a consistent view across heterogeneous data. Notice, however, that the view is homogeneous and consistent while the underlying data remains heterogenous and even physically separate.
Knowledge graphs encompass the large, diverse, and constantly evolving data found in modern enterprises using a comprehensive abstraction (that is, semantic graphs) based on declarative data structures and query languages. They combine key technologies that work together to unify data on a massive scale including a reusable, powerful data model; virtual graph capabilities to manage structured, semi-structured, and unstructured data; and inference and reasoning services.
Given the importance and potential of data, enterprises can’t ignore the costs that arise from not being able to access or apply the knowledge accumulated across the enterprise. In today’s hybrid multicloud world of increasing complexity and specialization, data sprawl and data silos aren’t really avoidable, but they are manageable so long as data can be unified across them. By applying knowledge graphs to truly leverage what the enterprise knows, they can grow in parallel with the business and enable users to utilize this untapped data and insight to help them innovate and achieve a true competitive advantage.
About the Author: Kendall Clark is founder and CEO of Stardog, an Enterprise Knowledge Graph (EKG) platform provider. You can follow the company on Twitter @StardogHQ.
Related Items:
Why Young Developers Don’t Get Knowledge Graphs
Cloud-Native Knowledge Graph Forges a Data Fabric
Why Knowledge Graphs Are Foundational to Artificial Intelligence
Applications:Data Management
Technologies:Middleware
Vendors:Stardog
Tags:big data, data fabrics, data integration, data management, data silo, graph database, Kendall Clark, Knowledge Graph, late binding, loose coupling
FAQs
What are the challenges for data integration and solve it? ›
- Data is Collected in Silos. Data silos are a major issues for businesses. ...
- Each Team is Using Different Systems. ...
- You Have Several Integration Use Cases. ...
- You Need to Scale Your Integrations. ...
- You Need Bi-Directional Integrations.
There are three issues to consider during data integration: Schema Integration, Redundancy Detection, and resolution of data value conflicts.
Why is data integration so hard? ›Volume—too much and too challenging for organizations. Variety—many types of data and sources. Velocity—data is flowing into organizations quickly and requirements for speed in analytics are growing. Veracity—there is a need for understanding the data that you can trust for financial reports, etc.
What are the challenges associated with using data from different sources? ›- Data Extraction.
- Data Integrity.
- Scalability.
One of the biggest challenges that will likely crop up during the integration process is dealing with data in heterogeneous forms. Most organizations collect data from multiple locations – customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, etc.
What are data integration techniques? ›Data integration is the process of combining data from different sources to help data managers and executives analyze it and make smarter business decisions. This process involves a person or system locating, retrieving, cleaning, and presenting the data.
Why is data integration important? ›Data integration allows businesses to combine data existing in different sources to provide users with a real-time view of business performance. Data integration is the first step towards reforming the data into useful & meaningful information.
How does integration of data affect an organization? ›Business data integration can improve return on investment (ROI) and total cost of ownership (TCO) of the organization's services and products. To do this requires an investment in data integration as a strategy for the organization. The initial costs or value of investment (VOI) will affect the TCO.
What is data integration with example? ›Data integration defined
For example, customer data integration involves the extraction of information about each individual customer from disparate business systems such as sales, accounts, and marketing, which is then combined into a single view of the customer to be used for customer service, reporting and analysis.
Integration is the act of bringing together smaller components into a single system that functions as one.
What is the problem with integration? ›
Integration is a difficult process. It is faced with various difficulties. Firstly, the complexity of society makes it difficult to co-ordinate all the structural parts and to socialise all the humans. As we know society is a complex of different structural forms and elaborates division of labour.
How do you handle data from multiple sources? ›- Download all data from each source. ...
- Combine all data sources into one list. ...
- Identify duplicates. ...
- Merge duplicates by identifying the surviving record. ...
- Verify and validate all fields. ...
- Standardize the data.
There are also following problems for big data visualization: Visual noise: Most of the objects in dataset are too relative to each other. Users cannot divide them as separate objects on the screen. Information loss: Reduction of visible data sets can be used, but leads to information loss.
What are the challenges and issues in system integration? ›There are many challenges in integrating 2 data systems. In this post we will cover the following challenges: lack of skills, lack of money, lack of resources, poor communication/planning, after go-live maintenance and difficult technical issues.
What are the big challenges that one should be mindful of when considering implementation of big data analytics? ›- Business analytics solution fails to provide new or timely insights. ...
- Inaccurate analytics. ...
- Using data analytics in complicated. ...
- Long system response time. ...
- Expensive maintenance.
Data integration is the process of consolidating data from different sources. Data integration is often a prerequisite to other processes including analysis, reporting, and forecasting.
How does system integration help in solving the problems of a business? ›The main reason for businesses to use system integration is the growing need to improve productivity and quality of day-to-day operations. The goal is to get organization and business IT systems to communicate with each other through integration. This accelerates information outflow and reduces operational costs.
What are the 3 types of system integration? ›Three types of system integration
Enterprise Application Integration (EAI) Data Integration (DI) Electronic Document Integration/Interchange (EDI)
- Define the project. Setting clear objectives for the project ensures that its success can be measured and monitored. ...
- Understand the systems. ...
- Design the data integration framework. ...
- Define how the data will be processed. ...
- Implement the project.
Data integration is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation.
What are the steps included in data integration process? ›
- Gathering business requirements.
- Determining data and quality needs.
- Data profiling or understanding data sources and associated quality both in the source system and across multiple source systems, if applicable.
ETL(Extract, Transform and Load) is a significant data integration component in data warehousing. The most well-known implementation of data warehousing is building a data warehouse for the enterprise side.
Who is responsible for data integration? ›Administrative officials have oversight responsibility for the integrity and integration capability of reporting data generated under their direction.
How can a company improve strategic integration? ›- Identify the Initiatives for Integration. ...
- Form an Integration Strategy Team. ...
- Collect Requirements from Different Domains. ...
- Carefully Examine Gaps in Existing Integration Capabilities. ...
- Choose Technology Last.
Data integration aims at securing that data flows seamlessly between different systems. This could be be between an ERP system and a CRM system. When you integrate ERP and CRM, each system handles data in a specific way according to the business processes they support.
What is common data integration? ›Data integration refers to the technical and business processes used to combine data from multiple sources to provide a unified, single view of the data.
What are the 5 stages of data life cycle? ›- Creation. The first phase involves collecting and creating data. ...
- Storage. During this phase, companies use various backup procedures to secure the data, which ensures protection from lost data or stolen information. ...
- Usage. ...
- Archival. ...
- Destruction.
What is a data integration platform? A data integration platform allows IT professionals to bring together data from multiple sources and provide a complete, accurate, and up-to-date dataset for BI, data analysis and other applications and business processes.
What are the components of integration? ›- Object structures. An object structure is the common data layer that the integration framework components use for outbound and inbound application message processing. ...
- Channels and services. ...
- Endpoints and handlers. ...
- Integration web services. ...
- External systems. ...
- API keys. ...
- Predefined integration content.
Integration experience means the integration and interfacing of large scale IT systems serving the health care sector, including the integration of complementary tools such as speech/voice recognition as well as biomedical, laboratory and mobile devices; Sample 1.
What are the reason why integration project fail? ›
In most cases, software integration fails because of unclear project requirements, scope creep, employee resistance, frequent technology changes, or inability to handle big data in real-time.
What is the actual meaning of integration? ›: the act, the process, or an instance of integrating especially : acceptance as equals into society of persons of different groups (as races) integration. noun. in·te·gra·tion | \ ˌint-ə-ˈgrā-shən \
Why is system integration important? ›The goal of system integration is to streamline and simplify communication between not only the organization's internal systems but also the third parties the organization works with. System integration helps accelerate the outflow of information and cut back on operational costs.
What are the five key Big Data challenges? ›But, there are some challenges of Big Data encountered by companies. These include data quality, storage, lack of data science professionals, validating data, and accumulating data from different sources.
What are the four Big Data challenges? ›Because of the constantly evolving data sources and the increasing amounts of generated data, companies face severe problems in achieving high-quality data integration. Those challenges altogether can also be called "The 4 V's of Big Data". They are data Veracity, Volume, Variety, and Velocity.
What are the 8 big challenges of Big Data? ›- Recruiting more cybersecurity professionals.
- Data encryption and segregation.
- Identity and access authorization control.
- Endpoint security.
- Real-time monitoring.
- Using Big Data security tools such as IBM Guardium.
- Surveys. Surveys are one way in which you can directly ask customers for information. ...
- Online Tracking. ...
- Transactional Data Tracking. ...
- Online Marketing Analytics. ...
- Social Media Monitoring. ...
- Collecting Subscription and Registration Data. ...
- In-Store Traffic Monitoring.
- Gather data. The data preparation process begins with finding the right data. ...
- Discover and assess data. After collecting the data, it is important to discover each dataset. ...
- Cleanse and validate data. ...
- Transform and enrich data. ...
- Store data.
Using a data blending platform, you can quickly mash together data from all disparate sources in a way that's fast and easy. Data blending is typically used for ad hoc reporting and rapid analysis. Traditionally, teams combined data sets through a process known as extract, transform, load (ETL).
What is the most challenging problem you have solved in your big data project? ›Challenge #1: Insufficient understanding and acceptance of big data. Oftentimes, companies fail to know even the basics: what big data actually is, what its benefits are, what infrastructure is needed, etc. Without a clear understanding, a big data adoption project risks to be doomed to failure.
What is your biggest challenge when working with information from multiple sources? ›
When working with multiple sources or vendors, it's difficult to ensure that datasets are regularly kept up to date. For data-driven decision-making, high-quality, frequently updated data is essential. Invalid or inaccurate data leads to faulty analysis, leading to potential losses.
What are the challenges that a data analyst can encounter while handling the data? ›- Lack of skilled resources with understanding of Big Data Analytics. ...
- Gaining meaningful insights using Big Data Analytics. ...
- Bringing extensive data to big data platform. ...
- Uncertainty of Data Management Landscape. ...
- Data Storage and fast retrieval.
This data integration challenge is commonly a result of depending on human power alone. Relying on developers to curate data from disparate sources and combine it takes time. And this is time that your organization should be spent on analyzing data insights and driving valuable business practices.
What are the challenges in application integration? ›- Complexity. ...
- Inaccessible Data. ...
- Lack of Information. ...
- Automate as Much as Possible. ...
- Opt for Multiple, Smaller Integrations over Large, More Complex Ones. ...
- Conclusion.
Integration ensures that all systems work together and in harmony to increase productivity and data consistency. In addition, it aims to resolve the complexity associated with increased communication between systems, since they provide a reduction in the impacts of changes that these systems may have.
What are the benefits of data integration? ›- Data integrity and data quality.
- Easy, available, and fast connections between data stores.
- Seamless knowledge transfer between systems.
- Better collaboration.
- Complete, real-time business insights, intelligence, and analytics.
- Increased efficiency and ROI.
Data integration defined
For example, customer data integration involves the extraction of information about each individual customer from disparate business systems such as sales, accounts, and marketing, which is then combined into a single view of the customer to be used for customer service, reporting and analysis.
Lack of integration creates information silos that make it hard to get a complete picture of how your business is performing. It creates inefficiencies that slow down decision-making and increase redundancies across the business.
What is the actual meaning of integration? ›: the act, the process, or an instance of integrating especially : acceptance as equals into society of persons of different groups (as races) integration. noun. in·te·gra·tion | \ ˌint-ə-ˈgrā-shən \
What does integration mean in technology? ›Integration services are detailed design and implementation services that link application functionality (custom software or package software) and/or data with each other or with the established or planned IT infrastructure.
Why is application integration an important part of running an online business? ›
Application integration is important as it assists in managing the data in your application landscape and reduces the chances of data duplication and data silos.
What are some of the challenges you have faced during data analysis? ›- Lack of skilled resources with understanding of Big Data Analytics. ...
- Gaining meaningful insights using Big Data Analytics. ...
- Bringing extensive data to big data platform. ...
- Uncertainty of Data Management Landscape. ...
- Data Storage and fast retrieval.
- Handling Enormous Data In Less Time: Handling the data of any business or industry is itself a significant challenge, but when it comes to handling enormous data, the task gets much more difficult. ...
- Visual Representation Of Data: ...
- Application Should Be Scalable:
Business data integration can improve return on investment (ROI) and total cost of ownership (TCO) of the organization's services and products. To do this requires an investment in data integration as a strategy for the organization. The initial costs or value of investment (VOI) will affect the TCO.