We live in a time in which information is the core element of business success for companies in almost any industry. Data is the key to making proper and timely decisions, building winning strategies, data can impact everything from revenue growth to customer satisfaction.
According to McKinsey, companies that heavily rely on data and make informed decisions are:
To obtain the necessary information, there are two primary challenges that need to be solved. First is the pace – the information must be provided as soon as possible, preferably in real-time. The second is trustable information you can take action on without questioning it. That’s a big problem, because almost half of data records contain errors that could mess up processes.
So, there is a lot of hype around technologies like Artificial Intelligence, Machine Learning, Cloud Computing, and the Internet of Things — but to benefit from them, companies should embrace digital transformation to its fullest. Right now, nearly 60% of data in companies is inaccessible for decision-making; analysts spend most of their time extracting and processing data. The real cost of poor data quality for businesses is $15 million a year on average.
The solution to all of those problems and challenges, as well as the key to unlocking the value of innovations in technology, is a data integration strategy. It is the only way for businesses to obtain accurate and detailed information that leaders will be able to take action upon.
Data integration is not just a list of technical processes, but rather a whole strategy that will make the necessary information accessible and understandable to anyone in every branch of a business. In this article, we will discuss what data integration is, how to achieve it, what tools we can use, and how to get the most out of your time and budget investments.
“The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of HP.
The definition of data integration is pretty simple – it is a process that aims to combine data from multiple sources into a unified view, making it accessible and actionable. You can measure the positive result of this process by getting information fast, accurate, and at scale, which will help you achieve your business goals and maximize your ability to use the latest technologies in your organization.
There is no unified approach or method to data integration, but all solutions have certain parts in common — such as a network of data sources, a master server, and users who need to access data from this server.
To give you an example, typically:
In most cases, information needs to be unified from a variety of sources before it can actually be used for any kind of analysis. Without data unification, creating a report could involve using information from different accounts on multiple websites, extracting data from apps, copying, reformatting, sorting, cleaning, and processing. After all of those moves, then the analysis could start. That’s where data integration comes into play.
First and foremost, it saves time on analyzing data and extracting value from it. Your employees will have access to the whole system without a time-consuming necessity to build connections between different subsystems. If you add automated tools for integration instead of manual coding, you can double the productivity of the organization — as 86% of the companies that used these tools did.
Collaboration and data sharing between different departments will instantly improve, despite their location or a project they are working on. Additionally, data integration will reduce the number of errors that could occur — automated solutions that regularly synchronize for updates prevent a lot of possible errors, boosting overall productivity as a result. But there are definitely even more benefits for businesses. Let’s look at them closely.
Your information has the power to boost business efficiency to the max, and make all departments work as the one unit. To achieve this, you need to connect all the dots in your plan. Precise planning and excellent execution will take time, but it is worth it. Here’s why:
Without a shadow of a doubt, your leading competitors on the market are most likely data-driven and looking forward to introducing innovation in their organizations. Industries like Finance, Retail, Manufacturing, Healthcare, or Food and Beverage could all be transformed and improved upon based on solutions that involve processing information. Winners do all they can to extract valuable and actionable insights that add to production, logistics, customer experience, brand image, marketing, and the future of the company. Sensors, networking, and cloud storage are cheaper than ever, resulting in an enormous amount of available information. Artificial Intelligence and Machine Learning technologies can make sense of all of that, with capacities far beyond human capabilities. All that needs to be done is unify data from all sources, and the algorithms will work their magic!
Available data is an advantage for your business; it’s as simple as that! Imagine that anyone in your company, or even your business partners, could have access to centralized information. It will be much easier and encouraging for your personnel to make reports and keep all processes up to date. Alpine Shire Council made a solution in which a wide range of complex information in multiple formats and even digital elevation models are unified. Integrated information is calculated to yield spatial and non-spatial results, and you can access it without delays using an iPad app.
Having access to all possible types of information that is regularly updated and synchronized makes it much easier to provide a higher level of security and prevent fraud. You can implement Artificial Intelligence and Machine Learning solutions to analyze any suspicious activity and have the opportunity to deal with it or you can even set automated algorithms to do it for you.
Browsing thousands of interfaces from different types of software is not easy or effective for any enterprise. With a data integration plan, you can optimize all of this and handle complexity while achieving maximum results and the best information delivery. The solution may require an accessible data hub that is easy to connect with. Going back to graphics, Shell Canada is making a 3D PDF dataset out of 2D, 3D, vector, and raster information easily accessible.
Data integration adds value to the data; that’s one of the main reasons to have a data integration strategy in the first place. Data quality techniques are becoming more common in DI solutions; these techniques detect the problems that need fixing and improve data characteristics to make data cleaner, more consistent, and more complete. The resulting datasets become much more valuable than raw data, because they are aggregated and calculated. Much like in manufacturing, data integration turns raw material (data) into an actual product (new datasets).
Integrated and accessible information opens up an entirely new world of possibilities for collaboration in and out of the company. Basically, anyone who is relying on your information will have a much more effective impact on the processes with the available data in the right format. People with whom you share this data can include internal teams, the whole company, and even your partners — with quick access to information from different sources, your organization will get a precise understanding of the current situation. In the United States of America, the State of Indiana was unifying the information of almost a hundred counties; the parameters included boundaries, parcels, points, streets, and addresses. As a result, all those data points were combined in an online GIS portal, making it convenient for each county as well as centralized and effective for the state government.
An integrated data strategy makes it possible to have information updates as soon as possible. If you include in your strategy cloud technologies, which we will talk about later, it could even be updated in real-time. There is a term “data silo,” which means a steady and isolated repository of information. Because of this isolation, the information is at risk of becoming outdated and inaccessible for collaboration. Data integration techniques will connect those silos in various areas of your business and offer you the maximum potential value out of it.
Organized repositories with a variety of integrated datasets will enable you and your peers to obtain an impressive level of transparency and understanding across the entire organization. Never before accessible nuances and facts about data will now be in your hands, helping you make the right moves just in time.
After our discussion of the reasons to obtain integrated data systems, let’s discuss how you can use it in particular cases.
ETL stands for “extract, transform, and load” — this is one of the key processes in any data integration strategy. It means extracting data from particular sources, transforming information to some unified format that is required for a certain business goal, and loading data in a new format to the data warehouse. This traditional way to do this is changing, as the Extract, Load and Transform (ELT) approach is gaining more popularity.
Big businesses often choose to use data integration for creating data warehouses, both on-premises or in the cloud. By doing this, they combine different data sources into a relational database. These data warehouses make possible for users to generate reports, run analyses, and have the opportunity to retrieve their data in a single format.
A unified understanding of all available information is boosting business intelligence to the max, providing a holistic view of all available resources and the current situation. This is resulting in improving the work of analysts, which can extract valuable insights for more accurate and timely decisions as well as form the vision of future strategies.
The main stores of structured and unstructured data can be very complex and gigantic, causing an additional problem due to the amount and types of data there. With all information coming from new data points and sensors, the lake will only get bigger. Being a massive advantage for businesses, data lakes can fulfill their potential only when they are handled properly — and that’s where big data integration could save the day.
For now, we will focus on the five ways of integration for your company. Each of them will be suitable for different scenarios, depending on the size of the organization, the goals that need to be achieved, and the funds available to deliver this innovation.
The manager or analyst takes ownership of every stage of integration, doing everything manually. The price is low here because this approach requires minimal support, involves just a couple of data sources, and you will have complete control over the whole process. But humans make mistakes, and there is plenty of room for error here. In addition, scaling is not an option because the larger scale will involve greater manual effort and time. It’s a good choice for quick fixes and one-time integration — but if you have a long-term strategy in mind, this is probably not for you.
This is a type of software that basically acts as an interpreter between legacy systems and modern ones; it connects those applications, so they can exchange information. This approach improves the speed of data streaming, allowing systems to easily connect with each other. On the other side, the middleware needs supervision from technical specialists all the time and it can only work with the particular types of systems. If your integration goal is only to connect legacy with new systems, this is a good choice; however, it is only a communication tool and you will struggle in the data analytics area.
If you pick this option, the software will handle everything, including finding, extracting, cleaning, and integrating information from various separate sources. The information that was separated before and had different formats will have an opportunity to be transferred from one point to the other. Analysts and managers can focus on something else because an automated application will take on everything, providing a seamless transfer of the data. Unfortunately, you still need to have a qualified expert monitor the actions of the program from time to time. Also, there is a problem with standardization — each service provider offers its own way to do it. But the biggest challenge of this approach is the actual creation of the application that will work in cohesion with every department across the entire organization — the time and effort of many technically proficient experts is required. It could fit big enterprises that have resources to do it right as well as a long-term strategy for integration.
This approach allows access to information from different locations and sets in one unified view, while the data stays in its initial place. Without the need to have multiple places to store information, storage requirements will stay low and you will be able to connect a number of systems. However, it has some risks in the data integrity area because a large number of sources will be used. The amount of the information could be quite large, so not all data hosts will be able to handle it without upgrades. If you have a company that requires unified access to separate systems and you have a powerful enough data host, it will be perfect for your case; you can get everything you need without major modernization processes.
This is similar to the previous method — but common storage means not just providing a unified view, but also copying all information to the data warehouse. Being far more versatile, this approach is considered one of the most widespread ways of integration. The host system is not that loaded with information anymore; thus, you will have an option to manage data versions. Also, analysts and managers could work with stored copy to run complex operations without putting data integrity at risk. But on the other side, there are some disadvantages to it — like the additional costs of storing the information and hiring the right technical experts to perform this integration as well as maintain it. Undoubtedly, this is the most sophisticated and expensive approach of the five; however, it could enable experts to work with the most complex queries, providing you the best insights possible.
To pick the right approach, it is necessary to clearly understand the scale of integration and the needs of your company. Summing up the above mentioned five approaches, here are the scenarios for organizations using each one of them:
|Scenario||The Best Approach|
|Combining information for simple analysis from of a small number of sources.||Manual|
|Providing connection and translation between legacy and modern systems.||Middleware|
|Connecting systems with automated software and providing more complex analysis.||Application-based|
|After connecting data, presenting it in a unified format for sophisticated analysis.||Uniform access|
|Storing a copy of unified information for the most detailed analysis possible.||Common storage|
To help you get a better understanding of the processes, let’s switch to the mechanics of cloud integration and its advantages over classic methods.
It’s hard to predict the future, but we can already agree on the fact that the role of mobile and cloud computing technology is increasing — freeing up managers, technicians, analytics, and bosses from their physical places of work. Location isn’t the problem anymore; specialists can interact with the system whenever they want with a little help from cloud technology. Data integration tools should be prepared to operate without a hitch on all types of devices, software, and networks. Even more, today companies need to share their information with other partner companies in real-time; that’s why data architects are being pushed to provide more sophisticated and effective solutions. Cloud-based is the right solution to share information on a bigger scale and speed than ever before. The classic integration processes ETL and ELT have their place and deserve their reputation for a reason, but moving everything to the cloud could drive innovation and provide significant improvements.
The definition and purpose
Cloud integration is a secure system of software, tools, and technologies that connects all kinds of IT environments, including systems, data warehouses, repositories, and applications, in real-time to exchange information among them. Information is combined and transferred to cloud services to be easily accessed via devices in a private network or via the internet. Cloud integration enables the processing of Big Data and information from the Internet of Things devices.
The goal of moving data integration to the cloud is to give the developers an opportunity to create a unified solution that will run perfectly locally and will have all the key benefits for the best performance. Since organizations often have multiple cloud-based solutions, integration must support native interfaces, models of SaaS application, cloud data warehouse platforms, and cloud storage.
The value of cloud-based integration for your business is higher than ever; here’s why:
Every employee will have their own personal password and username to access the cloud. Nothing can be modified or changed without permission, thanks to a set of security protocols.
Managing and storing information in the cloud is much cheaper, reducing the operating costs for your business. Additionally, there are some other areas for saving on your budget like cybersecurity expenses — these will also be lower.
A change in any application in the system will be reflected almost instantly because everything will be synchronized, providing users the latest information possible at the moment.
All modern applications are designed to work with the cloud, including ones for business practices such as analytics. Also, the cloud is now critical to data management disciplines such as data quality, data warehousing, reporting, and master data management.
In 2020, companies are using environments with hybrid cloud-to-cloud connections. Examples are Google Cloud Platform, AWS, and Azure — a company might use one of them or even all of them at the same time. Environments are now more complex and using different SaaS applications is normal. Cloud integration can deal with it better than any classic method.
As the pace of business processes keeps getting faster, it’s better to get all data-driven products as soon as possible. Integration platforms that are built on the cloud could compress development cycles and add new information sources and users fast!
With a cloud-based data integration solution, users could have access to all data sets and perform exploration, data preparation, and visualization by themselves without needing additional help from the experts. The cloud basically creates a central place of data sharing and collaborations for all users who have permission.
Cloud integration could lead to the putting together of an astonishing amount of Big Data that will be available for in-depth analysis by the latest tools and technologies. Leverage advanced analytics to get the most out of your company and the information that is available for processing.
Some companies choose to migrate their warehouse to the cloud partially and some do so entirely; in both cases, cloud integration is the way to go. You need to have a cloud integration solution for feeding your warehouse from hybrid sources during day-to-day operations; without it, your reports won’t be complete.
Another great thing that cloud integration could help achieve is the multi-cloud synchronization of your cloud environments. You can get the best out of Azure, AWS, and the Google Cloud Platform by synchronizing information among them and getting a unified view.
Choosing the right data integration approach is just one element of turning your business into a truly data-driven company. It’s not just selecting the right software or a single solution; a data integration strategy is part of your big puzzle — it’s a vision of innovation and growth. Answering these questions will help you better understand the direction of your strategy:
SPD Group has proven expertise in data integration, platform integration and unification, data consolidation, product development, and business intelligence solutions development. Working with our partners for over a decade, we have successfully built high-scale, enterprise-grade consolidated enterprise platforms that use cloud computing technologies to the max — with up to 80 connected services running at the same time.
We created a set of data migration and reconciliation tools to move information of any complexity with guaranteed security and integrity from legacy platforms to modern ones. If you have the vision to build solid and consolidated system architecture that will bring you cost optimization and free up business-critical resources, we can help you!
You will get a complete understanding of the information you have in your company and will have tools to drive insights out of it.
It can help businesses in any industry and any size extract the value out of the information available. Data integration could be a basis for a technological innovation such as Machine Learning in Finance.
Keeping up with the technological revolution, getting out of the legacy infrastructure, improving data accessibility, improving security, better understanding the value of every piece of information, and many more!
Cloud integration can help you unify all resources in your organization, store it in the cloud, and have the opportunity to extract valuable insights.
There are multiple approaches, each one better suited for a particular business case than the other. The most common are manual, middleware, application-based, uniform access, and common storage.
The most important thing about your custom data integration strategy is using it as part of a greater vision. It’s not just a one-time deal, but rather a step in your digital future. Feel free to contact us at SPD Group for a consultation regarding the data integration strategy for your business. We will help you to get out of the legacy infrastructure and make the best out of modern technology and approaches.
Originally posted here