We usually need a wide range of data to create an accurate picture of our playing field. However, information can come in many shapes and sizes, making it hard to fully integrate different data types to extract insights.
That’s why, in this age of big data, integration is the name of the game. Successfully implementing it will bring nearly boundless benefits to the organization. Just a handful of benefits include:
- Reducing cost
- Saving time
- Enhancing your digital marketing tactics
- Better understanding the market
- Improving internal operations
Therefore, if done correctly, data integration can bring huge benefits to any organization.
In this article, we will learn the various challenges that an organization will encounter when they start implementing data integration to enhance their productivity. This serves as a general discussion to guide you on creating a data integration plan.
A brief review of data integration
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Data integration includes combining, cleaning, and presenting data.
This involves combining data in a variety of formats and a wide range of sources, cleaning the data of duplicates and low-quality portions, and presenting it into a specified format.
While it looks like a linear process, this is an all-encompassing term that covers the following processes:
- Data retrieval
- Data quality control
- Data migration
- Data transformation
- Legacy data modernization
- Application integration
These “smaller” processes are important by themselves and will affect the success of the data integration plan of your organization. Each of these processes presents its own challenges! Answering these challenges is essentially what will bring success to your data integration plan.
Consolidating a wide variety of data formats
Organizing data has always been a challenge even before the advent of computers.
Even if you use the same set of data and information with your collaborators and fellow enterprises, you may also find consolidating them difficult due to the wide variety of formats that exist.
Several industries are served by a wide variety of software, which can either be regularly updated or a legacy system that an organization decided not to let go for a reason. The software can also be either proprietary or open-source. This creates a special challenge of combining even the same type of data but is stored in different file formats.
A common solution is to implement a separate solution to pre-process the data by unifying them into a single format before integrating them.
Often, companies engage with third-party services to handle this sole process of data conversion, thus freeing them of the need to invest not just resources but also divert some talent and expertise in creating the solution.
Making contracts with third-party services, however, creates inefficiencies in the data processing and slows down the process. Additionally, choosing the third-party service presents itself as an additional security risk.
To solve this, a more ambitious plan is to unify the data into a single format to get rid of the need for a third-party service or an additional system to preprocess the data before integrating them.
For example, the International Standards Organization (ISO) has created ISO 8601 to unify the date and time formats. Various date and time formats still exist today, but the standard implemented by computers follow the ISO 8601, easily communicating date and time information even between different systems.
Enterprises should also take advantage of the trend of data unification because of the following benefits:
- Easily access and readily incorporate data sets collected by other businesses
- Allows businesses to produce more robust functionality within their products
- Seamlessly exchange data amongst themselves
While there is a tendency among enterprises to be a bit possessive regarding their own data (especially if their enterprise is hitting the rocks), the foundation argues that the benefits far outweigh the costs of data unification.
Depending on the demands of the organization, it might be possible to implement a single format for unified data. Else, developing your own tools or outsourcing the consolidation of data to a third-party service can be chosen as the solution.
Scaling the system to handle growth
Computer systems also have to adapt to changing needs.
All business managers know that being unable to fulfill additional orders due to insufficient inventory translates to a lost potential income. They, therefore, plan ahead by ordering in advance before the demand outstrips what remains in their inventories.
Similarly, the positive growth of an organization translates to an increase in the amount of data to be processed and stored. Therefore the data integration system should be able to account for potential growth by considering the three performance bottlenecks in the hardware:
- Processing power
These are three technical problems by themselves encountered by large organizations of all types. Individually solving them is possible but can defeat the purpose of data integration through a mismatch in the hardware used.
However, as a competent IT manager knows, integrating both the hardware and the software being used for data integration drastically improves the performance of the system.
There are three ways to solve the problem of scaling the system.
1. Vertical scaling
This is a process of replacing the existing hardware with a newer, faster one. If you have been using computers for around 20 years, you would notice a massive change in the hardware demand, where you are replacing your computer with a newer and faster one. This is an example of vertical scaling.
During the first years of this millennium, your desktop computer can already run Windows XP with exceptional performance while having a Pentium 4 processor and a memory of 512 MB.
You cannot use the same system to run Windows 10! You will have to replace it with a modern system. Even if you are using a system with an Intel Core i3 processor and a memory of 4 GB you’d be mildly annoyed with how slow it sometimes runs.
The advantage of vertical scaling is that it uses less power and resources as you will only run a few minutes with sufficient computing power. The disadvantage, however, is that in the case of exceptional growth in the data analyzed, it may quickly overwhelm the existing systems.
2. Horizontal scaling
It can be more practical to acquire a set of computing units with similar architecture and processing power, and then connect time through parallel computing, with a “mother” unit coordinating the tasks submitted to the other units.
The advantage of horizontal scaling is that the system can keep running even if one of the units fails, as that unit can simply be isolated while the rest will continue running as usual. Additionally, you can easily upgrade the system by adding new units and coordinating them with the “mother” unit. Voila, more processing power!
One disadvantage of horizontal scaling is that it is more expensive to run and maintain – you are running an equivalent of several desktop computers in the same room. Running them requires you to dedicate a room with a robust air conditioning unit as it will get hot quickly inside.
Instead of investing in hardware, you can always look for a cloud-based solution. The main advantage is that you won’t have to invest a lot in both computing infrastructure and technical expertise to maintain those systems. The right cloud integration tools are scalable and flexible in how data is shared, stored, and accessed.
One disadvantage of using a cloud-based solution is that you may be limited in the amount of functionality you can use or add. Another one is on the question of security. While cloud-based services already offer good system security, there might be information that you cannot simply host elsewhere due to its critical nature.
All these three options offer their advantages and disadvantages. It is up to the needs of the organization to determine which one will be the best option.
Securing the system from cybersecurity threats
Data rivals money in its value nowadays. Securing data from people who are not authorized to see it is a necessity.
Unfortunately, we are gone from the age where we simply solve the malware problem by running reliable antivirus software and spending a few days to clean the infected system. Nowadays we have the newest threat to our data: ransomware.
Ransomware is a type of malware that encrypts all the files of an infected system and then locks the system. The ransomware then asks for ransom money for a decryption key to be released. Else, the files either remain encrypted forever (unless the decryption key has been discovered) or the programmers behind it release the files into the wild.
The first major ransomware first appeared in 2013. Since then, more ransomware started appearing in the wild. Even amid the pandemic, healthcare providers are no longer safe from ransomware attacks.
Clearly, this new challenge, on top of the threat of data breaches, which is ever-present today, can make you anxious and reconsider whether data integration is worth it. Due to how complex the issue of data security is, you should always consider hiring a security expert whenever possible.
Else, try to follow these best practices to enhance the security of your system:
- Creating an adequate access control policy.
- Adequate data protection, often with encryption.
- Protecting the communication and transfer of data from the sources to the data integration system.
- Using a real-time security monitoring.
- In case you opted for a cloud-based solution, look for a service with adequate protection mechanisms in place.
- Consider investing in a solution like identity and access management; this helps narrow failure points, like password breaches.
These recommendations will help you make better choices in designing the data integration system so that it will be more secure from data breaches and ransomware attacks.
Data integration is a process that requires a lot of investment and expertise but will enhance the performance of your organization. Both corporate and noncorporate organizations will benefit from data integration.
The three main challenges in data integration are as follows:
- Consolidating a wide variety of data formats
- Scaling the system to handle growth
- Securing the system from cybersecurity threats
Once you solved these three main challenges, your data integration system can now be designed better than ever before.
Author Bio: This article is contributed by Rai Bautista for lido.app, an all-in-one data analysis platform that lets you create custom reports, dashboards, and apps within minutes––no code required. Stop begging your engineering team and start building your own solutions.