In our modern age, few things are as valuable as access to the right data at the right time. Data access and integration have given rise to several professions and college majors dedicated solely to everything data.
For example, business data analytics is an emerging field, yet it's arguably among the most successful career paths. Megacorps like Facebook and Apple constantly improve their data quality tools to collect more relevant data efficiently and leverage it for massive profit.
The tension between organizations continues as each one tries to find or create valuable data faster to offer personalized products and services first to gain that sweet competitive edge.
But as business data analytics grows, its learning curve gets steeper, so how does a startup get into it?
Data integration. What’s that, you ask? Stick around to find out all you need to know about it!
Data integration is the process of accessing data from multiple different sources and integrating it (i.e., bringing it together) into one central location or event; such as another system like a data warehouse but could also be applications, solutions, devices, and more.
For example, if your business has multiple customer engagement platforms such as CRM, marketing or support they probably collect data individually. However, separately analyzing each platform’s data is inefficient, increases IT costs and won’t bring a remarkable result.
In contrast, if you integrate data from each platform, then it's the exact opposite. As a result, expect reduced IT costs, increased data quality, and use with a simplified base for data analysis. Therefore, your customers get a better experience with your service as it's more personalized and tailored to their needs and wants.
Having easy access to your business data to create 360-degree views of what is happening unlocks understanding. In return, that helps you make more effective strategic decisions that genuinely serve business continuity.
With control over data, it’s easier for you to analyze your business operations to learn and understand customers’ needs and wants better than before. Of course, the data gets better as your customers stick around for longer and you get to know them more.
In general, data analysis is very time-consuming – that’s a fact. Yet, it’s easy to see how having all your customer data centralized is much more time-efficient than if the data were scattered all over many separate platforms and kept decentralized.
When business data analytics is done more efficiently, it saves time and money. For example, your IT team doesn’t have to waste so much time delving through unorganized clutters of data, so expect IT costs to go down.
When data is isolated, it leads to poor (and sometimes stagnant) communication between your systems and processes because the information is often incomplete or inaccurate. Ultimately, it becomes harder to meet your business goals, customer expectations and make good decisions.
When the data is accessible through integration then fewer resources are being wasted. The result is your processes can work efficiently with your systems to prevent stagnation or avoidable losses.
The first decision everyone has to make is buy vs build when it comes to the integration solution. Either way, you’re going to need software. There are two ways of acquiring this software: building it up from scratch or configuring a pre-existing one. But what are the differences between these two options?
We’ll start with building your custom software from the ground up.
Your custom software will cater precisely to your company’s needs - it will be fit for purpose as developed by your in-house or contract integration specialist. But is it worth the downsides?
Unsurprisingly, building custom software is extremely time-intensive. Most companies don’t have IT technicians specialized in creating such software or business subject matter experts who know the system’s data, so you’ll have to look for developers that are fit for it. Even then, building the software could take months or maybe even years.
It’s also quite expensive. Just building the software can cost tens or even hundreds of thousands of dollars plus the cost of procuring the software from another vendor. On top of that are the maintenance costs. Custom software will need a lot of maintenance to keep it updated in a rapidly evolving industry.
On the other hand, there are configuration integration solutions which allow you to quickly work to connect systems without the high overhead of building.
For many businesses, configuring pre-existing software is the best option because it’s much more affordable and less time-consuming. Overall, it’s much more practical.
You can indeed buy software and configure it to your needs as much as you can, which will be enough for most organizations. Look for the buy solution to have a defined connector library, process orchestration capabilities, data governance, built-in AI and predictive capabilities as components of a complete offering.
A good configuration integration solution will allow you to customize it to meet your needs. Making it your own with custom fields, business rules and process automation steps.
To start back from the basics, data quality is what helps your organization run well, make good decisions and execute processes smoothly.
In other words, ensuring data quality is one the strategic reasons organizations invest in data tools. So whether a business needs data to offer its customers a personalized experience or sells the data to companies that need it, data quality is essential.
When the data is fallible, inaccurate, or incomplete; decisions and processes that depend on quality will degrade. Data quality errors can be an inconvenience or a major hurdle, but they hinder achieving business goals either way.
When data is integrated, business data analytics is easier on your employees, errors are minimized and goals are easier to reach.
When analyzing operating efficiency in its traditional sense, which includes ratios between revenue and costs, time to achieve an objective and how long a process takes to execute you’ll see a truer ratio after proper data integration.
This improvement is because instead of manually combining the data sets, the process is automated, reducing errors and the level of effort needed to do the analysis.
Additionally, you can delegate other more critical work to those who used to work on combining data. So it’s a win-win situation for everyone! The business achieves better results with its data at a lesser cost, and the IT technicians focus on meaningful work rather than tedious grunt work.
For those who aren’t so savvy about programming, a no-code data integration platform can allow you to create your data story without writing a single line of code.
Traditionally, a no-code data integration platform has a simple graphical user interface (GUI) and drag-and-drop functionality. So that non-technical people can visualize what they’re doing quickly.
Some no-code tools also streamline the development process for you by predicting customer behavior using AI and machine learning.
No code integration solutions such as Put It Forward allow users to work with the data in the way they need to while IT controls access to the underlying systems. This gives the users the power of an enterprise integration platform without the heavy lifting of writing code.
Since writing code often takes a lot more time, no-code platforms can be helpful for any organization with a full-time IT team that is resource constrained.
Since no-code platforms boast so many benefits, you may wonder why even choose a platform that requires coding. The critical difference is access to in-house resources and a team to build it for you.
The caveat of coding platforms is that you need to know how to code or have someone on the team who specializes in doing it. Additionally, you need resources that understand the systems being connected and the processes in place to manage a software development lifecycle (SDLC). With this path, your IT and project costs will rise.
Native connectors are API integrations built into the application itself by a software vendor so that data integration takes place directly (natively) within the application. In other words, no external software is required!
The most significant advantage to native connectors is that they allow direct communication between two applications. However, their configuration and settings are often either hard-coded to specific uses or have limited options since they're not dedicated to data integration. Ultimately, this makes it difficult to customize, support, and understand what’s going on inside.
Data integration is the process of gathering data from different sources or technologies into one shared interface to enable communication between them. This process can be manual or automated, though businesses understandably move towards automation as time goes by.
The process is crucial for businesses because scattered data is often incomplete, inaccurate, or flat-out unreadable in its raw form. Through data integration, companies can turn their raw, scattered data into useful and digestible information. Therefore improve their decision-making.
To understand API integration, you’ll need to know what APIs are in the first place.
APIs, or application program interfaces expose data or an event through a defined access point. For example, if person A has an API about weather data and person B is making a weather application. B could write a program to request the API from A, and A can respond accordingly.
You can probably roughly imagine how API integration works by now. It’s when several systems are connected via their APIs and exchange data in harmony. So in a way, the systems are “talking” with each other to keep each other up to date.
And since you don’t have to worry about versioning, this makes APIs especially great when handling data between entirely different systems.
Finally, we can't talk about data integration without mentioning ETL, a legacy method of integration that is still used in some instances.
ETL stands for "Extract, Transform, Load." And that’s a very apt and self-explanatory name.
First, data is extracted from the different sources from which you want to take data. Then, in many cases, the extracted data is validated to confirm that it's correct.
Next, the data is transformed to filter out unneeded data and make the rest more coherent. For example, empty values can be discarded, some values can be encoded, and numbers can be calculated or sorted depending on the business' needs.
Finally, the transformed data is loaded into the destination system in a format useful to the organization.
Usually, the data collected by an organization resides in multiple source systems. Being able to reach into those different sources, pull data from them and organize it together is called data wrangling. Often data wrangling is a key step in the data preparation process before data is loaded into the target system. An example of a target system is a data warehouse that runs on data from multiple sources.
Data wrangling is mainly used in businesses that get a fast influx of varied data such as customer or transactions or events (and subsequently associated data). Especially when the data is diverse.
Usually, a data scientist or analyst will use data wrangling solutions to quickly access sources and normalize the data before running it through the target system.
Often data wrangling is used within a data stage during the preparation phase before the results are integrated into the target system. Connecting a data stage using no-code integration solutions speeds the process of getting data into the environment and reduces the friction of doing so across the teams.
A 360-degree view means collecting all of the data of a particular subject or area of focus into a single location. A common 360 view criteria are of a customer or vendor which is used to centralize the understanding of this subject at any moment in time.
For example, having the customer's past buying patterns, behavior on social media, and information on what products they seek helps determine what exactly they need. Since each of these is collected from a different source, you can imagine how a 360 view wouldn't be possible without data integration.
Time series data is data plotted on a timescale to understand the customer's past and present behaviors and give analysts a way to forecast the future. It can be instrumental in predicting likely future trends, especially when a pattern is spotted between several customers.
The problem with raw data is that it's illegible, so it's impractical to try and analyze cluttered data. That's where reporting comes in to organize raw data into a digestible form so that analysts can perform standard analytics on the reported data.
Both of these methods work hand-in-hand to help decision-makers find helpful information in data.
Continuous integration is the process of automating the collection and merging of data from multiple sources as they change. Often this data is used to drive events and analytics out of the target system.
Data acquisition and utilization is often a race between organizations. Now they use continuous integration to try and acquire valuable data as fast as they can to make use of it.
Continuous integration helps stay up to date with trends, wants and needs being identified through the synthesis of all this data. This strategy can give you an edge over your competitors since you'll be able to take advantage of changes quicker.
Although continuous data integration can be helpful for almost any organization. It's especially so for those that depend on quick decision-making and following trends before their competitors or the market catches on.
AI is beginning to enter the data integration community, as automated systems are rising to take the grunt work off the analysts' shoulders.
Businesses continue to seek the help of machines to make better business decisions, and that isn’t likely to change soon. A 3-year-old survey found that AI and machine learning are the top priority of 61% of the surveyed businesses.
Embedded data intelligence is another way of visualizing the insight data, though in a collective form within a host application. Said another way it’s a dashboard view of intelligent insights embedded within another solution.
One common way is to embed the data and its visualization in software for the customer to see. And the great thing is it's simple to implement one in nearly any application and has many benefits for your organization!
AI can improve data quality and minimize data quality errors. Still, it's sort of a "chicken and egg" relationship, as good data quality can also help machines learn what good data and bad data are and thus improve later results.
By now, you should know that data integration is the process of combining data from different sources into a data warehouse. But what about workflow integration?
Workflow integration is the process we use to connect the systems themselves to allow for data integration. In a way, it's a prerequisite to data integration, but definitely not the same.
Events, triggers, and calls work harmoniously to keep data warehouses updated, often in this exact sequence.
An event is a change or action that happens in the data, and automated software can detect it. After detection comes the trigger.
There are usually different triggers for different events. The instructions in a trigger often call specific actions to be taken for the code. For example, call a delete function to delete certain information if needed.
You should use process logic in any automated process to verify the integrated data and the target systems. However, developing process logic will require testing because you don't want a logical flow to affect your business decisions.
As more and more events and signals are captured in systems which are deployed further across the enterprise - tools and solutions will need to be deployed to address these data needs at scale. Organizations that leverage the Put It Forward data platform gain a significant advantage in the marketplace with a single solution to scale in the data-driven era.
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