A 2020 Forbes report highlighted just how important data integration has become to US businesses. It highlighted that 80% of operations managers across all industries confirmed that integration of data was key to their success, while 67% of businesses were relying on integration for business intelligence. According to the report, 24% of US companies were hoping to introduce data integration within 12 months. Manufacturing was one of three industries that had widespread adoption of integration strategies, and 65% of all industries were deploying solutions from cloud and hybrid platforms.
It isn’t very often you come across data integration as a topic of discussion; it isn’t as commonly talked about as, for example, sales and marketing or customer service. However, from the above statistics, it is obvious that it is a concern for businesses, and they are spending significant resources to make it happen. They understand its value and what it means for business intelligence and strategy.
Companies are now paying good money for professionals who are well-versed in data integration. If you understand data and data analytics, you are in a position to not only make good money but to also rise quickly up the ranks.
A degree in data analytics is one way to get the requisite skills and set you up for an excellent job that pays well. An MBA data analytics salary, for example, can pay six figures, and those who are qualified rise quickly into senior management positions. Walsh University’s MBA in Data Analytics focuses on topics such as information systems, quality management, data integration and performance management.
By the time you complete your training, you will be highly skilled in data analysis and will understand big data and the architecture that is used to analyze it. You will be able to apply analytics at top-management level, creating and implementing strategies that give your company an edge in the marketplace.
Before you enroll in an MBA in data analytics, it is a good idea to acquaint yourself with data integration. What is it and why does integration of data matter? How does it apply to real-life business situations? What is the difference between data integration and data analysis?
This article provides the basics on data integration and answers all these questions.
What is data integration?
It can be defined as the practice of bringing data from different sources within the organization, compiling a complete data set that can then be analyzed to improve business processes.
Data integration provides a unified view of operations, and using data analysis tools, operations managers can then analyze whatever data they integrate and come up with accurate and concise business intelligence that they can use to create sound business strategies.
Each business is unique when it comes to data integration. Different businesses collect different types of data across different departments, and whatever they integrate depends on their needs. Some businesses, for example, choose to integrate data from sales and customer service so that they can see how customer service affects sales for each period and what they can do to improve the bottom line. A manufacturing business may want to integrate data from the supply chain with data from production lines so that it can get a better understanding of how lead time affects production numbers.
There isn’t a unified approach to data integration, and business process managers must think carefully about what data they want to merge and what information they can extract from it.
Why data integration?
Data integration is a time-consuming process that often requires expensive tools and complex platforms, so why are so many businesses doing it? It comes with distinct advantages:
It improves collaboration between departments
One of the biggest problems in businesses is the absence of unified action between departments.
Often, for example, you’ll find that the sales department doesn’t know what customer service is doing or that there is no coordination between logistics and supplies and the manufacturing floor. Likewise, those in HR often have no idea what is happening with employees and what training challenges they face.
These are just basic examples, but they can lead to crippling problems within businesses. Within each department, there are expensive and complex systems that collect data, but the data isn’t shared or consolidated to find out how the actions in one part of the business affect another. This leads not only to duplication but also to breakdowns in communication and encourages competition between departments that should otherwise be cooperating.
When different departments within a business work in tandem, it is easier to identify problems and bottlenecks and eliminate them. It also makes for a more cohesive work environment as employees have the bigger picture of what the business is trying to achieve.
When data is integrated, it makes it easier to achieve set goals, and growth is celebrated not just in one department but across the board.
Data integration saves time and resources
Duplication of effort is a big problem within businesses. Various departments end up collecting similar data rather than pulling together and coming up with a data collection and analysis strategy that takes a bird’s eye view of the company. With data integration, departments and businesses can save not just time but also valuable resources that can be used to improve business processes for a healthier bottom line.
Integration of data eliminates errors
When a business process manager unifies data from different departments, it becomes easier to correct and eliminate errors.
Imagine a scenario where each department collects and analyzes its data. If it contains errors, they are more difficult to find because they don’t compare what they have to data and information gathered by other departments.
Such errors can prove critical when it comes to creating strategy and can have a significant impact on profits and overall performance.
It produces high-quality data for better decision-making
When you integrate data, you see the bigger picture, which helps to eliminate errors and duplication. Whatever you are left with is data that is completely accurate and gives you a true perspective of what is happening within the business.
Whatever conclusions you draw from that data will be reliable, and you can use it to make decisions that will have a positive impact on the business. Business managers who integrate data can be sure that whatever conclusions they extract from the information that they have is accurate and of high quality. Whatever strategic decisions they make will then be driven by a true picture of operations.
Users can access all the data they need from one location
Imagine a salesperson has just lost a customer because they are not happy with after-sales service. When he tries to ask the customer, who is agitated with the company already, the customer tells him that they already logged the information with customer service, so the salesperson should go ask them.
Without data integration, the salesperson has to track down the employee who dealt with the customer and find out what went wrong and why. This is time-consuming and detracts from other more profitable activities. If all data is logged in one central repository, everyone from the sales department can see what happens in each part of the sales cycle and can act fast and efficiently when dealing with unhappy customers.
Business decisions are driven by solid data
Data integration indeed provides intelligence that can be used to come up with a long-term strategy, but it is also useful in making day-to-day decisions that can have a big impact on the bottom line.
Operations managers can spot problems as soon as they occur and take steps to correct them. They can also take advantage of opportunities because they are easier to spot.
How does data integration work?
There are different approaches to data integration, and whatever approach a business chooses is determined by its needs.
Before implementing a data integration approach, it is important for business managers to carefully consider what data they would like unified and why. They should consult with various departments to find out from the people on the frontlines what they think would be the best way to integrate the information that they collect.
Below are some of the approaches that are commonly used to unify data:
Extraction, transformation and loading
Data sets from different parts of the business are put together and harmonized and then fed into a database or data warehouse for immediate analysis.
Extraction, loading and transformation
This approach isn’t very different from the one above, except that once data is collected and harmonized, it is fed into a database to await analysis at a later time.
Change data capture
This approach tracks changes in data sets, and if it detects something different or unique, the data set for that particular period is transferred to a platform for immediate analysis. Whatever conclusions are drawn from the analysis can be actioned right away.
In this approach, data gathered in one database is transferred to another to keep it up to date. The process of replicating data this way is tedious and often creates more problems than it solves.
Rather than copy data from one database to another, data from different parts of the business is combined and analyzed virtually to give an overall view of the business.
Streaming data integration
Here, data from different sources is collected in real-time and analyzed right away so that at any time, management can see at a glance what is happening within the business.
Also Read: Tallyman Axis: The Future of Data Management
How does data integration apply in real-life situations?
There are numerous examples of data integration in the real world. In a retail business, it is important to integrate data from sales with customer service and account management.
In banking, customer acquisition should integrate its data with the loans department to see how many new customers take out loans and how many of them repay them as scheduled.
Data integration could highlight a weakness in the system if the bank signs up new customers who only come in for the loans and have a problem paying them back. It means that the overall strategy for customer acquisition and lending ought to be revised.
Many small businesses are keen to integrate data from sales and customer service. They want to know how customer service impacts repeat sales and what they can do to increase customer retention.
These are just three examples, but they give you an idea of how data integration works in the real world.
How does data integration compare to data analysis?
Data integration is only a small part of data analysis. It collects data from different areas of the business, which is then analyzed and applied to make strategic decisions.
Data analysis, on the other hand, examines data, identifies patterns and provides actionable insights. Just like data integration, it helps businesses implement data-driven decisions. Data analysis can happen within departments or in one central repository, such as a data warehouse or database. Data integration, however, gathers information from different sources to see what conclusions can be drawn and what intelligence can be extracted and applied to operations.
What is the best way to integrate data?
It all depends on what information you want to extract from the data that you unify. The best approach is to think about what conclusions you need to draw from the data that you collect from different departments and then decide on how you want to integrate what you’ve gathered.
If you want to know what is happening within the sales cycle every day, for example, you need to unify all the data that is collected from the relevant departments so that you can identify any bottlenecks.
A career in data
Data integration in businesses is becoming ever more critical as organizations seek new ways to attract and retain customers.
Companies are willing to pay good money to professionals who have the right qualifications to put together data from different departments and use it to extract useful business intelligence. If you enjoy working with large data sets and using them to compile information, this is a career that you should consider. It pays well, and all it takes to qualify is an MBA in data analysis or a similar program from a renowned institution.
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