Unless your organization has a good data governance program in place, your data is likely not all stored in one location––but it should be. Here’s why, and how!
Data collected from different sources on different tools is usually analyzed and stored separately. Integrating all your organization’s data for strategic and operational reasons is important, particularly when it comes to similar datasets that remain separated owing to the mode of collection.
Here’s why:
- It is an unnecessary complication.
- It denies you the chance to have a complete view of the subject.
- It’s a major inconvenience to the analyst who has to consider all related datasets for proper interpretation.
- And it makes it more difficult for workers to do their daily tasks if the information they need is scattered all over the place.
There is an alternative though!
You can combine related datasets once and for all. By merging similar data you can better control how the information is accessed because you create a single gateway with one key.
This will reduce the amount of time it takes to get to the data one needs for their job, boosting productivity. Further, data combining is the first step you need to take towards complete data integration, which should be your organizations ultimate goal.
That said, data combining is a delicate process. There is risk of data loss, damage to data, and corruption of the data pool. However, you shouldn’t avoid data merging just because of the risks but rather do it well to enjoy the advantages that come with it.
What is Data Merging?
Data merging, also referred to as data combining, is the process of bringing together two or more similar datasets. This is done to make it easier to analyze and access all the information related to a particular aspect of the business such as customers, employees, production, or service delivery.
There are many potential sources of data:
Let us take for instance the consumer data pool. The type of consumer data that you collect can be categorized into three main classes: Personal, activity, and soft data.
- Personal data can be collected through signup forms, one-on-one, among other ways.
- Activity data which includes variables such as engagement and behavior can be obtained through the website, in the purchase process, and email.
- Soft data such as customer satisfaction and attitude can be gathered through direct feedback, social listening, and other ways.
From this, we can see that data from the same consumers is being collected in very different tools, some of which don’t integrate easily. This means that while you have all the data you need to create a picture of the individual, it is a challenge to put the image together.
With data combining, all this data converges at a single point where anyone (with access) can easily make out who the individual is. Even better, you can set up your system so that any incoming data is channeled to the right merged dataset.
When Should You Combine Data?
Data combining should be done when you find that you have similar datasets that are not well connected to each other. And if you haven’t thought about it before, you probably need it.
Here are some instances where data combining is necessary.
1. During analysis
When performing a data analysis, it is always better to have all the raw data available to the analyst. The inconvenience of having to examine datasets that are scattered across different locations can negatively affect the process.
2. When digitizing data management
There are good reasons to have your organization’s data in digital form: Easier access, better storage, deeper analysis, etc. When you start digitizing your data, you may notice that various pieces of information belong together and should be combined.
3. After a merger or acquisition
When organizations merge, they have to combine their resources in order to operate as one. Data is one resource they have to share and if they have similar sets of information, combining them makes sense.
It’s crucial to understand company perception (from both a consumer and market standpoint) and the only way to have an accurate understanding of this is by combining the intel:
How to Combine Data
When you come down to it, what does the process of data combining look like? The procedure is designed primarily to protect the integrity of the data – one step coming after the other.
1. Profiling
Data profiling refers to the analysis of data in order to determine its attributes. This step allows you to get a better look at your datasets to decide how they fit together.
It is also important for evaluating the data quality to prevent any corruption. Data quality is measured by accuracy, completeness, consistency, relevance, and timeliness.
For instance, if you are combining customer data, you should analyze the different datasets from your CRM tool, your social listening tool, Excel, hard copies, and other sources to identify the common attributes.
You should also verify the sources and confirm that you have up-to-date information.
2. Standardizing
Standardizing your data means conforming it to the preferred mode. This may include transforming the diverse datasets into a common format, replacing unprintable with printable characters, and fixing broken links in the data.
It may also involve establishing legal compliance. Data standardizing enhances the completeness of the data, boosts uniformity along set parameters, upholds the company’s data policies, and makes it easy to store and access the data.
For instance, you may have the same information about a customer saved in many different formats e.g. names recorded in Excel vs. Word documents. During data combining, you harmonize it so that all text documents, images, and other such information is saved in the preferred formats.
3. Filtering
Data filtering refers to the refining of data to improve its quality. This may involve establishing criteria for which sets of data go into different buckets. It may also require you to delete certain data if, for instance, it is a duplicate of already saved data or of poor quality or valueless information.
For instance, check to see how relevant old records of customer information are.
You may find that some customer contact information has changed which may be reflected in your CRM software but not in your manual records. In this case, the details in the manual record ought to be filtered out so that the new dataset only has current information.
4. Matching
Data matching is done to identify the relationship between different datasets. This can only be done after you have profiled, standardized, and filtered the data as it is easier to recognize similar information and there is no interference from unneeded data.
For instance, a customer’s personal, activity, and soft data can be identified and categorized across all the different tools on which it is stored through data matching.
5. Integrating
After the data has been categorized, it is time to bring it together. In this case, data integration refers to the creation of a single view for similar datasets, effectively forming a new set of data.
The old state in which the data was scattered is abolished and the data is now identified as one complete dataset built upon a specific subject.
At this point, some of the advantages of data combining should be apparent: Access, usability, analysis, etc.
6. Review
As a final step, it is important to do a review of the data combining process. For this, you perform data profiling just as you did in the first step only this time it is for the entire dataset. You may still discover errors that might have been overlooked or emerged from the process.
This step will demonstrate how convenient it is to have similar datasets combined!
Challenges When Combining Data
There are a few challenges that come up during the data combining process:
1. Structural differences
Differences in the structure of similar datasets can make it harder to combine them. For instance, the highly unstructured social media data with the highly structured Excel data, or image and text documents.
2. Content format
Similar datasets with the same structure may have content in different formats making it harder to combine them. However, this only occurs if it is not addressed during the second step of data combining – data standardization.
3. Data duplication
It is easy to avoid data duplication during the process. However, if it goes unsolved, the error can have a significant impact on an organization. Besides causing wasted time and effort during the process and possible strains on staff productivity down the line, data duplication can affect customer service and cause difficulties with automation systems.
Automation and Data Combination
Automation is recommended as it speeds up data combining and it also reduces human error. Autonomous systems can process large datasets efficiently and accurately.
NetBase Quid® has two features dedicated to data combining. One is our Opus Upload capability that allows you to upload datasets from diverse sources including online, offline, and manually. With it, organizations can combine several unique datasets for analysis.
The other is the Intelligence Connector, which connects disparate datasets and feeds them into your business intelligence tool, creating a single view of your organization’s data where it can be processed by proprietary processing tools. We have a team that helps you sort out specifically tailored solutions if you don’t have one already in place!
Reach out for a demo to learn more about combining your data and create a powerful single source of truth for your company to build upon!