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Five Common Data Integration Use Cases


In order to choose the best tools, companies need to have data integration use cases which will add business value, as well as a data integration strategy that makes sense from a financial perspective.

Every organisations’ needs are different, depending on their industry, products, customers, workflows and other factors. The common factor is that they need data integration for similar purposes. Here are five of the most common data integration use cases that apply across a wide range of industries:

  1. Migrating data into a data warehouse
  2. Syncing records in multiple systems
  3. Receiving data from partners or suppliers
  4. Creating a sales or marketing dashboard
  5. Creating a single-customer view

1. Migrating data into a data warehouse

Data analytics has become an integral part of doing business. Organisations are creating repositories of big data which they aim to combine and process to compile business insights. However before organisations can run reports, perform analytics or develop insights, they first need to collect all their data in one place and get it into the proper format for analysis. This requires data integration.

The type of data integration will depend on the type of data repository that enterprises are interested in creating. Many organisations have a data warehouse that they use for business intelligence purposes. To create these repositories they need data integration tools that can collect relevant data from a wide variety of different applications and systems.

Because a data warehouse stores data in a structured state, the data may need to be cleansed or organised so that it is in the same format as other similar data. For example, some applications may store phone numbers with parentheses, as in 020(7) 321 4321, while others don’t use anything, like 0207 321 4321. Before data gets stored in the data warehouse, all those phone numbers need to be converted to the same format.

For that, organisations typically use a type of data integration software known as Extract Transform Load or ETL applications. Enterprises have been using ETL tools for these purposes for decades, and it is one of the most familiar types of data integration software.

These days, many enterprises have data lakes in addition to or instead of data warehouses. A data lake stores unstructured data and semi-structured data in addition to structured data, and they store all the data in its raw state rather than transforming it.

2. Syncing records to multiple systems

Many enterprises find that they have multiple independent systems that store the same data. Sometimes this occurs as a result of merger and acquisition activity. For example if two retailers merge with each other, the two may have many suppliers, partners, and customers in common and have information about all those entities in their respective databases. However the two brands may run different databases, and the information stored in those databases may not always agree.

Other times, the duplicate data is simply the result of siloed systems. For example, the finance software might be different than the receiving department software. While both systems likely may store similar data related to the supply chain, the two databases may be very different. And if the receiving department updates the address for a particular vendor, they might forget to alert the finance department, which would still have the old address stored in its systems.

Enterprises can choose to deal with these situations in many different ways. For example, they may try to combine the databases from the two merged companies, or they may try to move both the finance department and the receiving department onto the same enterprise resource planning (ERP) software in order to eliminate the siloes.

Most enterprises still end up with multiple data repositories, as much as they might try to avoid this. In order to keep all their databases up to date, they need a solution that can sync the records in the various independent systems.

This usually requires a data integration tool with data governance solutions and master data management (MDM) capabilities. It might be a standalone MDM product or a complete data integration platform that can remove duplicates, standardise formats, copy data from one system to another (data propagation) and provide a unified view of the master data in the organisation’s systems.

3. Receiving data from suppliers or partners

For as long as companies have been using computers, they have needed to send and receive data from their suppliers and partners. For example, a manufacturer might need to transfer shipping lists, invoice information or general product data. Or a hospital might need to receive patient records from independent physicians’ offices and labs.

In the past, partners may have simply faxed the relevant information, and enterprises would re-input it into their systems. But this method is time-consuming and error-prone.

One of the earliest solutions to this problem was a type of data integration tool known as electronic data interchange (EDI). First invented in the seventies, EDI is still used today by many companies, so many vendors incorporate EDI into their data integration platforms.

However, modern technology offers several alternatives to traditional EDI. For example, some companies transfer data via web services that rely on XML files, while many others make extensive use of APIs. And some companies use multiple different methods to transfer data to and from partners, in which case data integration tools that can manage these different types of data connections become appealing.

4. Creating a sales dashboard

Many organisations are in the process of creating a data-driven culture. A big part of the effort at most companies is making greater use of data analytics in the sales and marketing departments.

Today, many of an organisation’s interactions with its customers take place online. That gives enterprises more ability to measure and track sales and marketing efforts, such as counting impressions and clicks, tracking how long customers spend on their website or selling their products and services online.

These dashboards tell their sales and marketing teams how their efforts are going, and provides some idea on ROI. For example, a marketing dashboard might track leads generated in relation to:

  • Open rates
  • Conversion metrics
  • Lead quality
  • Bounce rates
  • Key performance indicators (KPIs)

Whenever possible, the data is presented in visual formats, such as charts or graphs, so that the users can see trend lines and make sense of the data at a glance.

To create these dashboards, organisations might use a data integration platform or a suit of several different tools. Some sales or marketing software includes the capability to create a dashboard. Or organisations may create their own custom dashboard that pulls data from several different internal and external sources. The application then runs any necessary analytics and creates graphs and images and updates them regularly.

This data integration use case is much more complex than ETL or syncing records, and so it requires powerful software.

5. Providing a 360-degree view of a customer

For many enterprises, the “holy grail” of data integration is to create a true 360-degree view of individual customers. The idea is that whenever a salesperson or other employee interacts with a customer, they have a single view that summarises the customer’s interactions with the company.

This often requires pulling customer data from multiple systems — the customer relationship management (CRM) system, the ERP application, support tracking system, marketing software, the e-commerce systems, and any other applications. It often gives users the ability to drill down into the customer’s history, seeing what they have purchased in the past and the details of any calls, emails or chat sessions with customer support.

Many of these 360-degree customer dashboards also benefit from data enrichment which means they bring in external data that isn’t included in the company’s databases. It might pull information from the customer’s public social media accounts or incorporate information available from data brokers.

A lot of today’s dashboards also incorporate predictive analytics, machine learning and artificial intelligence. They might offer suggestions for what the customer is likely to purchase next, or offers that the customer will probably find particularly appealing. They may even use sentiment analysis to gauge the customer’s emotional state and guide the staff member.

This data integration use case is the most complicated of all, and it requires very advanced data integration and analytics software. Many companies are making the necessary investments, however, in the hopes of seeing dramatic improvements in sales and customer service.

If you’re interested in bringing any of these to life for your organisation, please get in touch with us to help you get started. From a review of current infrastructure, to a full implementation project, we have a service for organisations at every stage.

Also see – How To Create A Data Integration Strategy

Karen