What data integration means for workforce efficiencies


Analyst looking at data visualizations.
Image: Andrey Popov/Adobe Stock

Businesses generate tons of data, but there is often insufficient time or resources to sort through it all. However, when the data is properly collected and analyzed, employees can make more informed decisions and take actions that drive better business outcomes.

Data integration enables teams to identify the most relevant information and the appropriate tools to achieve company goals. With increased productivity and fewer distractions, your team can spend less time on lower-value tasks to be more efficient.

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What is data integration?

Data integration is the process of combining data from different sources so it can be used in one location. Data Integration is often done with an extract, load and transform tool, a software solution that automates the steps to integrate disparate datasets into a single database or other data storage format

SEE: What is data integration? (TechRepublic)

This process simplifies access to information and boosts analytical capabilities by integrating heterogeneous datasets across multiple systems or departments within an organization.

Importance of data integration

Data is an invaluable asset to any company; thus, employees must be able to access relevant information to make more informed decisions. Advantages of strong data integration practices include the following:

Increased productivity

When workers have all the information they need at their fingertips, they can complete tasks faster than if they had incomplete information or were searching multiple systems to collect the same data points.

Support for strategic decision-making

In today’s fast-paced environment, business leaders need access to real-time data analysis tools that help them make timely decisions with limited resources. With data integration, companies can automate repetitive processes and focus on strategic initiatives to drive competitive advantage.

SEE: Data integration vs ETL: What are the differences? (TechRepublic)

As a result, organizations reduce manual tasks while improving operational effectiveness, using technologies such as self-service business intelligence and ETL software.

Stronger data governance

Data integration leads to more centralized data storage, which offers a variety of benefits in the realm of data governance. Increased data visibility and unification make it easier to apply new and evolving data governance rules to all of your data at once. The ability to change how you manage and govern data based on evolving requirements will ensure your organization’s compliance standards remain up-to-date.

Organizational analytics

Analytics can tell you everything about what works best in one department but not necessarily what another department needs to succeed. Data integration enables cross-departmental collaboration, allowing teams to analyze the organization holistically and discover new insights from new perspectives.

These insights enable individuals to understand their contributions to the larger organization and increase organizational learning. It can also lead to new cross-functional projects and goals.

Reduced customer data errors

Employees do many daily data tasks, like looking for a customer in different systems, importing Excel spreadsheets into database tables and combining datasets. Unfortunately, each task often introduces new errors, because there is no unified view of the customer across all databases. Consolidating disparate sources into a single platform reduces data redundancy by providing a single view of each customer across the enterprise.

Decreased complexity

Data integrations simplify IT infrastructure by streamlining storage costs while eliminating redundant servers and applications. This process saves money when upgrading hardware or migrating to newer operating system versions.

Increased process visibility

A typical feature of data integration tools is data visualization via customizable BI dashboards. This feature empowers teams, allowing them to see past data patterns that could inform future products or events. A byproduct of data integration is a reduction in hours spent analyzing data and retrieving information, which translates to increased process visibility and productivity.

How to increase workforce efficiencies with data integration

Data integration is a vital part of how businesses work today. Unintegrated data cannot be used to extract meaningful insights and often leads to error-prone workflows.

With data integration, you can analyze data sets to make decisions on how to improve workflow processes and create new, more efficient ones.

It’s not always easy to know where to start or how to get your workforce onboard with data integration best practices. In order to increase workforce efficiencies through data integration, follow these tips and best practices:

Identify the objective

Before you start a data integration project for your business, it’s important to determine what objectives and outcomes you hope to achieve. Start by asking yourself these questions:

  • What are you trying to accomplish?
  • Are you looking to increase workflow efficiencies?
  • Are you looking to enhance customer service?
  • Do you want to streamline your processes in general?

Answering these questions will help determine how much time and effort needs to be put into the data integration project.

Create a data integration plan with milestones, timelines and responsibilities

A more detailed data integration plan ensures that there’s no misalignment between what is expected and what is delivered. Five aspects of a good plan are clearly defined objectives, goals and milestones, resources, deadlines and evaluation points.

This includes identifying the data to be integrated and defining the goal of the integration. Next, decide which systems need to be changed or optimized. Finally, choose a data integration strategy and implement it. Measure your success and track progress against the original integration plan. Throughout the process, work with the team to establish realistic, measurable goals for the integration.

Determine which systems need to be integrated

Before starting any data integration project, it is important to have an idea of all the systems you currently use and all the ones that may need to be included to achieve the desired results. You should identify each system and where the data resides.

After this has been done, evaluate whether those systems need to be modified in some way before being integrated. If not, create a list of recommended integrations for those other systems that might require modification.

Develop an appropriate data integration strategy

Depending on your situation, many strategies for implementing data integration include redesigning databases, converting legacy applications and automating data processing through scripts and tools like ETL. In addition to selecting a strategy, select the most appropriate tool for your specific implementation.

Define a detailed integration roadmap

Once you have selected a strategy and tool, draw up an outline of the activities required for the implementation. Doing so ensures that nothing gets missed along the way.

Integrate data from disparate sources

To boost workforce efficiency, get data from disparate sources and integrate it into one unified information set. A streamlined operation gives employees access to necessary real-time intelligence without spending too much time digging through reports or sifting through files. This allows them to spend more time doing productive work while improving responsiveness. You can set role-based access controls as necessary to ensure maximum data security and privacy.

Analyze your data

Some data integration projects may require you to look at and analyze existing data. This is crucial to ensure you choose the right data integration approach for your organization, gather enough information, and make decisions based on accurate assumptions about your data.

Test connected systems

When integrating data, testing connected systems is always a good idea. This way, you can be assured of seamless integration and will be able to more quickly notice if anything goes wrong.

Companies that fail to do this risk experiencing data errors and incompatibility problems. Data errors or incompatibility can lead to slower speeds, higher costs and even lost customers, sometimes with disastrous effects on your company’s bottom line.

Monitor your integration

At this phase in your data integration process, it’s time to see what’s working and what needs to be tweaked. Then, perform a post-mortem by discussing with your team what worked, what didn’t and how you could do things better during the next data integration initiative.

​​Data integration tools to support your business

Integrating disparate data sources is no easy task. But some tools can make the job easier, such as Hevodata, Dell Boomi, SnapLogic and Oracle Data Integrator. With these solutions in place, you can ensure that different systems pull from a common database to create a unified view of customer data across your channels.

You’ll also be able to connect with other applications to automate processes like marketing campaigns and billing. The result? Your employees will spend less time doing data entry and more time delivering personalized service to customers.



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