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Data Integration – How an Integration Platform Can Unify Business Data

Unifying business data with an integration platform and data integration tools

Data integration platforms and tools play a crucial role in unifying business data by enabling disparate data sources to be combined into a cohesive, accessible and usable format. A dedicated integration platform and its data integration tools facilitate the consolidation of data from various systems, databases and applications, thereby creating a unified view that supports better decision-making and operational efficiency within an organisation.

Why do businesses need data integration?

At the heart of data integration is the need to bring together data from different sources that often have varying formats, structures and protocols.

Businesses typically operate with multiple software systems, each optimised for specific functions, such as customer relationship management (CRM), enterprise resource planning (ERP) or supply chain management. These systems generate and store vast amounts of data that, without integration, remain siloed and underutilised.

Therefore, data integration is an essential component within any organisation as it enables them to create a cohesive and comprehensive view of their data, which is essential for informed decision-making and strategic planning.

By integrating data from these diverse sources, businesses can combine disparate datasets into a unified repository. This consolidation is vital for several reasons.

First, it enhances the quality of the data by cleaning, transforming and standardising it. High-quality data is essential for accurate analysis because it reduces errors and inconsistencies that could lead to flawed insights.

Second, data integration significantly improves the decision-making process. When data from different sources is brought together, it provides a more comprehensive dataset for analysis. This enables decision-makers to identify trends, patterns and correlations that might be missed if data were analysed in isolation. As a result, businesses can make more informed and timely decisions, which can lead to better outcomes and a competitive edge in the market.

Moreover, data integration increases operational efficiency by automating the consolidation and transformation of data. This automation reduces the time and effort required to prepare data for analysis, allowing business analysts and other stakeholders to focus on extracting valuable insights rather than on data wrangling.

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How is data integration achieved?

Data integration relies on various connectors and tools to bridge these silos by extracting data from each source, transforming it into a common format, and loading it into a central repository, often a specified database, data warehouse or data lake.

Extracting data

The first process of data integration begins with data extraction, where data integration platforms and tools connect to various data sources and retrieve the necessary data. These sources can range from relational databases and legacy systems to cloud-based applications and external data feeds.

Modern data integration solutions support a wide array of connectors that simplify this process, allowing seamless access to diverse data sources without extensive custom coding.

For example, BPA Platform provides a range of dedicated connectors, many pre-configured, for CRM, eCommerce and ERP systems, including Amazon, Epicor, HubSpot, Infor CRM, Magento, MailChimp, Microsoft Dynamics 365 Business Central, Sage 200, Sage Intacct, Salesforce, SAP Business One, SAP Concur, Shopify, SugarCRM, SYSPRO and WooCommerce.

It also provides a variety of ‘execute’ tools that enable data extraction from a wide-range of applications, databases, systems and web services:

  • The Call Stored Procedure (OLEDB) Tool executes database stored procedures and functions to provide read, write, and automated data processing.
  • The Database Query (ODBC) Tool and Database Query (OLEDB) Tool allow the extraction of specific information sets from relational database systems such as ERP, financial, accounting, MRP, production, CRM, HR, project, and bespoke systems. Once obtained, the data is then available to other task steps for manipulation or delivery, or both.
  • The Database Query (HANA) Tool provides BPA Platform with direct access to an SAP HANA database. This tool allows you to design and run queries that return structured data otherwise unavailable when using the tools from the SAP Business One Integration Tool Pack.
  • The Import Flat File Tool imports a delimited or fixed-width file from disk into BPA Platform, extracts the file content including any file properties, then outputs it as either a recordset or as XML. The columns that make up the recordset, and the nodes and nested elements for the XML file, can either be inferred from an imported example or created manually.
  • The Import XML Document Tool imports an XML document to be used and processed by other task steps. To validate the XML documents at runtime, you can either import an XML schema file (XSD), inferred from an existing XML document, or manually constructed.
  • The Web Service Connector Tool allows BPA Platform to integrate with a wide number of commercial web services, making it possible to automate the passing of data requests and retrieval to and from the website that would normally be carried out manually.

Transforming data

Once the data is extracted, the next step is data transformation – also known as data interoperability. This involves converting the data into a standardised format, which is essential for ensuring compatibility and consistency across the integrated applications, systems and databases.

Transformation tasks include data cleansing, where errors and inconsistencies are corrected; data enrichment, where additional relevant information is added; and data normalisation, where data is restructured into a common schema.

Data integration platforms often include sophisticated transformation capabilities, such as ETL (Extract, Transform, Load) processes, which automate these tasks and ensure that the data is prepared correctly for integration.

Again, BPA Platform provides a variety of tools to convert and transform data so that the target system or database can read and manipulate the data:

  • The Convert Recordset to XML Tool allows the conversion of one or more flat-structure BPA Platform recordsets into a hierarchical XML structure. This way, it is used to provide “translation services” between the standard BPA Platform tools, such as Database Query (ODBC), and advanced BPA Platform tools that accept XML as their input.
  • The Convert XML to Recordset Tool is used to convert XML data into a flat-format BPA Platform recordset, allowing the data to be used by recordset-only consuming tools. This could then be used to power alerts via fax, email, or SMS, create reports, power a workflow, perform FTP transfer, and so on.
  • The Format as Flat File Tool takes a BPA Platform recordset and converts it into a flat file to be used by another task step or external program. The flat file output can be delimited or fixed width. You can also choose whether the flat file contains data for single or multiple record types.
  • The Format as HTML Tool is used to create a task step that produces single or multiple HTML documents. You can choose to create the HTML template from scratch or import a pre-existing template. Recordsets and other task step properties can also be used to populate the HTML template to produce dynamic content documents. These documents can then be consumed and then delivered by Output or Execute task steps.
  • The Format as Text Tool creates single or multiple text documents, using information provided by Input tools such as the Database Query (ODBC) tool. It requires no global settings. If required, you can produce dynamic documents such as, alerts and notifications, or data formats such as .CSV for spreadsheets or uploading to other systems.
  • The Microsoft SQL Server Trigger Tool uses the Microsoft SQL Server trigger functionality to create a trigger on a table of a specified SQL data source. When a specified data modification is attempted, such as an attempt to add or delete a table row, the trigger then causes a task to run.
  • The Transfer File (FTP) Tool is used to create a Task Step that uploads or downloads one or more files to and from one or more FTP Servers. This is a powerful tool which can be used, for example, in the automated publishing of key information to Intranet / Internet sites.
  • The Transform Data Tool is a Format tool that enables a new structure to be created from an existing XML or recordset data source. The output generated by this tool is ready for use in a destination system which consumes or requires XML.
  • The Filter Data Tool extracts a subset of data from a recordset produced by a Database Query (ODBC), Database Query (OLEDB), or Call Stored Procedure (OLEDB) step and provides the data for use in further steps.
  • The Parse Text Tool extracts specific data from variables populated by other steps and then maps the data to other variables for use in further steps.

Accessing and analysing data

After transformation, the data is loaded into a central repository via the targeted application or system, where it can be accessed and analysed. This repository, typically a specified database, data warehouse or data lake, serves as a single source of truth for the organisation, providing a comprehensive view of all business data.

Data warehouses are optimised for query performance and structured data, making them suitable for reporting and analytics. Data lakes, on the other hand, can store large volumes of unstructured and semi-structured data, offering greater flexibility for big data analytics and advanced data processing.

The unified data repository enables businesses to perform more accurate and comprehensive analyses. With all relevant data consolidated in one place, decision-makers can easily access all the information they require, or the data can be automatically transformed into reports, using tools such as a Run Crystal Report Tool or Run Microsoft Reporting Services Tool, so that they can gain insights that were previously obscured by data silos.

For example, a unified view of customer data from CRM systems, transactional data from ERP systems, and interaction data from customer support systems can provide a 360-degree view of customer behaviour, preferences and needs. This holistic understanding can drive more targeted marketing campaigns, improved customer service and more strategic business decisions.

Moreover, an integration platform often includes features for real-time data integration, enabling businesses to have up-to-date information at their fingertips.

Real-time integration is particularly valuable in scenarios where timely data is critical, such as in financial services for fraud detection, in manufacturing for supply chain optimisation, or in retail for inventory management. By integrating data in real-time, businesses can respond more quickly to changing conditions, make more informed decisions and maintain a competitive edge.

In addition to providing a unified view of data, an integration platform enhances data governance and compliance. By centralising data, an integration platform makes it easier to implement consistent data governance policies, ensuring data quality, security and privacy.

This is especially important in industries with strict regulatory requirements, such as healthcare, finance and government. An integration platform can enforce data access controls, monitor data usage and ensure that data handling practices comply with relevant regulations, thereby reducing the risk of non-compliance and associated penalties.

Furthermore, an integration platform can support data democratisation by making unified data accessible to a broader range of users within the organisation.

Advanced integration platforms often include user-friendly interfaces and self-service capabilities, enabling business users to access and analyse data without requiring extensive technical expertise. This democratisation empowers employees at all levels to leverage data in their decision-making processes, fostering a data-driven culture and enhancing overall organisational agility.

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How do companies use data to improve business?

Companies rely on data to improve their business operations, strategies and outcomes in several transformative ways. By harnessing data, organisations can gain insights into customer behaviour, optimise processes, enhance decision-making and drive innovation.

Firstly, data enables companies to understand their customers better. By analysing customer data, businesses can identify buying patterns, preferences and feedback. This understanding allows for personalised marketing strategies, targeted promotions and improved customer service.

For instance, eCommerce platforms use data analytics to recommend products based on past purchases and browsing behaviour, enhancing the customer experience and increasing sales.

Secondly, data-driven insights lead to more informed decision-making. Companies collect and analyse data from various sources to monitor performance, track key performance indicators (KPIs), and identify trends.

This empirical approach reduces reliance on gut feelings and assumptions, leading to more accurate and effective business strategies. For example, financial institutions use data analytics to assess risk, forecast market trends and make investment decisions.

Thirdly, operational efficiency is significantly enhanced through data utilisation. By analysing data on production processes, supply chain logistics and employee performance, businesses can identify inefficiencies and areas for improvement.

Manufacturing companies, for instance, use data to optimise inventory levels, reduce waste and improve production schedules, resulting in cost savings and increased productivity.

Moreover, data fosters innovation by revealing new opportunities and driving product development. Companies analyse market data, competitor performance and consumer trends to identify gaps in the market and innovate new products or services. Technology firms, for example, rely on data to develop cutting-edge solutions that meet evolving consumer needs.

Additionally, data supports strategic planning and risk management. By examining historical data and predictive analytics, companies can anticipate future trends and potential challenges. This foresight allows businesses to develop proactive strategies, allocate resources effectively and mitigate risks. Retailers, for example, use data analytics to predict demand fluctuations and adjust their inventory and staffing levels accordingly.

How to choose a data integration platform

Selecting the right data integration platform is essential for ensuring efficient data processing, high-quality data output and the overall success of your data strategy. Here are key considerations to guide you in choosing the best data integration platform for your needs:

1. Define your requirements

Start by clearly defining your data integration requirements. Consider the following:

  • Data sources: Identify the types and number of data sources you need to integrate. These could include applications, cloud services, databases, and more.
  • Data types: Determine the types of data (structured, semi-structured, unstructured) you will be dealing with.
  • Data volume and velocity: Assess the volume of data and the speed at which it needs to be processed. Real-time integration demands different capabilities compared to batch processing.
  • Business objectives: Understand your business objectives, such as data warehousing, analytics, operational reporting, or real-time data synchronisation.

2. Evaluate platform features and capabilities

Once your requirements are clear, evaluate potential platforms and tools based on their features and capabilities:

  • Connectivity: Ensure the integration platform can connect to all your data sources and destinations, including APIs, cloud platforms, databases, and on-premises systems.
  • Cloud-based or on-premises: Decide whether you want to install the integration platform on your own servers or benefit from iPaaS whereby a provider hosts the platform in the cloud.
  • Scalability: Choose a platform that can scale with your data volume and user base, both horizontally and vertically.
  • Data transformation: Look for robust data transformation capabilities that allow for cleansing, enriching, and converting data into the desired format.
  • User interface: Consider the ease of use and user interface. Platforms with intuitive graphical interfaces can be more accessible to a broader range of users.
  • Automation and scheduling: Check if the platform supports business process automation and scheduling to streamline regular data integration tasks.
  • Real-time integration: If real-time data integration is crucial, ensure the platform supports real-time or near-real-time data processing.
  • Data quality and governance: Evaluate the platform’s data quality and governance features, including data lineage, error handling, and compliance with data protection regulations.

3. Assess performance and reliability

Performance and reliability are critical for any data integration platform:

  • Speed: Test the platform’s data processing speed and its ability to handle large data volumes without significant delays.
  • Reliability: Look for a platform with high availability, robust error recovery, and failover mechanisms to ensure uninterrupted data integration.
  • Security: Ensure the platform has strong security measures to protect sensitive data during transmission and storage.

4. Consider integration with existing systems

The chosen platform should integrate seamlessly with your existing infrastructure:

  • Compatibility: Verify compatibility with your current systems, including databases, applications, and data warehouses.
  • APIs and customisation: Check for API support and customisation options to tailor the platform to your specific needs.
  • Ecosystem support: Evaluate the platform’s ecosystem, including community support, third-party integrations, and the availability of plugins or connectors.

5. Analyse cost and return on investment

Cost is a significant factor in the decision-making process:

  • Pricing model: Understand the platform’s pricing model, whether it’s subscription-based, license-based, or pay-as-you-go.
  • Total cost of ownership: Consider the total cost of ownership, including licensing fees, implementation costs, maintenance and support.
  • Return on investment: Assess the potential ROI by evaluating the platform’s impact on efficiency, productivity and data-driven decision-making.

6. Vendor support, services and resources

Vendor support and an active user community can greatly influence your experience with the platform:

Choosing the right data integration platform requires careful consideration of your specific needs, the platform’s capabilities, performance, cost and vendor support. By thoroughly evaluating these factors, you can select an integration platform that not only meets your current requirements but also scales with your future data integration needs, ensuring robust and reliable data management.

A unified view of data

Data integration platforms and tools unify business data by consolidating disparate data sources into a centralised, accessible and usable format. They facilitate the extraction, transformation and loading of data, ensuring that it is compatible and consistent across the organisation.

By providing a unified view of data, an integration platform enables more accurate and comprehensive analyses, supports real-time decision-making, enhances data governance and compliance, and promotes data democratisation.

As businesses continue to generate and rely on vast amounts of data, the role of data integration platforms in unifying this data and unlocking its full potential becomes increasingly vital.

For more information on the benefits of data integration or automating business processes and how they can help your business, download the brochure below or call us on +44(0) 330 99 88 700.

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