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Use Data Integration to Break Down Data Silos in Businesses

Breaking Down Data Silos: Why Data Integration Matters

Imagine running a bakery where you need to produce quality baked goods to satisfy customer order demands at fifteen retail locations and an online store that ships nationally.

You might track customers and wholesale clients, orders, and sales trends. You likely place sizeable ingredient orders, track supplier details, and maintain inventory. Lastly, a notebook holds recipes, recipe development information, and allergy modifications for reference among bakers.

Without a connection between each of these points of information, you might not realize that you need to buy more flour to keep up with increasing demand for your cookies, or that what seems like a simple allergy accommodation decreases profitability by 30% for that product.

This bakery scenario is a micro example of data silos, and it illustrates how keeping your data in different “silos” can slow your company’s growth, efficiency, and decision-making capabilities.

When data are separated, gaining a complete view of your business is challenging. You might struggle to identify trends and make informed, data-driven decisions that align with your goals.

Understanding Data Silos

Data silos create barriers between different parts of your organization, inhibiting seamless communication and hindering comprehensive insights into your business operations.

What is a Data Silo?

A typical data silo happens when information is stored in isolated systems, databases, or departments within an organization, making it difficult or impossible to access and use across the entire company.

The average company has more than 2,000 data silos, according to an Entrepreneur Magazine article. While I’m sure your organization is far from “average,” it’s still a common issue in companies of all sizes and all industries — and nonprofits, as well.

This article explores the causes of data silos and how data integration solutions can break them down, maximizing the potential of your company’s data.

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Causes of Data Silos in Businesses

Data silos often arise due to several common factors within organizations.

Disparate Data Sources

In a single business, different functions often use their own particular data sets. For example, finance may rely on month-end data from an enterprise resource planning (ERP) system, while operations works with machine-generated data, and sales tracks leads and prospects.

Even within sales, disconnects between prospect and customer data can happen when a lead converts to a client. It takes deliberate effort to integrate and synchronize data across functions — otherwise, these data sets are isolated from each other, creating a lack of visibility for trends and valuable data.

Legacy Systems

As businesses grow, add new divisions, or merge with other companies, they often end up with a mix of old and new systems generating overlapping data. We commonly talk with business leaders about processes where employees manually extract and combine data from both systems.

Lack of Clear Data Organization Plan

We often find that technology issues aren’t the main reason companies struggle to bring their data together — it’s most often because they lack a clear plan for organizing data across the entire organization.

Establishing procedures for handling data and a process for centralizing data to establish clear relationships takes an investment and is frequently overlooked.

Why are Data Silos Problematic?

Data silos cause several challenges for organizations, affecting their ability to make informed decisions and run efficiently. Here are a few common challenges.

Incomplete View of Performance

Data silos make it challenging for companies to get a thorough, timely view of their performance. When financials dip, leaders must dig through separate systems to uncover underlying causes. They might need to investigate sales data, operating systems tracking time and labor, and other sources to piece together the whole picture.

Time-Consuming Analysis

The process of gathering data from multiple locations is time-consuming and often results in outdated insights by the time an analysis is completed. Decisions made based on stale data can be ineffective or even counterproductive. These processes are often not repeatable.

Data Quality Issues

When data come from multiple systems, they often have inconsistencies and mismatches in data types, naming conventions, and structure, making analyses slow and painful. We frequently see data sources lacking primary identifiers for records that must then be manually matched or matched on text identifiers, taking significant time to overcome. As an aside, it is a best practice to clean data in a source system – through manual clean ups or changes to people and processes – so that when you are ready to leverage data in analyses, it is easier to use.

Manual Reconciliation

Reconciling inconsistencies between sources manually is painstaking and takes resources away from strategic initiatives. The risk of error and questionable reproducibility contribute to inconsistencies in the data and erode trust.

Lack of Trust in Insights

Untimely, sporadic, manual, and opaque analyses lead to a lack of trust in the insights from that data, leading to delayed decisions or misguided plans that can send your business down a path of wasting time and money.

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The Role of Data Integration

Data integration serves as the bridge connecting disparate data sources, facilitating a cohesive understanding of your business landscape and empowering data-driven decision-making.

What is Data Integration?

Data integration is the process of centralizing data from multiple sources and establishing relationships between sources. Depending on the organization, our goal is often to create a unified view of data that allows many departments to do something meaningful with it, such as develop insights from the data and make informed decisions.

By integrating data from various systems and silos, companies can set up clear links, such as connecting customer information across sales, marketing, and customer service, improving their ability to ask questions of the data and get complete answers.

How Data Integration Breaks Down Silos

Data integration tries to fix “shopping cart syndrome,” where you gather items from multiple locations. Just as a pharmacy may not carry fresh vegetables and a farmer’s market doesn’t sell toilet paper, data often reside in separate systems serving different functions. Collecting and integrating this data can be time-consuming, not unlike running all over town to complete your shopping list.

To streamline this process, organizations need to set up efficient data pipelines that automate the flow of data from source systems to a centralized repository, making it readily available for analysis and decision-making.

The Benefits of Data Integration

Data integration can streamline processes, enhance data accuracy, and foster a culture of collaboration and innovation, driving sustainable growth and maximizing ROI.

Improved Decision-Making

Integrating data enhances visibility across functions, activities, and events within a business. This visibility is particularly valuable for holding companies with multiple entities when making investment decisions or considering divestments. By having a clear, unified view of data across the organization, leaders can make more informed and strategic choices.

Enhanced Operational Efficiency

Data integration uncovers insights that might otherwise go unnoticed. For instance, if a company’s truck repeatedly visits the shop for the same issue, integrated data could reveal that replacing the vehicle would be more cost-effective than continued repairs. By bringing data together, organizations can find inefficiencies and optimize their operations.

Data Consistency

Centralized data eliminates manual errors and ensures consistent business logic. While the initial build may require effort, automated processes can run regularly to push out updated, consistent data. This removes the risk of individuals making contradictory, siloed decisions based on inconsistent information.

Facilitates Collaboration and Cross-Functionality

Breaking down data silos encourages interaction and collaboration among teams. With everyone looking at the same centralized data, there’s less word-of-mouth information sharing. This fosters a more data-driven culture and promotes cooperation and collaboration across business functions as they work toward your business’s strategic objectives.

Strategies for Implementing Data Integration

Implementing data integration requires meticulous planning, proactive identification of potential obstacles, and robust governance frameworks to ensure long-term success and maximize the value of integrated data assets.

Identify Current Data Silos

Before beginning a data integration project, our team at SVA Consulting starts on an expedition to find existing data silos and the specific problems they’re causing. Centralizing data for its own sake is never our goal. Instead, we want to increase visibility and address tangible issues with our data integration services.

We begin with the end in mind, documenting key business questions and metrics that if answered would impact the bottom line. We then identify the data required to answer those questions, and plan architecture that is right-sized for what’s being asked.

For example, if an organization lacks visibility into operations and the associated costs, establishing a data integration plan can eventually help find the source of potential problems by providing a full view of related operational data across departments.

Select Data Integration Tools

Choosing the proper data integration tools is necessary for successful data integration. At a minimum, organizations need a database to store the integrated data and typically also leverage an ETL tool (Extract, Transform, Load) or orchestration tool to manage aspects of the data integration process. These tools enable companies to extract data from various sources, load data into a central database, and transform it into a consistent format.

When selecting data integration tools, consider access capabilities, scalability, compatibility with existing systems, cost, and ease of use.

Establish Governance Policies and Standards

Data governance policies and standards often support and enhance the long-term success of any data integration project. These should define roles and responsibilities for data management, outline data quality standards, and provide guidelines for data access and security. Data governance practices help your company maintain the integrity and reliability of its integrated data over time.

Implement Processes and Workflows for Data Integration

Successful data integration requires well-defined processes and workflows. These should cover all stages of the data integration lifecycle, from data extraction and loading to transformation and reporting. As a result, these processes will help to maintain the consistency and reliability of your data.

We often recommend that companies perform regular testing and validation to identify and resolve any issues that may come up.

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Bring Data Silos Down

With all the strategic initiatives companies undertake, they can only benefit from breaking down their data silos and leveraging data integration services to maximize the full potential of their data. It’s a foundation for improving operational efficiency, leading to better decision-making and collaboration.

Data integration may seem overwhelming at first, but with the right strategy in place, businesses can transform their data from a liability into a valuable asset, driving growth and competitiveness.

Connect with our experienced data integration consultants at SVA today to assess your current data processes, identify opportunities for integration, and develop a strategy that aligns with your business’s goals.

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Authored by: Jacob McClendon

Authored by Jacob McClendon

Jacob is the Director of Analytics with SVA Consulting. In his role, Jacob assists clients by strategically architecting solutions that help unlock meaning in data. Alongside a strong team, he designs and builds client-specific data automation and visualization solutions to fit the unique needs of a given client.

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