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The increasing importance of artificial intelligence (AI) and data analytics in strategic planning for small businesses and entrepreneurial startups requires businesses to be organized around ease of access to accurate data. This becomes a challenge for small businesses and even larger enterprises when leadership allows an organizational framework to form that results in departments working in isolation from one another and data silos to form.
To combat data silos more effectively, it’s necessary for small business owners to understand what they are and the problems that they introduce into the data flow for companies that rely upon data analytics to make business decisions and power their AI-focused applications.
Data silos are business process-specific data repositories that are isolated from other departments and business units in the organization. While data silos occur often in large organizations, they can also occur in small companies that manage each business unit and department separately.
According to the Harvard Business Review, 84% of executives report suffering from the negative effects of data silos. [1] Data silos can occur because businesses intentionally organize operationally around their formation, or they can develop over time as department leaders seek to streamline business units in an effort to operate more efficiently. Not addressing the formation of silos can result in up to a 30% revenue loss annually. [2]
Regardless of how well-intentioned, however, allowing the formation of a data silo introduces several challenges and issues into the operations management space for small businesses and start-ups.
Data silos make cross-departmental collaboration difficult, as data and communication flows vertically within a silo rather than horizontally across departments.
Because each department’s data is self-contained and difficult to access across teams, data silos limit leadership’s ability to have operational oversight of company performance and financials.
Data silos make it difficult for analysts and leadership to make data-driven strategic decisions, as the isolated nature of data silos prevent the collation of data into a comprehensive foundation from which to draw insight.
The challenges that arise when data silos form can cost a business both in additional costs as well as have a more indirect impact on the financial performance of the organization. Silos are such a problem, in fact, that 47% of marketers surveyed by Treasure Data point to data silos as their biggest challenge in gaining insights. [3]
Data silos impact how well department leaders can manage their teams and impact overall productivity of the business.
Poor communication and data sharing, especially between departments that should be working to maintain a centralized customer relationship management (CRM) database, can lead to lost business opportunities and diminished customer service.
When customers have a poor experience or cannot find valid data on your site, they’ll lose trust in your brand and won’t find value in your services.
Operationally, data silo formation isn’t always an intentional occurrence, and it can be difficult to detect their formation because departments are operating independently of one another. While your IT data management strategy should include regular inventorying of departmental systems that are in part intended to keep track of how data flows between stakeholders, the formation of data silos can still be difficult to pinpoint if your analytics and data science efforts aren’t structured properly to connect the dots.
Data silos can skew the data that business intelligence and data science tools rely upon. Some of the signs that might indicate your departments are siloing data can include:
Departments report data during strategy meetings that tell conflicting narratives.
Leadership, looking to make a data-based strategic decision, might find certain departmental data missing from their reporting, preventing them from having a full understanding of their operations.
Because data is often used for forecasting and budgeting, the formation of data silos can result in unforeseen IT costs and business process-expenditures.
When data isn’t being shared between departments, clients accessing data externally and employees working with internal data sets alike might find gaps in the data or data that is out of date because the system isn’t pulling updates properly from all sources.
It’s in the best interests of businesses to break down data silos. Not only will doing so improve the flow of data across departments and improve, but data silo elimination will reduce costs associated with data management. Driven by this need for efficiency and reducing costs, there are several approaches that business owners can deploy in order to eliminate and prevent the formation of data silos.
One method of breaking data silos down is to leverage a centralized data storage solution. Depending on how it’s being used and accessed by business intelligence and data science applications, information can be stored as unstructured, semistructured, or structured data.
Data lakes can hold the totality of a company’s data, but are especially useful for organizations that need to store large amounts of raw data used in machine learning applications.
Data warehouses specialize in structured and processed data, the type of data needed to allow data science applications to provide insight and information to business leaders.
Data fabric is a digital mesh that lays across systems, allowing data to remain stored by department but eliminating the disconnect by virtualizing the interface for data access and manipulation.
Data mesh requires a business to establish domain teams to manage data access. Each team is responsible for a specific business domain and establishing the pipelines and methods to deliver data products to users within the organization on demand.
The overall intent of integrating data is to create methods for extracting and consolidating data from the various silos and then making them available across systems and departments. Data integration requires the ability to monitor and verify data integrity and to optimize the movement of data across systems to protect it. Data integration techniques include:
Extract, transform, and load (ETL)
Real-time integration
Data virtualization
Extract, load, and transform
One of the major factors leading to the formation of data silos is an organizational structure where departments do not work together. Setting organizational goals that encourage cross-team collaboration helps facilitate the sharing of information and resources and improves productivity. As teams see the benefits of collaboration, they will begin to naturally share data more effectively and avoid forming silos.
Data silos prevent businesses from being able maintain the quality and integrity of data as it’s accessed and used across departments. A proactive approach to preventing data silos, data governance frameworks are a collection of data quality standards, access protocols, processes and guidelines for inputting data, and rules on how data is accessed and used. A properly structured framework also helps organizations to adhere to increasingly complex regulatory requirements and laws governing the protection and security of consumer data. [4]
Of the tactics business owners can use to break down data silos and facilitate the movement and access of data across departments, leveraging digital data management tools is one of the most effective. Software platforms help to bridge the gap between departments and data sources and introduce more efficient workflows to negate the impact of data silos or eliminate the silos altogether. Explore GetApp’s Data Management Software directory to discover solutions to help eliminate existing data silos or prevent their formation in the future.
Eliminating data silos can have a massive impact on how a business operates, but it might be a challenge to figure out how to start addressing the foundational issues in play. Understanding how other small businesses and enterprises have addressed data silos can provide your team with a place to start the process.
For example, data ideology helped a healthcare organization to eliminate data silos, resulting in a $4 million per year savings in operational costs. [5] By eliminating data silos, your team can regain up to 12 hours a week they currently spend tracking down or chasing data sources to support their work. [6]
Data silos can cause significant issues in the flow of information across departments and hinder productivity. The impact on an organization’s bottom line of breaking down data silos can be tremendous.
Check out these additional resources from GetApp to learn more about how to structure and manage your organization to maximize access to data and improve the guidance provided by data analytics tools.
Disentangle From Data Silos: The Key to Unlocking the Transformative Power of Your Data Assets , Data Dynamics
How Siloed Data May Limit Your Business Growth (and How to Prevent It), Entrepreneur.com
Small Business Data Privacy Protection: GDPR Compliance and Individual Rights, UpCity
Eliminating Data Silos Saves Healthcare Payer Nearly $4 MM Yearly, Data Ideology
The Impact of Data Silos (and How to Prevent Them), Dataversity
David J. Brin