Breaking Down Data Silos: Five Essential Best Practices

This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Pimcore CEO Dietmar Rietsch offers advice on breaking down data silos through essential best practices you need to know.

Over the years, the volume and speed of data creation and generation have changed beyond measures of human comprehension. With the exponential growth of data, capitalizing on data is the new business differentiator. However, businesses face various infrastructure and data management complexities to become more data-driven and accelerate transformation. One such complexity stems from storing data across different departments/divisions in separate locations, further segmented by products, services, and functions. The diversity of this data does grow, albeit in silos.

Now even if multiple data repositories may seem harmless at first, its immediate resultant – siloed data – creates barriers to information-sharing, insightful analysis, and collaboration between departments while forcing IT teams to spend most of their time managing the data infrastructure. Due to data inconsistencies across systems, the data quality often gets compromised. In addition, data management functions and other high-level executives find it harder to get a holistic view of the organization’s data in time to make critical business decisions.

Moreover, suppose an enterprise runs on a siloed organizational structure and has a shortfall of appropriate technology and tools otherwise necessary to unify data. This only results in lack of transparency, efficiency, and trust within the enterprise and across customers. So, how should organizations handle different islands of data to create a positive impact?

Here are some essential best practices enterprises can follow in breaking down organizational silos and taking control of their data:

Breaking Down Data Silos

Establish a Data Governance Program

Building a data governance program helps outline the rules, roles, and processes an organization will need to follow in ensuring data quality. It facilitates establishing a comprehensive policy that assists in breaking down data silos along the way. Data governance policy covers a statement of purpose and defines the missions and goals of how data should be used appropriately across aspects such as organizational structure, data creation, data access, data ethics, instate procedures, disciplinary practices, and data sharing. It might sound like the IT department’s responsibility; however, organizations must realize that such a program is not just the IT team’s authority in setting and enforcing uniform data standards.

Instead, the program is driven by business managers, including a steering committee of senior executives and other data owners at the disposition to make policy-related decisions.

Define Data Ownership and Access Controls

While data governance focuses on high-level procedures and policies pertinent to data management, assigning data ownership or stewardship offers tactical coordination and execution. By defining data ownership or stewardship across departments, enterprises can effectively distribute the weight of carrying out data usage and security policies determined through data governance initiatives. This helps establish a liaison between the business side of an organization and the IT department. Moreover, inventorying of corporate data, accessing the same, and where it’s needed are best handled by assigning individual roles for each data element along the taxonomy.

This helps avoid spillage across divisions and designated data owners can seamlessly translate the enterprise’s strategic data needs into desired outcomes without duplication.

Implement a Data Discovery Tool

Data discovery tools are known for breaking down data silos by eliminating bottlenecks surrounding an organization’s information or data assets, making it easier for relevant stakeholders to understand. By implementing these tools, enterprises can readily access collated data from various sources on a single interface. Moreover, users can search for data just as they would on a website and make the most of advanced analytics and machine learning (ML) models via simple visual interaction for actionable insights.

By integrating business intelligence (BI) solutions via data integration tools and master data management (MDM), enterprises can easily clean, combine, and analyze complex datasets to harvest insights and make impactful discoveries that enhance customer experiences.

Train Users to Find and Use the Right Data

The power of driving a business using data insights is undeniable. But no matter how clear the path is to making key decisions based on a governed data framework, applying this approach is a different story altogether for many enterprises. From leaders and managers to various teams, the need of the hour is multi-dimensional training programs encompassing all the skills, knowledge, and strategies required to enable business users to become data-fluent and -confident.

Such programs also empower them to apply the correct data to their decision-making across the board using the right tools. But a few organizations may fall short of this, partly due to the lack of a data-driven culture that helps develop the required skills in harnessing the true potential of data and further deploying it to drive hyper-relevant customer experiences.

Build a Data-Driven Culture

To empower teams to become data-driven decision-makers effectively, there’s a need to establish a data-driven culture that provides the right kind of vision, structure, tools, and collaborative synergy. In addition, if enterprises are looking to transform their work culture to have a competitive edge, it is even more essential to instill data-driven decision-making as a core competency. When building a data-driven culture, a few aspects such as choosing KPIs carefully, not pigeonholing data scientists, fixing basic data-access issues quickly, providing specialized training based on roles, trading data flexibility for consistency, and more should be factored in.

Implementing these pointers is foundational to making all employees use available data while facilitating a shift in their mindset, which is otherwise considered a daunting task for some.

Eliminate Data Silos with the Right Kind of Platform

Disparate organizational data, more importantly, customer data housed in separate systems, applications, and other locations can lead to inefficiency, inconsistency, and reduced ability to use for insightful analysis. Here, integrating the right customer data platform solution can help consolidate customer data into a single repository, which can then be accessed by any department or individual across the organization.

Moreover, implementing an analytics tool on consolidated data helps derive better, deeper insights that can anchor hyper-relevant campaign messaging and enhance operational efficiency. Additionally, the solution allows businesses to implement data-driven initiatives like enabling marketers to stitch their data together in real-time, segment their audiences, and target them across the proper channels.

Here are a few other benefits enterprises can gain from integrating a customer data platform solution:

• Unify data sources by merging, auditing, and removing duplicates to create a consistent and real-time single-view of customer data
• Foster collaboration and enable teams to function as cohesive units that align as bedrocks for executing common goals in governance
• Drive data-driven decision-making across teams such as products, marketing, support, sales, operations, and customer success
• Speed up operations across departments by ruling out the need to spend time unearthing scattered data from multiple sources

Enterprises need to think holistically about data management solutions to eliminate data silos through centralization and standardization of organizational and customer data, replacing data stagnation with continuous innovation. In the future, the exponential growth of data will only accelerate.