4 Data Democratization Best Practices

Over the past decade, data analytics have increasingly been fueling better business decisions. Data democratization is shifting that trend into high gear by putting powerful insights directly into the hands of front-line users across the organization. That comes with some risks.

As companies increase data democratization, they also increase the possibility that data will be misinterpreted or even deliberately misused. If data silos are allowed to persist, users will be left to grapple with incomplete information. Poor data quality can hamper efforts to elicit meaningful insights, leading to a loss of confidence among decision-makers. Data security and data privacy can raise even bigger concerns, as compliance risks loom large for most companies, especially those who operate globally.

To get ahead of these problems, follow these data democratization best practices:

1. Eliminate Data Silos

Today’s enterprises rely on a diverse array of software systems to manage customers, vendors, products, and core financials. The old days of all-encompassing monolithic systems are over. Today’s companies are operating ERP, CRM, digital marketing automation, ecommerce, human resources, and more – often in different software systems.

Each of these elements represents a data silo. There’s a lot of redundancy across these multiple data sources, but there is often no direct connection between them. The customer stored in your ERP system might have a different shipping address from the one in your CRM database. When a customer updates their email on your ecommerce website, that information might not flow to the other systems that track individual customers. Duplicate entries are a frequent problem as well, especially in databases that store information on customers and prospects.

A robust and flexible data integration platform solves the problem of data silos by synchronizing changes in each major system to the other systems throughout the enterprise. This serves as a critical first step toward consistency and completeness of your data.

In the context of data democratization, it means your users can expect to have access to data that reflects the totality of each customer, vendor, product, or cost center regardless of where the source data originates.

2. Inventory Your Data Assets

The problem of data silos doesn’t end with major standalone systems like ERP or CRM. Virtually all businesses must also contend with an array of one-off data sources such as homegrown databases, Excel spreadsheets, and flat files. These often reside on shared file servers or even on individual users’ hard drives.

Any business that doesn’t inventory its physical assets is clearly operating with some major blind spots. In the same way, today’s organizations must have a clear picture of which data assets they own, where that information is located, who should have access to it, and how it is used.

By cataloging and profiling your data using enterprise-grade data quality tools, you can gain visibility into gaps in information security, data redundancy, additional data silos, and inconsistency. One of the key challenges with this process is that most enterprise landscapes are constantly changing. New systems are brought online, old ones are decommissioned, and new ad hoc data sources are created routinely.

Data democratization efforts must be accompanied by a comprehensive data governance across the enterprise, so that as information is made available to business users, the data analytics team can understand the lineage of that data and how it is used.  Enterprises need scalable data quality and governance tools with proven data profiling and cataloging capabilities.

3. Manage Data Quality Proactively

The first two data democratization best practices serve as stepping stones for the third one. As data silos are eliminated and your organization gains a deeper understanding of your existing data assets, your attention can then shift to proactively managing data quality.

For data democratization to serve your organization effectively, users must know that they are working with accurate, consistent, complete data sets.

The best data profiling tools apply AI and deep learning to understand your data contextually. Phone numbers, for example, align with different formats in various countries. They may often include punctuation such as dashes, parentheses, or plus signs. Sometimes they include a country code and/or area code, while in other cases that information is omitted. All of these anomalies can make it a bit complicated to recognize a particular string of characters as a phone number.

AI algorithms can automatically identify fields as phone numbers, provincial names or code, email addresses, and more. This is especially important when you consider the importance of data privacy regulations such as GDPR, CCPA, and Australia’s newly enacted privacy law.

4. Prioritize Security, Privacy, and Compliance

Data security and compliance have become hot topics. As data democratization expands, security and privacy are more important than ever.

The European Union started the ball rolling when it adopted the General Data Protection Regulation (GDPR) in 2016. Since then, other jurisdictions have followed suit. California’s Consumer Privacy Act aimed to assert the rights of individuals to control the personally identifiable information (PII) that corporations collect and store about them. Australia has updated its “Australian Privacy Principles,” (APP) to align with the same philosophy.

While there are differences among these various bodies of regulations, they all amount to more or less the same result; enterprises must take responsibility for managing and safeguarding the PII that they control. Automated data profiling and data governance tools help to identify information that may fall under the definition of PII under these various regulations. and how to used.  That makes it easier for organizations with diverse data assets to comply with GDPR, CCPA, APP, and other laws.

Data Democratization Best Practices in Summary

Precisely views data integrity as a holistic undertaking. The process begins with data integration to eliminate data siloes. Data discovery, data profiling, and data quality tools enable the detection and remediation of errors at scale. Enterprise-grade tools allow for continuous monitoring, which enables ongoing quality and consistency.

If you would like to learn more about data democratization read our eBook Managing Risk & Compliance in the Age of Data Democratization.