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Why Companies Fail to Implement a Data Governance Strategy

Author: Bankim Sheth | | March 10, 2022


 

According to Gartner, only 20 percent of analytics insights will deliver business results through 2022. In other words, 80 percent of companies’ Big Data projects will fail and/or not deliver results.

 
There are many reasons for this failure, but poor (or a complete lack of) data governance strategies is most often to blame. This article discusses the importance of solid data governance implementation plans and why, despite its obvious benefits, many organizations find data governance implementation to be challenging.

Understanding Data Governance

Data governance (DG) should be the core element within the overall organizational data management plan. Proper implementation of data governance allows businesses to use their data resources to achieve desired business outcomes.

Ideally, a solid data governance program should include the data governance team, data stewards, and the governing body, often a steering committee (data governance council) comprising top organizational stakeholders. Together, this group creates policies/standards of data governance and the procedures implemented/enforced by data stewards and stakeholders of the organization.

What is Data Governance?

There are many complex definitions for data governance. Simply put, data governance is the “set of processes/policies/strategies that manage the availability, usage/usability, security, and integrity of an organization’s enterprise business data.”

Data governance is critical in the present and future business landscape. It enables organizations to comply with ever-changing data privacy regulations and optimize their operations through sound, data-driven decision-making. An effective data governance program will ensure that the organization has trustworthy, single version of the truth, and consistent data, and prevents wrongful access/use and abuse.

Stakeholders of Organizational Data Governance

From business executives and IT professionals to data operators and end-users, several participants have key roles in ensuring data is collected, accessed, stored, and/or used appropriately. These are their specific roles:

  1. Chief Data Officer (CDO) – oversees the data governance implementation plan or program, and bears the highest responsibility for its success or failure.
  2. Data governance team – works on/coordinates the data governance program full time and reports to the DG manager/CDO, who also acts as DG manager.
  3. Data governance steering committee – business executives and representatives of data owners/operators who approve/govern DG policies and internal DG regulations and strategies.
  4. Data stewards – provide oversight of data sets to maintain data integrity and ensure implementation of policies from the committee and end-user compliance with the policies.
  5. Others – data modelers, data engineers, data architects, and data quality analysts also contribute to the DG process.

Steps to an Effective Data Governance Implementation Plan

A data governance program comprises the processes, rules, technologies, organizational structures, and policies that define how an organization implements its data governance. Every organization must document its DG framework and share/communicate it with all teams and data users.

Organizations will leverage DG software to automate various data governance and management tasks, called DG tools. These tools are often used together with tools that support master data management, metadata management, and data quality management.

Every data governance implementation plan must begin with the identification of custodians/owners of enterprise data assets. The CDO or another C-suite executive oversees creating the data governance program structure, including creating the DG team, identifying appropriate stewards, and convening the steering committee.

Once the structure is operational, the teams must develop policies and standards, authorization protocols, and protocols for access and usage. They must also implement rules that govern ongoing operations and define controls and audit procedures to ensure data consistency and operational compliance with internal and external regulations.

The data governance strategy should also define and document all data sources and procedures for its storage and security, i.e., how to protect it from unauthorized access, security attacks, and other mishaps.

Master Data Management vs. Data Governance

Data governance is often mistaken for Master Data Management (MDM), a closely related term. Master data refers to enterprise data that defines the subjects involved in business communications, events, and transactions–it defines the ‘who,’ ‘what,’ and ‘where’ of business subjects.

Therefore, MDM is a program that covers the operational processes and policies for manipulating master data to deliver understandably controlled, fit-for-purpose, and trusted master data. The rules implemented in MDM are defined within the data governance program, from creation and access of master data to its usage and eventual disposal.

Data governance does not operate as a separate process from master data management. However, it extends beyond MDM to govern and adjudicate all operational processes executed on enterprise data, master data, or otherwise. MDM requires data governance to function optimally.

The Benefits of an Effective Data Governance Program

The following are key benefits of having a solid data governance program:

  • Breakdown data silos – data silos rise where teams operate without centralized coordination, resulting in different data sets that have undergone different manipulations.
  • Harmonize organizational data – if all data in the organization is consistent, all operations will operate using the same data, increasing their efficacy and eventually improving performance.
  • Ensure proper data usage – authorized users access and use the data as required to avoid errors and block potential abuse and misuse of personal, sensitive, or proprietary data.
  • Balance privacy mandates with data collection practices – observe privacy regulations while collecting data to aid business decision-making.
  • Harmonize data policies – create uniform policies and procedures to monitor and enforce adherence to said policies continuously.
  • Improve data quality – this results in more accurate BI insights, eliminates data redundancy, decreases data management costs, and allows for stronger regulatory compliance.

 
There is a competitive advantage for the organization that has high-quality, uniform, and centralized data. Accurate insights result in accurate predictions/forecasts, and executives can work proactively to take advantage of opportunities or avert crises. This directly leads to increased ROI, revenues, and profits.

The Negative Impact of Data Governance Failures

Data governance failures can have disastrous consequences in the organization. The apparent result of non-existent, improper, or ineffective data governance is inconsistent, insecure, and illogically stored data. This makes it harder to merge, verify, and access data later. Incorrect data affects the quality of any analytics or business intelligence (BI) insights and reports gleaned from the data.

Weak data governance also impedes internal and external regulatory compliance. The company loses its customers’ trust after a disastrous breach. At worst, they have broken several laws and must pay hefty fines after the breach besides the lost revenue from defecting customers.

Other consequences of data governance failures include:

  • Security breaches – the latest Identity Force report showed over 300 million personally identifiable information (PII) was breached in 2020. Poor DG can lead to breaches that expose customers to identity theft. Businesses take years to recover after such breaches.
  • IP risks – businesses invest millions in research and development and can lose proprietary data on new and innovative products, services, and methods to external actors.
  • Expensive storage – most companies don’t even know what data fills up their storage and end up storing irrelevant data from unknown sources. Effective DG enables organizations to store only valuable and business-critical information to decrease storage costs.
  • Ransomware attacks – the worst outcome of a data breach is having the bad actors hold your company data hostage in an extortion scheme.

Common Reasons for Data Governance Issues

Many organizations don’t have a solid data governance implementation plan despite appreciating the importance of a solid data governance strategy. Some have documented their data governance strategy, but as their data ecosystem becomes more complex, the plan moves to exist only on paper.

There are several reasons for this:

Steering Committee

Implementing a data governance program may fail because the company does not wish to commit its resources to data governance efforts. Members of the DG steering committee often have other designations in the company, and their DG oversight role may play second fiddle to their primary job.

Business executives cannot be separated from the steering committee; understanding the business applications of data, they are best placed to define the rules, procedures, and metrics of the data governance strategy.

It is also easy for the steering committee to create a data governance program that is so high-level that no one in the organization can implement it. Conversely, they may become bogged down by the nitty-gritty of the program and fail to make progress building the foundational framework.

Unrepresentative Data

It is harder to implement a data governance strategy where different parts of the enterprise work with unexpected or unrepresentative data. End-users may apply data in ways the program makers didn’t expect because the strategy does not reflect the current business reality or other reasons.

Expectation gaps can result in data quality issues and disparate data sets that are more complex to integrate. Eventually, the business/stakeholders simply give up.

Inconsistent Data

Inconsistent data can result where different departments use different vocabulary to process their data. This creates confusion and leads to inaccurate/poor insights, and directly interferes with the initial data governance implementation plan.

Without a standardized process to define KPIs, the company will have disconnected reports, where the centralized assets don’t reflect the true health of the enterprise. There is no single version of truth to inform overall strategy, and the departments may end up undermining one another. In a vicious cycle, this makes it harder even to begin developing a data governance strategy.

Data Silos

Data silos are information repositories controlled by singular teams or departments and not visible across teams/departments in the organization. Isolated data leaves the organization unable to standardize its data repositories consistently, and other departments cannot benefit from the insights in the siloed data.

Business Value Demonstration

Many data professionals struggle right at the beginning – getting management to approve a data governance program. You can overcome this challenge by curating data quality disasters and connect how the program can prevent them while prioritizing the enterprise’s specific core objectives.

Data governance programs must be justified after implementation by developing quantifiable metrics that express how the data governance strategy adds value. It also helps to have the data governance implementation plan well-thought-out before approaching top executives.

Work with Datavail Experts to Implement Data Governance

Data governance failures cost businesses of all sizes billions of dollars every year. Just one company, Citigroup, was fined US $400 million in 2020 for ‘several longstanding deficiencies’ in their risk management and data governance practices.

Conversely, when implemented correctly, a data governance strategy can help to break down those pesky data silos, standardize and integrate your data, and provide accurate insight for business-wide action.

Do you want to learn more about creating and implementing a strong data governance program? Download our whitepaper to find solutions to the gnawing challenges of implementing your data governance strategy.

Ready to get started on your data governance implementation plan? Our Datavail expert team is at your disposal – contact us today!

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