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Strategic Investor Relations for Technology Companies

Ensure Compliance With Data Governance and Management

Business background

Many companies are seeking to adopt advanced big data analytics technologies to improve decision outcomes while mitigating risk exposures to meet compliance requirements.  A precursor to achieving success with these initiatives is to implement data governance and data management best practices.

Data governance and data management together demonstrate how a company understands and uses its data assets as well as how those assets are managed over time.  A formal data governance program coupled with effective data management represents a better path to improving return on data assets (RDA).

Companies with higher RDAs are more agile and competitive.  They outperform their peer groups in achieving corporate return on investment (ROI) and risk management objectives.  In turn, these factors drive higher market valuations.

Tech-Tonics believes that investments in newer big data analytics technologies should be a puzzle piece of data governance and data management strategies – and budgets.  Get these strategies right and it will be easier to integrate big data into analytics workflows to improve decision outcomes.

Data Governance for Risk and Compliance

The purpose of data governance is to ensure that information accessed by users is consistently valid and accurate to improve performance and reduce risk exposure.  The more users trust the quality of the data they are working with the more reliable and predictable their models and decision outcomes will be.  This particularly applies to the users of company financial data, including analysts, investors and regulators.

Data quality supports data governance by making sure that data assets are reflected correctly within data stores and throughout business processes.  As users’ appetite for data sources continues to grow, a framework that emphasizes data quality facilitates integration of existing data sources with external ones, including newer big data formats.  Data quality also ensures that financial communications are accurate and consistent across all reporting channels.

The key factor for success of a data governance program is an environment that promotes communication, collaboration and trust among and between business users and IT.  A formal data governance program includes safeguards on data handling and usage, with clearly defined rules, roles and responsibilities.

Working together teams can identify the existing information infrastructure, applications and data sources that drive workflows.  Understanding business and technical requirements to identify the value data provides is the first step to developing a data governance cycle.  It also helps pinpoint bottlenecks and inconsistencies in data elements that inhibit optimal data usage and oversight.

The Data Governance Cycle

Data Governance Cycle

Source: Tech-Tonics Advisors

While many companies have made substantial infrastructure investments to speed data delivery, bad data at the speed of light is still bad data.  Firms can only achieve higher RDA and mitigate decision risks with a long-term data governance strategy that supports the deployment of modern analytics technologies.

As analytics and decision-making bifurcates throughout the organization, assessing and managing the risks associated with data lurking within the enterprise – and coming from external sources – becomes more complex.  Data governance provides consistency to data that is strewn across departmental and organization silos.

Without data governance, it is impossible to know whether the information presented is accurate, how and by whom it has been manipulated, and whether it can be audited.  As departments maintain their own data – often in spreadsheets – and increasingly rely on outside data sources, a verifiable audit trail is compromised, exposing the firm to compliance violations.

It can also exacerbate stock price volatility as analysts and investors react to erroneous data.  This puts undue strain on investor relations, which must rapidly identify the bad data, make sure it is corrected, and then disseminate an appropriate explanation with the correct data.

Effective Data Management is a Key Factor for Success

Too often, decision makers only have access to pieces of information and make decisions based on an incomplete view of what is happening.  After data sources have been identified and harmonized, data needs to be integrated to provide connections between datasets in order to perform analytics.

However, integrating data from a wide variety of formats and data structures without considering data quality is not only costly but can also have a catastrophic impact on enterprise systems that rely on that data.  For example, in a recent poll, Gartner estimated the average cost of poor data quality to an organization at $8.2 million per year.  Among the respondents, 22% estimated their annual costs at $20 million or higher.

This argues for centralized data management.  With all users accessing the same data stored in a unified platform, or by having users access federated data stores that have been validated, trust in the data rises and results in more reliable models that drive decisions.

A Data Lake Centralizes Data Management

Data-Lake

Source: Dreamstime

A strong data management platform ensures users can access data assets as they need it, share information when required, and have the tools to “see” analytics results without the pre-definitions of restricted data sets inherent in legacy business intelligence platforms.

Although different business units may define entities differently, it is still important to make sure that all users access the same data.  Firms need to manage diversity of terminology and definitions by maintaining strong metadata while providing users with the flexibility to analyze data using modern tools.

Effective data management also eliminates the need for a separate big data strategy.  Big data becomes part of the firm’s data management strategy.

This is especially true as 90% or more of data that drives business processes and decisions are from traditional sources.  As more unstructured data is incorporated into workflows, technologies can be incorporated to aggregate, analyze and manage these newer data formats in an evolutionary manner.

How Advanced Analytics Helps

It simply doesn’t make sense for most companies to either rip-and-replace their existing data management infrastructure and analytics tools or to risk creating yet another data silo for the relatively small component of big data that drives their analytics and decision processes.

Data governance and management becomes more strategic as companies evolve from static, database-centric systems of record that report on historical results toward dynamic, real-time systems of engagement that generate insights faster and inform better decisions on fresher, more accurate data.  Modern systems allow IT to develop the framework that business users can access without having to rely on knowledge of what may be needed.

Advanced analytics can serve as an early warning system for investor relations and legal departments to prevent disruptions caused by trading errors, improper systems oversight or other compliance violations.  These solutions can provide better visibility into both operational and financial data, and reference past experiences to help predict system-impacting occurrences in advance.

The tighter controls a company has over its financial information, the less likely it is to experience the negative impacts of bad data dissemination.  These include a decline in share price, possible compliance violations, and damaged credibility with analysts and shareholders.

Contact us today for a briefing.

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