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How Data Governance Supports Analytics By Jason Rushin

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Data governance is essential to fully leverage the value of data analytics. Learn more about the challenges and benefits of data analytics and governance.
The post How Data Governance Supports Analytics appeared first on Alation.

How do businesses transform raw data into competitive insights? Data analytics. Modern businesses are increasingly leveraging analytics for a range of use cases. Analytics can help a business improve customer relationships, optimize advertising campaigns, develop new products, and much more.

As an organization embraces digital transformation, more data is available to inform decisions. To use that data, decision-makers across the company will need to have access. However, opening the floodgates of information comes with challenges. People might not understand the data, the data they chose might not be ideal for their application, or there might be better, more current, or more accurate data available.

An effective data governance program ensures data consistency and trustworthiness. It can also help prevent data misuse. In a governed data-driven environment, people can easily access data, trust it, and uncover meaningful insights.

What is Data Analytics? Value and Challenges

Data analytics is a way to make sense of raw data. Raw data includes market research, sales data, customer transactions, and more. Analytics can identify patterns that depict risks, opportunities, and trends. And historical data can be used to inform predictive analytic models, which forecast the future. The insights gained from analytics can provide value to businesses in several ways.

What Is the Value of Analytics?

Optimize Processes and Improve Efficiency

Evaluating historical data allows businesses to identify and mitigate potential problems early. Organizations can also compare operations under different conditions. This helps determine which processes had the best results, and which need improvement.

Drive Strategy and Increase Innovation

Data analytics is an effective way to gather information, make predictions, and drive strategy. Businesses also use data analytics to understand current market needs, trends, and opportunities. From there, they can adjust processes or develop new products based on market demands.

Inform Decision Making

With data analytics, decision-makers can identify why certain outcomes occur. They can analyze more efficient processes and even forecast success. Because of this, decision-making becomes easier and more efficient.

Boost Revenue

The use of data analytics can also reduce costs and increase revenue. An example of this is by optimizing resource use. Analytics track ecommerce activities, ad campaigns, and marketing campaigns. This provides feedback on their efficiency and effectiveness. With improved insight, resources are then reallocated for the greatest benefit.

Analytics can also be used to model useful information. For example, a Single View of Customer (SVC), provides a shared view of all customer data. This helps improve the customer experience, sales and marketing effectiveness, product development, and more. Creating a single view of any data, however, requires the integration of data from disparate sources.

Data integration is valuable for businesses of all sizes due to the many benefits of analyzing data from different sources. But data integration is not trivial. The challenges to data integration are also the same as those to implementing data analytics within an organization. Often, these challenges stem from the four V’s of data – volume, variety, velocity, and veracity.

What Are the Top Data Challenges to Analytics?

Volume

The proliferation of data sources means there is an increase in data volume that must be analyzed. Large volumes of data have led to the development of data lakes, data warehouses, and data management systems.

Variety

Despite its immense value, a variety of data can create more work. It requires advanced analytics to decipher and use for data insights, and can quickly overwhelm teams.

Velocity

Real-time streaming of data requires an equal acceleration in processing speed. Although companies used to be able to process data in batches, this does not work well in today’s data-driven environment. Companies now need faster and more agile decisions.

Veracity

Data now comes from so many sources that integrating, cleaning, and transforming it across systems is difficult. Data quality problems cause low productivity, inefficiency, high costs, and errors in decision-making.

Despite the overwhelming nature of some of these challenges, there are ways to mitigate their impact. Implementing an effective data governance strategy is one such approach. By controlling the management of data assets, businesses can ensure that data meets precise business rules and standards as it is entered into a system.

The Relationship Between Data Governance and Data Analytics

Together, data governance and data analytics make each other stronger. Data governance ensures that decision-makers can rely on analytics for actionable insights.

How? Data governance offers a path to standardization, so people glean insights from data by approved channels and methods. Aligning on language, too, is essential. Business glossaries and data dictionaries create a common language. This makes communication clear and expectations consistent.

Another benefit of data governance is the creation of transparent data processes. This includes systems for gathering, using, and storing data. A data governance framework ensures data management practices are compliant with laws like GDPR or CCPA.

It is important to not let governance impede organizational needs or slow the flow of information. A traditional “command and control” approach to governance can cause detrimental effects. More modern, active governance efforts create a dynamic decision-making framework where governance adapts to the organizational context and stakeholders are engaged in the governance process.

Furthermore, data governance helps instill trust across the organization. People are more likely to use analytics to gain intelligence if they trust and understand the data. Data governance forms the backbone of a good data management plan. Analytics helps boost its effectiveness and efficiency.

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The post How Data Governance Supports Analytics appeared first on Alation.