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.
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?
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.
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.
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.
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.
Benefits of Active Governance for Analytics
A strong data governance framework empowers all users to easily find, understand, trust, and use data. Before analysis can even begin, these core questions about an asset must be understood! Data governance labels and tags assets to classify data into domains and flag sensitive data as such, so people use it compliantly. This system of taxonomy allows users to answer important questions quickly. Questions like, “Which products in which markets will generate the highest revenue?” can more easily be posed and answered with a strong governance framework in place.
Many organizations are turning to active data governance to minimize the burden of governance tasks. Active data governance leverages AI and ML to automate manual tasks to build, monitor, and enhance the data environment in a way that enriches and enhances data quality.
How Does Active Governance Support Analytics?
Establishes Trust in Data
Trust is a key element of data analytics. Data inaccuracy undermines trust in the information users interact with daily. To build trust in data, governance provides tools like lineage, which show the development of data over time. With trust in the data, decision-makers can use the insights gained from it with greater confidence, further enhancing the use of data to drive strategy across the organization.
Uncovers Valuable Business Insights to Maximize ROI
More data is being collected and created by organizations than ever before. But keeping unnecessary information increases storage costs and makes it harder to locate valuable information. Creating an integrated data catalog, for example, helps your organization find, curate, analyze, prepare, and share data. This way, users can be connected quickly to the most valuable data with the highest ROI. Cataloging data can also help identify and remove “dark data.” Dark data does not add value to an organization and may even pose compliance risks.
Facilitates a Data-Driven Culture that Enables Collaboration
Communication is most effective when we share a common understanding. A business glossary can be used to foster communication and collaboration. When well-maintained, it can act as a “single source of truth” for an organization. It can also help unify employees by explaining how other departments think.
Active governance also enables organizations to develop “governance guardrails”. This is based on analytical models that can identify and reduce the misuse of sensitive data, without affecting legitimate activities. With a people-first data culture, data silos are broken down, allowing users to fully leverage data throughout a business.
The Role of Data Governance and Analytics for Decision Making
According to Gartner, a clear decision-rights model is essential for effective governance. “The value of AI and data and analytics in enabling change can only be realized if the right people use them to make the right decisions that drive the right business outcomes.” Placing expertise close to the source—and making de facto data experts responsible for the data they know best—is a key first step.
Implementing adaptive, transparent models of governance is another key step. A Gartner survey found that “fit organizations (highly resilient to disruption) rated clarity and effectiveness of IT governance at 60%, compared with fragile organizations (vulnerable to disruption) at 39%.” Many businesses today are implementing a data mesh architecture to launch transparent governance processes shepherded by human expertise.
Embedding self-improvement protocols into the governance framework, too, is crucial. “Organizations that are effective at data and analytics governance measure information value and its impact on business processes,” Gartner found. A data catalog that embeds data intelligence into its platform will help data leaders ensure their governance processes improve with time.
How Alation Can Help You Fully Leverage Data Analytics
Data governance is a key framework on which robust analytics programs are built. From a human perspective, investing in a data-driven culture that fosters collaboration (instead of control) is a key piece, as well. Modern, active data governance democratizes data by widening access and surfacing governance guidance at point of use.
Alation helps business users make accurate, compliant decisions. Data quality metrics, descriptions, and dashboards are collected and distributed to users in real-time. Users also have access to data lineage, statistical information, and numerical graphs on the data. With Alation, a combination of crowdsourcing and machine learning automates data categorization. By cataloging and curating enterprise data, teams gain unprecedented trust and insight into key assets.
Ready to launch your own data governance strategy that supports analytics? This whitepaper, Active Data Governance Methodology, walks you through the steps.