Data Management Best Practices: Six Ways to Improve Analytics Impact
Big data volumes are expanding, velocity is increasing, and variety is growing — but value often lags behind. While companies recognize the inherent potential of actionable information, making the connection between initial data ownership and data-driven output can frustrate IT teams and C-suites alike.
According to Gemalto's Data Security Confidence Index, although 89 percent of organizations connect effective analytics with industry advantage, 65 percent say they "can't analyze or categorize all the consumer data they store."
For you and many other business leaders, management is the missing link. And you're not alone. According to Tech Republic, 41 percent of business leaders report that no one in their organization is responsible for data management.
Perhaps more accurately, no one wants to take on this responsibility. Despite the broad availability of advanced analytics tools, roadmaps from implementation to insight are rare. The result, as you can see, is a paradox: Teams often have access to massive analytic power but are missing the practices necessary to standardize and streamline data insight.
Here are six data management best practices to help you bridge the gap and improve the impact your analytics have on business decisions and your bottom line.
Curate Data Collection
Two factors play a role in effective data collection: Regulation and Relevance.
Regulation speaks to the need for clear and concise notification when collecting any data from customers or business partners. You're probably familiar with everything from GDPR and HIPAA, as well as newly minted rules, such as the California Consumer Privacy Act (CCPA). All of these regulations share a perspective that companies are best-served by transparent policies around data collection and use that are clearly communicated to data owners. Informed opt-in policies, for example, both improve existing datasets and reduce the risk of compliance failure.
Relevance speaks to planning before possession. By identifying your highest-value datasets — from consumer purchase history to online behavior or product preferences — you can develop datasets tailored to high-value outcomes.
Enhance Data Cleanliness
Clean data delivers better outcomes. Poor data quality negatively impacts 95 percent of organizations and frustrates operational insight. As a result, it's critical that you establish data cleaning best practices before starting to create data repositories. You need tools that deliver in four key areas:
Error monitoring — Identifying errors in data entry and storage lets you eliminate potential problems before they impact analytic outcomes.
Data entry automation — Automating repetitive data entry tasks significantly reduces the scope and scale of errors.
Data validation — Ensuring source reliability and accuracy means less time spent backtracking to identify data issues.
Duplicate data removal — Removing duplicated data both saves on storage space and ensures analytic processing power isn't wasted by repeating the same work.
With clean data, you can more quickly arrive at data insights rather than cleaning-up the mess of duplicate or incomplete information.
Handle Data Quality
Even with effective data collection and cleansing practices in place, your enterprise must store and manage massive amounts of information. Handling data quantity means identifying the best storage solution: On- premises or in the cloud.
Advantages differ depending on the type of data stored. On-premise storage is often the best choice for data that demands real-time analysis and requires the highest level of control. For example, consumer financial data used to inform immediate purchasing trends may be worth storing on-premises to both improve access and reduce compliance complexity.
The cloud, meanwhile, offers distributed storage and resource access for large-scale datasets that inform long- term operational strategy. Knowing where to put your data — before it reaches company IT environments — can improve your analytics outcomes.
Ensure Data Quality
Quality, quality, quality. From marketing to sales to operations and the C-suite, data quality is the key to actionable insight.
When it comes to best practices, there's a simple rule regardless of location or use case: Context defines quality. Clean, curated data forms the foundation but without context, it's impossible to effectively influence outcomes and deliver value.
As a result, you need to prioritize staff skill sets and software solutions that will help you capable of understand the relationship between multiple variables and deliver contextually relevant insights, at scale.
Improve Data Assessment
Metrics matter. But knowing which metrics to measure — and how to measure them — forms the foundation of business value.
In this case, best practices around measurement must effectively replace existing cultural bias. With decision- makers often relying on personal experience and beliefs to define data outcomes, you must identify high-value, high-confidence metrics that your team can reliably obtain and easily communicate.
These might include context-driven predictions about consumer spending, or evaluations of predicted outcomes against actual results. By presenting C-suite executives with clear, contextual measurements that align with business strategies, big data initiatives gain critical support.
Define Data Access
Access matters for analytics. Teams must be able to leverage key datasets on-demand to deliver actionable insight — time spent waiting for specific permissions can reduce the value of recently-collected contextual data. Too-broad access, meanwhile, can potentially expose data to unnecessary risk and compliance challenges.
Effective access management demands best practices based on a zero trust approach. Verification — rather than assumption — is the cornerstone of this practice. By using role-based permissions, you can ensure that the right users have access to the right data at the right time. This empowers teams to discover applicable insight without putting unrelated datasets in harm's way.
The right analytics tools turn data into action — but they won't deliver results in isolation. Data management best practices for collection, cleaning, quantity, quality, assessment, and access are critical to bridge the gap between analytic abilities and actionable outcomes.
Underpin essential best practices with industry-leading processes. Go from simply having data to acting on it with Ki from Keyence.
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