November 18, 2021

White Paper
5 min

Data Management Strategies Enhance Data Analytics

Learn how the creation, storage, management and processing of data affects the achievement of business outcomes.

When implementing data analytics, a data management strategy is crucial. To ensure success, IT leaders must consider the outcomes they’re seeking and the kind of strategy that will help them achieve those outcomes. 

Data management strategy helps organizations develop a long-term approach for handling the data they create, store, manage and process. These strategies vary depending on an organization’s needs, yet all share the common goal of making the process more efficient.

As companies grow and generate more data, it is crucial to have a data management strategy to support this growth. Read on to learn about implementing a successful data management strategy.


The percentage of respondents who believe data management is a critical issue for their organizations

Source: Seequent, “Geoscience Data Management Report: 2021 and Beyond” (PDF), January 2021

Strategies for Data Analytics Success

Some IT leaders may get swept up in the technologies powering data analytics. Instead, stakeholders should first focus on outcomes and applications, then seek out solutions that will help them achieve their goals. 

Organizations should aim to have their data analytics initiatives guided by an overarching strategy. These strategies will vary from organization to organization, but nearly all analytics efforts will be propped up, at least in part, by the pillars of data management, artificial intelligence (AI) and machine learning (ML), and security.

Data Management

A data management strategy is essential to analytics success. It maps an organization’s use of data to its goals, ensuring that the disparate activities surrounding data management — from collection to collaboration — work together effectively, efficiently and seamlessly. Without a data management strategy in place, organizations often run into problems, such as incompatible, duplicative or missing data. They may find themselves running siloed projects that use common data yet rely on redundant hours and costs. Overall, data analytics efforts are more likely to consume time and resources in ways that do not contribute to an organization’s objectives if they are not guided by effective data management. 

To create a strong data management strategy, organizational and IT leaders must identify their business objectives, create effective processes and find the technologies that support their use cases. When identifying business objectives, it is often helpful to keep the scale small at first, focusing on a limited number of analytics use cases, and expand from there. Effective data processes include not only analysis but also data collection, preparation and storage. Stakeholders should ask themselves whether they will be using structured or unstructured data (or both), how they will transform data to prepare it for analysis, how analytics insights will be communicated and whether data will be stored on-premises or in the public cloud. 

A data management strategy should also cover data governance and employee training. Data governance incorporates security and privacy, as well as factors such as data quality and transparency. Training programs, meanwhile, are often the unheralded factor that separates successful analytics programs from less successful ones. Frequently, an organization’s data owners are not data experts, and it is critical to provide these stakeholders with the knowledge and skills they need to understand analytics insights.

Artificial Intelligence and Machine Learning

The use of AI and ML tools in data analytics programs makes it possible for organizations to obtain insights about their customers and constituents, expand their business, and optimize the quality and speed of logistics. Business and IT leaders should be willing to experiment when implementing AL and ML in their analytics efforts, and should quickly move past any unsuccessful projects, maintaining a “think big, start small, fail fast” mindset. By beginning with specific business outcomes that can easily be measured and understood, stakeholders will be able to accurately assess how well their AI and ML projects are working and use these lessons to make improvements. 

AI and ML tools have a wide variety of potential applications across industries. Firms in the financial industry use AI and ML to arrive at market insights, healthcare organizations use them to scan medical images and assist with diagnostics, marketers find new ways to personalize campaigns, and payment card companies can increase the precision with which they distinguish between legitimate and fraudulent transactions.

Like other data analytics capabilities, AI and ML skills are in high demand. Organizations implementing AI and ML should build up the skill sets of staff members in this area or seek outside expertise from a trusted partner.


The percentage of business leaders who see data management initiatives as becoming more urgent

Source: Experian, “2021 Global Data Management Research” (PDF), February 2021


Data analytics can be a powerful weapon in an organization’s security efforts, but organizations must take great care to safeguard their data storage and analytics infrastructure to prevent a potentially catastrophic breach. Security analytics tools can help stakeholders to identify changing use patterns, execute rapid analysis in real time and perform complex correlations across a variety of data sources ranging from server and application logs to network events and user activities. 

These security efforts require advanced analytics, as well as the ability to run analysis on large amounts of current and historical data. However, by integrating security into their analytics programs, organizations can improve their cyber resilience, limit exposure to malware and protect their reputations. 

A new generation of security analytics tools has emerged in recent years. These solutions can collect, store and analyze huge amounts of security-related data across an entire enterprise. Compared with traditional security offerings, these analytics-powered tools generate a smaller number of security alerts, which are ranked by severity and enriched with forensic details that lead to faster detection and mitigation of cyberattacks.

Story by Aaron Colwell, the leader and founder of CDW’s Data Platforms and Insight team.  He has worked for over 15 years in the IT industry helping customers solve complex problems and reach their desired technical and business outcomes with data platforms, analytics and security solutions.

To learn how data management strategies can make data analytics more efficient, read the white paper “Achieving Effective Data Analytics” from CDW.