Jul 05, 2022

8 Keys to a Successful Data Management Strategy: The What, the Why, and the How

Gabriel Greening
Grant Kassing
Joshua Newman
Randy White

Gabriel Greening, Grant Kassing, Joshua Newman, and Randy White

8 Keys to a Successful Data Management Strategy: The What, the Why, and the How

As the amount of data produced globally continues to rise, organizations are faced with the daunting task of managing an ever-growing amount of data, maintaining data quality, and ensuring value can be extracted from this data while remaining cognizant of regulatory commitments. To avoid being overwhelmed when reassessing or crafting your organization’s approach to these challenges, a good place to start is your organization’s overall data management strategy.

Understanding what the term data management strategy encompasses, why it is important to have one, and how your company can refine its own strategy will enable you to extract more value from your data and be confident in your data management processes.

What is a Data Management Strategy

A data management strategy is a high-level course of actions driven by the vision, goals, and prioritized work necessary to successfully manage data in a business. Developing a data management strategy allows you to take the first step in becoming a data-driven organization.

Your strategy should set out a plan of actions and policies to achieve a goal. For example: collecting, storing, managing, and using data effectively to support your business.

When setting out your data management strategy, your organization must consider several adjacent areas: data governance, data quality, ability to use data insights for action, efficient use of your data, and more. When successful, your data management strategy will lead to a structure and methodology for data management that is controlled, yet nimble enough to meet business demands.

Why Getting Data Strategy Right is Key

Getting this right will set the foundations for you to embed the correct structure and methodology, enabling your business to begin developing, or further refine, its data management processes with confidence.

You may think that crafting or retooling an entire enterprise data management strategy can be a daunting and expensive task, however bad data quality alone could be costing your business 30% or more of its revenue. When considering that data analysts spend half their time working around the challenges created by issues of data consistency, quality, and accessibility, it is more accurate to say that you can’t afford not to have a modern data management framework.

How to Make an Effective Data Strategy Plan

One of the biggest hurdles to implementing your data management strategy is knowing where to begin. Before expanding your strategy to include granular tasks such as how to ensure high data quality, how to formulate effective security and privacy policies, or how to design powerful automated systems, your initial approach should involve formulating a plan for what tasks your data management strategy will involve and how you will approach each one. Toward that end, we’ve outlined eight key ingredients to building a successful data management strategy.


In part one of our data strategy series, we noted that 87% of data governance initiatives entirely or largely fail. Credera’s experience has shown that many of the top reasons for this failure boil down to a lack of adoption from the data users. Because of this, our approach to help boost adoption of a new data management strategy is to approach the entire project with the data consumer in mind. This is particularly important for the data governance piece (described more below) but is an approach that should characterize every aspect of your data management strategy.

If your strategy doesn’t support the goals and needs of the people who will be consuming your data there is an incentive against adoption right out of the gate. Our approach focuses on understanding how people want to use data and prioritizes a strategy that enables their efforts rather than hindering them.


Beginning to form a data management strategy is a shared responsibility because it will frequently span multiple departments within the company. The question of who specifically owns the data management strategy can vary widely depending on how your company is structured.

In general, however, the data management strategy would fall within the purview of the chief information officer. Proper implementation of the data management strategy will frequently require the involvement of many other areas of the business including human resources, finance, and technology.


Data governance is the set of policies, procedures, roles, and metrics that “govern” how your organization interacts with and uses its data. It is one facet of creating an effective data management strategy that is often overlooked or given insufficient attention.

Considering governance as a distinct and holistic component is daunting, but before the rest of your strategy for data management can be effective, it needs to be given the direction of proper boundaries within which to work. Without an iterative process of review for data governance, businesses struggle to generate value out of their data and often have difficulties with data quality, documentation, and scale.


The three key components of data security are confidentiality, integrity, and availability (CIA).

  • Confidentiality involves making sure your data is given the appropriate level of privacy.

  • Integrity means ensuring your data is trustworthy and tamper-free.

  • Availability involves making sure your data can be easily accessed by appropriate parties.

Effective data security should be considered at every stage of your data’s lifecycle.

Data security must comply not only with your own data governance standards, but also with relevant laws and regulations around the use, storage, and transmission of data.


Almost all companies believe they have inaccurate data (it’s reported to be as high as 98% of companies). Often this form of bad data quality causes confusion that stems from lacking a single source of truth. Formulating a plan to ensure high data quality at your company includes thinking about your data during its ingestion, persistence, and destruction.

The best place to start identifying any gaps in your existing plan, or to build a new plan from the ground up, is by stationing checkpoints for data quality at every stage in the pipeline or workflow. These checkpoints will help ensure the value in the data is maintained and not lost through the many processes it takes to realize that value for your business.


Analytics can be a powerful tool to drive decisions and help discover new ways of performing tasks. To access these valuable insights, you will want to include a visualization plan built on top of your overall data strategy. Effective visualizations and reporting should target specific business questions, avoid ambiguity, and prioritize agility as requirements change.

An important rule of thumb when building visualizations on your data is that “simple is better.” Flashy, complex graphs and charts are great for showing off the capabilities of a business intelligence tool, but when it comes to communicating important business data across a company, you want the viewers of the report to understand the impact as quickly and easily as possible.


Data architecture refers to the set of systems involved in the movement of data through your organization. Any tool, website, or other system that accesses or uses your data is part of your data architecture.

One of the most important steps to an effective data architecture is to design a comprehensive model and keep consistent documentation. Optimizing the performance of your architecture should also be a priority; if systems in your architecture run slowly or are otherwise difficult to use, it increases the likelihood your data users will look elsewhere for solutions to their needs, which can compromise many aspects of your data management strategy.

Lastly, ensure your architecture is scaled to the size of your data and employs the appropriate tools for each use case. This may seem like an obvious point, but oftentimes it is not immediately apparent where the use of one tool should end and another should begin. For example, many analytics or data transformation tools end up being used as makeshift data storage when a proper data warehouse is not included in the architecture.

While it is possible to use those tools in this way, it is not how they are intended to be used. Doing so can lead to poor data quality, data loss, and performance issues such as long load times for developers and report viewers. When you design your architecture, make sure you take proper account of the needs of your data users, the scale of the data you will be handling, and the proper tools to use for meeting each need.


Automation is an emerging area to consider in your data management strategy. Building a connected environment of tools that can automatically process your data can help minimize the workload on your developers and in many instances provide constant improvement to the workflow over time.

It is important to note that automation, like some related terms such as artificial intelligence (AI) or machine learning, is a buzzword. Companies often jump at the chance to incorporate automation into their business simply because it is a hot topic. However, you should never adopt automation just for the sake of using automation. The goal of automation is to reduce the workload on humans and to reduce human error. You should only incorporate automation when it is able to accomplish one or both of these goals.

For your company, the question around utilizing automation should focus on how you can use AI or tools with automation features to reduce the workload on your employees, thereby streamlining your data management processes.

Start Your Data Management Journey Today

Incorporating these data management best practices into your enterprise strategy will help your business progress toward being an effective data-driven organization, raise the effectiveness of your people, and increase the returns on future growth investments.

At Credera, we love helping organizations understand how to leverage or create their data management strategy. Please reach out to us at to start a conversation.

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