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The guide to Data Management: What it is and why you need it for your organization

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The way companies use of data has dramatically changed over the last ten years. As businesses have increasingly used data to drive competitive advantages, their data set has become more integral to their success.

Yet with high volumes of data flowing in from multiple sources, working with data has become complex.

As organizations need access to high-quality, actionable data to make faster business decisions with more confidence, how organizations manage data is a growing concern for many.

Whether you are new to data management or looking to brush up on your data management skills, use the below resources to bolster your understanding of data management.

What is data management?

According to Gartner,

“Data management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.”

While there are numerous definitions out there for data management, it comes down to how organizations manage their central data asset

It’s no secret just how valuable data is for an organization, with 63% of businesses relying on data to mitigate risks for their organization. However, your data’s value is tied intrinsically to how well you manage the data to empower every part of your organization.

Good data management requires having a strategy in place to manage data throughout its lifecycle.

Why is data management important?

Data management exists to increase the quality and accessibility of the data that companies interact with. As the volume of data and the number of sources increase, there’s a growing need to successfully manage and maintain the large volumes of data that companies generate daily. 

Data management systems empower companies’ data processes, ensuring they have access to complete and accurate data. Just how costly can mismanaged data be for a business?

According to Dun & Bradstreet

“Nearly one in five (19%) businesses have lost a customer by using incomplete or inaccurate information about them.”

Without reliable, accurate data, game-changing use cases such as personalization or machine learning cannot be achieved.

Two major benefits of data management

There are plenty of advantages to having a data management system in place. When thinking about data management, one question to ask yourself is, how can you make your data work harder for you and enable data compliance?

1. Increased data productivity

Driving the productivity of your data is a crucial part of data management. Distributing your data to the people that need it in a form they can understand and take action with benefits the whole organization.

To be productive with your data, users have to trust the data and easily interpret the data. Information should be readily available for employees to drive value from the data and use it to inform their decisions.

This is done through data governance, which drives high-quality data. Part of data governance drives the predictability and meaning of the data.

Data governance plays a huge role in driving productivity from your data. It is a set of practices that are a part of data management.

Data governance answers the following questions when dealing with data productivity:

  • How did we produce that data?
  • Where did the data come from?
  • How is the data structured?
  • What steps can we add to build assurance that the data is accurate and complete?

When organizations can answer these questions and map out how the data was created, they can ensure the data meets the required standards of quality because it’s tightly governed.

Tightly governed data makes it easier for someone to look at the data set and understand what the information means, driving data productivity.

2. Improved data compliance

Compliance has gotten a lot more complicated over the years, especially around personally identifiable information. The European Union has GDPR, and in California, there is CCPA, which limits what data you can collect and where you can process that data.

Data collected in one system shouldn’t be moved to and processed in another system for security reasons. As a result, data management has become much more complicated, and to avoid hefty fines, companies have invested a lot of resources into compliance.

In many companies, the responsibility of data management rests with individual teams since different teams utilize data in their own way. This means that data sits in silos across the organization, and the task of governing and ensuring adherence to data compliance regulations is incredibly challenging.

To combat this, organizations must validate their data before it enters their warehouse.

Validating data makes sure data comes in the expected format, making it quicker and easier for analysts to work with since data is the same format across the organization.

Why have companies invested time and money into compliance?

Three best practices of data management

While best practices vary for various reasons, here are three best practices that can be applied to most use cases.

1. Make data security and protection a top priority

We can’t stress enough how important data security is for an organization. Data security is a big part of the data compliance that we mentioned earlier.

Data security deals with how data is collected, stored, analyzed, and if needed, sent off to third parties. Organizations need to have procedures to handle data correctly and train employees on data security best practices.

Data security is also tied into how accessible your data is to members of your organization. While it’s nice for everyone to access important information, make sure that authorized personnel only have access to information relevant to their everyday jobs. Thus, companies have set up different levels of permissions to ensure employees can only access data that is necessary for their work.

2. Create a single source of truth

“We could finally track all of our products and events in one place and in a consistent way and centralize this in a single source of truth, saving effort on the data collection front and ensuring quality on the analytics end.”

Pedro Gemal Lanzieri, CTO, PEBmed

Building out shared rules for generating data is a crucial part of data management as it aids organizations in ensuring data comes in an expected form that everyone in the business can work with and understand – a single source of truth

Diagram for creating a single source of truth, across websites e.g. React, and including consumers of raw data e.g. analysts, or even technical marketers. Includes data validation.

Picture from our own blog on breaking down communication barriers with a universal language in data

Without this, data consumers would be left on their own to dig through documentation to figure out what a particular field means. To help organizations build a single source of truth, Snowplow has schemas that strictly govern how the data will look, and data will require very little (if any) cleaning once it enters the data warehouse.

This is because data is validated up front as each event is checked to see if it matches up to the rules laid out in your code.

3. Build a data culture for your organization

To truly be a data mature organization, the culture for data excellence must be brought in from the top-down. Data mature organizations understand the importance of data and treat it as a strategic asset for their company. Leaders of these organizations understand data analytics and create data collaboration across their company.

While there is no single path to becoming a data-driven organization, leaders have more technology at their fingertips to support them than ever. The right technology can help you become a data-driven organization and open up your company to new ways of achieving tasks.

Embrace the change — it will be worth it in the end!

Further reading: Why you should centralize your data

How to implement a data management strategy at your organization 

Implementing a data management strategy might seem like a daunting task for your organization. You might even be wondering, “Where should we start?” Don’t worry, you are not alone.

Think of your data management strategy as a roadmap for your business to use data to achieve your goals. A good data management strategy will aid your organization in all activities involving data. 

Here are four steps you can take when implementing a data management strategy for your organization.

1. Identify business goals and align them to data management

Your organization likely has access to a high volume of data, which is great, especially if you can align your company’s goals with what you want to achieve with the data.

When you understand what you can do with the data you have, you can set aside any superfluous information. This step is where most businesses fail when trying to become a data-informed organization.

The way data is captured, processed, modeled, or transformed needs to fit your business model and unique goals. You need to capture data in a way that makes sense for your business and ideally not be limited by packaged analytics tools.

According to Dun & Bradstreet

“Often, data management has not been connected to the overall business plan, and only 57% agree that they have had an effective data management strategy in place across the organization.”

For example, an organizations’ goal might be to put their customers’ needs first and increase their net promoter score (NPS) score value.

To achieve that, they have to allow the right people to access customer data to serve clients better. This means customer service can access customer data that aids them, engineering can access data that helps them debug problems, and product managers can access data that assist them in understanding their clients to build better products that serve their needs.

There is a different level, quantity, and type of customer data that each department should access in each of these cases.

2. Find the right data management tools for your organization

After you identify your business goals and align them with data management, it is important to have the right tools at your fingertips to build out a data management strategy.

Sixty-eight percent of businesses are investing in data management software tools to get more from their data. When picking the right tools for your organization, you should lean towards tools that give you full control and ownership over your data and data management infrastructure. Our friends at Tourlane put it well:

We own the data so it’s transparent – our hands are never tied by not knowing what is going on. Other solutions were like black boxes, and that is not the direction we wanted to take. We wanted a solution to become a core part of our business.”

– Kevin James Parks, Data Engineer, Tourlane

We have broken down the data management tools we recommend for the following areas below:

Data collection tools

Data enrichment tools

Cloud platforms

Data flow / streaming tools

Data warehouses

Data lakes

Data integration tools – IN to warehouse

Data integration tools – OUT of warehouse

Data pipeline orchestration tools

Data transformation tools

Data analysis tools

Data quality tools

Data cleansing tools

Data validation tools

Data science tools

Data observability tools

Business intelligence tools

Data catalogs

Behavioral data platforms

3. Build out a data management team

To get the most from their data, businesses will need employees who can analyze data and understand the technologies associated with data management.

To get started with a small centralized team, you’ll need a data engineer, analytics engineer, and in most cases, an analyst to support data access.

According to Dun & Bradstreet

“Only a quarter of organizations (25%) have people dedicated to the management of data.”

The same report mentions that

“41% of leaders say that no one in their organization is responsible for the management of data.”

Once you find the right employees to handle data management, the next thing to figure out is how you should structure your data team. Should you centralize your data team? Or should you embed data team members in different departments? We dove into this in a previous post here.

Further reading: How should I structure my data team? 

4. Ensure data is accessible for users

We mentioned this earlier, but it’s arguably the most crucial part of the data management process. With data playing a huge part in your business, it’s important to make sure employees have access to data relevant to their role.

To ensure this step is accomplished, set up different permission levels depending on their role in the organization. Typically, in companies, team leaders and executives have higher access to customer data than accounting or sales representatives. 

Here are a few things to consider when working with data across your organization:

  1. Ensure the data is understandable, meaning employees who consume the data know what it is and how to interpret it.
  2. Make sure the data is clean and well structured. This will save data users time as they can now use the data without spending hours cleaning it.
  3. Make sure the data is relevant to data consumers’ goals and objectives.
  4. Make sure the data is modeled in a way that has meaning. For example, a marketing team will work with data differently than would a product team.

How to get started in data management

Implementing a data management model across your organization will take time. Even once it is implemented, you will constantly be evolving how you work with data as you scale your business.

How organizations manage data is just as important as the data itself.

As data continues to become increasingly complex for organizations, take the opportunity now to truly understand the fundamentals of data management. Over time, a data management strategy will allow you to stay ahead of your competitors and effectively manage risk.

Making high volumes of data accessible to teams can be a challenge. For Strava, they were collecting anywhere from three to four billion events a day, which was a challenge in itself. With Snowplow’s help, Strava was able to make data accessible to teams of analysts to serve their individual needs.

“We would not have achieved our current level of self-serve data without Snowplow. It has enabled us to democratize our data culture, significantly improving our analytics coverage and deepening our insights.”

– Daniel Huang, Data Engineer, Strava

Learn more about how you can power your data management strategy with Snowplow. Book a demo

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