Best practices for designing and managing data infrastructure

Most modern business owners collect and store data to some capacity, but few maintain an organized data infrastructure. In the age of big data, it’s becoming increasingly important to have a system for storing, integrating, analyzing, and protecting data. Unfortunately, there are no one-size-fits-all solutions; infrastructure maintenance is company-specific, so no two models will be alike. However, there are data integration best practices you can implement to keep your infrastructure in check and in accordance with compliance requirements.

Understanding data infrastructure

Before establishing a plan, understand what a data infrastructure is and what it means in relation to your industry and customer demographics.

Establishing the meaning and scope of data infrastructure

What does a data infrastructure mean to you and how will it play a role in areas of sales, marketing, retention, and customer service? Some companies incorporate data in all aspects of business operations, from product development to hiring. Others may use data to hone in on a singular front, such as brand touchpoints.

Key components and stakeholders involved

Identify who will be impacted by the new data infrastructure model. Customers often are the first to come to mind, but this may also encompass shareholders, sponsors, employees, and suppliers. These are the people you need to involve in your infrastructure and business continuity planning.

Impact of data infrastructure on business outcomes

How much impact, or the degree of impact, is the data infrastructure expected to have on business goals? This is an essential component when setting key benchmarks, creating data-reliant campaigns, and devising cost optimization strategies.

Best practices for data infrastructure planning

Here are a set of practices used across industries for road-mapping and executing your infrastructure blueprint.

1. Define your data and analytics strategy

To define data means to identify the data sets pertinent to a particular campaign or set of campaigns. Examples include first-party data, behavioral data, etc. You also need an analytic strategy. What metrics will you use to gauge real-time performance?

2. Prioritize your projects

You may have more than one campaign going on at once. Determine which ones take precedence. A good model to use is the prioritization matrix. This is a system of ranking priorities based on variables, such as profitability, success probability, spending, etc. This ensures data and other resources are put to use in areas most likely to yield the highest net gains.

3. Evaluate environments

Evaluate the current environment of your data storage setup. Is data stored in in-house legacy systems, or have you migrated to a cloud-based data infrastructure? Either way, assess whether the current data architecture is sufficient for the new infrastructure you have in mind. Ideally, the system should be a solution-friendly environment in areas including but not limited to:

• Data privacy practices
• Data monitoring and management
• Disaster recovery planning
• Scalability considerations

4. Build a flexible data model

Create an organized structure and rule set for filing, labeling, and merging data points. At the same time, the data model needs to be flexible, with exceptions allowed to the same rule sets if there are circumstances where structural changes may be beneficial.

5. Document data lineage

It’s important to understand where your data originated from and the data pipelines it went through before arriving at your data warehouse where the arriving form is converted into analytics. This helps you understand the data lifecycle management, in turn, helping you to identify errors or areas of improvement in data migration.

6. Step back and assess performance

Regularly evaluate analytic reports and examine performance from a backend perspective. This includes performance elements on the IT front, such as:

• Monitoring extract, transform, and load (ETL) performance
• Data refresh cycles
• Timeliness of incremental loads

7. Implement a data governance program

Establish data governance policies at the company level. However, the rules should follow the same initiatives already set in place by existing compliance standards at the federal and global levels. Medical and pharmaceutical companies, for example, should heavily model their governance guidelines after HIPAA.

8. Optimize your data infrastructure

Upon assessing performance, identify areas where you can fine-tune performance optimization. Examine your key performance indicators (KPIs) and create a remedial plan in areas that fell short of your benchmarks. For example, your KPIs may reveal shortcomings in areas like:

• Updating data access and permissions logs
• Data security measures, such as procuring the latest security patches in a timely manner
• Gaps in data compliance regulations (i.e. discrepancies between your governance plan and industry standards).
• Inadequate filtering that leads to data quality and integrity issues

9. Evaluate and select the right data infrastructure tools

Identify the features you need in a SaaS product or tool to satisfy the above best practices. This may just require a minor upgrade of an existing system, or a complete transition to third-party-hosted data storage technologies. The best tools provide the latest in generative AI and machine learning packed in a turnkey yet customizable package. It also has integrative data team collaboration tools for remote and cross-collaborative work.

Developing, maintaining, and fine-tuning your data infrastructure with Lytics

Data maintenance is an ongoing process; it’s a whole other element that needs to be rolled into your daily operations. Fortunately, with Lytics’ solutions like Cloud Connect and Conductor, much of the maintenance is automated, eliminating much of the manual- and guesswork.