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Data Modeling 101: Everything You Need to Know

Data modeling is getting a lot of attention these days, and with good reason. Here’s the what, why, and how that ITOps should know about data modeling.

Today’s IT environments are complex, hyperconnected, and composed of highly disparate components. All the bits and bytes being generated and streaming through the network, coming from a host of different machines and applications and with a wide variety of purposes, can be overwhelming to IT operations. Even pieces of equipment that do the same job, but that come from different manufacturers, can generate data with significantly varied characteristics.

Yet, hidden within that data are powerful insights that can be used to make decisions, solve problems, and increase the efficiency of the systems and services that drive your business. But without a way to understand the meaning and context of that information—without data modeling—it is nothing but noise. And the noisier your IT environment, the harder it is to recognize the signals that are important and put that data to work. That means you can’t manage your environment to achieve peak performance.

What is a data model? What is data modeling?

A data model is an abstract way to organize disparate elements of data and to standardize how those elements relate to one another and to the properties of their associated configuration items (CIs). For ITOps, it is important that there be a single data model that is used to create an operational data lake—the resource all devices, systems, and services draw on for simplifying analytics.

When you have a single source of data, unified with a common data model, you can reduce the number of tools needed to perform all the tasks required for IT operations monitoring and management. But the data model is only the format used to make the data useful. You aren’t storing that information in a static data warehouse to use for a specific task at some point in the future. You need that data to be dynamic, working all of the time. That’s where data modeling comes in.

For ITOps, data modeling is the ongoing process of collection, refinement, enrichment, and application of data from all of the disparate sources of data throughout the enterprise. Data modeling is the answer to the question, “What do you need your data to do to add value to the organization?”

Data modeling is a way of keeping the constant ingestion of data relevant to the needs of the enterprise, even as the elements of your IT estate change, as new technologies are added, as the scope of your enterprise grows, and as your organization’s mission evolves. Data modeling is how you put your data to work.

Without data modeling, you are likely to end up with technology and organizational silos. A manufacturer might keep their shop floor equipment and controls separate from those associated with other departments, like logistics, research and development, accounting, purchasing, and others so that the data can be analyzed within the context of the business or IT function. Or an organization that is growing through mergers and acquisitions may expand its IT footprint but need to keep different divisions separated by geography.

Why is data modeling important?

data modelingThe problem is modern enterprises are hyperconnected. No device or service operates in isolation. Adds, moves, and changes have downstream effects on performance, and all the information has value to the organization.

Data that is generated by equipment in an on-premises data center can have an effect on the resources that operate in the cloud. The failure of one device may have negative effects that ripple downstream. Events affecting operations in the EU may presage trends that will also affect operations in the U.S. If such information is isolated, it is of limited value. But when it can be combined and understood within the context of the organization’s entire operation and mission, it may yield surprising insights.

Getting there is a process of three-parts in a continuous, data modeling loop that looks something like this:

  • Front-End Data Collection – data modeling starts with the ingestion of data from every source in the IT estate, in real-time, translating the data into a unified data model, and collecting the data in an operational data lake.
  • Back-Office Enrichment – once the data has been modeled and collected in the data lake, it is supplemented and enriched with metadata that is relevant to the needs of the enterprise in order to support specific functions.
  • Ecosystem Application – enriched data can now be shared and put to work across the enterprise for functions like IT operations management, DevOps, service level agreement compliance, load balancing, trouble remediation, process automation, and more.

This process can only work when it is feeding into and drawing from a clean, continuous data lake. It has to be as up to date as the most recent change to the IT estate or else the data modeling breaks down and the data lake can no longer be a trusted resource. That means operating at machine speed and with the intelligence available through machine learning.

Data Modeling, AIOps, and SL1

The demands inherent with IT operations management (ITOM) in today’s complex, ephemeral technology environments are too much for traditional approaches to IT operations. Only IT operations that have adopted AIOps can keep pace, and data modeling is a prerequisite to the adoption of AIOps. What’s more, data modeling is necessary for achieving the full potential of AIOps.

That is where the ScienceLogic SL1 platform comes into play. SL1 gives you a complete, real-time view of all your data sources, whether on-premises or in the cloud, no matter how persistent, mobile, or ephemeral that source. This complete, cross-platform view means you have the confidence of ingesting all the data you need, automatically transforming it into the single data model you need to create that clean, continuous data lake vital to IT operations. SL1 then affects the data modeling loop that allows you to combine disparate forms and sources of data, add the necessary context, perform advanced analytics, and put the data to work in a way that maximizes its value to the enterprise.

Data modeling, as the old saying goes, is not a destination; it is a journey. And it is a vital part of your journey to AIOps and IT transformation. If you’ve been considering taking that journey, or if you’ve embarked on the journey and are struggling to find your way, ScienceLogic can help. Why not get in touch with us. We’d love to have a conversation about your situation, understand your needs, and help you navigate your way successfully down the path.

Ready to learn more about data modeling and AIOps?  Read the eBook, “Your Guide to Getting Started with AIOps”>