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04. June 2022

Lessons in Intelligent Data Management

Intelligent data lifecycle management entails a series of steps to locate, identify, clean, and secure data, so it can ultimately be optimized and leveraged to meet a variety of use cases.

The explosive growth of data is old news. The fact that data can yield powerful insights and drive better decision making? That’s expected these days.

What data architects, data modelers, data stewards, engineers, business executives, and others involved in working with their organizations’ data want to know is “how do we efficiently and cost effectively manage our data so we can make the most of it?”

There’s no single answer to that question because data is a complex entity. It goes through a sequence of stages from its initial generation to archival and/or deletion at the end of its useful life.

There are seemingly limitless and diverse use cases for data. And its quality and characteristics (like volume, velocity, veracity, variety, and value) can vary greatly and affect its ultimate use.

A number of solutions exist for helping with data management at various points in the data lifecycle. What is needed, however, is a comprehensive “big picture” view of the processes that ultimately take data from point A ─ where it’s generated ─ to its final destination.Equally important are recommendations for the best practices and tools to enact those processes, so data isn’t just managed. It’s managed intelligently.

Data Management To-Do’s

The following steps provide a starting point for intelligent data management:

1. Locate the data. We all know that most companies have a lot of data. They know they have a lot of data. The problem is none of us know just how much data, where it, or if its structured, semi-structured or unstructured.

That’s why any attempt at intelligent data management starts with finding data wherever resides ─ on-prem servers, DAS/NAS/SAN resources, cloud-based data warehouses and data lakes, shared drives, email servers, the edge, and even end user devices.

2. Identify the data. It’s hard to work with data unless you know what you have, which is why data classification and identification is an important next step. It entails labeling data sources and elements with metadata that provides context into how data should be organized and handled.

3. Conduct data hygiene. Regardless of how and why data is used, its value will be greatly diminished if its quality is poor. Poor quality stems from data that has errors, is full of duplicate files, has no value, etc.

4. The cleaning process ─ fixing errors, removing redundant files, etc. ─ can be a tedious undertaking, depending on the tools and processes used. But it’s the act of finding the data that requires cleaning that can be especially time consuming, particularly if it relies solely on human action. Automated filtering and search processes that locate data that requires cleaning can cut down on the time requirements significantly.

5. Secure the data ecosystem. Data is an important asset, and not just for the organizations that generate and use it. Cybercriminals find it extremely valuable too, hence the reason that incidences of ransomware attacks and other cybercrime are so high.

But it’s not just hackers that data owners have to worry about. Data can be lost or corrupted due to equipment failures, theft, negligent actions by employees, and more. As such, there’s a critical need for multi-layered security across the entire lifecycle and wherever data exists.

Data security also affects data privacy; both are subject to various regulatory requirements, industry standards, and government mandates. Things like data retention and data access come into play as well. Failure to deal with them can cause compliance issues and possibly open an organization up to unnecessary risks and security vulnerabilities.

Additional and Next Steps

Making the most of data also requires optimizing it so it can be used how, where, and when needed. Understanding the specific use cases ─ i.e., applications powered by machine learning, behavior AI analysis, fraud prevention etc. ─ and their requirements and goals will make a difference in terms of next steps. It’s also essential to ensure that all stakeholders are actually using the same data to make decisions by establishing a “single source of truth.”

In many cases, the use of tools and policies that can automate actions, such as copying, moving, archiving, retrieving, and deleting specific types of data, can help reduce errors and generate more accurate results.

For more specifics about intelligent data management, download Aparavi’s free eBook: The Art of Intelligent Data Management. In addition to providing details about the steps for intelligent data management, including two steps not covered in this blog, it covers how to:

· Identify the opportunities and risks underneath your enterprise data.

· Understand the lifecycle of data across all of its uses and applications.

· Distinguish between data you need, and what should be thrown away.

· Successfully apply intelligent data management principles with real-world use cases.

For questions about how Aparavi’s DataAutomation and Intelligence Platform can facilitate intelligent data management, or to try out the platform for free, contact us.