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Best Practices for Enterprise Data Management

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Best Practices for Enterprise Data Management

With more data available than ever before, proper data management is critical for enterprises.

· Database Zone ·
Free Resource

In today’s business, that old maxim information is power is more relevant than ever.

The challenge for many enterprises, though, is ensuring the data they now have access to is organized and stored in such a way that makes it accessible throughout their organization—all while still adhering to proper governance and security.

How can you make this happen? By following these four best practices for managing your enterprise data:

1. Get to Know Your Data

Enterprise data used to be relatively simple. How many widgets were sold where, how much time and resources were spent on production, and so on.

But with the proliferation of connected devices, unstructured data has exploded. Now an enterprise can collect an unprecedented amount of information and use it to not only make smarter decisions (traditional business intelligence) but also to identify new opportunities for revenue and growth.

Realizing these benefits takes more than simply capturing data, however.

Modern data infrastructure — particularly the cloud — may make storing information easier and more cost-effective than ever before, but without a thorough understanding of your data, you won’t be in a position to capitalize on it.

In order to understand your data, you need to catalog:

  • Where your data is coming from
  • The quality of your data
  • Any gaps in your data that need to be filled

2. Make Your Data Discoverable

Advanced technologies like predictive analytics, artificial intelligence, and machine learning — tools that help you make smarter decisions — need a massive pool of data to play with in order to be effective.

They also need structure, which is why ensuring a proper data cataloging process is in place is essential.

Without structure, all the data you are storing is basically a house with no doors. Your advanced analytics models cooked up by your data scientists may know where the information they need is stored, but they have no way to gain access to it.

The good news is, there are a number of tools available to accelerate this process. Google’s Big Query, for example, provides you with a rigid platform to ensure cataloging happens consistently. Amazon’s AWS and Microsoft’s Azure offer similar products as well.

3. Democratize Your Data

One of the goals of building out an effective data platform is to break information free from the silo of IT and allow access throughout your organization.

In order to do this, you need to ensure proper governance,  monitoring, and security measures are in place.

Marketing has different needs than data scientists, after all, and while there may be overlap in the data sets they both require access to, not all of the information in those sets should be made available across the board.

For enterprises with a global footprint, in particular, proper governance is critical to democratizing data. Different countries and regions have different regulatory requirements when it comes to privacy and security, so strict rules need to be in place for who is and is not allowed to access information.

4. Know What You Want From Your Data

While not explicitly under the umbrella of data management, knowing what you’re trying to achieve with your data can be the difference between putting data to work effectively and not.

As a general rule, all your data should have a reason for being captured and stored, whether that reason is specific or, in the case of data science projects, used strictly for experimentation.

It’s common for enterprises, especially those new to the world of advanced analytics and the cloud, to attempt to bite off more than they can chew. This, in turn, usually leads to projects failing. So, start with the small and achievable, then build upon your successes.

Topics:
data management, data management solutions

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