Locating the data you need in the time you need has always been difficult. Finding a specific image, document, or other file type can be like finding a needle in a haystack—or, in the case of an enterprise dataset, a few hundred haystacks.
You should be able to simply open a browser, select a few criteria like file type and create date, and find all the relevant documents, right? Or, you could type in a keyword that you know is contained within the file content. Alternatively, you could use tags to find documents related to a particular classification policy. These processes should be fairly straightforward, yet, today they rarely are.
Metadata filters, content indexing, and classification tagging are critical capabilities for effectively finding and culling data. The majority of available tools do not offer all three of these capabilities, are limited by storage tiers, and tend to be needlessly complicated and time-consuming. They also lock customers into their proprietary system and demand extra storage resources to achieve results.
How can you save time, money, and effort by simplifying the process of finding and accessing the right files? We’ll look at how most applications work and explain how you can take the pain out of finding data.
To make the process of locating data effective, we must first understand the problem: the typical enterprise manages billions of files in their storage hardware, files are in disparate locations, and data sprawl makes finding the right data a huge time killer. Additionally, these closed proprietary systems tend to only work with their storage, when the typical data center has multiple systems and storage vendors.
A tool that can find data effectively will save an organization a significant amount of time and money. Whatever the use case, it allows quick and easy creation of complex queries, enabling even a novice user to find their needle in a haystack.
There are many applications out there that call themselves a powerful tool for locating data because they can crawl file systems and collect a limited list of metadata fields. Finding data based on simple fields like file name, file type, age, or date range may be adequate for some operations. When those filters are insufficient, modifiers such as “all of these words” or “contains any of these words” can help cull down the results.
These applications effectively capture metadata, but they usually do not create a content index that allows users to search through the contents of files. They may use content caching for classification purposes, but they do not supply a way to store indexed content and access that index quickly.
Many organizations that use cloud storage wind up having to restore gratuitous files and paying unnecessary egress fees to find a file that fits a complex set of criteria. Because they lack insight into files’ content, metadata, and tags, the only solution is to restore various files and have a human evaluate their contents. This is, of course, a difficult, time-consuming, and expensive task. The good news is there’s a better way.
Certain organizations, such as those in the finance, healthcare, and federal government verticals, process more regulated data than others and constantly deal with high-risk data. Finding that high-risk data can be challenging unless you have an intelligent solution for classification tagging.
Classification tagging requires the ability to clearly define a classification policy and its rules. From there, the solution should evaluate the metadata and contents of a file, add a classification tag, and move the file through the system accordingly. The classification tag should be a searchable field that allows users to determine the risk factor of a file.
Identifying high-risk data is essential for heavily regulated organizations. Classification tagging and finding files based on those tags should be easy for all users, regardless of expertise.
The Aparavi Platform’s capabilities are simple enough for even the most novice user yet powerful enough to handle highly complex metadata queries. Why is Aparavi more powerful than first- and second-generation data intelligence tools? It comes down to indexing content so it can be kept for future actions. The Aparavi Platform does not limit the metadata fields it stores; it captures all fields and makes them easily searchable.
What’s the difference? Let’s look at a search an organization might require:
An end user needs to find the following document that fits the following criteria:
Let’s try to find a file with these fields defined.
And the results!
As you can see, we inputted the criteria, and the query found 32 files that match. Aparavi’s indexing of file content allows users to find files and details about those files easily and quickly.
With Aparavi’s data intelligence, there is no need to restore or access files until you have determined you have found exactly the file you need. Aparavi’s superior metadata collection and content indexing make it easy to find your needle, no matter how numerous or disorganized your haystacks may be. Contact us now to learn more about Aparavi Index and Find.