Unify your enterprise data with enterprise research software

Many modern organizations rely heavily on third-party services for some or all of their day-to-day workflow. Software-as-a-Service (SaaS) solutions offer inexpensive, convenient services that allow businesses to work productively without the responsibility of developing or maintaining the software that powers the platforms. This has many benefits, but it can also lead to fragmentation of your organization’s data. This is the problem that enterprise research aims to solve.

Why is data fragmentation and data access a problem?

Data fragmentation is a somewhat dated term that applied to single computers. Sometimes, data on a hard drive became fragmented, which meant that it was spread over different sectors of the hard drive and became difficult to recover. This old problem has a very similar modern parallel, so similar that it is often called massive data fragmentation. The problem here is very similar: enterprise data is fragmented, except this time it’s because the data is stored in different physical locations, third-party services, data centers and remote offices. Organizations continue to suffer from the same issues – data is difficult to find and to unite when needed.

Why should research data be unified?

There are different pressures on different organizations that are why data unification is a good idea. Some organizations rely heavily on multiple third-party SaaS solutions, while others use a very limited software stack. Regardless of this, all organizations must at a minimum comply with regulatory requirements regarding the use and storage of data. Regulations such as GDPR in Europe and CCPA in the United States have placed constraints on organizations on how they store and retrieve data.

Common to these regulations is that an organization must be able to retrieve all data held on an individual upon request and also be able to delete all of it upon request. Without getting bogged down in regulatory details, this essentially means that all organizations must be able to find and retrieve all information held on a particular individual at any time.

This can be a significant problem for companies with significant massive data fragmentation. A customer’s data can be distributed across an organization’s CRM, marketing campaign manager, sales database, and internal LAN storage devices, to name a few possibilities. Finding and retrieving all of a customer’s data manually can be a daunting and time-consuming task.

How Enterprise Research Software Combats Massive Data Fragmentation

Enterprise search software is the functional equivalent of having a private version of Google, Microsoft Bing, or elasticsearch for your organization. When you search the web with Google or another search engine, it does not matter where the data is stored, in what language it is written or in what file format it is stored. Contemporary search engines are capable of returning relevant search results for text documents, emails, videos, images, audio files, slideshow presentations and many other formats.

Enterprise search software makes solving the problems listed above relatively simple. First, all data sources used by an organization must be brought together. This data does not have to come in any particular structure, come from any particular source, or be formatted in any particular way. Instead, many pre-built connectors for existing popular SaaS services and other platforms are readily available, simplifying data collection. Once the data is collected, it is then analyzed and indexed. This important step helps establish relationships between datasets and enrich the data.

However, enterprise research software is much more powerful than simple keyword research. Advanced linguistic concepts and natural language processing are used to understand not only the surface content, but also the deeper meaning of a text. When that means, in a context relevant to many organizations, the ability of enterprise search software to understand when a name is mentioned in multiple disparate documents, to understand that it represents a person, and to automatically create a profile of that person using the information collected. This is all done automatically through machine learning processes using existing data.

Data-driven content analysis

Enterprise search software has the ability to not only unite, but also analyze the data that an organization connects. For organizations that typically rely on multiple third-party services, this may be the first time all of this data has been analyzed collectively. This can highlight previously unknown relationships between data sets – for example, sales data and employee attendance records can be cross-referenced to compare employees working on the best and worst performing days.

Secure search engine software for your organization

Enterprise search software works the same way as a private, internal Google or Microsoft Bing search engine that is specifically trained on your organization’s data. It can be searched in the same way and can represent relationships between different sets of unstructured data. Individual customers and suppliers can be identified and grouped from different platforms and services without any manual effort. Enterprise search software not only brings together all of an organization’s data, but also analyzes it to build relationships while making all of that data as easily accessible as web search on Google.

This article does not necessarily reflect the views of the editors or management of EconoTimes

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