The Big Data Revolution
As a series of advanced methods, Big Data is used to process voluminous data sets that conventional business systems cannot handle. The purpose of Big Data processing is discovering hidden patterns and connections, customer preferences, and market trends that enable businesses to better understand the information in their data, subsequently making superior decisions and gaining an edge over their competitors. Indeed, businesses will be able to achieve new revenue opportunities, enhanced customer service, and improved operational efficiency.
The Impact of Big Data on ECM
Big Data has become a fundamental drive for the growth and transformation of the Enterprise Content Management (ECM) market. In fact, it has emerged to reinvent the way ECM solutions are perceived.
ECM systems, which have now been around for 3 decades, were initially designed to store, organize, and manage large quantities of unstructured data, making them securely accessible and meaningful.
But today, differentiating between structured and unstructured data is no longer important, especially with the emergence of Big Data, which enables businesses to create value from all kinds of information it deals with.
Furthermore, storage has become cheap, so storing large quantities of data is also no longer a concern. Today, the major focus of ECM solutions is to create value from this data by interpreting it to derive useful meaning out of it. Therefore, ECM solutions are now complemented with Content Analytics tools, with one main objective: providing stakeholders with tools that enable them to pull higher insights using chronological, statistical, and geospatial views.
How is it done?
According to Gartner, Big Data is high Volume, Velocity, and Variety information assets that demand cost-effective and innovative forms of information processing for enhanced insight and decision making. This is known as the Gartner Big Data three “Vs” paradigm to which we add two additional dimensions: Validity and Volatility.
[list-ul type=”circle”][li-row]Volume: very large Volumes of data ingested and managed in Big Data structures that require adequate automation in order to obtain better insights and views of the managed data.[/li-row]
[li-row]Variety: variety of types and formats of ingested and managed data that flows into Big Data structures in various formats, which require advanced connectors and adaptors to manage their ingestion.[/li-row]
[li-row]Velocity: velocity of incoming data flow and processing, which poses high technological challenges related to the ingestion and validation of incoming data flowing in different peaks.[/li-row]
[li-row]Validity: mainly concerns uncertainty of data. A large amount of time in data processing is spent on removing duplicates, fixing partial entries, eliminating null/blank entries, concatenating data, etc.[/li-row]
[li-row]Volatility: for some sources, the data will always be there; for others, this is not the case. Rules should be established in the lifespan, during which data should be valid as it might need to be preprocessed repeatedly.[/li-row][/list-ul]
A solid and performing framework is needed for storing and processing Big Data in a distributed manner on large clusters of servers. Basically, what the framework is requested to accomplish is massive data storage and very fast processing. The Big Data framework should then provide the following characteristics: Computing power, Scalability, Storage flexibility, Clustering and self-healing capabilities, and Completeness.
Subsequently, ECM vendors have considered making important changes in their architecture in order to support Big Data. The core of the Big Data management framework consists now of a powerful Enterprise Content Management System that can manage the ingestion, storage, and processing of very large content volumes coming in various flows and in different formats. On top of that, it contains a state-of-the-art Records Management System that is fully integrated with the Content Management System to manage physical security archives. Furthermore, it includes a powerful distributed processing sub-system that manages the Big Data distributed storage, and exposes its services to the Big Data analytical layers, as well as to other third party applications.
Big Data Analytics
Organizations are in need of a modern and comprehensive Content Analytics Solution in order to access the information residing in many sources and in different formats, thus being able to perform their main tasks: monitor, find, decide, and act swiftly.
Now, since data takes many forms and formats, it isn’t always persistent and, most of the time, requires logical interpretation and decoding, so the adopted solution should provide ways to query, analyze, link, and interpret all kinds of data.
The Content Analytics Solution should provide easy and quick ways to access existing data in any “compliant” database, link all information together, as well as with other data sources, search in various “fuzzy” ways in order to pull superior insights from existing and new data sources. As a matter of fact, these analytics tools made it possible to reach further understanding and achieve improvements on the level of ECM solutions. In reality, Big Data analytics have improved automated workflows and ECM applications, and provided businesses with insights that assisted them in automating and optimizing their business processes and applications by uncovering previously hidden information and making it available.
In conclusion, ECM solutions have now become Big Data centric; and with the ever growing amount of data, structured databases have become a fundamental asset to businesses. Big Data has also made ECM solutions more intelligent and agile by converting them into decision-making solutions that rely on advanced analytics. This will add up to better decisions, more satisfied clientele, reduced fraud, and so forth; which will bring businesses significant economic benefits.