Unified Data Strategy

The amount of data being created, captured, and managed worldwide is increasing at a rate that was inconceivable a few years ago. Data is a collection of discrete units of information but like the stars in the night sky taken together form an organized structure.

Unstructured data comes in many different formats including pictures, videos, audio, PDF files, spreadsheets, documents, email, and many other formats. 

Sometimes unstructured data lives within a database. Sometimes the database acts as an index for the unstructured data. Often the metadata (information about the data) associated with the unstructured data is larger than the data itself. Consider the example of a set of videos. Although the files may be small in size, the information stored regarding the content within a particular video may be very big. Often unstructured data is also called big data.

Certain business functions require analysis of massive amounts of data.

Multiple systems are being utilized to manage different forms of disparate data. Companies need to adopt a comprehensive and holistic approach to managing these many systems and incorporating them into a combined system.

Modern IT systems should be able to ingest, access, store, manipulate and protect data within a wide variety of disparate formats. These multiple data formats may exclude the necessary flexibility, elasticity and alacrity that many modern business functions require. There are situations when data must be accessed so quickly and data management systems should be able to accommodate such situations. Each of these systems recognizes a particular style of data with a fairly well-defined set of attributes and manages that data to satisfy a particular business function.

A Unified Data Strategy (UDS) is a broad concept that describes how massive amounts of data in a multitude of forms can and should be understood and managed. UDS is also a specific individualized methodology developed by each data owner to manage that data in all its forms in a comprehensive but interrelated manner.

By adopting a UDS, data owners will be able to develop comprehensive, customized methodologies to manage their data. By taking into account the interconnected nature of the various sources of data and tailoring the management of that data to the specific business requirements the maximum value can be achieved.

UDS can be used to address the task of comprehensive data management. Cloud computing may provide the solution to this data management and recognition problem. Virtualization, the foundation of cloud computing, is the cornerstone of this strategy. The capabilities and architecture enabled via a virtual/cloud infrastructure can help companies to develop a UDS to address the movement in data management and practice.

Exciting new technologies and methodologies are evolving to address this phenomenon of science and culture creating huge new opportunities. These new technologies are also fundamentally changing the way we look at and use data.

The rush to monetize big data makes various solutions appealing. But companies should perform proper due diligence to fully understand the current state of their data management systems. Companies must learn to recognize the various forms of disparate and seemingly extraneous forms of information as data and develop a plan to manage and utilize all their data assets as a single, more powerful whole.

The transition from traditional relationally-structured data to a UDS could be complicated, but can be navigated effectively with an organized and managed approach to this effort.

To successfully adopt a Unified Data Strategy, companies should focus on the following:

1. Develop a thorough understanding of how the business consumes, produces, manipulates and uses information of all types.

2. Determine how the business can use data to both understand external factors and to assist in making internal decisions, as well as to understand how the data itself is relevant to influencing the business.

3. Analyze the "personality" of each data form so that it can be matched with tools that appropriately acquire, filter, store, safeguard and disperse the data into useful information.

4. Select infrastructure and tools that automate or eliminate traditional high-cost tasks such as import, provisioning, scalability, and disaster tolerance. A highly virtualized infrastructure with complementary tools should provide the majority of these capabilities.

5. Commit to the process of learning as an entirely new approach to technology, and to adopting it in risk-appropriate increments.

Any organization with a significant data infrastructure should be aware of the pitfalls that could occur if a company rushes into acquiring new technologies without understanding their requirements. Thorough analysis will lead to an understanding of the current state of their data management systems, and subsequently to better control of their existing data.

Ultimately, organizations should be able to recognize, manage, and utilize new forms of disparate and seemingly extraneous information as data. Companies, that develop a plan to comprehensively address all their issues around managing and utilizing all useful data, will gain significant strategic advantages.