The Future of Data Warehousing

The Future of Data Warehousing

As the data industry continues upon its exponential expansion, companies and organizations alike have quickly found themselves dealing with an overwhelming amount of data. Initially, these enterprises managed data via multi-million dollar infrastructures called data warehouses. Yet, with the data industry booming in an unprecedented fashion, many companies found themselves dealing with a larger, more diverse amount of data than they could have predicted.

Due to such influx, data warehouses were soon found to be inefficient, as most were designed to handle structured data (i.e. ERP systems) as oppose to the unstructured data (i.e. social media, mobile devices) that currently dominates the data industry. For example, Richard Solari, a director of a Information Management service line, estimated that, “90 percent of the data warehouses he’s observed process only 20 percent of an enterprise’s data.”

The Expansion of Unstructured Data

If any of the aforementioned information was troubling, consider this: the amount of data that data warehouses are able to process is projected to further diminish with the progression of unstructured data.

Two primary elements of unstructured data refer to mobile devices and social media. Both platforms allow for the generation of massive amounts of data due to their ease of use. Individuals of all ages can now create, consume and share data at an unprecedented frequency. Such influx of information creates a unique opportunity for enterprises to discover and understand their audiences.

Yet, this situation puts many companies at a crossroads, for their current infrastructures are unable to handle such colossal amounts of data. Furthermore, not utilizing the opportunities provided by Big Data would put them at fatal disadvantage within their particular industry.

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Moreover, the creation of unstructured data is likely to increase as mobile technology is expected to expand into one of the supreme industries of the next decade. Considering this, what exactly can enterprises do? Must they ditch inefficient data warehouses and spend millions re-designing a new infrastructure?

Introducing the Savior: Analytics Warehouses

Analytics warehouses are considered to be very similar to data warehouses (so much so that the initial infrastructure need not be destroyed), yet differ in that they are able to process unstructured data equally as well as structured data. For example, in the past, enterprises couldn’t merge structured and unstructured data due to a lack of a universal system – i.e. each respective technology required specifics file formats in order to process the different forms of data.

With analytics warehouses, enterprises are now able to integrate various forms of data, which thus provides them with an entire 360-degree view of their collected data. They can then collect this data and convert it into priceless information without having to be concerned about missing a huge chunk of potential information.

Examining the Structure of Analytics Warehouses

Companies that are concerned about transitioning to analytics warehouses need not worry, for analytics warehouses are constructed upon the following three main components of the data warehouse design:

1. Both forms of data – structured and unstructured – that are stored in back-end systems can be transferred over to the data warehouse in real-time.
2. A file distribution system is situated between the data source and the data warehouse. This system is able to process massive amounts of unstructured data, which, once the processing is complete, can be entered into data warehouses for further analysis.
3. Both warehouses engines function in a similar fashion, with the main objective being the identification of patterns within Big Data.

Examining the Future of Data & Analytics Warehousing

By implementing analytics warehouses, enterprises create for themselves the possibility to take full advantage of Big Data. And naturally, with new access comes new ways in which data and analytics warehousing can be utilized, examples of which are highlighted below.

Consolidation via Engineering Systems

By incorporating analytics warehousing, companies and organizations are now able to consolidate their data resources, which in turn creates for a more financially efficient process. Much of this consolidation is achieved via purpose-driven engineering systems that combine hardware and software to function far more efficiently than traditional industry practices.

Data Transformation

With data transformation, data values organized in a format recognizable by the source data system is converted into the data format of the desired data system. Within this transformation process, there are essentially two steps:

1. Data Mapping: which maps and identifies the necessary transformations for proper conversion
2. Code Generation: which creates the program that will transform the data from the source’s data format to the destination’s data format.

Overall, there is myriad current and future potential within the data industry. Yet, enterprises that solely use data warehouses will be unable to take full advantage of all the valuable information being generated. Therefore, enterprises should take a serious look at implementing analytics warehouses in order to gain a competitive edge in their respective industry.


Sources

http://www.oracleimg.com/us/dm/dw-top-trends-2014-v13-2075572.pdf
http://deloitte.wsj.com/cio/2013/07/17/the-future-of-data-warehouses-in-the-age-of-big-data/
http://geospatialworld.net/Magazine/MArticleView.aspx?aid=30512
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