Big Data association and aggregation

Organizations are dependent on Information technology, They are forced to collect information as a part of their day to day electronic transactions. Such collected information silos are scattered across various cooperating and independent organizations. Management models have evolved into collaborative business models leading to a win win for member or partners of the coalition. Such organizations with excellent chemistry have exercised partnerships to exploit their combined strength to win. These firms include, enterprises, large organizations and government departments have aggregated volumes of data on individuals, spread across their verticals, profiled individuals, and are today looking for synergies and opportunities for cross-fertilization leading to better insight and better business. Such firms churn data with a greater velocity and variability through the multitudinous elements composing the business canvas.

Apart from the operational data addressing the IT systems, which are internal, customer centric data from elements like blogs, social media networks, and location based services, browser based data, which impact the business portfolio and provide business variations in terms of USP to the respective firms are generating valuable insight on customer profiling. Adding fuel to this is the dynamic business environment shifting gears to consolidation and Cloud computing where data could be aggregated. For example cloud oriented and / or localized application and infrastructure events provide a real time insight on usage patterns. Such events governed by SIEM modeling methods  would benefit on Big-data. For example, ATM transactions are profiled to manage location based profiling, which can trigger on abnormalities and throw additional checks. For example, social network and blog entries have come up with their specific search engines to analyze data in real-time. If such aggregated data could be exploited to trigger on data-patterns, organizations would be in a position react with appropriate change management initiatives. On a parallel lookout this is similar to the established just-in-time strategy with the difference being micro managed scope of coverage.

Author - IT for Management, Oxford Press

Big data ecosystem

Digital data explosion is beyond control as evident from the digital data being pumped into the digital world without relevance. The data so generated calls for special techniques, to manage and distill the essence out of it. Data follows the data lifecycle. In a larger parlance data co-exist  in a digital world and is a part of digital ecosystem. The digital ecosystem is still at its infancy. To start with digital data is brought to life through the interaction with digital gadgets by the digital users on a minute by minute basis. This is followed by aggregation of such data, analysis, and interpretation. There are other elements like the data platform, associated operating system, data storage, access mechanisms, data processing software, specialized resource, associated vendors, etc forming a part of this large ecosystem. Digital data ecosystem contributes to the genesis of bigdata ecosystem. Big data initiated by Douglass Read Cutting is an envelop term describing the management of very high volume data.  Interactions within the digital ecosystem by itself a study and calls for larger understanding of the system and their dynamics.

Looking at the volume of interactions every minute the generated digital data is beyond comprehension. In a business sense, the business community is interested to understand the digital data and exploit it for their bottom line management. This is in line with the Law of survival, looking for more cheese.  The same could be achieved through the data distillation process with or without appropriate catalyst into something newsworthy and the info-graphic chart which is used to carry the news to whomever it may concern. However digital data management is different from digital info-graphics and each of them calls for different skill sets creating a paradox.

Some of the larger questions include, the ways and means of addressing big data capture and storage management architecture, Ways and means of redefining the infrastructure architecture, modeling business intelligence to look for specifics either by heuristics, fuzzy logic, or investigative statistics, Development of info-graphics capabilities like timeline info-graphics, statistical info-graphics, process oriented info-graphics, etc, within the organization, remodeling organizational data strategy to address organizational dynamics on demand and reshaping the organization to circular designs. Big data strategy needs to also account for new sources of data, and ways of integration for timely, accurate access to information leading to business decisions.

Author- IT for Management, Oxford Press.

MoneyWise-Topline and Bottom line

Information technology (IT) is today woven into our daily life. In a globalized economy, business life without informational technology driven applications would be impossible transforming information technology as a commodity. From a niche market, information technology has evolved into a commodity market. In order to sustain, information technology is envisaged to create value.  Today business with information technology is adopting bottom-line strategy to increase efficiency by reducing overall costs and top-line strategy to generate new revenue through new products and services.