Resolving Data Complexity with Flowcharts

“Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to cloud to gaming.” – Chris Lynch, Former CEO, ex-Vertica

Complexity is inherent in natural processes and defines many aspects of the physical world, as we know it. The interaction of solar radiation with various layers of the earth’s atmosphere creates complex visual effects. Meanwhile, the impact of atmospheric water vapor on various geographies is manifest in complex outcomes such as dense vegetation in equatorial areas and sheets of snow high in the mountains of cooler climes. Similarly, complexity in digital data is an emerging fact of life. Data complexity is a modern phenomenon powered by multiple factors such as the size of digital data, the structure of data, and the sheer variety of digital information. A range of actors in the modern digital domain is working to analyze and resolve the many aspects of this complex modern phenomenon. Flowchart diagrams can help in such investigations.

A data diagram represents one of the techniques that may help resolve certain issues pertaining to data complexity. Various stakeholders can design a flowchart diagram that contains multiple elements that power the phenomenon of data complexity. The multiple parts of this diagram may typically include a query language, the type of information being processed, the growth rate of various elements inside the data, the dispersion of data inside a system, the size and structure of data, etc. This effort to map data and its various aspects allows stakeholders to gain the proverbial handle on the burgeoning explosion of digital information. It also allows reviewers to gain a sense of the various facets of complex data, and evolve a strategy to deal with such phenomenon. In addition, the structure of the flowchart allows actors to review their own inputs and survey the impact thereof.

The rapid proliferation of digital data across a system represents one aspect of data complexity in the modern world. This represents an opportunity for various actors to deploy flowchart diagrams in a bid to (visually) capture the spread of data across the many elements of a system. The illustration can help map multiple moving parts such as data resident inside a data center, the use of file analytics to analyze the data, the transit of data through remote file servers, the creation and use of multiple data back-ups, the establishment and operation of a compliance mechanism, digital archives, etc. The act of mapping these moving parts through a flowchart empowers actors to tackle data complexity that arises inside modern digital infrastructure. A consistent survey of these elements also allows actors to strategize and devise multiple techniques that may help resolve the said issue.

Challenging data environments are a sign of the times, and industry observers have stated on the record that compound annual growth in data will likely touch almost 50% each year. In this regard, flowcharts are uniquely positioned to lend the proverbial helping hand to efforts that seek to fashion tech policy that may resolve data complexity. Data scientists may deploy such illustrations to execute data modeling tasks and the various moving parts therein. The flowchart may help map the moving parts, which include data attributes, the various sub-types of data, (emerging and actual) relationships between various forms of data, the rules that preserve data integrity, etc. Certain other aspects of the flowchart may explore the parameters related to performance considerations, the different types of physical data models, the technical environment that promotes optimized data modeling, etc. These actions, when marshaled into a coherent whole, may assist in the mission of resolving data complexity.

Modern business organizations can leverage the analytical power of the flowchart in a concerted bid to tackle the problem of data complexity. The retention of a sharp focus on data itself remains an essential feature of this approach. The various actors involved in such efforts can apply a digital tool to said flowchart with the aim of ‘mashing up’ a set of data, combine data from various sources, and subsequently, derive business intelligence from this digital apparatus. Intelligent business operators may elect to refine this strategy in line with the unique demands of their business. Additionally, the smooth operation of this technique may promote future initiatives such as data visualization and data discovery. These techniques, when operationalized across a business enterprise, may allow organizations to draw better conclusions from their business data. In addition, said techniques may provide a bulwark against the threats posed by new ad emerging forms of data complexity.

Some observers have noted that the phenomenon of data complexity faces aggravation when organizations fail to undertake suitable application analysis initiatives. In such a scenario, organizational actors can devise flowcharts in a bid to view an application from the viewpoint of the organizational matrix. The flowchart illustration can help businesses to gain a clear view of which applications actually exist, the various modes of the interaction, the value of each desired function, etc. These actions can generate outcomes wherein data complexity is reduced and actionable business outcomes find clear expression. Certain sections of the flowchart may conduct a detailed analysis of the system information and feed into the parent process operating in the flowchart.

In recent times, data complexity has posed incremental, fresh challenges to all attempts at resolution. Experts in the domain of Big Data aver the increasing complexities that attend data modeling challenges, as also new challenges in data indexing and the aggregation of data. Flowchart diagrams can be deployed as an innovative new tool that can help tackle certain elements of data complexity. Data scientists and digital specialists can use these diagrams to assess the outcomes of multiple strategies that aim to resolve aforesaid challenges. In addition, groups of researchers can study flowchart illustrations in a bid to investigate the efficacy of new analytical models. The outcomes of such efforts may create a significant impact on the problems that attend the emerging face of Big Data.

New studies that seek to tackle the menace of data complexity indicate that collaboration among multiple stakeholders may lead the way. Modern data workers can sketch flowchart diagrams to block, for instance, the creation of duplicate data assets. Such an intervention may be augmented by devising combinations of machine intelligence technologies and human ingenuity. The outlines of such collaboration can be depicted inside a modern flowchart, thereby bringing to life expert strategies that can resolve emerging issues in the domain of data complexity. In addition, flowcharts can also help spotlight trends in the realm of complex data, an action that may allow insights to emerge from a bunch of data. Thereby equipping data workers with insights that may help them win the war on data complexity.

We have explored some of the aspects of tackling data complexity in the digital domain. Flowcharts have emerged as the premier tool that allows data analysts & scientists to fashion a variety of techniques and strategies. Interesting variations of these illustrations may help data workers to shine the proverbial light on the dense undergrowth of complex modern data. Further, data resolution assumes importance because data-driven decisions are driving modern businesses. Business operators that can derive succinct business insights from data can steal the proverbial march over the competition and sharpen their competitive edges. In light of the above, data resolution techniques and technologies using flowchart diagrams have emerged as critically important.

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