Milestones based Methodology for implementing Big Data

In the modern world, fashion absolves no one. Cloths, vehicles, homes, slang, travel, furniture, behavior, rules, mingling, art, music, hobbies: you name it, everything is marked by the trend. In the business world, fashion is equally prominent: mergers and acquisitions methods, IT, offering methods, management designs, modifications and optimizations, trainings, profession and knowledge, and growth. Some walk the floor, some follow the trend; in our own different ways, we are all subject to the fashion magnetism.

Presently, among the trendy patterns on the business market is without doubt Big Data. From a cursory Google search, you develop more than 50M … practically the like when you look for David Guetta.

So, lots of companies like and want to have Big Data. For some it is a must, for others it is a good thing to display. Nevertheless, entering the world of Big Data is more complex than buying a new vehicle or training your selling group to pursue a more aggressive business development practice. It is complex due to the fact that it needs substantial investment in resources and careful planning without any quantifiable “in time” and money return on investment. Big Data, like any new business initiative, needs a strategy and a plan, otherwise the needed investment can go through the roofing system with results maybe never ever actually attained.

The crucial thing for Big Data or any data-driven effort to be effective is to have a methodology for implementation and the appropriate team in place. If we recall over the history of data advancement, we can see that data has been around permanently which Big Data is actually nothing new to the clinical world. Scientists have constantly begun data collection, data mining and conclusions in a project based structure operating in flexible groups. Also, an important premise is constantly beginning any research with the question currently in mind.

In a business environment, successful Big Data initiatives must follow the very same pattern: be project based (which means that they must have a beginning and an end date), have actually clearly defined questions (goals, targets) and can count on a multidisciplinary team (data researchers, PMO, subject matter specialists, etc.). There is no plainly specified project methodology for a Big Data project. Lots of use the Agile method as data initiatives have strong dependency on IT and heavy calculation, others have actually adopted the SMART Model, some have developed their own internal methodology.

An often used method (especially in SMEs) for a Big Data start is resulting in the use of DMADV (originally used in Six Sigma jobs) as it incorporates all the essential stages for structuring a data initiative however in this case, rather of focusing and examining internal data and procedures, the attention is on external data or a combine of internal and external data.


The Project Milestones

Define: state objectives or the concerns we want to find responses to (customer requirements, expectations, habits patterns, interdependencies).

Measure data (internal and external). If data is not in location, categorize technical demands for data collection, storage and analysis, established collection points, collect data and measure.

Analyze: exploratory data analysis, recognize patterns and patterns (by using analytical tools: inference, regression models, etc.). Develop best growth alternatives (new offerings).

Design: choose best new offering, develop it and the supporting processes to fulfill customer expectations, run DoE

Verify the design (the product, process, or service output and performance versus customer demands): established pilot runs, execute the production/marketing/sales/ customer care process and hand it over to the process owner(s).

Without a structured methodology which has actually clearly defined turning points, launching a data-driven effort is merely ineffective. Nevertheless, there is one extra and extremely important aspect to any methodology for Big Data: the training of individuals to think, work and deliver in a new method. Big Data requires a various set of minds, ones that measure in order to decide. And I am not discussing the Big Data stakeholders, the ones who do the number crunching and evaluations. Training can have various tactics and scope for different organizational levels, but it is compulsory as Big Data may be fashionable now but a type of fashion that will dominate in time.

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