Big Data More Is Not Always Better For Your Big Data Strategy

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1 Big Data More Is Not Always Better For Your Big Data Strategy Abstract Everyone is talking about Big Data. Enterprises across the globe are spending significant dollars on it and endeavor with their strategy. The people, the processes, and the technologies that are needed to understand and solve the complex problem of processing enormous amounts of data remains elusive. We are storing more and more data at staggering amounts. Our fast paced lifestyles are only adding to the requirements for faster and faster machines to endlessly gather all forms of unstructured, semi structured, and structured data. We are simply hungry for data without any for seen ability to satisfy our appetite. We are data rich and information poor. The global data stores are growing exponentially. In 2013, Big Data has been projected to produce an annual revenue of $187 Billion in 2015 (reference: annual report of IBM, 2013). Big Data is a Big Business. Big Data is also a Big Deal, a Big Financial Deal. It can offer Big Business gains, but hidden costs and its complexity present barriers that organizations will struggle to mitigate. Standards organizations like the National Institutes of Standards and Technology (NIST) are working in collaboration with government and industry to define Big Data and the issues associated with it. One overarching issue involves security aspects, and the significant challenges that have yet to be solved. Information security like availability, back-ups, and disaster recovery is also a big deal regarding hidden ongoing costs. While technology firms and numerous vendors across the globe are developing new software tools and services to mine, query, analyze, and to make the data stores meaningful much is yet unknown as to the payoff of Big Data. Now is the time to look for and apply proven methods from other sciences to the Big Data discussion. The science of 3D coordinate measurement metrology (3DCMM) as a discipline of Dimensional Metrology (DM) has been successfully practiced for over 25 years and offers some of the answers. 3DCMM is the science of calibrating and using physical measurement equipment to quantify several physical characteristics from any given object. It embraces and optimizes the value of data. The purpose of this paper is to serve as a catalyst for a paradigm shift, while we have vast stores of data, analytical projects could use the 3DCMM approach that first identifies the minimal dataset required, then use an iterative control loop as a feedback mechanism to detect the end for gathering datasets. Simply said, more is not always better. If the 3DCMM modular, structured approach would be applied to the Big Data strategy: Just enough data, the right data, at the correct location would produce the right conclusions. Therefore, the endpoint results in risk elimination and reduced financial costs, ensured customer satisfaction, and production of THE requested product. The overarching application of 3DCMM applied to Big Data ensures that data stewardship and management are interwoven into the company culture. These characteristics are embraced by outstanding companies that have zero tolerance for waste and errors, or defective outcomes. Again, be careful! There will not be a better result automatically even if there are more datasets involved. The key will be to adapt the best evaluation procedure depending on the identified

2 page 2 requirements. Some people often think that new approaches like Big Data produce a better outcome. This is a misconception. Introduction While Big Data has the potential for organizational Big Wins the Big Data runaway train has left the station. Depending on your organizational situation you might say that the Big Data train is full steam ahead, a runaway, or it is barely chugging along. Is your Big Data team giving you the indication that they think they can, they think they can. Whatever your situation please keep reading and take we encourage you to take action now. As Jim Collins said it well in his book Good to Great, "technology is certainly important but it comes into play only after change has already begun". If we apply this to the Big Data buzz then it makes sense to reassess with transparency, our approach to data management. It makes sense to leave no stone unturned and be sure that any and all organizational concepts are exposed and reconsidered. It is all about decision making that supports the bottom financial line thus ensuring the organization can continue to fulfill its mission. As important, Jim says, "confront the brutal facts"! So what are your organizational brutal facts and misconceptions that need to be exposed. Why, who, where, when, and how is the data train rolling down the tracks? As you work every day to ensure your organization is staying on top, you are storing more and more data, your stakeholders are storing more and more data, using new applications and tools that you are not able to use. Depending on your situation you may need to start at ground zero but: Do you have a data strategic plan? You should not forget to ask the following questions: Who has the data maps, are they accurate, what about the source of truth, data integrity and validity? Perhaps now is the time to look outside of the box with a "disruptive approach". Perhaps it is time now to apply a new approach or simply to try to adapt some incipient stages from other disciplines. Approach Dimensional Metrology (DM) is the umbrella engineering term for a number of technologies that allow the manufacturing industry to produce quality products, the right product, within the best time, and price by evaluating geometry. In DM it is all about the datasets and the expected work output. There are many different techniques under DM specialized for different workload: The evaluation of geometry in general (based on vectorial description), The attestation of specific form deviation on a particular form element, or The measure of the roughness or texture of a surface One technology within the DM umbrella is the 3D coordinate measurement metrology (3DCMM). With 3DCMM it is about evaluating the real geometry of workpieces out of the production process. Ideal geometry is defined by CAD with nominal- and tolerance- values. Real geometry has to be evaluated and compared with the ideal geometry. Especially the 3DCMM is predestinated to fulfill the evaluation very flexible, well structured, and efficient. While computer resources and networked

3 page 3 connections in the early eighties were limited, the visionary approach of 3DCMM emerge as the rewarding attempt. This approach enables to evaluate the well-balanced amount of datasets according to the given circumstances. The possibilities of numerical methods in computer technology (regression analysis according to the method of Gauss; also known as Gauss best-fit algorithm) to compress a large number of data points were used to get a quick feedback of the workpiece geometry (position, direction, shape and geometry deviations in dimension). Structured approach gathering and evaluating datasets By the structured approach to take the datasets on form elements and combine them to describe and evaluate the geometry of the workpiece is very worthwhile. This is a classical bottom up approach. On the level of the form elements the datasets will be evaluated to parameter sets: Workpiece (Geometry Description and Evaluation) Form Element Form Element Form Element Computers equipped with the appropriate software are highly accurate to control precise measuring machines and provide quality reports concerning the geometry of the workpiece in a reasonable time (some seconds). This process is characterized by really doing only that what is going to!

4 page 4 The adequate amount of datasets well distributed all over the workpiece's geometry is only been taken. A very sophisticated combination of expert knowledge and closed loop procedure during gathering the datasets facilitated a resource effective measurement process. This approach allows: Gathering only the minimum needed amount of datasets (intelligent closed measuring loop behind) Describing and evaluating the workpiece geometry (based on a well predefined data structure) Overview General Definitions Approach Recommendations Data Governance Probing geometric form elements like planes, cylinders, cones, spheres Try to structure data in small parts so that they are easier to handle. Detect outliers, mark and eliminate them Data Integration Best fit algorithms to evaluate the geometry parameters of the workpiece Check the same instance for evaluation and reveal same results. Reproducibility is important Data Quality Data Virtualization Closed loop defines the end result depending on given accuracy Storing data, show deviations, numerical / graphical interfaces According to given resolution results are expected in confidence intervals that is accuracy! Double check conclusions with other approaches and look if you get the same results. Perform plausibility tests Provide only as much as needed Master Data Management Dashbords, statistics, longterm evaluation, process optimization Divide the important data from the unimportant data

5 page 5 In addition to the pure measurement process statistical methods were often used to make improvements in the production process (accuracy) over the time based on the workpiece individual evaluations (samples). Conclusions Big Data skips the past existing boundaries, respectively. There are almost no limits today! Quantity (volume), speed (velocity) as well as diversity in the application (variety) is virtually unlimited. In computer terminology memory size (memory and storage), processing power, connectivity (network/internet) provide the crucial factors which give new possibilities. Combining an enormously large amount of basic data offers completely new strategies! Software support brings real-time analysis options in addition. But: Be careful! There is no better result automatically even if there are more datasets involved. Key will be to adapt the best evaluation procedure depending on the given requirements. With the approach of the 3DCMM to divide an entire task into manageable parts, each object had to be described (parameterized) and is included for the efficient evaluation. Intelligent closed loops during the phase of data collection prevent needless datasets. A clear data structure at the very beginning is the outcome and levels the science base for efficient evaluation and creation of worthwhile information. But it makes sense to be careful and explore strategies that will decrease and mitigate the risk within yours organization. To develop a "big strategy" it means you had better get the right team together and you internal intellectual property is at its peak. You have intellectual wisdom, open thinking, and look at industries outside of yours. There are many different industries looking for help: Some industries usually lagging other industries when it comes to the adoption of new technologies. Take the chance and start the challenge today! Author : CEO, founder and owner of WPS-System, a full service web development, business optimization, and consulting firm. His information technology and software engineering background encompasses over 20 years as a specialist in dimensional metrics, software development, quality assurance, and project management. Additionally, he teaches as an informatics vocational instructor. He educates the next generation workforce in the latest advancements in computer architecture, programming systems, software engineering, databases, and information management. As a teacher he supports his students to perform at their highest potential. He ensures they obtain extensive hands on experience that enables them to easily obtain and assimilate into corporate information technology positions. His students work on innovative and technically challenging projects. Los Angeles, the 10 th of February 2015

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