Ting Hsuan Lin | September 29, 2016
While the term “big data” has become a trending catch phrase, it is actually nothing new. The need for data, and the need for rapid access to that data, has existed for years; however, the world has entered data-driven era in which every facet of human existence is literally driven by some form of data. Whether it is in the world of scientific research on in the study of customer behavior, business, academics and social engagement, it is all driven by data in some way.
When it comes to the world of business, more and more companies, whether large or small, are beginning to use big data and the analytic approaches and techniques associated with it as a means of developing strategies that will assist them in better serving their customers.
To better understand big data analytics, it is necessary to put today’s data, and the manner in which is applied, into proper prospective. According to one recent study, by 2024, the enterprise servers throughout the world will process the digital equivalent of a stack of textbooks that would extend more than 4.30 light years, which is enough to reach Alpha Centauri, the closest neighboring star system within the Milky Way Galaxy. With all of this data being processed, the challenge of being able to process and analyze it in a manner in which it can be understood is paramount.
Big data analytics are an effective method for preserving the intended context of data in order to create the balance between data that is considered to be intentionally dirty in comparison to the data that has been cleaned of unnecessary digital exhaust. When compiling data from multiple data sources, it is important to understand that there will be certain differences that will have to be accounted for when attempted to analyze the data. For instance, there will be differences in granularity, lifespan, perishability, velocity of changes, and dependencies of participating data sets.
The convergence of mobile, cloud, social and big data technologies presents new challenges and new requirements, such as ensuring that the right information is forwarded to the customer in a timely manner, ensuring the reliability of external data that is outside of the control of the local data facilitator, effectively validating relationships between coexisting elements, identifying gaps and data synergies, identifying biased and skewed data and creating provenance of the data that is provided to others.
With data becoming more prevalent for businesses, it is becoming increasingly important for these businesses to develop the capacity to effectively categorize and analyze the data that they collect in an expeditious manner. Basically, data without lucid meaning is absolutely useless, and big data analytics are what gives data its meaning in any specific category.
Getting the Right Information for a Particular Business
It is important for businesses to develop a lucid perspicacity of what type of information is vital for the success of their business. The business has to have a clear understanding of what is important to the business in achieving specific goals over the short and long term. When a business is clear on what is important, it makes obtaining a better data context easier. During a recent presentation at the TeamQuest ITSO Summit, the managing director of CMS Motor Sports Ltd., Mark Gallagher, shared with the crowd how Formula One teams have successfully analyzed data in order to ensure the safety of drivers, and win races.
Gallagher explained how engineers are able to analyze massive amounts of data in real time, allowing the team to make strategic adjustments on the fly. With today’s technology and access to data, any engineer that is a part of a Formula One racing team can immediately call a halt to a race if they become aware of a potential threat to the driver or public, based on the information that is constantly being computed and analyzed.
In a case like the one mentioned above, the engineers are looking for anomalies, which only represent less than one percent of the data that is being processed. While 99 percent of the data that is processed reveals that everything is fine, it is that one percent that could mean life or death during a race. There will also be specific indicators within the data that allow engineers to make certain adjustments during the race that give the driver the best chance to win.
In formula one racing, the driver’s steering wheel is actually a highly functional laptop that is constantly feeding the driver the information they need in order to make the best possible decision at any given moment during the race. Drivers have the ability to scroll through a 10-point menu, all while driving at speeds that exceed 200 mph — having the ability to make adjustments to the parameters that will positively impact the performance of the vehicle. The ability to make these types of adjustments is made possible because the driver is able to get the right data at the right time. Much in the same way, businesses are able to collect data, and then organize, categorize and analyze it in order to understand it, allowing them to gain certain advantages within their specific market. The faster, and more accurately, that they are able to complete this process, the greater the advantage they create for themselves within their market.
Using Big Data to Prove and Enhance the Value of IT to Businesses
With the evolution of technology, it is becoming increasingly necessary for IT managers to prove the value of their departments to company executives. The fact that many businesses are resorting to cloud-based computing, the need for traditional IT services is on a rapid decline; however, this does not mean that IT is obsolete. It simply means that IT services are rapidly evolving to meet the unique needs of the contemporary business world. One problem that IT has had to face is the difficulty in measuring the cost in correspondence with the results that are produced through the IT processes. Big data can actually provide the necessary information that will allow IT managers to provide a clear measurement of results in comparison to IT costs.
The focus has to be placed on business goals, while having an understanding of how the use of IT services will contribute to the attainment of the business goals in mind. Not only will measuring these goals provide the proof of value for IT, but it will also provide the information necessary to plan future services. The vast majority of CIOs hold to the belief that the IT department has the capacity to increase the value of the services it delivers by improving the efficiency of cost measurements.
Traditionally, the efforts to measure cost and value by IT departments have been conducted on a macro level, focusing on total capital costs for the construction of data centers, and the associated annual cost to operate these centers. With big data analytics, IT costs and results can be measured on a more micro level, evaluating individual elements and components of specific services and processes.
The Importance of Good Analytics in Business
Companies have been placed in a situation in which they have a need to make informed decisions at an immensely rapid pace, allowing them to create a performance and cost optimized system infrastructure that provides the capacity for them to deliver the best possible services to their customers. While having the capacity to gather an enormity of data is important, it is equally important for companies to have the capacity to effectively organize, categorize and analyze the data they collect.
A good example of a business using analytics to improve its performance and cost efficiency would be a company that is looking to launch a new marketing campaign in a new geographical territory. The company will need to develop a reasonable expectation of what the sales penetration will be in that area. One way that these expectations will be built is through analyzing data that reveals the outcomes of recent marketing campaigns in similar demographics. The company will be able to correlate current expectations with past sales activities in similar demographic audiences, allowing the company to also determine what necessary adjustments will have to be made to make the current campaign more successful and engaging that the previous ones.
While the average business person should be able to see the value in developing good data analytics, IT personnel will have a more lucid understanding of the fact that there is much more involved.
The key is to find a way to bridge the gap between business and IT.
Bridging the Gap Between Business and IT
In order to produce a situation in which situation in which automatic provisioning of resources will create a synergistic dynamic in which the process will be simultaneously service risk minimizing and cost effective, there must be an understanding of application performance that is deeply integrated with the data center management tools in use. When properly set up, the automated provisioning of bandwidth, storage and computing power becomes the primary benefit of virtualization; however, without the use of integrated business intelligence what will likely happen is rapid sub-optimal decisions will be made that will produce sub-standard performance and poor cost efficiency.
When advanced analytics are intelligently applied in the planning and functional testing phases of projects, the risk of under-provisioning is reduced drastically. It does not take a data scientist to understand the potential for loss in money and time when under-provisioning causes under performance.
A good intelligent data analytic system will provide the capacity to measure and analyze more than power utilization effectiveness; it will allow the company to achieve optimization in risk reduction, decreased use of capital, increased revenue growth, and ultimately, an enhanced customer experience.
Basically, no company invests in building or using a data center for the purpose of simply circulating electricity throughout the data center in an efficient manner; they make the investment in order to increase work capacity and the efficiency of work processes. The use of good data analytics creates a process in which the company will be able to correlate their efficiencies with the work that is accomplished and the customer experience. All components involved must be optimized in an automated, continuous and highly integrated manner.
At the end of the day, businesses will find that they can achieve a high level of success when they are able to view the right data in combination with a powerful and effective analytic system. Good data that is evaluated through a powerful and accurate analytic system will result in better business results.