The who, what, when, why and how of Big Data analytics
“Big Data”. With more than 300 million search results to choose from when you Google those two simple words, that makes them more than ten times as popular as Dunkin’ Donuts. According to Google’s standards, that is. But what do those two words, that turn up nearly ten times as many results as the Empire State Building, almost twice as many results as Barack Obama and even exceed Michael Jackson (only just), actually mean? This article will give you everything you need to know, from basic definitions and explanations about how to use Big Data to details of who uses it, what exactly it’s capable of, the most common mistakes associated with using it – and more.
Think you’re pretty well brushed up on Big Data? Read on and see if you can tell the difference between myth and truth, and discover some impressive statistics around exactly what Big Data has done, can do, and will provide you in the future.
First things first: What is Big Data and how is it used?
In its simplest definition, Big Data collates vast amounts of information from numerous sources, before analyzing a portion of the information collected to make educated predictions on future outcomes.
This allows businesses and organizations to use information provided by various digital sources to influence future decisions. This form of predictive analytics works due to the high volume of data being collected. Though only a percentage of what is collected is actually used to help form these decisions, the sheer volume of data that amounts to is sufficient, and smart enough, to make profitable, efficient, beneficial decisions in a variety of industries and environments.
Big Data is used by anyone and everyone – from multinational corporations to SMEs and charities. Essentially, any organization that will benefit from saving time, money and resources. Find three industries below that all benefit in their own way from Big Data, perfectly highlighting its many capabilities.
The automotive industry is a vast one. From vehicle manufacturing to showroom sales and customer aftercare, there are many facets that need to be considered. With seemingly separate information coming from different funnels, using Big Data to collate this means quicker, more direct actions can be taken to solve problems.
For example, if customer service is recording numerous instances of faulty issues or complaints across a certain product line, Big Data can be used to predict the volume of future complaints that may stem from this, and ultimately how much revenue could be lost as a result. A decision can then be made to address this at production level to prevent the same complaints from reoccurring, resulting in a vast amount of revenue savings long term.
Other uses of Big Data in the automotive industry include the analysis of showroom performance. Using Big Data analytics can produce invaluable statistics on demographic and sales data for individual dealerships, which can then be shared regionally. Big Data tools can then form accurate estimates on exactly what type of customers shop where and why – tailoring their offering in accordance with this to improve sales.
Cost reduction and sustainability are key factors in the construction industry, which is why the predictive analysis provided by Big Data is so valuable. Using the right information and acting on it at the right time can garner massive savings for construction companies – especially when supplying products for larger developments.
It’s often the less obvious problems that cause the biggest complications. Something as simple as the weather, a local event or any other form of unscheduled road block can have major effects on traffic conditions. This can slow down distribution of materials which can ultimately create a snowball effect when it comes to staying on schedule for the build. Using Big Data to anticipate weather and local traffic conditions, as well as planning for any re-routing contingencies, can help to prepare for the unexpected and keep timelines on track.
Big Data management can also be used to stay within budget when it comes to purchasing materials. Using Big Data statistics from completed and on-going builds can be used to establish realistic costs of materials – helping to stick to budget and prevent waste. For example, if the price of materials has been fluctuating, then Big Data can be used to form an estimate on when the most cost effective time would be to make the purchase. This, coupled with an accurate estimate of the volume of resources required, can mean significant cost-savings long term.
When it comes to something as fast-paced as manufacturing, Big Data is arguably one of the most cost effective solutions that can be implemented. Amassing data from the production line through to end sales can be used in tandem to impact future strategies.
The main goal of manufacturing is to sell – which is why any sales performance data collected is always welcome to help influence future production. Big Data analysis systems are capable of amalgamating data from current sales, previous year’s trends and any seasonal movements to form an estimate on the volume of sales, and therefore forecast demand. This can ensure that the levels of production are in line with the projection, resulting in maximum profits through avoiding over or under production.
Big Data can also get quite granular, which can make all the difference when it comes to mass production. When used in conjunction with live sensor data from the production line, any faults or equipment failures can be picked up in real-time – preventing further faults from occurring, saving significant amounts of time, money and resources.
What else is Big Data capable of?
Although Big Data analytics is used by many businesses large and small to increase profits, efficiency and cost-savings – it’s much more than just a money-making business tool.
Crisis management is an essential part of response and recovery in the event of natural disasters. While it is often easy for the disarray to become unmanageable, using Big Data in the right way can help address the situation from all angles, providing help and support to those who need it most. It can also be used to make future prevention, preparation, response and recovery efforts more effective – as seen in the development of the application of Big Data analytics across three recent natural disasters.
Japanese tsunami – 2011
Technology firms, media outlets and major manufacturers came together in the wake of the Japanese Tsunami 2011 to form Shinsai Big Data, an effort to collect all possible information surrounding the incident to form a detailed picture of how the events unfolded, and what could be done to prevent the same issues from occurring in the future.
Collecting information from mobile phones, social media platforms, and even sat navs – speculative analysis was formed surrounding the movement of people on the ground. Pattern analysis from mobile phone reception data and satellite navigation from vehicles helped experts analyzing the data to understand where people were and when. This information was subsequently used to form better response measures for future emergencies, by putting safer procedures in place.
Philippine typhoon – 2013
The Philippine Typhoon in 2013 saw the use of Big Data analytics aid recovery efforts by highlighting the areas where help was required the most, allowing emergency services to act quickly and efficiently to those in the most desperate need. Crowdsourcing is a form of data collection that uses witnesses’ accounts in large volumes to form an accurate depiction of unfolding events. Digital communication such as email, text message and social media can be collated to create clear understanding of the magnitude of a disaster, pinpointing specific zones where intervention is needed and helping to prioritize the emergency response.
Nepalese earthquake – 2015
Far from being reactive alone, by 2015 the access to and understanding of using Big Data for crisis management meant that focus on prevention and preparation was just as important as response and recovery. Although natural disasters are almost impossible to prevent, with companies like Terra Seismic using reams of satellite data to predict major earthquakes, the ability to now predict quakes up to 30 days in advance is a massive step forward in preparing for them.
Prevention of further damage or fatality is also a major benefit that can be harnessed via the sharing of information. The quicker response and recovery can be sent to a location; the more lives can be saved. Large tech firms like Google and Facebook created features that allowed users to confirm their location and safety in the wake of the Nepalese earthquake, not only allowing connectivity between loved ones but also aiding recovery efforts.
The contribution of Big Data to crisis management is extensive, proving that the collection and accurate analysis of information is more than just a business tool – but can also have a large impact on the improvement of humanitarian aid and recovery.
The top 5 Big Data mistakes
With such large-scale capabilities often come difficulties. Though that’s not to say the below mistakes aren’t easily avoided, as long as you’re clued up on exactly what you want, and exactly how to get it.
Collecting data indiscriminately just to bump the numbers up doesn’t help. This affects the quality of the data and could result in skewed results that don’t really offer any insight that can be actioned.
Alternatively, that doesn’t mean forgetting about certain data sources either. Even the smallest pieces of data relevant to the information you’re researching can provide some of the most informative insights. As long as it’s relevant, it can provide answers.
Big Data has such vast and versatile capabilities, that it can provide insight and information into almost any area. This can help cost-effectiveness, profitability, efficiencies and even the widespread sharing of information, as seen with crisis management.
Big Data is only as useful as you make it. It can provide you with answers to current, real-time data, but if you don’t act on it quickly enough, the results may not be what you were expecting.
Collecting data without a purpose is one of the biggest Big Data mistakes out there. Having detailed goals highlighting what information you want to find is the best way to curate effective, useful data.
Myths about Big Data
With Big Data’s abilities stretching so far, it naturally faces some skepticism, which subsequently results in myths being created that then gain traction. Uncover the truth below to see which are Big Data myths and which are Big Data truths.
Big Data is complicated
Big Data solutions are as simple or as complicated as you make them. Volume isn’t synonymous with confusion, and as long as the right amount of relevant data is tackled in the right way by the right people, it can be more fruitful than many other processes. What’s complicated about that?
Big Data is an old concept
Though the buzzword that is ‘Big Data’ appears to have come along fairly recently, that doesn’t mean that the process of collectively sourcing information and then interpreting it to make educated assumptions did too. The autonomy and predictive analysis side of Big Data may have grown in recent years – but Big Data solutions are far from new to many industries.
Big Data doesn’t have to be expensive
Many people will be surprised at this one. Though it can be very expensive dependent on how it is implemented – and it was very much reserved for the heavy hitters and blue chip companies in its infancy, like many new technologies, accessibility to the resources required has increased, and therefore cost has come down as a result.
Big Data is only needed by large companies
Its namesake might suggest otherwise, but even the smallest SMEs can gain from the implementation of Big Data tools. If you consider the essence of Big Data and what it can offer, then it makes perfect sense that any company can benefit from using it. After all, when it comes to business data that is collected to help make future decisions, if anything, it’s the smaller companies that can reap the greatest rewards as they can better compete with larger companies.
Let’s crunch the numbers
So, with the digital landscape offering more avenues of information than ever before, exactly how big is Big Data?
So, which companies out there have used Big Data successfully? From using it to improve production consistency and efficiency to creating entirely new products using detailed data analytics – you might be able to find some inspiration in some of the most innovative Big Data examples to date.
If there was ever going to be an example that really showed the sheer capabilities of Big Data, it was going to be with one of the biggest manufacturers in the world. Coca-Cola famously used Big Data to influence the production of their orange juice line, ‘Simply Orange’. Analyzing countless variables by using satellite imagery, crop yields and cost pressures to name but a few – the company formed an algorithm, known as Black Book, which enabled them to produce consistent tasting orange juice all year round.
One of the fastest growing companies within the last decade, Netflix has effectively utilized Big Data to grow and adapt its offering. Using the reams of data collected on who is watching what, when they’re watching it, when they pause, rewind, fast forward or drop off completely, they were able to form detailed hypotheses on what makes successful content. This allowed them to curate and effectively target their own productions in order to maximize their success.
Presenting a true masterclass in how to leverage Big Data is Amazon. With more than 150 million user accounts and ten plus years in the online market place, Amazon is one of the biggest success stories when it comes to using Big Data to adapt and progress. Using click and purchase data among many other variables, the e-commerce giant is credited with founding and mastering the now well-known personalized recommendations and upselling technique that has generated it, and many other businesses who copied them, considerable profits.
The final word on Big Data
Big Data. There’s a lot to it, and there’s even more to come, which is why jumping on the data train now is as good a time as any.
With the myths, stats, case studies, definitions and industry intel all at your disposal right here – there’s no reason why you can’t implement Big Data within your organization to help influence your future decisions. You know it’s not expensive, you know it’s not exclusive, and you know it’s not expendable. Which means you can rely on Big Data to deliver anything and everything your company needs when it comes to future decision making.
About the author:
Hi, I’m Chris and a huge congratulations to you for making it this far down the article!
After working within the Big Data industry for a number of years now, I feel like I’m in a good position to put my spin on this popular topic.
It may not be the most exciting topic, but Big Data is growing at such a fast rate that everyone should be aware of the basics so that future adoption will be that bit easier.
Thanks for reading.
Feel free to read my other articles here.
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