What are the top 10 Big Data facts that you need to know about Big Data? What are the most important aspects of the Big Data hype that your organisation should be aware of when developing a Big Data strategy? BigData-Startups has the answer for you and has created a list of the most important Big Data facts that you cannot ignore in the coming years (in any order). If your organisation requires help in developing that Big Data strategy, do not hesitate to contact us.
1) Big Data Requires a Different Culture
In order to truly take advantage of Big Data, it is important to turn your organisation into an information-centric company. This culture shift will help to make more data-drive decisions and will give employees the opportunity to develop new operational, tactical and strategic plans based on data instead of guesses. A Big Data culture does require that employees are encouraged to ensure that data is collected at any moment in the customer contact journey. Subsequently they need to be encouraged to ask the right questions and solve them with data.
2) Hadoop is not the Holy Grail
Data in a Hadoop cluster is broken down into smaller pieces (called blocks) and distributed throughout the cluster. In this way, the map and reduce functions can be executed on smaller subsets of larger data sets, and this provides the scalability that is needed for Big Data processing. With the combination of these technologies, massive amounts of data can be easily stored, processed and analyzed in a fraction of a second. If a top layer such as Hortonworks or Cloudera is added to it, real-time analytics becomes possible with Hadoop giving it great advantages and making Big Data analytics possible.
However, Hadoop is not the Holy Grail. There are many advantages such as linear scaling with commodity hardware that is easy to implement and not expensive. Combined with the simple programming model that allows the end-user to only write MapReduce tasks and the fault tolerance of the system as all data is copied several times over different nodes and clusters, Hadoop offers many advantages.
However, there are also quite some substantial disadvantages of Hadoop. Getting Hadoop operational is difficult and requires specialized engineers that are expensive. Subsequently cluster management is hard and debugging is pretty difficult. Organisations will need special trained IT personnel to install a complete Hadoop server.
Luckily there are more and more Big Data startups who develop Big Data as a service platforms, taking away the need to build an own Hadoop environment.
3) The Real Driver Behind Big Data is the People Within the Organisation
We already mentioned that a shift in the organisational culture is necessary to ensure a successful Big Data strategy. However, in order for the Big Data strategy to happen it needs to be created by people. Especially managers and executive levels should be aware of what Big Data is and how it can be applied to their organisation.
Very important to know is that Big Data is not an IT party. IT is merely a means to achieve your Big Data strategy. As was the case, for example, with social media. A few years back everyone thought that social media were the Holy Grail for marketing. Today we see it, correctly, just as a means to achieve the goals stated.
Therefore, the people who need the insights should drive a Big Data strategy in an organisation: marketing and strategy managers. They should drive IT to build a Big Data system that gives marketing and strategy the answers to the questions they have.
4) Big Data is Everywhere, Even at Places You Did Not Expect
Everything that is digital is data and more and more items are digitalized and are connected to the internet. This results in new data flowing into your organisations from completely new areas, previously not thought of. With the Internet of Things movement it is clear that any product or device can be connected to the internet and therefore provide data. Organisations should use this information and not be afraid to digitize products. Big data literally is up for grabs, you only need to open your eyes to understand where it can lie and how you can find it, analyse it and use it.
5) Big Data Engineers Will Be Scarce, so Better Start Looking Around
McKinsey predicts a shortage of about 140.00 190.000 Big Data engineers in America alone in 2018. They also predict a shortage of 1.500.000 Big Data manager who can manage the Big Data engineers and are able to connect the IT aspect of Big Data with the strategy aspect. So, Big Data engineers will be scarce in the future. Organisations should already start to train their IT personnel to become familiar with Big Data technologies. Universities should create Big Data engineering courses that prepare students for the Big Data future ahead of us. Luckily more and more universities are already offering a Big Data study, as well as open online platforms such as Coursera are offering a Big Data course.
6) Big Data Does Require Big Security Measurements
Whenever organisations gather large valuable datasets, criminals are on the look out to steal those data and use it to their advantage. In the recent period quite a few large online organisations have been hacked. Linkedin, Evernote and even Bitcoin. However not only online organisations are being hacked. Governments and government organisations are also under attack quite often. Therefore, it is of extreme importance to protect any data that is collected. There are several ways to secure your data, of which correctly encrypting all your data is of course the most common known. But of course there are many other ways to protect data and whenever an organisations wants to deal with Big Data, security should be part of the team. Organisations should however also have a crisis plan ready when it does goes wrong and the organisations is hacked. Surprisingly, there are still many companies who have no clue what to do in case of a crisis.
7) A Public Debate About pPrivacy Issues is Inevitable
With Big Data come big privacy issues. In the age of Big Data, big brother will be watching everyone whether it is online or offline. If the data is not correctly anonymized, there is the risk of guidelines will be necessary as well as a public debate about how far we want organisations to go.
8) Venture Capital Firms are Investing Massively in Big Data startups
The Datalfoq platform aggregates a lot of different Big Data startups that have developed a solution for Big Data. At the moment there are dozens startups mentioned on this website and together they have received over $ 890 million in funding in the past years. As these Big Data startups are just a small part of all existing startups worldwide, the amount of money invested in this industry is enormous and it will only grow in the coming years. Especially the United States is ahead with investing in Big Data start ups compared to other regions in the world.
9) Governments are Increasing their Big Data Efforts All Over the World
In 2012 the United States government made $ 200 million available for research and development in the field of Big Data. Also the European Commissioner Neelie Kroes is a supporter of Big Data and she wants Europe to be in the front of it. More and more governments are starting to see the possibilities of opening up and sharing their public data sets with the public in order to develop applications that can solve problems. These data sets can be found on platforms like Infochimps or Datamarket where governments from around the world are sharing up to 45.000 data sets. There is still a long way to go and it is clear that also governments can significantly benefit from the opportunities of Big Data.
10) Big Data IT Spending Will grow in Coming Years, but Remain Small Compared to Total IT Spending
Gartner predicts that Big Data IT spending will grow to $ 43 billion in 2016 from $ 28 billion in 2013. With a total IT spending of $ 3.7 trillion in 2013, this is only 0,75% of total worldwide IT spending in 2013. This is a relatively small number for a trend that can will such a massive impact on organisations and governments. Gartner furthermore predicts that social media network analysis and content analysis will grow 45% on an annual basis until 2016. By 2020, they expect that Big Data will be normal and part of the baseline of enterprise software.
