Blog: Data : The new Oil
The word data means “known facts”. Data especially refers to numbers, but can mean words, sounds, and images. Metadata is data about data. It is used to find data.
Originally, data is the plural of the Latin word datum, from dare, meaning “give”. Datum is rarely used in English. So data often gets used as if it were a singular word. Some people like to say “data are”, not “data is”.
Have you ever wondered why only since the past few years there is a huge noise around data? It is very important to note that since past few years we have seen an exponential growth in the amount of data being generated around the world. Since the inception of digitization recording facts has become very easy and feasible. Gone are the days when one had to maintain notebooks or files with data points recorded in them. Today one has access to technology and devices which can be leveraged to record data in no time and also one can even automate the process. Now, on widening the scope of our thoughts we can see that their are n number of organisations all around the world, if each one of those generates and keeps their data then just imagine, how huge would be the size of this data .
From the figures shown above one can just estimate the amount of data which we are dealing with. A study on Corporate data growth says that “Like the physical universe, the digital universe is large — by 2020 containing nearly as many digital bits as there are stars in the universe. It is doubling in size every two years.”
This huge amounts of data is termed as “Big Data”. This data is not only huge in size but, it has a very high velocity and variety too.
Data is to the Information Age as is Oil to the Industrial Age.
How we make products, solve human problems, and use data in a constructive way will define the next wave of technology. Oil has evolved the world into a better place by creating an enormous amount of wealth and prosperity. Data perhaps holds the similar potential and is already responsible for creating four of the five most valuable brands in the world.
Data has become the most valuable resource on the planet. However, it needs to be ethically extracted, refined, distributed and monetized. Like the way oil has driven growth and produced wealth for powerful nations, the next wave of growth will be driven by data.
How can Big Data be leveraged
While talking about Big Data how can one forget to mention Data Analytics, which refers to the set of quantitative and qualitative approach in order to derive valuable insights from data. It involves many processes that include extracting data, categorizing it in order to analyze the various patterns, relations, connections and other such valuable insights from it. Today almost every organization has morphed itself into a data-driven organization and this means they are deploying an approach in order to collect more data that is related to the customers, markets and business processes. This data is then categorized, stored and analyzed in order to make sense of it and derive valuable insights out of it.
The larger the size of the data the bigger is the problem. So big data may be defined as the data where the size itself poses the problem and this needs newer ways of handling the data. So the analysis of data at high volume, velocity and variety means that the traditional methods of working with the data do not apply here. There are various tools in Data Analytics that can be successfully deployed in order to parse the data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale means that the tools need to be specifically able to work with such kinds of data.
How important is Data for Machine Learning Projects
Access to data is very crucial to ML project’s success, ultimately no level of algorithmic sophistication will make up for a poor set of data.
Think of an AI application as a three legged stool.
1. The first leg of the stool is the AI algorithm itself. Open source machine learning libraries like TensorFlow and Theano have removed a lot of the low
level complexity involved in designing and building AI applications. These tools are free, well documented and supported by vibrant communities.
The availability of these tools has made building machine learning applications far more accessible to developers.
2. The second leg of the stool is computing horsepower, both in the form of raw CPU power and large scale data storage solutions. Cloud services like
Amazon Web Services, Google Cloud, Microsoft Azure and others make renting servers, virtual machines and big data tools as simple as pushing a few
buttons (provided you get your credit card out first!).
3. The last leg of the stool is Data. Before you can consider hiring data scientists, renting servers and installing open source machine learning libraries, you must have data. The quality and depth of data will determine the level of AI applications you can achieve.
Now lets talk a bit more about Big Data, specifically about the its types which one would encounter.
By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner.
Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email, Video, Audio files, Photos, Social Media data are the examples of unstructured data.
Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Web pages are a very good example of Semi-structured data.
Advantages of Big Data
- One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
- By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services.
- Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money.
- Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, the can understand better where to invest their time and money.
- With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behavior to understand the customer trends and provide a highly ‘personalized’ experience to them.
One thing is for sure, by employing continuously evolving technology on Big Data in a smart and intuitive way, we can definitely unearth quality insights which can be further used for improvement of existing processes and also in discovering and inventing the unknown.