Data and analytics is getting more strategic and business-critical. As a consequence, your strategies must reflect the same grip and insight. It’s time to up the ante a little bit on data and analytics strategy. Gartner
Historically when we look at storing data, in the past data has been entered and stored by employees, with the advent of internet the volume of data collected and stored has scaled up with the users saving data using Facebook, twitter and various social media channels. Now data storage has scaled-up even more with biometric machines, IoT and satellites collecting and saving data. This progression of saving data from employees to machines is immense and expected to increase at a flashing velocity creating colossal volumes of data. The words Big Data, Data Science and Data Analytics have become buzzing words in the world of data, what’s the difference?
What is Big Data?
In most of the organizations the enormous volumes of data, otherwise called big data is the reality of doing business. This huge volume of data is a mix of structured and unstructured data that is created by people, tools and machines. Managing this data requires scalable technology to collect, host and analyze to derive real-time business insights that relate to customers, employees and suppliers enhancing productivity and performance resulting in a better ROI, enhanced share value and improved customer satisfaction and retention.
Big data includes information gathered from data internet-enabled devices (including smartphones and tablets), social media, machine data, voice recordings & video, and the continued preservation and logging of structured and unstructured data. The following four “V’s” characterize big data:
- Volume – Huge volume is the main characteristic that makes data “big”. This voluminous data is a result of data collection by the organizations through variety of sources like business transactions, sensors, machine to machine data and social media. Storing and analyzing data has been a problem since long, however now new technologies like Hadoop, Spark, Flink and many more technologies have made it easy.
- Velocity – This data is increasing at an unprecedented speed and and need to be analysed at a regular basis. Satellites, RFID tags, CCtv cameras, sensors and smart metering are driving the need to deal with data in near future.
- Variety – The collected data is not uniform as it is collected in various forms like email, text documents, audio, video, financial transactions, and stock ticker data.
- Veracity – Veracity refers to data reliability. Though a good manager knows that there are integral inconsistencies in all the data collected. It is about can the manager trust on the fact that the data is demonstrative or not.
Eventually any big data project is to add value to the company. So, Big Data is to collect and manage large amounts of varied data to serve large-scale web applications and vast sensor networks. Data Science helps to create models that capture the underlying patterns of complex systems, and codify those models into working applications.
What is Data Science
Although both Big Data and Data Science offer the potential to produce value from data, the fundamental difference between the two can be summarized by quoting “Collecting Does Not Mean Discovering”. Let’s see an example, creating a statue out of an ore requires science in purifying the ore, tools to collect and craftsmanship to create it. So is big data and data science.
Industrial Revolution has brought the ability to efficiently convert raw material into valuable products at scale. It is not to accumulate more material but to build tools that scaled and mechanized the expertise. The expertise that could meet the changing market demands, convert more and more raw materials into products.
Data Science is the expertise in the world of data, it converts raw data to useful information. A science is needed to convert a raw resource into a usable product because extracts from the ore are an amalgam of valuable elements, soil and unwanted things. As it is not in a useful form its need refining and cleansing processes to bring it to usable form.
Here, Big Data is the raw form that is littered with useless noise, irrelevant information, and misleading patterns. We need to convert it into that value which requires a study of its properties and finding a working strategy that captures the behavior that the company is in search of.
What is Data Analytics?
Big data is data in raw form and the primary value from big data is not from the data in its raw form, but by processing and analyzing it and the products, services and insights that emerge from analysis.
The comprehensive changes in big data technologies and organization approaches need to be accompanied by likewise dramatic shifts in how data supports decisions and product/service innovation.
Why is big data analytics important?
Big data analytics helps organizations to utilize their data in creating reports that enable them identify new opportunities, take smarter business decisions, improve operational efficiency increasing productivity improving profits with higher customer satisfaction and retention. The following are the benefits of implementing Data Analytics:
- New products and services. Analytics brings clarity of customer-needs and satisfaction that helps in quicker moves to create products that satisfy customers improving branding and trust of the company in a highly evolving market. Big data analytics is helping more and more companies to create new products that meet customer demands.
- Cost reduction. Storing large amounts of data with more efficient ways of doing business is easier and cost effective with Big data technologies like Hadoop and cloud based analytics.
- Faster, better decision making. Analyzing information immediately and take necessary decisions in-time is possible with the ability to analyze new sources of data combined with the speed of Hadoop and in-memory analytics
There is strong perception of the need for data analytics. Businesses are becoming aware of the benefits it can bring and the methods to achieve success. It’s not a trend so much as a permanent fixture in the organization which will have measurable long-term impact upon companies and institutions both great and small.