Making big data work in a developing country as ours can be overwhelming. Big Data explained in its simplest can be illustrated with all data in an individual’s phone; Messages, Applications, documents, music, videos, pictures that amount averagely to 40 exabytes. Let’s do a little math, 40 exabytes multiplied by 185,742,016 will give us 7,429,680,640. This is the average mobile data in Nigeria, this is minus computer and internet data, Social media Data.
Here is a per minute break down of internet use; 2.1m snaps are shared on snapchat, 3.8m search queries on google, 1m people log on to Facebook, 4.5m videos are watched on YouTube, 118m emails are sent. All these amounts to data too large for a traditional computer to handle. This mass of these data is what is termed “Big Data”. Summarily, the huge availability of data is what the term ‘Big Data’.
The term Big Data first appeared in the 60’s, but it is taking on a new importance nowadays. It is measured by volume, Velocity, Variety, Veracity and Value. Cassandra, Hadoop and Apache Spark are some frameworks that can be used to store data.
Types of Big Data
Behind Big Data, there are three types of data – structured, semi-structured, and unstructured data, each of these structures can be used for different tasks/projects.
Structured data is fixed-format and frequently numeric in nature. So, in most cases it is something that is handled by machines and not humans. This type of data consists of information already managed by the organization in databases and spreadsheets stored in SQL databases, data lakes and data warehouses.
Unstructured data is information that is unorganized and does not fall into a predetermined format because it can be almost anything. For example, it includes data gathered from social media sources and it can be put into text document files held in Hadoop like clusters or NoSQL systems.
Semi-structured data can contain both the forms of data such as web server logs or data from sensors that you have set up. To be precise, it refers to the data that, although has not been classified under a particular repository (database), still contains vital information or tags that segregate individual elements within the data.
Big Data, big opportunities
The Big Data revolution, powered by fast computers, mighty computing algorithms and a proliferation of data sources, is cutting across and transforming various industries.
Artificial intelligence (AI) and big data penetrates manufacturing floors in the form of robots, sensors, and machinery that make production systems faster and more efficient. Many of these technological advances in smart factories result in continuous data collection of the production systems, and big data and artificial intelligence further advance the smart manufacturing process by integrating the system and helping businesses better leverage the data collected.
With big data and analytics, companies have got a chance to make better real-time decisions about asset usage and operations scheduling. Nowadays, data can be generated by hundreds of thousands of machines and parts, from valves to monitors equipped with sensors and wireless capabilities.
Insights everywhere for everyone – not just the elite. Take big data, business intelligence, and analytics to 100% of the population. Everyone can use analytics just like everyone can read and write.
Exciting developments have delivered some great success stories, ranging from image recognition technologies being successfully used for patient diagnostics to the first prototypes of driver-less cars. Big claims are being made about data science and AI: that their development is one of the most important events in the history of humanity.
Most of us in business insight functions believe that Big Data and AI-powered multi-source data analytics can and should be used for business insight generation. Multi-data source analytics has recently become a buzz word in our industry, and rightly so. Many companies across industries are investing in data analytics teams and data science capabilities. We all are on the same exciting, challenging and often painful journey of discovery.
What has it got to do with market research?
Market research is data and insight based, the research in Market research and Data in Big Data, screams connectivity. Big Data MR is the art and science of combining consumer data, behavioral data, attitudinal data and advanced analytics to produce better and faster decisions that yield superior business results. It is the convergence of two disciplines — big data (transactions, orders, steps taken, images, etc.) and market research (and the analytical disciplines that go along with all of it) — that yields enhanced products and services, which in turn create a competitive advantage.
Big data analytics involves examining large amounts of data. This is done so as to uncover the hidden patterns, correlations and also to give insights so as to make proper business decisions. Basically, organizations have realized the need for evolving from a knowing organization to a learning organization. Essentially, businesses want to be more objective and data-driven, and so they are embracing the power of data and technology.
While Big Data is evolving at an exponential rate, so too is the world of market research. Online market research provides the same benefits as traditional formats; however, it is also much more interactive and introduces some new tools that can supplement and improve insights.
Big Data is here to stay, there is no doubt about that, but this will never replace the power of market research to understand human behavior. Highly analyzed data, combined with rich qualitative insight is still the best way for marketers to understand the subtle nuances of human behavior.
These data can be presented to your customers, use it to create new products and functionalities, make business decisions, and so many more opportunities.
The Future of Big Data in Market Research
Big Data and data science is here to stay, and it will continue to disrupt market insights. However, as great as Big Data is, Big data does not equal good data, it will also not be able to address all industry informational needs. Primary research still remains the optimal means of identifying and understanding the real person behind the data. But in today’s multi-data source reality, primary research is likely to become shorter, more agile and focused, and will be used in combination with other sources more often.
The unlikely combination of data science and qualitative research will be key for marketing insights professionals of the future, with qualitative research likely to become the best friend of a data scientist. Finally, there will always be the need for a person who ‘tells the story behind the data.
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