Transform Big Data to Connected Data to Generate contextual Intelligence

The recent happenings during the COVID-19 pandemic have been an eye opener. Individual perspectives changed overnight as every living entity moved to a back foot and into a sustenance mode. As most nations switch focus on finding the silver bullet at the best, data is being generated by the nano second. Big Data suddenly has moved into prominent focus to resolve bigger issues. But yes, haven’t we been collecting huge amounts of data in the eCommerce, banking & financial services & Insurance (BFSI), and fast moving consumer goods (FMCG) sectors for long. Are we any different right now? Yes, we are. Today, we know the difference between Big Data solutions  and Connected Data.

Big Data and Connected Data – The difference eons apart

We have instances where efforts to gather huge amounts of data for consumer research were less successful than the intended goal. What went amiss?

The key that would have made all those years of research successful simply was Connected Data. The lever here is to connect the data points together to transform the Big Data into CONNECTED DATA.

Not that we hadn’t known the power and leverage that CONNECTED DATA would bring to us…but some of the use cases (and, lack of primary data!) during the pandemic, showed us ‘how’, a bit better!!

Preparation is an important aspect of Connected Data. Specifically in this context, the journey towards Connected Data starts during the data gathering and data collection stage itself. This constitutes your PRIMARY DATA. Knowing the purpose of data collection and listing your key parameters at the beginning is a must.

What is also very important is knowing the kind of SECONDARY DATA or EXISTING DATA, which you are likely to infuse with your primary data. Here, ‘PLANNING’ with a ‘MUCH LARGER’ end in mind would bring in tremendous benefits.

Hence, devising your data questionnaires accordingly and connecting points across researches, as in a jigsaw puzzle, in the ENDEAVOUR TO SEE THE BIGGER PICTURE is CRITICAL.

CURATING the data, PREPARING it as you would build the building blocks to impart strength to your research, and VALIDATING it at every point is essential. 

Understanding the consumer mind is a profound task. However, CONNECTING the CURATED DATA POINTS gives a glimpse into the unfathomable consumer mind across demographics.

 How Connected Data Works – An example

In BFSI, digital savviness may not at all be the perspective of many customers. But conducting REMOTE OPERATIONS in a SECURE ENVIRONMENT or having the ability to seamlessly shift assets to better avenues is a primary ask, for customer A, as in the following example.

How do you arrive at such a conclusion about your customer A?

A bank cluster head has data about banking relationships of the customers in their cluster. He wants to sell his mutual fund products and wants to shortlist the prospective customers from the available database. After analysing the data, he can deduce the following about customer A – 

• Food habits – Customer A orders food from a distant, popular fast food joint. Similarly, he orders or gifts food boxes to his family and friends from the food joints in the vicinities of his home. The periodicity and the bill amounts indicate his gourmand or foodie persona.
• Health status – Customer A does periodic health checks from a particular diagnostic centre. Since a long time there is no variation in the amount he spends per visit. It implies that his health is stable despite the health condition that he gets monitored and that he has healthy food preferences. This is HARD CONNECT DATA or a direct deduction from data.

Another observation is that after each health check, Customer A almost regularly has a cheat day and orders food from the distant, popular fast food joint. This is SOFT CONNECT DATA or an allied deduction from data. This can be also termed as a CONTEXTUAL REFERENCE.

• Banking choices – Customer A recently liquidated a term deposit and bought a term life plan from the confines of his home. He rarely visits the bank but has frequent video calls with his bank relationship manager.
• Mall visits versus Online shopping – Customer A visited malls only 3 times in a given year as compared to the online purchases done quite frequently.

It can safely concur that Customer A has the liquidity to buy a mutual fund or mutual fund SIP.

Now, Customer B also does frequent online shopping of consumer durables and is prompt in paying his credit card bills.

Customer C is prompt in paying credit card bills too. However, his spend does not go beyond a certain limit almost on a monthly basis.

It can be assumed that the persona of Customer A and B are similar. This is again SOFT CONNECT DATA. Hence the mutual fund products can be cross-sold to them. In the case of Customer C, it is safe to cross-sell mutual funds in SIP form. This logic can be safely extended to similar kinds of “customer personas”, which is again SOFT CONNECT DATA.

UNIFYING the consumer responses across geographically, socio-economically, and culturally diverse data sets helps to AMPLIFY, TAP, AND LEVERAGE the real sentiment of a CUSTOMER SEGMENT.

The UNIFY TO AMPLIFY exercise helps to develop a 360 DEGREE VIEW of your existing customers in order to take constructive steps and build the BUSINESS and hence the NATIONAL ECONOMY.  

In short, mere data or big data gathering simply creates a swamp – a data swamp. But connecting it across relevant parameters helps to BUILD CONNECTED DATA to solve BIGGER ISSUES.

Connected Data and Contextual Intelligence

In order to deliver research results at speed, the data sampling and data analysis have to be done in tandem across multiple geographies across multiple demographics and multiple personas. Building on each others’ research notes across geographical silos into a connected data ecosystem gives a critical advantage in the quest of searching for the silver bullet.

Generating a holistic understanding of the research results by weaving in contextual references, to resolve contradictions, is of paramount importance while building the CONNECTED DATA ECOSYSTEM. The exercise offers high-quality data for generation of actionable insights as well as a richer contextual perspective, at the same time, so essential for SEEING THE BIGGER PICTURE to RESOLVE EQUALLY GREATER PROBLEMS. 

Business impact of Connected Data in a Connected Global Environment

The most important impact of Connected Data is the faster goal achievement and success that otherwise would have spanned years of research and analysis. 

The faster processing of observations across a globally diverse, connected ecosystem allows to quickly pick up where the other has left towards the dawn of richer perspectives and increased momentum.

Graphical representation of the Connected Data allows faster analysis to quickly move to the next stage on a sure foot.

Connected Data delivers exponential value. It can be mined further for different parameters & perspectives. This data offers a critical advantage in research work conducted as a race against time.

The Connected Data paradigm ensures global collaboration in the research & analytics field and offers connecting across culturally and demographically diverse sampling sets.

Connected Data in a connected global environment offers a higher thrust in research by roping in the inherent strengths of different races, cultures, and creeds towards a successful and workable solution.

Also Read: Big Data Management Tools That You Should Try in 2021

In summary

The journey of BIG DATA to CONNECTED DATA to GLOBALLY CONNECTED PERSPECTIVES allows research & analytics personnel to expedite the research and achieve better and faster results, in almost all business sectors. The Connected Data can be mined further to conduct several other parallel research & analyses to generate contextual intelligence, resolve strategic business questions and bigger problem statements at a global scale, in a race against time.

Author Bio 

Carol is a software developer with over 10 years of experience in creating and maintaining high quality software applications. Carol enjoys spending her time learning software technologies, reading and writing. She loves writing on topics such as software development, lifestyles, gadgets, technologies etc.