Investor *Sales and Marketing Pioneer * Author* Technology Champion *Philanthropist * Leadership Mentor
Digital analytics, metrics & scales of data sets alone don’t speak for transformative behavioural change; when humans go by their guts & behaviour. This is universal human nature by default.
The result? A blind spot, a vacuum to big data still exist.
Algorithms go by artificial rules, grammar and set standards followed by marketers that are automated to unlock marketing opportunities. But most of us fail to derive value from metric-based data intelligence that lacks the human element.
Take this simple example.
Suppose you are looking for location data and the best route of reaching a destination. Your first instinct is to search online. Right?
Thanks to the Google maps- you are able to find the direction chart that perfectly works as per the predictive model, giving out directions in Kilometres at specific timings, suggesting travel mode, popular hotspots, distance, bus stops etc. Still, to avoid traffic jams & getting stuck mid-way, you will follow a short cut of your knowledge that gets you to the destination point! This is human intuition!
And, of course, digital marketing is not devoid of human connection. Human insights & intuition start even before the marketing metrics results are out.
The biggest challenge
Presently, 66% of global companies are investing US 5$ million in analytics. Out of it, only 12% of companies are able to reap benefits of business intelligence analytics. Why are companies not being able to derive the value from the big data they harvest? No doubt, analytical marketing metrics has proved to be a benchmark in perfecting customer journey throughout the purchase lifecycle. But how successful was the analytical hype that includes data intelligence once it fails to transform and interpret human judgement?
Unify human output with data computation
This is where most organizations are missing out to derive the value of marketing analytics. The challenge is to unify human input with data computation that can augment business intelligence producing real time business decisions based on human judgement that matters.
To harness the potential of big data without losing human value, big data needs to play the role of a transformative tool that can drive business change. But unless you debunk a number of big data myths, false expectations arising from misleading data are enough to cloud your opportunistic judgement. So, let’s bust out some of the biggest myth that stands as barriers in capitalizing on big data insights.
Myth 1: Big data is equal to big insight. Wrong
Tons of data is generated each hour, each minute and each second of the day. Data changes with time which means it will lose its value if it is not in its usable structure. It is not the big data but the right data that you need to extract and analyse to derive innovative new insights & apply at the right moment.
Myth 2: Big data is about analytics, numbers & stats. Wrong
Often data type is mismatched by the results extracted and analysed. Tracking the right type of data that defines customer solution can only unlock the value and has a huge competitive advantage.
Big data is about customer relationship management. Wrong.
Big data is not about customer relationship management (CRM) score but about what score which is important for customers. It is also about the predictions that can open new solution-driven insights for the customers.
Every data analytics should be able to answer a couple of fields or condition with the analytical objective identified. Wrong.
It is wrong to assume data volume, variety & velocity (3 Vs) as a predefined threshold to gain “big data” status. Organizations should relate data intelligence based on two aspects- tactical & strategic.
Tactical: When organisations IT infrastructure face data scaling issues being unable to cope up with the 3 Vs cost-effectively.
Strategic: When organization fail to analyse a broader range of data; thereby, misinterpreting the objective & its outcome. At the same time, when a new information asset complicates the existing data set standards.
Small data quality issues are negligible. Wrong
The effect of small data quality issues might seem to have a smaller influence on the whole dataset; but might impact huge in the long run. Big data discrepancies often start with tiny mistakes that eventually ends up with organizations paying up for a loss. Data quality is not same at every point of time and needs to be upgraded for particular touch points-email ID, Facebook etc. Understanding your data quality problems by defining the variables will help you focus your attention on the quality issue.