The proliferation of AI and predictive analysis will leave a void in its wake that will have to be filled by the organizations to stay parallel with evolving trends. A new set of challenges will constantly emerge, one that involves skill, application, and much more. How will marketers be prepared for the change? Read ahead to find out.
We have already seen several examples of predictive analytics in play and influencing the way decisions are made. Whether it Amazon's product recommendations or Google's Waze, the predictive market is all set to become futuristic. AI began as a premium technology that garnered the attention of major corporations, but nowadays, due to increased accessibility, it is available to all players alike. However, effectively harnessing its benefits are not as easy as installing another new software.
Analytics industry has fast evolved from what it was until a few years ago. Artificial Intelligence has a special place in the present-day world, thanks to the evolution of analytics and industry's predilection for predictive models. A myriad of improvements in the field of technology has resulted in shifting of focus from rule-based automation to the fractal edge of sentience.
Humans may never have the right ability to make predictions that are truly dependable. But Artificial Intelligence is already powering up industries in a big way with highly accurate forecasts based on Big Data. You don't have to take our word on it. Have a look at our compilation of latest trends in predictive market research -
In an independent survey, it was found that poor data quality acts as a barrier to global adoption of predictive analytics. The qualities of a good data include format consistency along with accuracy (without gaps in data sets). Artificial Intelligence (AI) works best when data is unambiguous because the results produced by predictive analysis hold true only if data is pristine.
AI technology has its limitations as well. It is a system that is designed to transform the data fed from data lakes into meaningful predictions. Businesses must become aware of this limitation and take collective steps to provide data not only in huge volume but also in the accurate form. Hence your teams must have the right skill to distinguish ideal data from misfit ones.
Predictive analysis as a technology is becoming more advanced. But there is an equally growing concern because user's knowledge is not advancing at the same rate.
A survey carried out by Capgemini concluded with 77% of respondents agreeing that successful digital transformation is hindered by the lack of right skills. In further analysis of this report, analytics was ranked as a core skill noting its potential in marketing. However, analytics is the area where biggest skills gap still exists.
Analytics cover a wide variety of data types and techniques for conducting a thorough investigation. Analytics of the present day is a pursuit to uncover two questions – "what happened?" or "why it happened?" According to predictive market research trends, the demand for skilled staffs to handle analytics will gradually fade out. But, until then it will be essential for businesses to train their staff extensively before investing in state-of-the-art AI-based data analytics systems.
The upcoming trends in predictive market research are all about organizations funding the training process to ensure their staffs are well-qualified both theoretically and practically. However, not every member of the marketing department needs to be aware of what happens under the hood of analytics platform. This holds true considering high interdependence of analytics platform and machine learning for the creation of complex predictive models. Without the right test cases and knowledge of technology limitations, results would be obsolete.
With the mind-blowing growth of Internet of Things (IoT) devices, dearth of data is never a concern. Almost every analytics company has potential sources ready to provide mined data at the flick of a switch. A cluster of data is held conveniently in the Cloud, making it remotely accessible when required. However, to get meaningful insights, companies will still have to feed data into the analytics platform and having data warehouses like Hadoop is no exception.
When General Data Protection Regulation (GDPR) comes into effect in April 2018, businesses will have to take care and precaution to ensure their data management policy is fully compliant with new regulations. It also becomes mandatory to discard old data which does not comply with the new rules.
According to predictive market research, in the future, AI cannot be completely sentient to make decisions on regulatory requirements. AI predictive models will yield projections based on historical data that is fed by analysts. If sufficient care is not taken to ensure the correctness of the Big Data, any decisions that companies take by basing the predictive models will become invalid. Also, any efforts to retrace the process will be a cumbersome task involving high overhead expenses.
Predictive analytics and AI have well-defined roles in various industry. The ability to predict what the future holds based on historical data is invaluable for businesses because it helps to make decisions that would drive growth and profitability. In marketing, the way in which predictions impel growth can vary slightly from that of other industries due to limitations imposed on industry, especially one that is driven by latest ideas.
As humans are influenced by technology, our role as creatures with creative wisdom is quite unique while collaborating with AI-driven content creation. Artificial intelligence still cannot match the way in which humans compose a solution through innovation and creativity. Hence our technology dependency should be limited to just freeing workers from certain tasks while not eliminating human role in the long run.
Upshifting businesses into AI-driven predictive analytics model from an existing one is easier said than done. When people, data, and technology is a key part of an organization, getting every asset to collectively take the next big step towards change is an uphill task. Sometimes, the challenge is greater than financing a new business model. If the transition is too fast, staffs may find it difficult to catch up. Training staff members is essential to allow a seamless transition to a new business model.
Some of the hurdles faced by AI-driven predictive analytics needs to be addressed before the digital transformation begins.
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