The history of predictive analytics dates back to 1689 with Lloyds of London, an insurance company. To say, predictive approaches have been here for more than 337 years irrespective of the emergence of technologies and machine learning. Though the concept of predictive analytics had been in practice, it was a mix of tenacious manual labor and with several trial and errors; unless as in the current time, with methodical approaches and presence of artificial intelligence and machine learning.
Gist on how predictive analytics in 2018 is better:
Several companies are taking advantage of the vast cloud repository to store years of data.
The dual hindrance of data collection and accessibility has been finally solved now, helping in the implementation of predictive analytics.
As mentioned in the start, previously the raw computing potential was something different. Andrew Pearson of Intelligencia notes says that in absence of any significant investment of hardware predictive analytics either would not have been possible or too gradual to be fruitful.
And Pearson continued to say that, with more powerful information systems like the huge cloud repository, there now predictive analytics has become more real-time.
Judah Phillips, the founder of SmartCurrent, says that we live in a real-time world of predictive analytics. Arrival time in Waze is a simple way of analysis whereas complex predictions occur several times globally, to match various kinds of digital advertising.
As per a research by Dresner Advisory Services, only 23%, and this says the figure remained the same as last year. Very few businesses making use of predictive analytics though many are planning to do so.
According to the findings of the research, about 90% of companies attach some significance to the predictive analytics which is very advanced.
Let's see what questions, some businesses answer with predictive analysis:
In online marketing, Phillips mentioned several cases of predictive analytics, which allows prediction of the following:
Predictive analytics allows the fact which can be predicted. The historical data can lead to good prediction. Only you need the data.
When you have enough data! Data sources are the priority.
As per Phillips, some thousands of records with necessary count on positive and negative result is enough for prediction of sales, marketing, and yield.
According to experts, there is two kind of data, one is own database, ready for modeling. Another is a collected data source, managed by an external vendor not possible to be tracked.
Also, there are external data sources like weather reports, make essential inclusion into data lakes, of the small businesses. For instance, restaurants can use analytics to send notification in a situation when they can sync weather report data and their notifications about iced tea when the temperature in an area rises.
Suppose you have a huge and well-organized dataset, what next? How to transform data into predictions?
Though data insufficiency is no more a dilemma, the later is.
Businesses, need a dedicated team of data scientists to interpret the data, or a potential software suite to do the same and rapidly.
In a market, predictive analytics software has soared high. Pricing might vary with a count of users and, in some cases, data availability, but typically the starting price is about $1,000 per year, though now scaling more higher.
The market is shifting with several software coming out with advanced requirements and more manual adjustments.
To mention, user-friendly SaaS models allows predictive analytics to become more accessible to marketing teams without the data scientists.
And if you want to outsource the process, you can get your requirements stated clearly to the outside vendors and they would make models, translate data and make predictions for you.
They are agencies offering desirable solutions. With the increasing rate of innovations and new implementations, they happen to be driving force behind outsourcing…acquiring the right toolset, skill set, and dataset… to get your data interpreted.
After saying all, let's agree that predictive analytics has also faced its own criticism. GDPR rejects some similar collection methods which cause swelling of data lakes. And not every prediction, though accurate, are acknowledged.
Experts say that the algorithms search patterns and not the values themselves. Also to mention that owing to data insufficiency there is some hold back in wearable, IoT and other modes of data collection that supplement conventional methods.
The SaaS platform is yet emerging. For many businesses, developing models and making predictions from sets of data yet demands a dedicated team of the employee to work with complex predictive analytics software or get the task outsourced.
To add, IBM has been recently named a leader in the Forrester report for Predictive Analytics and Machine Learning. And many companies are eager with accepting the new trends of predictive analytics.
What do you follow for your business? Share your experiences with us in the comment section below.
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