Do All Businesses Require Machine Learning in Data Analytics?
In this post, we will try and answer the question – do all businesses need machine learning in data analytics?
In part one of this blog post, we looked at how artificial intelligence (AI) can help business managers or owners take informed decisions beneficial to their business.
We don`t think so.
Many organizations can benefit from traditional data analytics without the need for more complicated ML applications. Data analytics can help quantify and track goals, enable smarter decision-making, and then provide the means for measuring success over time.
For all of that, in many cases, traditional data analysis is enough to do the job. You can generate reports or models of what happened in the past, or of what’s happening today, getting useful insights to apply to the organization.
One key difference between the traditional method and ML-based algorithm is that the former applies a strict mathematical approach, while ML is more data-oriented. In short, if your business really has vast repositories of big data, and making sense of it is all is beyond the scope of your team of human analysts, then deploying machine learning in analytics is better.
Whether you’re trying to estimate future sales, optimize your supply chain, or choose the optimal product price, forecasting is about predicting the future using past data. When it comes to analyzing such large amounts of data, that too in real-time, nobody can beat a machine, right?
Progress in recent times in neural networking is pushing ML technology to new levels, like providing businesses answers than mere models to predict answers.
Product price optimization is one of the many use cases of ML. AI tech writer Igor Bobriakov explains:
Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitor`s offers. The tools for data analysis bring this issue to a new level of its approaching.
…the data gained from the multichannel sources define the flexibilitprices, taking into consideration the location, an individual buying attitude of a customer, seasoning and the competitors’ pricing. The computation of the extremes in values along with frequency tables are the appropriate instruments to make the variable evaluation and perfect distributions for the predictors and the profit response.
As ML evolves as a predictive analytics tool, coupled with business intelligence tools like Tableau, business managers can make even better sense of their big data.
Here are some use cases where machine learning in data analytics works:
Marketing: Common use case of ML is in identifying and acquiring prospects with attributes similar to existing customers. They can also prioritize known prospects, leads, and accounts based on their chances of taking action.
E-commerce: To predict customer churn or even fraudulent transactions.
Customer service: ML can be used to process outcomes from earlier encounters with clients like total time taken to resolve a ticket, response-time of customer relationship executive, and so on.
When enterprises employ ML-based predictive analytics, it is essential to discover hidden patterns in unstructured data sets for new information. But do remember, to build comprehensive data analysis and predictive analytics strategy, an enterprise requires big data and progressive IT systems, so the cost factor, too, has to be factored in. Till such time, your organization can get along using the traditional data analytics methods.
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