Machine Learning in Data Analytics: How It Works for Your Business?
In this post, we will try and answer the question – how does machine learning in data analytics work for your business?
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 machine learning (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.
The Difference Between Traditional Method and ML-based Algorithm
One key difference between the traditional method and ML-based algorithm is that the former applies a strict mathematical approach, while machine learning (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 the 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 flexibile prices, 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 is the appropriate instrument 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.
Leading organizations are using machine learning tools to extract valuable insights from raw data to solve complicated, and data-oriented business problems.
Machine learning algorithms enable computers to discover various types of hidden insights and play a key role in expanding the business analytics community.
Machine learning in business improves business operations and enhances business scalability for global companies.
Companies are making use of AI to enhance customer loyalty, automate finance, detect fraud, and measure brand awareness.
If ML is implemented in the right way, it can serve as a boon to a wide variety of business problems, and predict customer behaviors.
Major technology giants including Google, Adobe, Microsoft, and Amazon are using their own Cloud Machine Learning systems.
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Machine learning has become popular because of different factors including growing volumes, affordable data storage, and cheaper computational processing.
Hence, businesses, if they want, can learn how to use machine learning in their operations to get benefits from it.
How is Machine Learning being used in Specific Industries for Data Analysis?
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.
Almost all eCommerce and retail platforms use machine learning based algorithms to boost complete shopping experience.
It recommends products according to past purchases, app or website activity, brand references, etc.
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.
Healthcare: Machine learning algorithms help hospitals and their staff customize treatment plans, make administrative processes more simpler, and track transmittable diseases.
Finance: Machine learning technology can support decisions related to investment by looking at risks according to probability statistics and past data.
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.
What are the Benefits of using Machine Learning in Data Analysis?
Data analysis in combination with AI and ML has modified the operations of businesses.
Businesses use machine learning techniques for data analysis to come up with feasible solutions to achieve higher revenue, minimize churn, and boost sales.
However, this needs expertise on big data solutions, machine learning and data analysis.
The following are some crucial benefits of using machine learning in data analysis:
Reduces manual data entry
Today, the most common problem faced by businesses is inaccurate and duplicate data. Both, ML and predictive modeling algorithms use discovered data to avoid such errors.
Hence, employees can make better use of their time by deploying machine learning.
Machine learning adds more value to businesses by detecting spam. Earlier, email service providers were making use of already-existing, and rule-based approaches to filtering out spam.
Nowadays, new rules have been created by spam filters with the help of neural networks to detect spam, and fraud messages.
With huge volumes of both accurate, and quantitative historical data, it is abundantly used in financial analysis.
It is widely used in finance for detecting frauds algorithmic trading, managing portfolios, and loan underwriting.
Furthermore, upcoming applications of machine learning in finance will cover customer service, various interfaces for sentiment analysis and chatbots.
Today, the most common problems faced by marketing professionals are CLV prediction and customer segmentation.
Businesses can use large quantities of data to derive valuable business insights as they can access such data easily.
Both, data mining and ML can understand the browsing, and purchase histories of users, and based on that send the best offers to them.
Identify fraudulent transactions
Machine learning has been transforming the globe for years, and today, data analysis helps organizations make the correct decisions for their clients.
Now, organizations are creating an algorithm with the use of machine learning that inspects the data sets.
This leads to the identification of invisible connections between behavioral patterns and fraudulent activities.
The interesting aspect of machine learning for data analysis is that once the process begins, it operates automatically. It can point out fake profiles by accessing private data.
Another significant aspect of machine learning algorithms for data analysis is monitoring irregularities in the datasets to make sure that clients are secure.
Payment gateways widely use this kind of algorithm to identify fraudulent activities.
Minimize customer churn
Customers purchased your product when it was launched, but gradually they started not showing interest in it.
Today, this is the most frequently seen problem faced by almost all companies. Consequently, the overall revenue of the business declines.
The truth is that almost all businesses rely on the consumer space, whereas churning has had a significant impact on the subscription policies of others.
The major benefit of churning is that it allows businesses to measure the satisfaction of customers with their products. This data acts as a platform to identify clear outcomes and forecast their next steps.
Predictive analytics is used by titans such as Amazon, Netflix, and Google to boost profits and prevent client churn.
Apart from this, customers’ data is monitored to sustain customer satisfaction and reduce churn.
Machine learning strategies for effective data analysis include the use of the latest trends in big data to generate leads and provide customer experience, which is a crucial element in boosting sales.
The surveys and client feedback are examined using ML algorithms to increase customer experience.
Companies can use this information to understand the consumers who might have problems in the future and take preventive actions as soon as possible.
Businesses can obtain better outcomes using relevant machine learning algorithms for data analytics to attract potential customers.
Also, businesses know that customers have become smarter and their needs are not the same all the time.
This makes them look for new techniques using data analysis concepts to produce leads and convert them.
Personalization can be analyzed using data, and companies use it to alter the way they think about their clients.
This speeds up the entire process and ensures smoother customer onboarding.
The machine learning algorithms assist in collecting the data through channels and messaging to guarantee that the correct product is introduced to the market.
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What are the Challenges of using Machine Learning in Data Analysis?
There are multiple challenges that machine learning encounters in studying data behavior. Let’s explore them in detail:
Getting vast amounts of data
According to Google’s reports, it is noted that Google processes nearly 25 pebibytes of data every day.
It is challenging to process such massive data. To overcome this challenge, frameworks that are dispersed with parallel computing should be selected.
Data processing performance
One of the primary challenges experienced in processing big data is that as the data grows, the complexity of computation grows, thus leading to dangerous operations.
Since algorithms require time to conduct operations and computation with massive data sets, this may render them useless.
The architecture used to move and store data starts to impact the performance of algorithms.
So, data size impacts performance and results in a rethinking of the relevant architecture used in evolving and implementing algorithms.
Navigating through various types of data
Today, the size of data is always growing and will ultimately rise in the future. Variety is a crucial attribute of big data. Three categories of data exist:
- Unstructured data
- Structured data
- Semi-structured data
These data types are again classified into high-dimensional, non-linear, and heterogeneous data.
This issue can be addressed using data integration by learning from such a huge dataset.
Navigating through disputed and partial data
Earlier, machine learning algorithms produced more accurate outcomes because the data they were provided was more perfect.
Today, the data offered by different origins is usually incomplete and doubtful.
Thus, machine learning in analytics has numerous challenges. Unknown data can be produced via wireless networks because of fading, shadowing, noise, etc.
A distribution-based strategy should be used to address this problem.
Navigating through the low-value density
The primary objective of machine learning-driven data analytics solutions is to process massive data sets and pull out useful data for commercial advantages.
Value is a crucial attribute that big data requires. It’s not easy to identify massive amounts of data with a low value density. Knowledge discovery and data mining techniques are required to find useful data.
What is the Future Scope of Machine Learning in Data Analysis?
Task automation for data analysis
Machine learning can automate many routine tasks including transformation, basic analysis and data cleaning quickly by inspecting huge datasets.
Constant development via machine learning
Machine learning facilitates the progress of AI models with the availability of new data.
Retraining the models on a timely basis to look at the fluctuations in the data and adapt to the ever changing needs of the business.
This guarantees the accuracy and relevance of the analyses for a longer-time period.
How can Express Analytics Support Advanced Data Analytics with Machine Learning?
Express Analytics is a US-based AI and ML-based solutions provider for your data analysis needs.
Our team of professionals can adapt innovative strategies to automate your day-to-day operations and revolutionize your business.
Our data analysts can use algorithms to experiment with hypotheses that result in stronger recommendations and strategic decisions.
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