When the term Machine Learning is used, it is usually assumed to be the niche of trained data scientists and statisticians. These technologies utilize the mathematically complex concepts, however, not everyone in the organization needs to know them in-depth, to extract value from them.
Technical •January 16, 2023• 5 Min. Read
3 way to augment business intelligence with machine learning
With the overwhelming data outburst of today’s consumers, business intelligence methods with the traditional tools are inadequate in keeping up with business requirements.
Most companies do have a data analytics strategy where data-based insights are often sought to aide in decision making. Incorporating machine learning in this process is the need of the hour in today’s scenario. The question is – how a company can get started with applying machine learning hypothesis in the existing business intelligence strategy?
There is no linear roadmap to this activity, however, few key measures can help in initiating the AI journey of any organization.
Hiring the right talent
A team of expert data scientists can formulate and integrate the machine learning models to utilize the available data for executing business intelligence strategies. The alignment of machine learning models with business goals can result in considerable benefits in terms of performance and customer satisfaction.
A commonly misunderstood area is the job description of data scientists where the tasks of BI professionals are included under the category of data science. Data science jobs are not for generalists but for people with specialized mathematical training as well as experience in forming models that can find patterns in data sets.
Initiating with a small team helps to streamline the process. Gradually getting the momentum will help in the formulation and completion of your BI goals.
Spot real-time anomalies
Does your organization track real-time data? Analyzing the historical data adds value for getting insights, however, it is far better to spot outliers in real-time, when they take place.
Immediate detection leads to quick insights and reduced data to action time. This can be quite useful in sensitive scenarios such as financial frauds, fraudulent online transactions, etc. when taking immediate action can be a decisive factor.
Being able to act on an anomaly in real-time results in greater efficiency as well as better customer experience. Machine learning can enable companies to create the anomaly detection ecosystem.
Invest in forecasting anomalies
The term “forecasting” is usually met with suspicion and intrigue. Nonetheless, it is very closely associated with the “insights” of data. Forecasting is now utilized for diverse business functions such as estimating sales, optimizing the supply chain, choosing the right option for e-commerce shopping. When it comes to recommending best from myriad options, machine learning is unbeatable. Recent researches have raised this technology to a level where the forecasted insights are as accurate and accessible as the historical data.
Getting valuable business insights have always been a key challenge for most organizations. With the overwhelming data outburst of today’s consumers, business intelligence methods with the traditional tools are inadequate in keeping up with business requirements. Machine learning presents a new option which offers speed and accuracy, which can augment the organizational decision-making strategy to a higher horizon.