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Business Analytics: ML in Action

Author: Jorge Anicama | | March 3, 2022


For eons, humans have gathered data to help them make sense of the world. That quest for information has evolved from studying the patterns of star systems to studying patterns found in the vast quantities of immense data lakes.

The tools used to gather and investigate data have evolved, too. Today’s advanced technologies provide data analytics programming to understand, learn from, and harness the values hidden deep in those data center depths.

Emerging business intelligence (BI) and analytics software offers unmatched opportunities to companies of all sizes to meet their current market demand and thrive into the post-COVID era. Datavail’s BI and machine learning (ML) experts are adept at designing and implementing complex BI and ML systems to help their clients glean the most value from their data.

Data Science = Business Intelligence

The proliferation of data analytics programming is advancing industries and economies, driven by cloud computing and the ever-widening expansion of digital connectedness. Billions of wired devices from around the world stream trillions of data bits every second, all of which are fed into and through corporate data stores and much of which is critical to the success of the individual enterprise. Making sense of it all are data scientists, who are not developers or IT technicians (although they can be either or both). Instead, data scientists focus specifically on finding relevant BI information within the databases to drive corporate success. To achieve success in this quest, they often use ML programs to enhance their effort and to discover deeper, more meaningful insights.

Business Intelligence = Analytics

The simple gathering of information doesn’t make it intrinsically useful. Instead, it becomes useful when users understand the relevance of the data and act on the lessons it offers. To gain valuable and actionable insights from those vast reaches, analytics programming searches for information directly related to BI inquiries. The data scientist is the specialist who devises those inquiries to elicit the information needed. The questions themselves follow the formatting of the different modes of analytic programming design: descriptive, predictive, and prescriptive.

Descriptive analytics provide information on what has happened to the business in the past. This software analyzes patterns in data stores that reveal events that already occurred, such as how often production machines have broken down, the number of positive customer service reviews, or any other data bit relevant to the company’s core activities.

Descriptive analytics can be beneficial to identify the costs involved in both positive and negative corporate outcomes. When the data shows a positive ROI on a specific activity, then those investments are justified. Different decisions would be made, however, when the information tallies the long-term expenses of maintaining aging equipment.

The nature of descriptive analytics programming is also significant.

  • Sometimes, the best business information is that streaming in minute-by-minute. ‘Real time’ analytics programming provides leaders with informative insights as they emerge so appropriate decisions can avoid immediate disasters or capture evolving opportunities.
  • Diagnostic analytics, another form of descriptive analytics, go deeper than just reporting what went wrong. These programs can also determine why the problems occurred so management can make appropriate corrective decisions.

Predictive analytics look the other way, towards the future, and attempt to predict what will happen based on what’s happened in the past. The process is more of an approach to computing than an individual technology. Predictive analytics are used across the enterprise and are particularly helpful at fraud detection, malware detection, and image analyses.

Prescriptive analytics can guide decision-making by providing recommendations on how what’s being done can be done better, faster, more economically, etc. The software culls through vast amounts of data to identify relevant factors, then offers suggestions as to how to respond to those realities.

This mode of analysis is particularly helpful when leaders have several options available and want to know which is the best (or worst) choice. For example, when sales are slumping, prescriptive analysis can identify which sales channels are most effective (print ads, social media blasts, or radio spots, i.e.), which products are lagging most, and which markets or customer lines are most engaged. This information can direct investment decisions to increase or decrease budgets, select new vendors, or hire more workers.

The three modes of BI analysis program models use a series of computing programs, including machine learning algorithms, advanced math calculations, and statistical modeling, to develop appropriate responses to specific BI inquiries. As each model rolls through the data, its specified mathematical calculation aspect seeks information relevant to the question. The ML aspect ‘learns’ the patterns within the data, then ‘learns’ again as new information is added. The value of the ML aspect is that it ‘learns’ on its own – its automation ensures that each particular BI question will gain relevant, accurate, and timely responses with every inquiry.

Using BI and ML analytics programming, today’s global business leaders can glean critical information from their complex computing programs that improves operational efficiencies, fine-tunes sales and marketing campaigns, and ultimately enhances productivity and profitability. Datavail’s experienced data scientists can help your organization develop and implement these complex technologies so your enterprise can thrive into the future, too. For more information regarding Machine Learning and setting it up to get the most from it, download our white paper, “Tips for Machine Learning Model Selection.”

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