How data analytics is evolving

Data analytics has become a cornerstone of modern decision-making, enabling business people to derive actionable insights from multiple datasets. As organizations increasingly rely on this information, the evolution of data analytics tools continues to transform industries worldwide. A popular advancement is adding financial software to the data platform so that operational and financial reporting can be created from the same place. As technology evolves, so do these business intelligence solutions. In this blog, we will discuss three types of data analytics, the emerging role of artificial intelligence (AI) in processing data, and the evolution of data analytics.

The foundation of data analysis

Data analysis began as a manual process, relying on spreadsheets and relational databases to organize and interpret information. Excel and SQL were foundational tools, empowering analysts to make sense of structured data and draw conclusions. Over time, advancements in data technologies and the rise of descriptive analytics allowed business people to better understand historical data by helping them to understand trends and patterns with user-friendly visualizations.

Modern advancements in data analytics

Today, the analytics landscape is dominated by advanced technologies like machine learning or artificial intelligence. Cloud data storage allows for a detailed insight into consolidated financial and operational information and people can access information from anywhere. These innovations from data analytics providers like Phocas have enabled organizations to process large volumes of data quickly and extract deeper insights. Tools like Phocas have made it easier to create interactive dashboards and automate data processing, enhancing the accuracy and use of data.

Descriptive analytics

As the name suggests, descriptive analytics describes what has happened in the past. This is accomplished by taking raw historical data, whether from five minutes or five years ago, and presenting an easy-to-understand, accurate view of past patterns or behaviors. By understanding what has happened, we can better predict future outcomes. Many businesses use descriptive analytics to understand customer buying patterns, sales trends, historical cost-to-serve, supply chain patterns, financials, and much more.

Predictive analytics

Predictive analytics uses historical data to forecast future outcomes, increasingly integrating with real-time analytics to create more dynamic decision-making frameworks. Understanding the likelihood of future outcomes enables companies to better prepare based on probabilities. This is achieved by combining historical data from various sources such as ERP, CRM, and POS into one version of the truth. Wholesale businesses use predictive models to optimize stock levels by analyzing sales patterns and seasonal trends. This can lead to cost savings, less dead stock and improved customer satisfaction

Decision-makers also need to re-forecast on-demand or run rolling forecasts. Finance teams preparing budgets in data analytics solutions benefit from all the data they can access. Financial data analytics software allows budgets and forecasts to be live, making re-forecasting straightforward because it’s clear what’s budgeted and what’s achieved year-to-date. Many finance teams also carry out scenario or what-if analysis. The data analytics tool enables them to determine what a margin would look like if they did 'X', allowing managers to make measured judgments and better predict the next 12 months.

Prescriptive analytics

Prescriptive analytics is the newest evolution in data analytics. It takes previous iterations to the next level by revealing possible outcomes and prescribing courses of action. In addition, this solution shows why certain outcomes will happen. Prescriptive analytics answers the question: What should we do? Although this is a relatively new form of analytics, larger companies are successfully using it to optimize customer experience, production, purchasing, and inventory in the supply chain to ensure the right products are delivered at the right time. In the stock market, prescriptive analytics can recommend where to buy or sell to optimize profit.

The role of data scientists and analytics tools

Data scientists are at the forefront of transforming raw data into actionable insights and often work in government offices and large financial institutions. With the rise of user-friendly data analytics tools like Phocas, more people in an organisation can also be their own version of a data scientist. These accessible tools help people mine data relevant to business operations to learn more about customers and products. The combination of data from a number of sources into structured databases allows the data analytics solution to also create financial statements and budgets and forecasts.

Big data

Big data refers to the large volumes of data generated every second from various sources such as social media, sensors, transactions, and more. Managing and analyzing these large datasets require advanced analytics tools and frameworks like Phocas. Big data analytics helps businesses gain deeper insights into their operations, customer behavior, and market trends, enabling data-driven decision-making.

Machine learning

Machine learning, a subset of AI, involves using algorithms to analyze data, learn from it, and make predictions or decisions without being explicitly programmed. Machine learning algorithms are essential for predictive analytics, enabling businesses to forecast future trends and outcomes based on historical data.

The future of data analytics

The evolution of data analytics has been driven by technological advancements and the increasing amounts of data generated. Initially, analytics solutions focused on descriptive analytics, providing insights into past events. As data volumes grew, predictive analytics emerged, allowing businesses to forecast future outcomes.

As technology continues to advance, AI is becoming more prevalent by making analytics substantially more powerful. A decade ago, analytics solutions only provided descriptive analytics. As the amount of data generated increased, solutions started to develop predictive analytics. As AI evolves, data analytics solutions are also becoming more sophisticated. BI software vendors are currently competing to offer limited AI-enhanced prescriptive analytics.

AI can help sales-based organizations by providing specific recommendations that sales representatives can act on immediately. Insight into customer buying patterns allows prescriptive analytics to suggest product bundles, ultimately leading to increased order sizes, reduced delivery costs, and fewer invoices.

Predictive ordering has enabled companies to send products you need before you order them. For example, some engineering part companies send replacement parts based on predicted usage patterns, ordering replacements for you before the heads begin to fail.

Real-time analytics empowers organizations to respond quickly to changing circumstances, with significant impacts seen in industries like retail, where inventory management benefits from real-time tracking. Cloud computing and automation have made it possible to analyze large data sets instantaneously, providing businesses with the agility needed to remain competitive.

If you are considering enhancing your data analytics capability and adding artificial intelligence, we encourage you to seek out a software vendor that offers industry-matched data analytics that is easy and intuitive for everyone to use. This means dashboards, scorecards, and alerts developed with standard KPIs for your industry, pre-built. Collaborating to customize the software to fit your business and augmenting it with newer predictive analytics and machine learning-based AI is the next step.

As technological advancements continue, organizations will increasingly rely on advanced analytics to gain a competitive edge. Innovations in data visualization tools, formats, and frameworks will further enhance the ability to make informed decisions.