Data analytics has come a long way since organizations started realizing the importance of data. As the demand for the “new oil” increased, we witnessed path-breaking technological advancements in recording, gathering, storing, and analyzing data. Specialized use cases, newer applications for real-time insights, and increased demand from frontline decision makers further fueled the evolution of data analytics. From being technology-centric to becoming user-centric and from extracting business intelligence to delivering decision intelligence, data analytics today empowers decision makers across hierarchies, roles, and departments.

Through this blog, let’s trace the evolution of data analytics and understand the advanced data analytics techniques that today’s data analytics platforms must offer for unlocking the true potential of data.

Evolution of Data Analytics

As data and its importance grew, data analytics also evolved to offer new analytics techniques for addressing the insight needs of organizations.

First, there was Data

For as long as businesses have existed, they have been maintaining data about customers, products, transactions, and expenses in various forms. It ranges from undocumented business insights passed on from generations to hand-written sales records to complex accounting systems. To a small extent, this data is usually analyzed to identify preferences and gauge demand. Your local grocer may know your food choices, brand preferences, and monthly purchase quantities to recommend new products, offer personalized services, and stock the right amount of inventory. But as businesses grow, it becomes humanly impossible to maintain this data, interpret it correctly, and use it effectively to service business needs.

Then came Business Intelligence

Computing technologies and information systems enabled businesses to record and aggregate huge amounts of data. The advent of Business Intelligence (BI) software helped query this data and collate it into reports. Even though the data was relatively smaller, it still took a painstaking and long process, often extending to weeks or months, to clean and prepare datasets for analysis. Also, special competencies were required to analyze data using the early BI software. This gave rise to the role of data analyst who processed data, extract insights, and compile them into reports. Instead of relying only on intuition, decision makers could access actual facts but were restricted by the delays in reporting and the historic nature of insights. The insights informed on what happened in the past but gave minimal guidance on how to prepare for the present, let alone the future.

Big Data explodes, revealing shortcomings of traditional BI

The success of internet-based companies and social networks, increased digitization of workflows, and the race towards digital transformation lead to the explosion of Big Data. Businesses were generating more data from internal and external sources than they could handle. It came in various formats like structured and unstructured texts, images, audio, video, user-generated content, system logs, location details, telemetry, and so on. Traditional data analytics and BI software tried to achieve faster processing of Big Data to some extent. Visualizations like charts and graphs were introduced to make the findings understandable. Dashboards helped compile the insights in an organized manner. Extracting insights from current data became possible, but it would still take days for them to reach the actual decision maker.

However as data continued to explode, the following limitations of traditional data analytics techniques were revealed:

  • Analysis limited to past data leading to lack of clarity on the current state of business
  • Delayed decision making due to lack of actionable insights with traditional analytic techniques
  • Special competencies required to query data and extract insights
  • Heavy dependency on analysts to prepare reports and make sense of data
  • Lack of timely insights resulting in missed opportunities and losses
  • Useful insights getting lost in overload of lengthy reports and static dashboards
  • Lack of self-service capabilities for decision makers to get real-time insights
  • Lack of scalability and agility as data and demand for insights increase
  • Complex and time-consuming deployments requiring infrastructure overhaul
  • Inefficient resource allocations and below-par returns on investment

The era of Decision Intelligence and Data-Enriched Products beckons

In today’s world, whatever industry you are in – product innovation, cloud services, retail, marketing, financial services, logistics and supply chain, content creation – every business activity creates a trail of data. This data becomes important not only for improving internal decision making but also providing enriching experiences to your customers. In the midst of information overload, decision makers within the organization are demanding decision intelligence in the form of focused and actionable insights for making quick and confident decisions. On the other hand, customers are looking for data-enriched products that improve their experience and give them control to fulfill their needs.

To harness data to its full potential, you need a modern data analytics platform that combines the best of machine learning (ML), artificial intelligence (AI), and user-centered design to offer advanced data analytics techniques in a self-service manner.

Advanced Data Analytics Techniques

The following are some advanced data analytics techniques that modern data analytics platforms provide to address the ever-growing need for insights.

Decision Intelligence

In high-pressure make-or-break situations, where there is no time for extensive manual analysis, decision intelligence is the advanced data analytic technique decision makers need to make fast, confident, and data-driven decisions. Decision intelligence is an advanced insight discovery process that uses natural language processing, automation, ML and AI to distill data into actionable intelligence quickly and accurately.

Gartner predicted that by 2023 more than a third of large organizations will have analysts practicing decision intelligence. Decision intelligence helps overcome analysis paralysis by presenting the most suitable option upfront, without overwhelming decision makers. This way, they can get focused guidance and targeted recommendations to make decisions quickly.

Self-Service Analytics

Self-service analytics democratizes analytics and empowers anyone to ask questions, without much effort and time delays. Modern data analytics platforms that offer self-service analytics for performing advanced analysis help transform business users such as floor managers, customer representatives, accountants, or sales executives into business technologists.

Intuitive user interfaces, intelligent search, easy dashboard and report creation, interactive visualizations, automated delivery of actionable insights, and personalization are some must-haves of a self-service analytics platform. These advanced analytics techniques help decision makers become self-reliant, data-driven, and confident in their work, thereby also enabling organizations to reap the benefits of greater user engagement and better returns on analytics investment.

Detailed Analysis

Root cause analysis or why analysis reveals “what” happened, uncover “why” it happened, and recommend “how” to proceed in the best possible manner. Predictive analytics helps identify trends and outcomes that may happen in future. Prescriptive analytics goes one step further to assess risks and success possibilities, and recommend actions to take to ensure businesses are better prepared to handle them. Actionable insights like anomalies, outliers, analogies, segments or clusters, distribution, and related useful information focus on important developments that users may not have thought to search on. Tracking business metrics and identifying influencing factors help businesses monitor what is important and specific to them. These advanced analytics techniques require precision, high-quality and real-time data, and robust AI and ML models with greater processing power. Modern data analytics platforms that simplify these advanced analytics and make them available to non-technical business users help in democratizing insights across the organization.

Embedded Analytics

Embedded analytics ensures rapid integration within existing systems, provides a seamless user experience, and removes complexities of learning a new system. Modern data analytics platforms offer embedding capabilities like search, insights, charts, and dashboards in regular business. When embedded analytics is infused in daily business workflows, business users can get insights automatically and use them instinctively without interrupting their work or getting distracted by switching to other systems. In addition to their internal users, organizations can extend the benefits of embedded analytics to their customers too. By embedding analytics, organizations can convert their applications into data-enriched products, thereby differentiating themselves from competition. The insight-infused products increase customer engagement, create more value for customers, and improves customer satisfaction.

Data Observability

Data observability refers to monitoring, tracking, and triaging of incidents to prevent downtime. Downtime occurs when data is inaccurate, partial, missing, or contains errors. Data quality directly affects the credibility of insights. When issues in data are not identified, it can lead to inaccurate decisions, losses, and wastage of resources. A clear visibility into data issues and recommendations to fix such issues can encourage timely action, prevent misinformation, and avoid losses. A modern data analytics platform that focuses on data quality and observability is a must-have today, especially when data comes through multiple sources and in varied formats. Choosing tools that evaluate data quality on multiple parameters and identify gaps and inconsistencies can help prevent expensive mistakes.

Top Benefits of Advanced Data Analytics Techniques

Modern data analytics that offer advanced data analytics techniques benefit businesses in the following ways:

  • Faster data-driven decision-making: Using advanced analytics techniques, decision makers can access insights easily and in a timely manner for making faster data-driven decisions, thereby identifying threats early, minimizing risks, and converting opportunities.
  • Easy and quick access to advanced analysis: The time taken to perform detailed analysis such as root cause or why analysis, demand forecasting, churn calculations, trends prediction, and prescriptive analytics can be reduced from weeks to minutes using advanced analytics techniques.
  • Improved agility and productivity: With easy access to decision intelligence, personalized recommendations, and contextual insights, business users can focus on their work and actions better instead of struggling with analysis.
  • Reduced dependency on analysts: When business users become self-reliant in getting answers to their daily information needs, it reduces the pressure on IT and support teams and frees them up to tackle complex tasks.
  • Consumable and personalized insights: By providing bite-sized headlines, interactive visualizations, chart options, and audio-visual data stories, business users can consume insights in an engaging and easy-to-understand manner.
  • Improved visibility of data issues and risks: Advanced analytics techniques give clear visibility into data issues and provide recommendations to fix them, thereby reducing errors, minimizing risks, and improving quality of insights.
  • Increased returns on analytics investments: Advanced analytics techniques simplify complex processes and democratize access to data, resulting in increasing adoption of analytics, reduction of overheads, and maximum returns on analytics investment.

Unlock Advanced Data Analytics Techniques with MachEye

MachEye is a modern data analytics platform that offers all the advanced data analytics techniques that businesses need today to convert opportunities and achieve growth. MachEye’s self-service platform empowers business users to explore and analyze data intuitively, gain personalized insights and recommendations instantly, build dashboards, and share their findings easily all by themselves, removing the dependency on analysts.

It provides an end-to-end solution for advanced data analysis, data quality and observability, and data governance, along with embedded analytics capabilities. MachEye’s interactive data visualizations turn data into audio-visual insight stories that help users to engage with data easily and to make better insight-driven decisions. By enabling users to gain the what-why-how of business insights easily, organizations can unlock the true value of their data with MachEye.