Data analytics is the process of businesses analyzing raw data in order to draw conclusions about the information that can help the organization progress in terms of functionality.
This has come about as a result of improvements in digital technology that has facilitated the compilation of massive amounts of information by business organizations.
Data-driven businesses are those which, through necessity and the nature of the organization, make heavy use of data analytics, and in general are continuously striving to improve their data analysis methodologies.
Utilizing an analytics technique reveal metrics and trends that are important to decision-makers in an organization whilst filtering out extraneous information that would otherwise obscure useful information. The filtered data is used to optimize processes, thereby increasing overall efficiency.
Many of the systems used in data analytics have been automated. Productivity software and business productivity suites input raw data, which is processed through algorithms that enable human interpretation.
Advanced analytics refers to an examination of data that engages sophisticated digital tools and techniques that surpass conventional data analytics paradigms. Advanced analytics uses highly developed methods to forecast future behaviors, trends, and events, giving organizations the ability to compile advanced statistical models and anticipate roadblocks to organizational objectives.
Business Intelligence engages historical data to present a picture of where an organization has been. This allows decision-makers to predict competitive responses and changes in consumer behavior.
Advanced analytics resources facilitate superior predictive analytics and supply real-time insights as changes are taking place; thus, organizations can react more responsively, with more accurate forecasting.
The analytical solutions market is ever-evolving, and the availability of user-friendly, self-serve tools are leading to enhanced data driven decision making, at a level that was not possible in previous years. These solutions employ sophisticated algorithms and analytical techniques so that users don't have to be data scientists in order to derive insights from data inputs.
A self-serve advanced analytics solution incorporates computational linguistics, analytical algorithms, and data mining into a self-serve environment and provides an easy-to-use NLP search capability for swift, accurate data analysis. It suggests relationships and provides insight into previously hidden data so business users can explore and discover' crucial business results, patterns, trends, issues, and opportunities and improve productivity and smart decision making across the organization.
The benefits of advanced analytics are manifold, but due to the sophistication of the solutions and platforms available, user empowerment is probably the most important advantage as regards implementing advanced analytics.
Most of the people involved in data analysis are not going to be statisticians or data scientists, and the companies offering these solutions are creating them with this fact in mind. This is commonly referred to as data democratization, in which all parties in an organization or department have access to all data, and that there are no other parties nor procedures creating impediments to accessing data.
According to Forbes, The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.
Other benefits of smart data discovery (advanced analytics) include data sharing, timely and accurate decision-making, day-to-day support for business decisions, fast hypothesis and prototyping, and an improvement in the value of business analysis overall.
It can be said that advanced analytics serve as a method of troubleshooting aggregated data, rather than providing stand-alone utilities for attaining direct insights, as is the case in some areas of data analytics. In traditional data analytics, data is processed to provide information relating to the past performance of business operations. Data is gathered and formatted for reporting purposes, and users and/or decision-makers gain insights after interpretation.
Organizations are increasingly concerned with business analytics and real-time representations of customer behavior or processes, their ability to recognize the causes of success or failure in operations and to eliminate future uncertainty to the extent that this is possible. These are requirements that cannot be met by conventional analytics dashboards, nor can they be supported by the existing analytics architecture.
Due to the wide range of applicability across several disciplines, business organizations have found more than a few uses for advanced analytics. Engaging sophisticated software systems, advanced analytics allow businesses to decrease the amount of time taken on analytics, which reduces the time between data inputs, insights, and decision making. This results in a competitive advantage and lowers costs, which justify the expense of related software solutions and suites.
Just a few of the myriad applications for advanced analytics include-
- Manufacturers use advanced analytics to put systems into place that effectively monitor and maintain capital equipment. This aids in forecasting and scheduling maintenance and repair cycles, as well as providing early warning reporting to avoid costly down time. Manufacturers and retailers also bring advanced analytics into play in order to maintain and monitor supply chains.
- Warehouse managers and inventory specialists use advanced analytics to observe inflows and outflows, orders, and sales. This allows them to either expedite or forestall ordering and reduce waste. The data sets they generate are also used by executives above them in the organization.
- Marketers use advanced analytics to interpret data on consumer preferences, buying patterns and habits, and emerging trends in these areas. This allows them to formulate marketing strategies and ad campaigns with a great deal more precision and confidence, as their insights are rooted more firmly in hard data and less on intuition and history.
- In similar fashion to manufacturing, organizations within the energy infrastructure have begun to make use of advanced analytics for everything from monitoring the state of heavy equipment (transformers, wind turbines, etc.) to energy usage patterns and trends for residential customers, businesses and the regions these organizations serve.
The implementation of big data and advanced analytics has become a major priority in the corporate world. At this juncture, most consumers have read about how tech giants like Google and Amazon are exploiting these resources in order to stay out in front, and many have experienced aspects of big data and advanced analytics in action while patronizing these organizations. To convey an idea of how fast-paced development is in this area, venture capital and private equity investments in advanced analytics are currently at an all-time high.
The tools available for performing advanced analytics are, at this point, too numerous to count. While companies like Microsoft and other large tech-oriented companies are investing massive amounts of capital into developing their own big data solutions for business, there are innumerable boutique developers and industry-specific ventures that specialize in solutions for industries involved in everything from insurance to geospatial analytics.
Right now, the chief areas that encompass advanced analytics include data analytics, data mining, and big data. Methods can include one or more of these, or all three at different junctures along a given process, depending on organizational objectives. Advanced analytics also includes novel technologies like artificial intelligence (AI), machine learning, visualizations, semantic analysis, and neural networks. Such tools and techniques include-
Data mining provides raw data to be used by predictive analytics. This is useful in securing existing insights and determining relationships between data points and data sets. Data mining also helps to identify anomalies in larger amounts of raw data. Data mining is used in numerous applications, such as marketing, fraud detection, email filtering risk management, and surveys.
Machine learning employs algorithms to reveal patterns and the implications of data. It also creates statistical models to produce long-term, reliable results in arriving at insights. Seen as a subset of artificial intelligence, the algorithms in machine learning improve automatically through experience. Examples of machine learning include image recognition, speech recognition, prediction, classification, and medical diagnosis.
Complex event analysis compiles and analyzes event data from multiple sources in real-time. Also called complex event processing (CEP), complex event analysis identifies cause-and-effect relationships across data to provide insights that can focus and improve decision-making. In this paradigm, streams of events provided by users or other devices are analyzed via queries to determine patterns.
Cluster analysis reveals differences or similarities data sets and present data in order to make comparisons between them easier to interpret. Relevant examples of cluster analysis could include marketers who wish to discover distinct groups within their customer bases or insurance companies that need to identify groups of policyholders with high average claim costs.
Retention analysis engages advanced data analytics to interpret cohorts of users, customers or individuals identified by other parameters. Insights provided can aid in determining factors that illustrate customer growth strategies or influence retention. Retention analysis is sometimes referred to as survival analysis.
Cohort analysis is used to develop comprehensive insights through the examination of people's behaviors. Cohort analysis allows an organization to identify relationships between characteristics of a given population or group and a given behavior or behaviors. Analysis of a group's behavior over time allows for consistent and reliable comparisons within that group.
Finally, predictive analysis is used with data mining, statistics, and machine learning to predict future outcomes and inform decision-makers. The predictive analysis employs current and past data to facilitate predictions about future events or other unknown factors. Using predictive analysis, one can determine the likelihood of coming events or certain situations arising in light of the data being analyzed.
An example of predictive data analytics would be companies involved in eCommerce that use this tool to offer certain products likely to interest visitors to their websites, with the predictions being based on the specific visitor's past purchases and online window shopping habits.
As is probably evident by now, advanced analytics tools empower organizations to study and utilize more data, and at faster processing rates. Insights gleaned also facilitate superior and more precise decision-making.
Advanced analytics also allows decision-makers more time to focus on strategy. With the right advanced analytics tools, organizations can minimize needed resources and streamline the process of sorting and analyzing data. With deeper insight into data, decision-makers can transform data into actionable, fact-based information far more quickly.
Achieving a better return on investment (ROI) is always a factor in organizational strategy. The tools offered in advanced analytics help organizations determine when and where to deploy resources, reducing costs, and increasing operational efficiency. These tools also reveal things like operational deficiencies and customer needs, so that the organization can modify systems, improve products and services, drive innovation, and gain an edge on the competition.
While the sheer number of data analysis and advanced analytics tools may be confusing at times, here, an automotive analogy may prove helpful in simplifying the concept. It's been known for many years that a well-oiled machine performs best.
Over the years, technological improvements led to the development of synthetic and specialized motor oils that dramatically improved the performance and longevity of automobile engines. There may be a dizzying number of options available at the auto parts store these days, but this only reflects market demand and increased opportunities for augmented performance, or in this case, superior efficiency and a better bottom line.