Types of Data Analytics


Data Analytics

The bigger the volume of data, the better the insight and the more precise the inference. Companies are fast adopting analytics solutions to improve decision making and to even constructively achieve business objectives with deep perception. Simultaneously, there is also a need to analyze historical data to optimize S& OP efforts to forecast what could happen imminently. Data-driven companies enjoy huge ROI’s in the form of lowered operating costs, augmented revenues, value-based product mix, and improved customer service. In the age of information, we are today, a data-oriented world where everything is more or less dictated by analytics and data techniques that are aimed at streamlining costs, increasing operating margins and bettering human resource decisions. In the next few decades, experts predict that the influence of data analytics is all set to grow multifold and companies will rely heavily on data analytics for their development.

Different types of DA or Data Analytics

Data sets are expansively evaluated to draw conclusions that predominantly aid existing software and operating systems to function better. Commercial industries widely use data analytics to make informed decisions to verify or disapprove of theories, hypotheses, and scientific models. BI or Business Intelligence, OLAP or Online Analytics Processing and other forms of advanced analytics, in combination, or based on their usage, are referred to as Data Analytics.

XCEL Corp proffers Data Analytics services to improve operational efficiency and to augment customer service efforts. Organizations can respond to emerging market trends faster while gaining a competitive edge over other contenders. We have one final goal when we use DA and that is to boost business performance. Data analyzed may consist of either new information or historical records which are processed using real-time analytics through a combination of external data sources and internal systems.

Descriptive DA

One of the most common types of data analytics, Descriptive Statistics, is normally employed as a retrospective where it answers questions from the past to decide about the future. Considered as the backbone of reporting, BI tools and dashboards cannot exist without it.Descriptive analytics is further broken down into canned reports and ad hoc reporting. Ad hoc reports are unscheduled and created by analysts on a need-to-know basis, while a canned report is pre-scheduled and revolves around a particular subject. Ad hoc reporting provides the organization with an in-depth view, for example, about your social media presence along with rates of engagement and demographics. Generally, interpretations are drawn to better strategies and formulate a better future. Although descriptive analytics does come with some limitations – in other words, the methodology only introduces us to the problem rather than alleviating it. Descriptive Analytics, therefore, for several organizations, is the first step towards big data.

Predictive DA

Causations, trends and other correlations are interpreted by the most common category of data analytics known as Predictive Data Analytics. Further categorized as Statistical Modeling and Predictive Modeling, this type of data analytics is meant to envisage the probability of an occurrence. Predictive modeling analyzes the statistics of more than one target audience present at disparate demographics while providing probable revenues for each. Predictive analytics also comes with some minor drawbacks where the model is at a danger of being built around inaccurate information, leading to imprecise inferences. Data analysts have to constantly refine, govern and tweak all analytics to generate the right conclusions.

Prescriptive DA

AI and big data meet at Prescriptive Analytics to predict the results by drafting each step in a process. Optimization and random testing are two of the main types under this category. Without having to spend time trying to understand and evolve a particular variable, predictive analytics provides with you the best results after having tested all of them. It even forecasts or suggests a newer variable that comes with greater chances of generating favorable results. One of the biggest drawbacks of the prescriptive model is its cost. Not a widely incorporated model, the analysis under this can be quite expensive and data scientists with the knowledge of prescriptive analytics are in high demand.

Diagnostic DA

Diagnostic Data Analytics helps us understand the cause of an event and even finds its dependencies. It comprehensively identifies patterns to provide in-depth insights into an existing problem. Furthermore, it uses several techniques such as correlations, data mining, data discovery, and drill-down to derive results. You get a more detailed report when you use Query and DrillDowns while Discover and Alerts notify you of a forthcoming issue before it occurs. This means it gives you alerts so that you can discover constructive information benefiting your organization. Unfortunately, diagnostic analytics rely on the speed of machines and human analysts often misinterpret patterns as causes of a business issue. They seldom use this information to support decision-making. Predictive analytics has to be used when you are trying to look ahead into the future for some valuable answers.

At XCEL Corp

XCEL Corp typically analyzes data to uncover business insights to improve performance. We also build comprehensive software applications that bring many interesting concepts to the table. If an organization does not have sufficient data or is not aware of the opportunity that data brings, then experts at XCEL Corp scale-up decision-making by gathering, managing, processing, engineering and using the data and integrating it with the company’s decision lifecycle. As an information technology consulting firm, we cover a wide variety of analytics and present detailed insights and storyboards to improve the future of your business.

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