Statistics is a topic that is very important in any technology, but it is particularly important when working with data since data is the foundation of all of the use cases we work on, and statistics ideas are essential to grasp and evaluate conclusions drawn from data. Once you understand the fundamental ideas, it will be much easier for you to study and apply the concepts of Machine Learning.
Statistics is a kind of mathematical analysis that makes use of quantified models and representations for a particular collection of experimental data or real-world research to arrive at a conclusion. The most significant benefit of statistics is that information is presented in a straightforward manner. The statistics resources were revised and arranged recently to prepare you for your career as a data scientist.
Recognize the Different Types of Analytics
- When we look back in time, descriptive analytics informs us what occurred. It also helps a company understand how well it is doing in the present by giving context to help stakeholders evaluate information.
- A diagnostic analytics approach takes descriptive data one step further by assisting the user in understanding why something occurred in the past.
- Forecasting the most probable events in the future and providing firms with actionable insights based on the information are two of the most important applications of predictive analytics.
- Prescriptive analytics makes suggestions about actions that may be taken to take advantage of the predictions and steer the potential activities toward a solution based on the predictions.
When it comes to phrases like “big data,” “the cloud,” and “the internet of things,” they seem to be far more complicated than they really are. In this day and age, almost every company collects information on its users and clients. Sure, businesses have been doing this for a long time, but the situation is a little different now. A telecom operator may be gathering not just call records, but also location data, mobile surfing history, a profile of the items you prefer, and the websites you visit on a consistent basis. But what are they doing with all of this information? Most significantly, what role does big data play in the consulting industry? Big data consulting assist customers in extracting important business insights from their data in order to truly comprehend their audience, estimate demand, decrease risks, avoid cost overruns, and a variety of other tasks.
The Use of Big Data in Consulting
As SAS correctly points out, “it is not the quantity of data that is crucial.” It is what organizations do with the information that is important. “Big data can be mined for information that can be used to make better judgments and make more smart business decisions.
Historically, management consulting and information technology consulting have been considered separate fields. A business consultant would utilize a great deal of data and research to provide recommendations on a new company model, fee structure, or marketing plan. When they had finished delivering the suggestion and action plan, the project would come to a close. A new Enterprise Resource Planning (ERP) system, on the other hand, maybe implemented by an information technology consulting business. The costs and benefits of implementing such a system are unambiguous. It is just necessary to teach the business users on how to utilize the system.
The use of big data analytics, on the other hand, does not truly fit into either paradigm. Effective big data consulting is a never-ending process of discovering and applying new tools and approaches to existing data sets. Depending on the circumstances, the customer may need continuing assistance or training in order to fully harness the potential of the analytics. Large-scale data innovation is a continuous process that customers might have to investigate with a consulting partner over the course of many months. As a result, big data is a fantastic collaborator for consultants in this area.
Is Big Data Posing a Threat to Consultants’ Employment?
In the end, big data does not threaten to eliminate the consultant’s position, albeit it may make it feasible for a team of three analysts to replace a team of four analysts. In fact, with wages on the rise and utilization rates being constrained, companies are likely anticipating that Big Data would assist them in shrinking the size of their customer interaction teams.
In general, big data apps can crunch numbers faster than an army of undergraduates, but someone still has to make sense of the findings and provide meaning to the data and outcomes they generate. In this aspect, artificial intelligence or robots will have a difficult time replacing people. For the time being, consultants may take comfort in the fact that big data is a useful tool rather than something to be dreaded.
Conclusion
Big data is a technique for analyzing and extracting information from enormous data volumes. It has the ability to have a positive influence on a company’s bottom line by increasing the success of campaigns and tactics developed in response to those insights. Companies are increasingly adopting big data consulting and even establishing their own deployment teams in order to capitalize on this growing segment.
Humans are still required to give meaning to data that is significant and to establish theories in order to guarantee that data analysis is as efficient as possible, despite the enormous potential of big data in consulting and other businesses. As a result, although big data and artificial intelligence will not completely replace consultants, consulting firms will have to be knowledgeable about the applications of big data!