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    Choosing the Right Data Analytics Strategy for Your Business: A Step-by-Step Approach

    Synopsis

    Data analytics is the study of analyzing raw data to draw inferences and gain insights. It is beneficial for businesspeople and students looking to operate their own company as it helps in obtaining consumer insights from a data set. There are four types of data analytics: descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Descriptive analysis provides historical insights, while predictive analysis makes predictions about future events. Diagnostic analysis delves deeper into the data to identify underlying causes, and prescriptive analysis determines the optimal course of action. Companies choose different types of analytics based on their decision-making process.

    Choosing the Right Data Analytics Strategy for Your BusinessET Special
    The study of analysing raw data to draw inferences from it is known as data analytics. Businesspeople or students who wish to operate their own company can benefit from taking data analytics courses because of the major uses of this field in obtaining consumer insights from a data set.

    Top professional courses on Data Analytics

    Offering CollegeCourseWebsite
    Indian School of BusinessApplied Business AnalyticsVisit
    IIM KozhikodeProfessional Certificate Programme in Advanced Data Analytics for ManagersVisit
    Candidates who intend to run a firm in the future enrol in data analytics courses. Gaining consumer insights from a sizable consumer dataset is made possible by data analytics courses. One of the industries with the fastest global growth is data analytics.

    In many aspects, data analytics is essential for a corporation. Data analytics courses are crucial for anything from business optimisation to research-based decision-making and risk management. Descriptive analysis, diagnostic analysis, prescriptive analysis, and other forms are some of the subcategories of data analytics. Beginners in this discipline should start with descriptive analysis because it is the simplest.

    What is Data Analytics?

    The term "data analytics" is wide and includes a variety of techniques. Any type of data can be put into data analytics techniques to have a better understanding of how to make things better. For instance, gaming businesses use data analytics to design prize schedules for players that keep most of them interested in the game. Similarly, various business types use data analytics to satisfy their unique needs.

    The study of analysing raw data to draw inferences from it is known as data analytics. Data analysis techniques let you carry raw data and spot patterns to draw forth important concepts. Today's data scientists use data analytics in their fundamental studies. Many businesses employ data analytics to form well-informed decisions. Any type of data can be put into data analytics techniques to have a better understanding of how to make things better.

    Types of Data Analytics

    Every industry uses one of four different types of data analysis. Despite the fact that we have categorised them, they are all related to one another and build upon one another. The level of labour and resources needed increases from the simplest to the most complex types of analytics. The amount of new value and comprehension increases at the same time. However, cognitive analytics is another sort of data analysis that is currently being employed by data analysts in the modern era.

    Descriptive analysis, diagnostic analysis, prescriptive analysis, and other forms are some of the subcategories of data analytics.

    Descriptive analysis

    The first type of data analysis is descriptive analysis. The foundation of any data analysis is it. In the corporate world of today, it is the most fundamental and common application of data. The dashboard-based presentation of historical data in the descriptive analysis answers the query "what happened" by explaining what occurred.

    Using a range of raw data sources, descriptive analytics juggles information to produce significant historical insights. These results, however, don't explain why something is right or wrong; they only show that it is. As a result, rather than depending just on descriptive analytics, data consultants encourage highly data-driven organisations to combine it with other types of data analytics.

    The tracking of KPIs is the most typical application of descriptive analysis in business. KPIs show how an organisation is doing in comparison to a set of benchmarks. Examples of how descriptive analysis is used in business include the following:
    • KPI-based dashboards
    • Reports on monthly income
    • Sales Leads Overview

    Predictive Analysis

    Comparable to descriptive and diagnostic research, predictive analysis goes further. Based on historical data, this type of analytics provides predictions about upcoming events. The predictive analysis makes sensible predictions about what will happen next by using the data we have acquired.
    • The foundation of predictive analysis is statistical modelling, which requires more resources in terms of both humans and technology to forecast.
    • Additionally, it's critical to keep in mind that forecasting is really a guess and that accurate forecasts require thorough, high-quality data.
    • A type of advanced analytics known as predictive analytics provides several advantages, including sophisticated analysis based on deep learning or machine learning and a proactive approach made possible by projections.

    Numerous business applications exist for predictive analysis, including:

    • Sales Prediction Risk Evaluation
    • To determine which leads are most likely to convert, customer segmentation is performed.
    • Predictive analytics may help customer success teams.

    Diagnostic Analysis

    To identify the underlying causes of the results, diagnostic analysis delves further into the descriptive analytics data. Businesses utilise this type of analytics because it creates more connections between data and identifies patterns of activity. A crucial component of the diagnostic analysis is gathering comprehensive information.

    There are several business uses for diagnostic analysis, including:
    • A goods company is investigating the cause of a delayed delivery in one area.
    • A SaaS company investigated whether marketing initiatives increased the number of trials.

    Cognitive Analysis

    Smart technology called cognitive analytics integrates many analytical techniques to assess large data sets and organise unstructured data. A cognitive analytics system searches its knowledge base of data for pertinent responses to questions.
    • Cognitive analytics refers to analytics with intelligence resembling that of a human. Examples of this include deciphering the context and meaning of a phrase or identifying certain items in an image given a large amount of data.
    • Cognitive analytics commonly employs machine learning and artificial intelligence techniques, allowing a cognitive application to mature over time.
    • Cognitive analytics can reveal correlations and patterns that simple analytics cannot.
    • A business may utilise cognitive analytics to monitor consumer behaviour trends and identify patterns.
    • This enables the business to anticipate future results and modify its objectives in order to perform better.

    Prescriptive Analytics

    The prescriptive analysis, which is at the forefront of data analysis, determines the optimum course of action in a particular scenario or option by utilising the knowledge obtained from all prior research. Modern data management methods and cutting-edge technology are employed in prescriptive analysis. It requires a big organisational investment, so businesses must make sure they are ready to put in the time and effort required.
    • Artificial Intelligence (AI) is a prime example of prescriptive analytics. For AI systems to continuously learn and reach wise decisions, a sizable amount of data is necessary.
    • Well-designed AI systems are able to communicate and even take action on these conclusions.
    • Without human interaction, artificial intelligence enables routine completion and optimisation of corporate tasks.
    • The bulk of large data-driven companies (Apple, Facebook, Netflix, and others) today use prescriptive analytics and AI to improve decision-making.
    • For certain firms, the switch to predictive and prescriptive analytics may be challenging.

    What Type of Data Analytics Do Companies Choose?

    For the 2016 Global Data and Analytics Survey: Big Decisions, more than 2,000 executives were asked to select the category that most accurately represented the decision-making process at their respective companies. The most popular types of analytics employed by the C-suite were also questioned. The results were as follows: Descriptive analytics led in the "rarely data-driven decision-making" group (58%), followed by diagnostic analytics in the "somewhat data-driven" category (34%), and predictive analytics in the "highly data-driven" category (36%).

    The poll results support ScienceSoft's real-world experience because they show that different sorts of analytics are required at different stages of a company's growth. For instance, businesses that intended to make informed decisions found descriptive analytics to be insufficient and added diagnostics or even predictive analytics. 2,800 executives were surveyed as part of BARC's BI Trend Monitor 2017 about the growing importance of advanced analytics. Predictive and prescriptive analytics were referred to collectively as "advanced analytics."

    What Type of Data Analytics Are Right For You?

    To choose the best mix of data analytics types for your business, we suggest answering the following queries:
    • What is the current data analytics scenario at my company?
    • How deep do I need to dive into the data? Is it clear what the answers to my problems are?
    • What is the difference between the data insights I currently have and the ones I need?

    These responses can help you choose a data analytics strategy. The strategy should, in theory, provide the gradual introduction of different analytics types, starting with the most straightforward and working up to the most intricate. The next step is to design a data analytics solution that uses the best technology stack and has a thorough implementation and launch roadmap.

    You could try to handle all of these responsibilities with the aid of internal workers. You will need to hire and train highly skilled data analytics experts in this circumstance, which will probably take time and money. To maximise the return on investment from adopting data analytics in your business, we advise contacting a knowledgeable data analytics provider with experience in your industry.

    A seasoned vendor will offer best practices and take care of everything, from analysing your current data analytics status to choosing the right combination of data analytics to realise the technology solution. Our data analytics services are available to you if the method described above appeals to you.

    Frequently Asked Questions

    Ques. How do data analytics work?
    Ans. The tools that businesses use to analyse raw data in order to make informed decisions about their strategy and performance are referred to as data analytics. Numerous methods and technologies are used in data analytics, many of which are automated using algorithms. In enormous amounts of data, these algorithms might quickly spot some trends and indicators that would otherwise go missing.

    Ques. What is the price of data analytics?
    Ans. An organisation can use data analysis software like SAS or SPSS, work with a custom consulting company like IQR Consulting, or develop in-house data analytic capabilities to satisfy its analytical demands. Nowadays, businesses use a mix of the aforementioned techniques.

    Ques. How soon should I implement an analytics strategy?
    Ans. Analytics is not a one-time or special-event activity; it is a continuous process. Analytics should not be overlooked by businesses, and they should work to integrate it into daily operations. The business needs to establish data collecting, cleaning, and analysis as normal tasks that assist functions that don't have the appropriate expertise.

    Disclaimer: This content was authored by the content team of ET Spotlight team. The news and editorial staff of ET had no role in the creation of this article.
    The Economic Times

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