What do you mean by Data Analytics?
Data and analytics refer to the management of data for operational and analytical uses. In data analytics, data is analyzed to drive business processes and improve business results through highly effective decision-making and enhanced customer experience (CX). To put it in better words, data analytics is the process of extracting meaning from raw data sets. Data analytics companies examine data sets to explore trends and draw conclusions about the information contained by these data sets. Data analytics is increasingly used with the help of specialized software and systems. Data analytics organizes, groups, and streamlines data through advanced algorithms to reveal patterns while delivering business insights. Data analytics technology integrates machine learning (ML) algorithms, automation, and artificial intelligence (AI).
Primarily, data analytics can be defined as a range of applications, right from basic business intelligence (BI), online analytical processing (OLAP), and reporting to multiple forms of advanced analytics. In this context, it is similar to business analytics in its nature.
There is wide use of data analytics technologies and techniques in commercial industries. Organizations can thus make well-informed and prudent business decisions. Scientists and researchers also use them to disapprove or verify scientific models, hypotheses, and theories. Irrespective of their size and type, businesses may benefit from the information and insights that data analytics provides to them. This way, businesses can comprehend past trends and forecast future patterns while empowering business leaders to make informed decisions. The information that best data analytics companies gather from data analytics programs helps businesses better understand customers. Through a better understanding of customers, businesses can personalize products, services, and content they offer to their customers. Data analytics plays a crucial role in boosting business performance and lending businesses – a competitive edge. Besides, data analytics helps businesses improve operational efficiency, increase revenues, optimize marketing campaigns, and accelerate their customer service efforts.
The Data Analytics Process
The data analytics process, or data analysis steps, consists of collecting all the information, processing it, exploring the data, and using it to explore patterns and other insights. The following steps make up the data analytics process:
Data Requirement Gathering: In this step, data analytics companies see why data analysis is done, the form of data analysis that should be used, and data planned for analysis. Criteria are also determined to group the data. Data can be classified by various criteria, like age, income, population, or sex. The data values may relate to numerical or categorical data.
Data Collection: Once you have identified your data requirements, you should collect the data through multiple sources. Sources can be surveys, case studies, direct observation, questionnaires, interviews, and focus groups.
Data Organization: After the data has been collected, you should organize it to analyze it further. Best data analytics companies use a spreadsheet for data organization or other software, taking statistical data into account.
Data Cleaning: Not every data you gather will be of use to you, so you should clean it up. In the process of data cleaning, you get rid of redundant records, white spaces, and basic errors. Data cleaning is a must before sending the information further for analysis.
Data Analysis: In this step of the data analytics process, you use data analysis software and other tools that help you interpret and comprehend the data to arrive at conclusions. A few of the data analysis tools that best companies for data analytics use include Microsoft Power BI, Python, Excel, Looker, R, Chartio, Rapid Miner, Redash, and Metabase.
Data Interpretation: Once you have your results post data analysis, you should interpret them and bring up the best courses of action, basis your findings.
Data Visualization: In this step, data analytics companies in the US graphically depict your information in a manner that people can read and understand. They use graphs, charts, bullet points, maps, and several other methods. As you compare datasets and also look at relationships, visualization enables you to obtain valuable business insights.
Forms of Big Data Analytics
Data analytics is considered to be a broad field. Among the four fundamental forms of data analytics discussed below, each form has a different goal and a unique place in the process of data analytics. These forms are also the primary data analytics applications in business.
Descriptive Analytics – Descriptive Analytics works with either entire or selections of summarized numerical data. It demonstrates means and deviations in continuous data and frequencies as well as percentages in numerical data. This form of data analytics enables you to answer questions on what happened. Such techniques summarize big datasets to explain outcomes to stakeholders. As you develop key performance indicators (KPIs), these strategies help track successes or failures. Several industries use metrics like return-on-investment (ROI), and then there are specialized metrics to track performance in some specific industries.
Prescriptive Analytics – Prescriptive Analytics assists you in answering questions about what should be done. In this form of big data analytics, there is a mix of all the insights gained from the other forms of data analytics. For instance, as data analytics companies use insights from predictive analytics, they can make data-driven decisions. Businesses can thus make informed decisions in uncertain times. Prescriptive analytical techniques are used when an issue can’t be resolved only with one form of analysis and needs multiple insights. These techniques depend on ML strategies that can discover patterns in large datasets. As you analyze past events and decisions, you can anticipate the probability of varied outcomes.
Predictive Analytics – Predictive analytics stresses the events expected to happen in the immediate future. It helps answer questions about what will happen in times to come. Predictive analytical techniques use historical data to explore trends and ascertain if they are likely to happen again. These techniques are various machine learning (ML) and statistical methods, including decision trees, neural networks, and regression. Predictive analytical tools offer valuable insights into what may take place in the future.
Diagnostic Analytics – Diagnostic Analytics helps answer questions about why things took place the way they did. Diagnostic, analytical techniques complement more basic descriptive analytics. Deducing the findings from descriptive analytics, diagnostic, analytical techniques, dig deeper to figure out the cause. The performance indicators are investigated further to find out whether they became any better or worsened with time. Big data analytics companies probe into the performance indicators in this way. First of all, anomalies are detected in the data, like there may be unanticipated changes in a metric or a specific market. Then, we gather the data that relates to these anomalies. After that, statistical techniques are used to discover trends and relationships, explaining these anomalies.
Data Analytics Technologies
There is a growing volume of data and advanced analytics technologies providing deeper and valuable insights faster. Few of the key technologies that power data analytics and best companies in data analytics use include:
Machine Learning: Machine learning is a subset of artificial intelligence (AI). It involves algorithms that can learn on their own and are significant for data analytics. ML gathers data, analyses patterns, and predicts results. ML enables applications to encompass data and analyze it to predict results. Through machine learning, you may train an ML algorithm on a small sample of data, and the system continues to learn as it collects more data, getting more accurate as time passes.
Data Mining: Data mining is the process of classifying large amounts of data to discover patterns and explore relationships between data points. It uses scientific and mathematical models and methods to explore trends or patterns of the mined data. Through data mining, data analytics companies in the US can sift through large datasets and find out what is relevant and what is not. As you mine relevant information, you can easily and quickly conduct analyses, one that informs your business decisions and makes them smarter.
Learn how to harness the power of data mining to make smart business decisions.
Data Management: Data management refers to the process of establishing data standards to ensure storage, quality, organization, and maintenance. Before you effectively analyze the data, it should be high quality and well-organized. There should be processes to manage data flow in and out of your systems and keep your data organized. Organizations should access the data when needed, which is why they should collect the high-quality data in a central data management platform (DMP). As data analytics companies help you set up a data management program, you can rest assured that your organization is on the same page on organizing and tackling data.
In-memory Analytics: In-memory analytics is an agile data analytics technology that queries data when it resides in the random-access memory (RAM) of a computer, unlike querying data stored on physical disks. When the cost of RAM decreases, in-memory analytics becomes suitable for businesses. In-memory analytics results in largely shortened query response times, enabling faster implementation of analytic and business intelligence (BI) applications to support quick business decisions. Besides, in-memory analytics may mitigate or eliminate the need for data indexing. You may not also need to store pre-aggregated data in aggregate tables or OLAP cubes. All this leads to reduced IT costs.
Text Mining: Text mining is an artificial intelligence technology that uses natural language processing (NLP). Using NLP, text mining transforms the free or unstructured text in databases and documents into structured, normalized data appropriate for analysis or driving ML algorithms. The structured data that text mining creates can be integrated into data warehouses, databases, or BI dashboards. This data is used for descriptive, prescriptive, or predictive analytics. Through text mining, data analytics consulting companies identify facts, assertions, and relationships. Text mining has become highly pragmatic for data scientists and other users owing to the development of big data platforms and deep learning algorithms, analyzing large sets of unstructured data. As organizations mine and analyze text through big data analytics companies, they explore valuable business insights.
Benefits of Data Analytics
As per a 2020 MicroStrategy survey, 90% of business users opine data analytics to be indispensable to accomplish the digital transformation goals of their organization. Using data analytics tools, big data analytics companies assist businesses to minimize costs, enhance customer experience, improve efficiency, and maintain a competitive edge.
1. Improves Decision Making
Organizations may use the information they get from data analytics to guide and make decisions, leading to improved results. To put it in better words, business leaders obtain meaningful and reliable insights that empower improved decision-making through data analytics. There is no more guesswork, and organizations can efficiently prepare marketing plans, produce goods, and deliver their customers’ services. Top data analytics companies use advanced data analytics technologies to collect and analyze data continuously, thus better comprehending changing situations.
2. Enhances Customer Experience
Business data analytics offers you more insights into your customers. Data helps reveal information related to your customers’ habits, interests, preferences, issues they face, and much more. Thus, you can enhance customer experience, customize customer service to their needs, and strengthen relationships through personalization. Besides, maintaining a central location for your data guarantees that your entire customer service team, including marketing and sales teams, are on the same page.
3. Increases Operational Efficiency
Data analytics companies aid businesses in streamlining their operations, saving resources, and improving the bottom line. Through data, businesses can better understand their target audience’s needs, thus mitigating costs and investing their resources only where required. They can even forecast fluctuations in the workforce, customer habits, or supply chain.
4. Mitigates Risks
Data analytics aids businesses in understanding risks and adopt preventive measures if required. For instance, a retail chain may decide to run a propensity model to determine which stores are at the highest risk for theft. Businesses may use this data to ascertain the kind of security needed at the stores or if they should divest from any location. Besides, businesses may use data analytics to check on their losses if a setback occurs. Business data analytics enables you to ascertain the optimal price for a clearance sale to reduce inventory. You may even create statistical models that automatically recommend how to resolve repeated problems.
5. Builds Better Security
Businesses encounter data security threats. As they use data analytics, they can diagnose the causes behind data breaches in the past by processing and visualizing related data. For instance, the IT department may use data analytics applications to analyze, process, and visualize their audit logs. This helps them to find out the origins and course of an attack. The IT department can thus quickly locate vulnerabilities and patch them.
In addition to the benefits discussed above, data analytics leads businesses to increased innovation. Through improved insights into market trends, businesses can innovate faster and develop cutting-edge and customized products.