More businesses are increasing revenue by delivering value to customers faster, and cutting costs by optimizing operations. How do they do it? They’re sharpening their competitive edge with data analytics.
Data analytics defined.
Data analytics is the process of gleaning insights from data that is extracted, transformed, and centralized to discover and analyze hidden patterns, relationships, trends, correlations, and anomalies, or to validate a theory or hypothesis.
In the past, data was analyzed to make future decisions. Today, data can be analyzed to make real-time decisions, spot emerging trends and uncover insights that would not be evident using legacy data processes.
The business case for data analytics.
In a recent industry survey, respondents from five countries indicate that the top uses for data and analytics are: to drive process and cost efficiency (60%), to drive strategy and change (57%), and to monitor and improve financial performance (52%).
Respondents also said that the top three trends with the most significant impact on their analytics initiative are: cloud computing, big data, and artificial intelligence/machine learning. Cloud computing, in particular, impacts the speed at which businesses can get insights from data, and this speed has created a culture where customers are empowered to expect more. They expect better products and better services, and they expect it now.
Benefits of data analytics.
Data analysis can help improve business processes. The data can provide a clearer picture of what’s efficient and what’s not, and analysts can drill deeper into the data to discover root causes.
Data analysis boosts revenue by allowing people to make faster and more informed decisions. With enough data to analyze, businesses can predict customers’ behavior, understand their needs, and respond in real-time by changing or adding products to meet the indicated demand. This can result in a competitive advantage, improved customer experiences, and improved acquisition and retention of customers.
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Data analytics process.
Taking advantage of the benefits of data analytics requires a business unit to get its data house in order so that accurate, reliable information is available for analysis.
The first step in the data analytics process is to determine what data is needed to support the organization’s business objectives. Generally, companies use internal data, supplemented by data from outside sources. The data is then organized into logical groups.
The next step is to collect the data into a central location for analysis, generally a data warehouse. This is a technical process that involves matching data elements from source databases to the warehouse. Each field is mapped from the source to the destination, and formulas are applied to convert data formats to meet the requirements of the data warehouse.
Analyzing different types of data.
Legacy systems are good at analyzing structured data, but traditional methods are not designed to extract value from unstructured data. Modern data analysis is able to combine structured and unstructured data to add depth and context.
Structured data is organized in a relational database in a way that’s easy for traditional technology to process and manipulate. Examples of structured data include: phone numbers, ZIP codes, currency or dates. Structured data tends to reflect the past, which is great for historical analysis.
Unstructured data includes things like: email, social media posts, articles, satellite imagery or sensor data. It may be stored within a non-relational database like NoSQL. Unstructured data better reflects the present, and can even help predict the future.
Once the data is collected, it is validated to discover and fix data quality problems that may affect the quality of the analysis. This includes running data profiling processes to ensure the dataset is consistent and complete, and running data cleansing processes to ensure duplicate information and errors are eliminated.
The data now is ready for analysis using data visualization tools to discover hidden correlations, patterns, and trends that can be used to drive business decisions.
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Six types of data analytics.
The types of data analytics range from descriptive to advanced, and an organization may choose one (or more) of these types based upon its own stage of development or decision-making processes. Organizations that are not data-driven, or those that make decisions reactively, may rely on descriptive analytics for reporting purposes. But data-driven organizations that must make decisions quickly likely will rely on predictive/prescriptive analytics.
Descriptive analytics reports what happened in the past, and is the most common type of analysis offered through traditional technology. Examples include inventory counts, production numbers, average dollars spent per customer, and year-to-year changes in sales
Predictive analytics predicts what may happen in the future based on present data. For example, predictive analytics can anticipate customer behavior, equipment failures, or how the weather might impact sales. Predictive analytics also is used in fraud detection, optimizing marketing campaigns, and forecasting inventory. Credit scores are an example of predictive analytics, showing the likelihood of default based on past behavior.
Prescriptive analytics is on the leading edge of data analytics. It prescribes a course of action to solve a problem or take advantage of an opportunity. It can assess a variety of possible outcomes based on taking specific actions. This is an extension of predictive analytics. Once the future is predicted, prescriptive analytics suggests possible steps to avoid a problem or seize an opportunity.
Data mining is a form of advanced analytics. It is the process of turning unstructured data into useful information like patterns, correlations, and anomalies. Data mining helps to find the proverbial needle in the haystack.
Artificial intelligence (AI) and machine learning (ML) are also considered advanced analytics. AI is a computer’s ability to process information in a human way, such as understanding a question and being able to answer. ML refers to a computer’s ability to program itself. AI and ML are a powerful combination that can remove the friction from the data analysis process by automating nearly every part of it, including finding new data sources, structuring data for analysis, and suggesting courses of action.
Text mining, another form of advanced analytics, supports natural language processing (NLP), which is the ability for a computer to read text or listen to language. AI systems scour the web regularly to find new information to support an organization’s analytics goals, or text can be scanned from books and documents as research material for the system.
What is big data analytics?
Big data is the catch-all term used to describe gathering, analyzing, and storing massive amounts of structured and unstructured digital information to improve operations. Big data analytics is the process of evaluating that digital information into useful business intelligence.
As big data gets bigger, more tools and techniques are coming online to make the process easier and more efficient. The cloud is the most practical environment for big data analysis as it’s designed to store massive amounts of data at a reasonable cost. It makes data analysis available to decision makers across an organization (not just IT or data experts) by inviting collaboration. The best tools for data analysis are moving to the cloud, and tool providers are putting more power into cloud versions of their software.
Migrating and integrating data into a data warehouse also is best done in the cloud. Extract, transform, and load (ETL) processes work seamlessly in the cloud to extract data from a source, transform it into a format compatible with the destination, and load into the warehouse.
Big data analytics for business focuses on extending traditional business intelligence and reporting to online analytical data processing (OLAP), which provides trend analysis, and advanced analytics such as predictive and prescriptive analytics.
There are a number of analysis tools in the cloud, such as Hadoop and NoSQL, that are designed to store, structure, and retrieve big data quickly. Hadoop is an open source platform for data analysis designed to run big data fast. It’s free and designed to run on commodity hardware — low-cost desktop workstations, or simple server hardware, that can run scaled-down database environments instead of upgrading to more expensive server equipment — which helps keep costs down.
Data analytics and the cloud: driving business forward.
Data analytics, including big data analytics, are helping businesses drive growth. A business that can turn its data into actionable insight may reap the benefits of improved processes, faster decision-making, increased productivity, clearer insight into how customers use their products, the development of new products and services, and more. The ways in which organizations can benefit from real-time, advanced analytics are still being discovered.
To get there, most businesses will need a data integration platform to tie their historical data into new data sources by connecting traditional, on-premises data and external data sources to a cloud-based data warehouse.
Talend’s cloud-based data management and analysis tools that streamline the process of getting data into a warehouse and extracting insights for decision making. Talend Data Fabric is a suite of data management apps that includes powerful tools to make cloud migrations successful. It provides access all of Talend’s solutions and capabilities from a single interface, providing consistency and control for all of your enterprise data.