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Data is a vital part of every organization. While not all data is used for analysis, analytics can’t happen without data. The tools needed for managing data, using it in different ways, and analyzing it come in a wide range. This explains why the term “data and analytics” (or “data analytics”) is used differently by organizations and vendors.
When we talk about “data,” we mean using that data in day-to-day operations, like in business applications such as banking systems, resource planning, or customer service. On the other hand, “analytics” (also called “data analytics”) refers to using data for analysis, often after transactions have taken place.
Table Of Contents
- What Is Data and Analytics?
- What is the role of data and analytics in business?
- What are examples of data and analytics use cases in business?
- How do you create a data and analytics strategy?
- What is the future of data and analytics technologies?
- What is the future of data and analytics technologies?
- What are the core types of data and analytics?
- What is “big data” analytics?
- How does big data analytics work?
What Is Data and Analytics?
It involves how companies handle data to assist in various tasks and analyze information for better decision-making, improving business operations and results. This includes identifying new business risks, challenges, and opportunities.
What is the role of data and analytics in business?
The purpose of D&A is to help businesses, their employees, and leaders make better decisions and improve decision outcomes. This applies to all sorts of decisions, like big-picture ones, small details, quick decisions, regular ones, long-term plans, short-term tactics, and day-to-day operations. It can lead to new questions, creative solutions, and opportunities that leaders hadn’t thought of before.
Forward-thinking companies use data in various ways and often need data from sources beyond their direct control to make smarter business choices.
D&A is crucial for digital transformation because they enable faster, more accurate, and more relevant decisions in complicated and fast-changing business environments.
Both individuals and teams in organizations make decisions. For instance, an individual decides whether to buy something, while a business team figures out the best way to serve a customer or citizen.
Making decisions based on data means using information to figure out how to improve decision-making processes. This involves creating a decision model, which can include techniques that suggest specific actions to take. Different types of analytical models can help with different kinds of decisions, such as describing a situation, diagnosing problems, predicting outcomes, or suggesting actions
Decisions not only lead to action but also determine when it’s best not to act.
Forward-thinking companies are integrating D&A into their business strategies and digital transformations. They do this by envisioning a data-driven business, measuring and sharing business results, and encouraging business changes fueled by data.
Read More: Analytics Across Pharma and Life Sciences
What are examples of data and analytics use cases in business?
Expanding digital business makes decision-making more complex and calls for a blend of data science and advanced techniques. Having both predictive and prescriptive abilities helps organizations adapt quickly to changing needs and limitations.
The complexity of a problem decides whether and how to use prediction, forecasting, or simulation for predictive analysis. (You can also refer to “What is advanced analytics?” and “What are core analytics techniques?”)
Here are some examples showing how predictive forecasting and simulation, along with prescriptive abilities, can be combined:
Predicting the risk of infection during surgery and using set rules to take actions that reduce the risk.
Forecasting orders for products and using optimization to proactively handle shifts in demand across the supply chain, without relying solely on incomplete or unreliable historical data.
Simulating customer segmentation based on risk and using optimization to quickly evaluate various scenarios and decide the best response strategy for each.
Businesses use D&A differently for various decisions. To make better business choices, leaders must understand when and why to complement human decision-making with the strength of data, analytics, and AI.
How do you create a data and analytics strategy?
Every organization should consider what role D&A play for them and what projects and budgets are needed to take advantage of these opportunities.
The key steps to plan a D&A strategy are:
- Start by understanding the mission and goals of your organization.
- Figure out how D&A can impact those goals strategically.
- Prioritize actions that will help achieve business goals through D&A.
- Create a strategic roadmap for D&A.
- Implement the roadmap through projects, programs, and products using a consistent and up-to-date operating model.
- Communicate the D&A strategy, its impact, and results to gain support for execution.
The enterprise model for D&A should also address gaps in the data ecosystem, data architectures, organizational approaches to delivery, and skills such as data analysis, data science, and data engineering, which are necessary to carry out the D&A strategy effectively.
How do you create a data and analytics strategy?
Every organization should consider what role D&A play for them and what projects and budgets are needed to take advantage of these opportunities.
The key steps to plan a D&A strategy are:
- Start by understanding the mission and goals of your organization.
- Figure out how D&A can impact those goals strategically.
- Prioritize actions that will help achieve business goals through D&A.
- Create a strategic roadmap for D&A.
- Implement the roadmap through projects, programs, and products using a consistent and up-to-date operating model.
- Communicate the D&A strategy, its impact, and results to gain support for execution.
The enterprise model for D&A should also address gaps in the data ecosystem, data architectures, organizational approaches to delivery, and skills such as data analysis, data science, and data engineering, which are necessary to carry out the D&A strategy effectively.
Read More: Build A Winning Data Strategy With Marlabs
What is the future of data and analytics technologies?
In the past, the teams managing D&A were separate entities. They used different technologies, with data management tools being distinct from analytics tools. However, this is changing in several ways. For instance, data management platforms now often include analytics features, especially machine learning (ML).
Analytics and business intelligence (BI) platforms are also evolving to include data science capabilities. New platforms are emerging to cater to specific functions like data visualization or governance for D&A. The rise of cloud service providers adds another layer of complexity, as they increasingly dominate the platforms on which these services run.
Traditional platforms across the data, analytics, and AI markets are struggling to keep up with the growing number of use cases. This forces organizations to weigh the high costs of existing on-premises solutions against the need for more resources and advanced capabilities. For example, new capabilities like natural language queries, text mining, and analyzing unstructured data are becoming essential.
The future of D&A requires organizations to invest in flexible, enhanced data management and analytics architectures to support advanced analytics. Modern D&A systems and technologies are likely to include:
Data management solutions:
- Master data management (MDM): Ensures consistency, accuracy, and governance of shared master data assets.
- Data hubs: Facilitate data sharing and governance, acting as a centralized hub for producers and consumers of data.
- Data centers: House servers are evolving based on the benefits of cloud migration.
- Data warehouses: Collect detailed transactional data for predictable analyses.
- Data lakes: Store unrefined data for exploration and analysis, complementing other systems.
Data fabric:
- An emerging design for data management that enables seamless integration and sharing across diverse data sources.
D&A in the cloud:
- Cloud platforms offer increased value, simplicity, and agility for data modernization and handling complex analytics.
- They support various D&A components like data ingestion, integration, modeling, optimization, security, quality, governance, reporting, data science, and ML.
Organizations must carefully consider their evolving needs and choose a mix of technologies that best suit their use cases as they move forward with D&A strategies.
What are the core types of data and analytics?
It can be broken down into four main types:
- Descriptive analytics: This uses tools like business intelligence (BI) and data visualization to answer questions like “What happened?” or “What is happening?” For example, it can help procurement teams track spending on different commodities and identify key suppliers.
- Diagnostic analytics: This involves deeper data mining to answer “why did X happen?” For instance, sales leaders can use it to understand the behaviors of successful sellers.
- Predictive analytics: This deals with forecasting probabilities and predicting outcomes over time. It helps answer “What is likely to happen?” but doesn’t suggest what actions to take.
- Prescriptive analytics: This type aims to find the best way to achieve a desired outcome or influence it. It combines with predictive analytics to suggest actions, answering questions like “What should be done?” or “How can we make a certain outcome happen?”
Using techniques like machine learning, and prescriptive analytics helps generate actionable plans. Combining predictive and prescriptive capabilities is crucial for solving business problems and making smarter decisions. Understanding these types of analytics helps organizations identify the skills, infrastructure, and technologies needed to become truly data-driven, especially as analytics merge with artificial intelligence (AI) capabilities.
What is “big data” analytics?
Big data refers to huge amounts of different types of data—like structured, unstructured, and semi-structured data—that keep coming in fast and in large quantities. Big data is usually measured in terabytes or even petabytes. Just to give you an idea, one petabyte is equal to a million gigabytes. For example, a single HD movie takes up about 4 gigabytes of data. So, one petabyte could store about 250,000 movies. Large datasets can range from hundreds to thousands to even millions of petabytes.
Big data analytics is all about finding patterns, trends, and connections in these massive datasets. To do this kind of analysis, you need specific tools and technologies, as well as a lot of computing power and storage space that can handle such a large scale of data.
How does big data analytics work?
Big data analytics involves a structured process consisting of five key steps to analyze large datasets effectively.
Step 1: Data Collection involves identifying and gathering data from various sources, following either ETL (Extract Transform Load) or ELT (Extract Load Transform) processes. ETL transforms data into a standard format before loading it into storage, while ELT loads data into storage first and then transforms it as needed.
Step 2: Data Storage is crucial and can include options like cloud data warehouses or data lakes, depending on the complexity of the data. Data warehouses are optimized databases for analyzing relational data from transactional systems, with predefined structures for efficient searching and reporting. On the other hand, data lakes store both structured and unstructured data without predefined schemas, allowing storage without precise design considerations.
Step 3: Once data is stored appropriately, the Data Processing step converts and organizes data for accurate analytical queries. This can involve centralized processing on a dedicated server, distributed processing across multiple servers, batch processing for accumulated data, or real-time processing for continuous data streams.
Step 4: Data Cleansing is an essential step to scrub data for errors like duplications, inconsistencies, redundancies, or incorrect formats, ensuring that only relevant and accurate data is used for analytics.
Finally, data analysis transforms raw data into actionable insights through various types of analytics:
- Descriptive analytics helps understand what happened or is happening in the data environment through techniques like data visualization.
- Diagnostic analytics delves deeper to understand why something happened, utilizing techniques such as drill-down and data mining.
- Predictive analytics uses historical data to forecast future trends, employing techniques like machine learning and predictive modeling.
- Prescriptive analytics goes a step further by not only predicting outcomes but also suggesting optimal responses and potential implications of different choices, utilizing techniques like simulation and recommendation engines.
By following these steps and leveraging the right tools and technologies, organizations can extract valuable insights and make informed decisions from their big data analytics efforts.
Also Read: Data is the future. But what is the future of data?
Frequently Asked Questions About Data and Analytics
Data and analytics (D&A) help companies manage data, analyze information, and improve decision-making, operations, and results. This includes spotting new business risks, challenges, and opportunities.
Descriptive analytics uses BI tools and data visualization to answer “What happened?” Diagnostic analytics involves deeper data mining to answer “why did X happen?” Predictive analytics forecasts probabilities over time, while prescriptive analytics suggests actions to achieve desired outcomes.
Big data analytics uncovers patterns, trends, and connections in huge datasets, requiring specialized tools, significant computing power, and ample storage space.
Data analytics assists businesses in improving performance, efficiency, profitability, and strategic decision-making. These techniques are automated into algorithms that process raw data for human use.
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