Business Decisions Informed by Big Data Analytics

Making Business Decisions with the Help of Big Data Analytics

Many sectors employ big data and business analytics to support in-the-moment business decisions. Data scientists are devoting time and resources to researching the numerous uses of big data management and storage as a result of the big data analytics’ rising relevance.

Big Data Analytics: What Is It?

There is a continual flow of fresh data being generated every second due to the pervasiveness of artificial intelligence (AI)-driven technology, mobile devices, social media usage, and the Internet of Things. Big data refers to the information produced by sensors, transactions, smart devices, and online activity.

No matter what form of data it is or where it originates from, big data analytics is the capacity to gather, organise, and process it quickly. Big data analytics handle structured, semi-structured, and unstructured data and help organisations access, utilise, and profit from it.

Modern solutions like NoSQL databases are used to manage terabytes to zettabytes of incoming data because traditional relational databases are unable to handle huge data.

Different kinds of analytics are important for various industries:

Cyber, Prescriptive, Diagnostic, Descriptive, and Predictive

Big data analytics’ importance to business

Why is big data analytics so important to the success of businesses? Effective big data analytics has an impact on decision-making speed, which improves operational effectiveness and lowers costs.

Think about the four strategic outcomes that big data analytics addresses.

strategic choices based on data

Big data analytics allow for the use of up-to-date data in company choices since they continuously examine data from all user sources. This makes it possible for an organisation to spot pertinent trends, insights, correlations, patterns, and client preferences.

While some businesses rely on stream processing solutions for real-time analysis, others employ batch processing to analyse massive data. Both approaches use big data analytics to aid in the decision-making process, but stream processing offers quicker answers at a higher cost.

Cut expenses

Cost-saving business decisions may be made using the most recent and pertinent data. Numerous advantages that directly or indirectly lower costs are unlocked by using big data analytics services to improve company intelligence, including:

 • Daily operational choices result in less waste and more revenues

Customer happiness improves (affecting return rates and average customer value), processes speed up, and productivity rises.

management of risk

Business intelligence and big data analytics also improve risk management initiatives. Most of the data being received for analysis will come from outside sources. Data scientists examine both internal and external data to acquire a broad overview of the state of the sector.

In order to identify and mitigate risk, organisations rely on AI technologies and machine learning to process and analyse enormous volumes of data in real-time. Big data analytics is playing an increasingly important role in risk management, from seeing fraud sooner in the financial services sector to spotting issues in production and manufacturing.

Product development Businesses must continuously evaluate client demands as they develop services and goods. The more thorough the assessment, the better because clients’ demands frequently change fast. Both consumer happiness and efficiency in their creation and delivery must be reflected in products and services.

Things to think about

Big data analytics have numerous advantages, but they also present management issues with the generated databases. Know the probable obstacles and their fixes for each of the following.

Security

Big data analytics produces a valuable asset—a vast data mine that is very alluring to potential attackers. It’s critical to identify fraud and breaches quickly. Good security practises and rules are the first step in securing resources against unintentional sabotage as well as inadvertent loss or modification. It also depends on the technologies and platforms an organisation utilises and their security protocols.

accessibility to data

Giving important stakeholders access to examine and utilise data as needed is referred to as making data accessible. Tools for visualisation may be quite helpful in addressing these needs. Take into account the needs of the team members in your organisation who will be converting information-querying requests into reports.

choose the appropriate platform

Make sure you select the appropriate platform for this sort of analytics, one with all the tools and capabilities you need to keep data organised and safe, in order to categorise data efficiently and acquire the necessary search results in big data. Take into account your unique analytic requirements, who would have access inside your organisation, and what security measures will keep your information safe.

Who is big data analytics for?

We’ve looked at the importance of big data analytics, but who is it for? Big data applications may help businesses in almost every sector make better decisions and streamline operations. Industries across all sectors—retail, healthcare, and government—understand its importance.

Here are just a few examples of big data analytics at action in various industries:

Fraudulent transactions provide a persistent risk to the financial sector. Banks may use predictive analysis to identify patterns and spot fraud early. They can take action themselves in response to these abnormalities or alert account holders to possible problems. Furthermore, bank marketing can gather information on consumer spending patterns to target certain offers at clients at the precise time when they are most likely to want products (loans, credit cards, and other financial programmes).

Retail

The retail sector has various prospects thanks to big data analytics. Extending company intelligence through big data analytics saves time and money by monitoring supply chains and personalising marketing initiatives.

By taking into account elements like population, demographics, geographical accessibility, and more, data scientists may also use big data analytics to define potential locations for a company to grow.

Healthcare

In a hospital context, linked sensors and devices can measure more fresh data, but medical history is just as crucial for deciding the best patient treatment. Big data analytics assist in processing and gaining access to patients’ overall health. Real-time social patterns captured and analysed by big data business analytics may help guide public health recommendations, as was demonstrated during the pandemic.

production, and more

Big data analytics are widely used in the manufacturing sector and offer producers the knowledge they need on the state of their products at all stages of development. Big data analytics provide the feedback required to maintain productivity, safety, quality, and efficiency while monitoring manufacturing. Large data sets guarantee that manufacturers have a full picture.

We’ll wrap off with just two more big data analytics applications because the possibilities are almost unlimited.

• Customer choice information is used by media streaming services to provide entertainment options and raise satisfaction.

• Big data analytics are used by government organisations to make decisions on policies relating to national security, housing, and agriculture.

Cloud Lyve: Store and examine without boundaries

Understanding the value of big data analytics is only the beginning. Success in adoption depends on choosing the provider that best suits your requirements. Seagate® LyveTM Cloud offers everything you need for worry-free storage and analysis to deal with data expansion and sprawl—a significant problem for many enterprises seeking to manage big data.

Enterprises can more effectively use big data analytics thanks to Lyve Cloud’s ability to scale data up or down as needed, with predictable capacity-based pricing, best-in-class availability and durability, and seamless data transfer across public and private cloud environments without additional fees for egress or API calls.