How Companies Are Using Big Data and Analytics

Aashish Yadav | April 1, 2022 | 866 views

How Companies

Data are becoming the new raw material of business.”

Craig Mundie, Senior Advisor to CEO, Microsoft

Currently, the most valuable asset that a company has is data. By analyzing a large quantity of data and drawing valuable insights, companies can use raw materials (data) to work more effectively. In addition, many big data analytics case studies show that data gives businesses a big advantage over their less tech-savvy competitors.

Let’s explore more about big data and analytics in this article.


Why Do the C-Level Executives Need Big Data?

Every C-level executive is on the lookout for new insights that help them keep their company viable. In recent years, the use of data analytics has become crucial for business leaders to make important decisions.

According to McKinsey & Company, companies using big data analytics extensively across all business segments see a 126% profit improvement over companies that don’t. With the use of big data analytics, these companies see 6.5 times more customer retention, 7.4 times more outperformance than competitors, and almost 19 times more profitability. Here are some top reasons why the C-suite needs big data.

Take Calculated Actions

Harvard Business Review estimated that 70% of companies don’t feel that they understand the needs of their customers well enough to recognize what initiatives will drive growth. In such cases, you already know what you need to do, i.e., leverage big data and analytics.

Big data analytics for businesses can help in recognizing customer preferences and customer segments on the basis of those preferences. C-suites in any industry can align their structure and product offerings to create value and take calculated actions.

Recognize the Data

According to Statista, data creation will increase to more than 180 zettabytes by 2025, which is a huge number. So, you can’t keep an approach of ‘gather now and sort it out later.’ With this approach to big data, you will be buried under tons of non-structured data. Start tracking the data early and capture the ones that are customer-generated and provide value to your company.

Segment Your Customer’s Experience

Analyze your present data and utilize your analytics to evaluate which characteristics a group of customers have in common and which aspects they don’t share. Segment and organize customers according to their preferences to build a clear lifecycle structure for every segment.

Biggest Concerns About Big Data Analytics

According to Concepta, 80% of C-suites think that data analytics will be a transformative force for businesses, but only 1 in 10 deliberately use it. 48% describe analytics as critical to decision-making, but only 7.4% say they use analytics to guide corporate strategy. So, what are the issues or concerns that tech-savvy C-level executives face when it comes to big data and analytics?

Integrating Data with Current Technology

"Tech inertia" usually disrupts certain businesses from evolving. Sometimes, the analytics framework businesses have in place is outdated to accommodate new techniques. According to Concepta, more than half the C-suite feel their analytics infrastructure is too rigid, and 75% say that due to inflexibility, they could not fulfill their business needs. Changing or upgrading the current technology would result in a loss of productivity.

Companies must get the appropriate tools like Oracle Data Integrator 12c, SAP Data Services, MuleSoft, etc. to handle their data integration challenges. Another option is to seek professional assistance. You may either engage seasoned specialists who are far more knowledgeable about these instruments. Another option is to hire big data consultants.

Big Data Silos

There is a lot of unstructured data that is collected by different departments within a company, which leads to big data silos. The C-suite plays a critical role in developing a strategy, ensuring all departments communicate and integrate data from various sources to get a holistic picture of their business operations.

  • Integrating your software that collects and stores data correctly is one of the most effective ways to avoid data silos
  • Make a decision to use an all-in-one tool to unify and speed up your data management
  • Spare some time to filter your outdated data

Big Data Security

Big data security is one of the most difficult tasks. Businesses are often so preoccupied with understanding, storing, and analyzing data that they overlook data security. Unsecured data repositories may become fertile grounds for malicious hackers. A data breach may cost a company up to $3.7 million.

Businesses are hiring more cybersecurity experts to protect their data. Other measures taken to secure big data include: encryption of data, data segregation, identity and access management endpoint security implementation, real-time security monitoring, and use of big data security technologies such as the IBM Guardian.


Key to Big Success from Big Data

To get the most out of your big data and overcome the associated challenges, we have listed some key pointers that make a business successful and show how companies using big data are standing out.


Have a Calculated Approach

While laying the foundation of big data and business analytics, it is important to have a calculated approach as it reduces the risk in the early stages of setting up big data analytics. So, rather than attempting to implement it all at once, businesses should focus on resources that drive value from big data.

Programmatic Integration

In an action-driven system, success demands synchronizing big data, relevant analytics, and decision-making platforms at the appropriate time. The most successful companies using big data get insights directly from the data analytics tools used by executives who can act immediately according to the insights from the data.

Focus on Building Skills

Businesses must expand the big data capabilities of current workers through training and development since data analytics talent still remains one of the major challenges. 54% of the CEOs say that their companies have already set up in-house technical training programs for their employees.

State-of-the-Art Technology

To create strong big data and analytics capabilities, you need the right tools and technologies. Unfortunately, those who don’t have access to efficient big data analytics tools like Hadoop find themselves falling behind.

Conclusion

There's no going back when it comes to technology. Business decisions and activities are now made based on the use of data, so businesses that don't learn how to use their data will soon be out of date because data is now at the heart of everything.

Businesses can align their data structures according to the requirements of their product offerings to generate value by utilizing big data and analytics. It helps to determine consumer preferences and segment consumers based on insights.

FAQ


How much data does it take to be called “Big Data”?

There is no definitive answer to this question. Based on the current market infrastructure, the minimum threshold is somewhere around 1 to 3 terabytes (TB). However, big data technologies are also suitable for smaller databases.

Do I Need to Hire a Data Scientist?

The decision to hire a data scientist for your company is often a difficult one, and it depends entirely on your business's position. While there has been a huge demand for data scientists over the last few years, they are not easily available. Many businesses just use the support of a data architect or analyst.

How are big data and Hadoop related to each other?

Hadoop and big data are almost synonymous. Hadoop is a framework that specializes in big data processing that has grown in popularity with the advent of big data. Professionals may use the framework to analyze large amounts of data and assist companies with better decision-making.

Spotlight

Lenovo

Lenovo is a US$50 billion Fortune Global 500 company, with 57,000 employees and operating in 180 markets around the world. Focused on a bold vision to deliver smarter technology for all, we are developing world-changing technologies that create a more inclusive, trustworthy and sustainable digital society. By designing, engineering and building the world’s most complete portfolio of smart devices and infrastructure, we are also leading an Intelligent Transformation – to create better experiences and opportunities for millions of customers around the world. To find out more visit https://www.lenovo.com, read about the latest news via our StoryHub and follow us on the social media platforms listed below.

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The Opaque Confidential AI and Analytics Platform is designed to specifically ensure that both code and data within enclaves are inaccessible to other users or processes that are collocated on the system. Organizations can encrypt their confidential data on-premises, accelerate the transition of sensitive workloads to enclaves in Confidential Computing Clouds, and analyze encrypted data while ensuring it is never unencrypted during the lifecycle of the computation. Key capabilities and advancements include: Secure, Multi-Party Collaborative Analytics – Multiple data owners can pool their encrypted data together in the cloud, and jointly analyze the collective data without compromising confidentiality. Policy enforcement capabilities ensure the data owned by each party is never exposed to other data owners. Secure Data Sharing and Data Privacy – Teams across departments and across organizations can securely share data protected in TEEs while adhering to regulatory and compliance policies. Use cases requiring confidential data sharing include financial crime, drug research, ad targeting monetization and more. Data Protection Throughout the Lifecycle – Protects all sensitive data, including PII and SHI data, using advanced encryption and secure hardware enclave technology, throughout the lifecycle of computation—from data upload, to analytics and insights. Multi-tiered Security, Policy Enforcement, and Governance – Leverages multiple layers of security, including Intel® Software Guard Extensions, secure enclaves, advanced cryptography and policy enforcement to provide defense in depth, ensuring code integrity, data, and side-channel attack protection. Scalability and Orchestration of Enclave Clusters – Provides distributed confidential data processing across managed TEE clusters and automates orchestration of clusters overcoming performance and scaling challenges and supports secure inter-enclave communication. Confidential Computing is supported by all major cloud vendors including Microsoft Azure, Google Cloud and Amazon Web Services and major chip manufacturers including Intel and AMD. About Opaque Systems: Commercializing the open source MC2 technology invented at UC Berkeley by its founders, Opaque System provides the first collaborative analytics and AI platform for Confidential Computing. Opaque uniquely enables data to be securely shared and analyzed by multiple parties while maintaining complete confidentiality and protecting data end-to-end. The Opaque Platform leverages a novel combination of two key technologies layered on top of state-of-the-art cloud security—secure hardware enclaves and cryptographic fortification. This combination ensures that the overall computation is secure, fast, and scalable. The MC2 technology and Opaque innovation has already been adopted by several organizations, such as Ant Group, IBM, Scotiabank, and Ericsson.

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