Power of Data: Navigating Big Data Analytics for Business Excellence
Power of Data: Navigating Big Data Analytics for Business Excellence
Big data is defined primarily by three V’s – Volume, Velocity, and Variety. Hence, Big data incorporates the analysis of massive volumes of datasets, coming at ultra-fast speeds (Velocity), from a variety of sources, including social media, sensors, and electronic financial transactions.
However, big data is nothing without analytics, necessitating big data analytics – the process that inspects, modifies, eliminates errors, and interprets vast and intricate data sets to reveal patterns, trends, and valuable insights.
Having big data and analyzing it is important for a business as it holds significant growth potential. Analyzing such volumes of varied data will reveal insightful information and patterns that a business can capitalize on to grow and make informed decisions.
The Evolution of Big Data in Business: Historical Context and Growth
Big Data rises from an aged background dating back to the earliest storage of information by humans in a single location – the Great Library of Alexandria in Egypt. However, it was not until the 1990s that David Ellsworth and Michael Cox, two NASA scientists, officially created the term. Subsequently, they used it to describe the challenge of analyzing and visualizing larger volumes of data from supercomputers.
In 2001, Doug Laney, a data analytics expert, offered more insight into big data in one of his published works. He titled it: 3D Data Management: Controlling Data Volume, Velocity and Variety.
The 21st century also saw big data evolve into a more complex resource for analysis with increasing web traffic. For instance, Facebook and Twitter emerged with more unstructured data in images, videos, GIFs, and direct messaging services. These images, videos, and messages were exchanged between users across the world. Specifically, they were exchanged in massive volumes, varieties, and high speeds, requiring no other revolutionary tool but big data analytics.
Core Components of Big Data Analytics Services
There are key components and stages at the heart of big data analytics services that define the productive outcomes and benefits businesses enjoy. These include the following:
Data Sources and Storage
Collecting data is the primary step in data analytics services and without gathering data, there will be no analytics. Therefore, businesses must assemble data from different sources, including social media, research publications, sensors, etc.
After gathering data, the next stage is to store it in a safe repository for easy access. Subsequently, big data can be fetched, processed, and analyzed for accurate decision-making.
Data Processing
Data processing is the refinement of data by changing its form and presenting it in a more easily identifiable variant for analysis. It involves the organization (arranging data into categories), cleansing (eliminating inconsistencies and flaws), integration (assembling data from varied sources), and transformation of data (presenting data in a consistent form).
Machine Learning and Predictive Analytics
Machine learning is one of the core elements of big data analytics that rely on algorithms to observe, study, and memorize patterns in large data sets. These patterns can range from customer spending habits to the type of music they love listening to on weekends. The computer learns of these habits via automated data analytics to provide businesses with insights on what customers want, why they want it, and when they want it. This system of data analytics is exactly what a music brand like Spotify deploys to suggest songs to listeners based on what they love listening to from previous records.
The ability to predict the future is a power every business will be proud to have and happy to use. This key component of big data analytics helps a business predict customer behaviors, and market trends and identify potential markets to explore using AI tools that study historical data, existing trends, and patterns.
At Infomineo, we exceed every client’s expectations by providing reliable, excellent, and unmatched big data analytics services.
Benefits of Big Data Analytics Services
Big data analytics is critical to the growth and expansion of businesses, especially in making consequential decisions. One wrong decision can. The right decision defined by valuable insights can transform a business for good, while the wrong one can set up a business for failure. With that said, here are some remarkable benefits of big data analytics services for businesses.
Enhanced Decision-making
Big data analytics services provide decision-makers with real-time data, which allows them to make informed decisions, initiate radical growth, and boost the Return on Investments (ROI) of their businesses. Consequently, they work with eyes on the past, present, and future, and predict future patterns via descriptive and predictive analysis. Thus, a business serves its clients better, gains their loyalty.
Cost Efficiency
Big data analytics services can reduce operational costs for businesses. For one, wrong decisions can cost businesses lots of money. However, with big data analytics, each decision comes with a profit potential. Predictive analysis can also help a business identify future losses and avert them.
Improved Customer Experience
Big data analytics via descriptive, diagnostic, and predictive analytics, and the use of artificial intelligence, can devise new ways to satisfy customers. Using AI and machine learning algorithms in big data analytics will identify customer pain points, their buying habits, preferences, and what they will even buy next. Armed with this information, a business can tailor its marketing campaigns, products, and services to address the dire needs of its customer base and improve their experiences.
Choosing the Right Big Data Analytics Services Provider
Choosing the right big data analytics services provider is a critical decision that can reward a business with the substantial benefits of big data. Hence, it is vital to conduct a thorough analysis of service providers before deciding on one. Here are some things to consider to find the right one for you.
Level of Specialization
When choosing, consider specialization as a primary criterion for selection. Some big data analytics providers specialize in specific niches like mobile data, social media data analytics, health, finance, or others, while others focus on big data in its comprehensive form and provide experts per niche.
Expertise and Prior Expertise
You would want to be sure the big data analytics services provider is not a newbie in the field. By evaluating the provider’s expertise, you can be confident that such a provider is the best fit for your big data analytics needs.
The following are some important questions you must ask to have an insight into the quality of service you’ll be getting and to evaluate the expertise of the provider:
- How long have they been in business?
- How many clients have they satisfied?
- What does their feedback section look like?
- How many big brands have they worked with?
- What are the latest technologies they are deploying?
Collaboration Models
When implementing big data, consider collaborative models that allow for the seamless collection of data across internal teams. From sales and marketing to customer service and auditing teams, a collaborative model facilitates easy assemblage of data. Subsequently, the collated data goes to the data analytics team for processing and provision of valuable insights.
At Infomineo, we help clients navigate the challenges of implementation with our team of data analytics experts.
Skip the stress of looking for a reliable big data analytics services provider that can specifically address your business.
Implementing Big Data Analytics in Your Organization
Implementing big data analytics in your organization will bring the necessary change that drives growth, boosts profit potential, and keeps your business in the competition. But how do you implement this profoundly beneficial big data in your business? Here are 5 integration strategies to utilize.
1. Evaluate Your Data Needs
Before making the bold step of implementation, evaluate your data needs by answering a couple of questions such as the following:
- What are the challenges that big data analytics services can solve in my business?
- What are the profitable opportunities that big data can uncover for my business?
- How much data does my business need to grow?
- What type of data would my business require?
- What are the goals and objectives I seek to achieve with big data analytics services?
Answering these questions will help you find the right big data analytics services provider, the type of analysis you need, your data sources, and other information you may require while integrating big data.
2. Select a Big Data Platform
Next, you need to choose a data platform to integrate into your existing IT infrastructure. Data platform here refers to the data infrastructure and software that will manage, process, analyze, and store your data for easy access. There are many options, including cloud, on-premise, or both (hybrid). Your choice will depend largely on your budget and the data needs you determined in the first step, in terms of room for scalability, compatibility with existing systems, performance, and features of the data platform such as data quality, security, and integration.
3. Choose Agile Implementation
It is paramount to consider agile techniques when implementing big data analytics. Agility in the business context is the ability of a business to instantly respond to changes as they happen without obstructing its day-to-day operations. Hence, using agile techniques will help your business adapt to changes that may arise within the implementation process, which may demand unforeseen changes. Agility not only keeps your business prepared for changes as they come but also in line with what’s trending.
4. Implement Big Data One Step at a Time
Depending on the size of your organization, you may have to implement big data analytics one step at a time, starting with your existing data. Delving into the process all at once and involving all data sets can be counter-efficient as the results will be too complex, unreliable, and challenging to analyze. Start the implementation process with your existing data sets, and work your way to the bigger data sets from varied sources.
Industry-specific Applications of Big Data Analytics
Big data analytics services have grown in prominence across different industries globally. Some industry-specific applications of big data include the following:
Banking
In the banking sector, big data analytics is a powerful resource for tracking the spending habits of credit card holders, detecting unusual patterns, and flagging suspicious activities that may lead to fraud. Banks also deploy it to keep an eye on employee performance, and measure and report KPIs, including its operational activities to manage risks.
Healthcare
Big data analytics services in the healthcare sector are having a huge impact in transforming healthcare service delivery globally. Big data analytics is the brain behind the prediction of global epidemic outbreaks, research speeds, electronic health records, and early detection of disease symptoms to avoid contamination. Also, some hospitals deploy big data analytics by utilizing data in smartphone apps from millions of patients to provide evidence-based medicine instead of the rigorous process of carrying out medical and lab tests on every patient.
Manufacturing
The manufacturing industry benefits from big data analytics services in diverse ways. Some examples include predicting product failure and maintenance costs, reducing production time, and assessing the best price point for both businesses and customers.
Retail
The retail industry is one of the biggest beneficiaries of big data analytics services. Data is sourced from Point of Sale (POS) scanners, Radio Frequency Infrared Devices (RFIDs), loyalty cards, inventories, and specific demographics. Afterward, they are used to market products, improve customer experience, retain customers, and even reduce fraud.
Government
The government is perhaps the biggest collector of big data. For example, The FBI uses it to keep an eye on different markets and identify criminal business transactions, and the Federal Housing Authority uses it to predict mortgage repayment rates and defaulters. Also, Government agencies like the IRS rely on big data analytics services to detect and limit tax fraud.
Challenges and Solutions in Big Data Analytics Services
Big data services can influence positive change in the profit landscape of any business, however, it is not free of challenges. Its massive volume, ultra-fast speed, expansive variety, and sheer complexity are making it difficult for businesses to integrate it into their existing operational systems. Some of the challenges of big data and how to address them include the following:
Data Security
With big data, there is a heightened risk of data security challenges and privacy violations, as cybercriminals tend to focus on sensitive information stored in volumes. They hack into data banks, costing organizations time, money, and reputation. Deploying artificial intelligence solutions can help guard data sets, maintain round-the-clock surveillance, and raise an alarm on discovering any suspicious activities.
Data Quality
Data quality refers to the completeness and accuracy of data, which can be difficult to attain with big data, leading to wrong decisions. Organizations can overcome the challenge of data quality by using automated tools that sweep through data, searching for errors, inconsistencies, duplicates, and other irregularities.
Communication and Alignment
When dealing with big data, various complex data sets are in the mix, which may lead to communication challenges between big data analytic teams. Some of these challenges include the following:
- The use of technical jargon by the Data Analytics team that other non-technical teams involved may not understand clearly
- Poor communication, feedback, and evaluation across teams
- Lack of alignment on the goals, scope, and objectives of the big data analytic implementation program
Potential solutions to overcome those challenges could be the following:
- Simplifying complex terms and explaining acronyms
- Maintaining open communication and accurate feedback across teams
- Investing in high-end communication tools
- Onboarding all involved parties to the goals, dynamics, objectives, and scope of the big data implementation program
Big data is here to stay and can only get better with time as it evolves. The future looks extremely bright judging from the pace at which it spreads. Also, the increase in web traffic and web-based applications only mean that big data will continue to rise in relevance and in the future.
Emerging Trends in Big Data Analytics
As big data continues to play a pivotal role in the growth of businesses and brands, some trends are emerging to incorporate more industries, and technologies, and offer more benefits.
Here are some of the emerging trends in the big data analytics landscape:
- Machine Learning as a Service
- Data-Centric AI
- AI-Powered Analytics
- Edge Computing
- Deep Learning
Predictions
With machine learning as a service, artificial intelligence solutions, and cloud-based data analytics as technological forces driving the evolution of big data, the future looks promising. Going by the trends, the future of big data may likely witness the following:
- More businesses will deploy machine learning and artificial intelligence to optimize big data analytics.
- Quality, processed, and ready-to-use data will become more prominent in the market as a product.
- More businesses will rely on cloud storage with the rapid increase in data volumes.
- Smart assistants like Siri and Google Assistant will become more common as they depend on big data analytics to provide solutions after being trained on volumes of data.
- Companies will anchor their most critical decisions in creating new products and services on big data analytics.
How Infomineo is Leading the Way in Big Data Analytics with its Distinctive Methodology
Infomineo’s expertise spans across data engineering, extraction, analysis, and visual representation. We gather information from various online sources like mega Google search, social media platforms, and websites, and organize the data based on distinct characteristics. Subsequently, we focus on data correlation to detect patterns that we present through interactive interfaces such as dashboards using tools like Power BI, Tableau, and advanced Excel Charts. Our forte lies not only in the analysis of large datasets but also its merger with our business acumen, allowing us to provide tailored insights aligned with each client’s strategic goals.
We enrich business’ internal data by complementing and benchmarking it against external contexts. Our Data Analytics team comprises professionals well-versed in diverse industries, ensuring that their solutions harness the power of data and align strategically with industry-specific challenges.
Frequently Asked Questions (FAQs)
How can I tell if my business demands big data analytics services?
You can tell if your business will require big data analytics services by evaluating your current data needs, and the size and objectives of your business. This evaluation will reveal how much data you have, how much more you need, and the type of analytics you need, among other relevant information.
What are the applications of big data analytic services?
Big data applications cut across various niches and industries, ranging from healthcare and banking to the government and manufacturing sectors.
How can I choose the right big data services provider?
You can make the right choice by conducting your due diligence or background check on the provider. Look for reviews and testimonials, check their client base, identify their technological resources, and find out if they can address your business’s specific needs.
Conclusion
Data is a fundamental resource for every business whereas big data is a transformational tool for growth. Deploying big data analytics services gives a business unlimited access to a large pool of data sets in their variety. With the variety and sheer volume, data analytics services can identify patterns and relationships, learn customer habits via machine learning, and predict the future with AI. These services can help a business stay on top of trends, grow exponentially, satisfy customers, stay in the competition, and avoid surprises via predictive analytics.