Contact us anytime to know more — Abhishek P., Founder & CFO CISIN
Size is no longer a competitive advantage. While large companies still have an advantage in resources and talent within their walls, it's less important today.
Mid-sized companies can now access the same intelligence, computing power, and insights as large corporations, often with greater flexibility to respond to potential opportunities. The next generation of market leaders could be companies with a solidified Big Data and Analytics strategy.
What Is Big Data?
Big data is information that's too complex or large to be analyzed by traditional data processing methods. Big data is data at its core; however, the overwhelming volume and complex nature of this data requires specialized analysis tools.
Big data also grows exponentially, requiring innovative solutions to manage, store, parse, and analyze information. Big data is often associated with "three Vs.": volume (or velocity), variety, and variety. These three V's take data from the traditional world of data analysis into big data.
Consider the following three V's to understand big data better:
- Volume: Unstructured data in large quantities, such as Twitter feeds, may require terabytes and petabytes to store. Word documents are usually only a few dozen Kilobytes in size.
- Velocity: As Internet usage grows, businesses receive more data at once. This means that they need more processing power. The amount of data generated by each user increases as a business grows.
- Variety: Think about the variety of extensions in your database, including MP4, DOC, and HTML. The more file extensions you have, the more diverse your data.
The nature of the topic continues to change as the field of Big Data continues to mature. Oracle, for example, has noted that there are two new V's in big data.
These are value and integrity. The core of big data remains the same, despite the many Vs. This is using larger and more complex data sets to discover patterns, trends, and other important information, which would only be possible to find using smaller data sets.
Big Data Is A Valuable Tool For Business
Big data can offer various benefits to an organization depending on the information collected. Big data is a multiplier.
With enough data, businesses can better predict and respond to certain business use cases in various fields. Big data is an ever-evolving field with many benefits. Here are some examples of how big data can be used:
- Product development.
- Machine learning and automation.
- Fraud monitoring.
- Retention of customers through proactive measures.
The benefits of big data are numerous and include:
- Find out the root causes of issues, defects, and failures.
- Analyze the market, customers, and trends to avoid relying solely on gut feelings.
- Create coupons instantly at the point of sale based on customer buying habits.
- Recalculate a risk portfolio quickly.
- Cyber-attacks can be detected before they cause the worst damage.
- Your data and analytics strategy should be integrated.
Big data can be used to improve operations in any business. Big data can be used for various purposes, including reducing inefficiency, improving customer retention, identifying and combating fraud, or anything else.
The Benefits And Challenges Of Data Analytics Are Closely Related
The growth in IoT adoption has been the main driver for big data analytics solutions.
Sensors and devices are a great way to turn legacy equipment smart. They can provide valuable insights into products that can be analyzed with ERP systems, financial statements, and customer satisfaction metrics.
IoT sensors that track foot traffic in retail brick-and-mortar stores optimize the customer experience across the digital and physical worlds. Farmers use sensors to monitor their equipment and the environment, while drones monitor crops.
These and many other use cases represent huge opportunities for midsized companies across all sectors. To unlock these opportunities, AI-enabled analytical tools and IT infrastructure is required.
According to an article published by the Harvard Business Review, mid-sized organizations need help extracting value from Big Data strategies for two reasons: their internal culture and limitations in scalability. The IoT will become more reliable and faster with 5G and WiFi 6. Those who fail to prepare will likely face more Big Data challenges.
How Can Mid-Sized Businesses Use Big Data Analytics?
Big Data analytics can be used by mid-sized businesses to gain various benefits. Here are three major use cases.
- Understand What Makes Your Customers Tick: The data and intelligence innovations have completely changed the game for sales, marketing, and customer service departments. IDC reports that real-time data from smartphones, GPS devices, wearables, and other Internet-enabled devices, combined with behavioural and transactional data, business intelligence, and other sources, makes it easier for brands to improve the customer experience and innovate new products. Marketing analytics platforms and automation platforms are not new. However, more sophisticated solutions, such as experience management platforms, take things to the next level by combining operational data with customer insights.
- Identify Opportunities And Trends: Traditional BI tools used to be reserved for analysts and consultants. Today's self-service solutions are affordable and more user-friendly than old, clunky reports. Real-time data visualization can be used by business users without any data science experience to analyze the factors that affect performance. Brands can respond to emerging trends and identify untapped markets with potential customers who are likely to purchase. Analytics platforms with AI and ML capabilities add value through guided decision-making and predictive modelling.
- Use Predictive Analysis: Today's predictive analytics solutions use data from multiple sources to model and predict the effects of different scenarios. AI-enabled platforms are used by brands for a variety of purposes, such as fraud detection, marketing campaign optimization, product development, and more.
Mid-Sized Enterprise Data Analytics: Key Strategic Elements
The mid-sized enterprise has a different room for error than, say, Netflix or Amazon. Implementing a Big Data Analytics strategy can be both a high-risk and high-reward initiative.
Here are some tips to help you create a Big Data strategy tailored to your business:
- Define Your Goals: Decide what you want to achieve with your Big Data Analytics strategy before starting. Do you want to have a 360-degree customer view? Are you hoping to take advantage of trends such as deep learning, machine learning, and dark data ahead of your competitors? You can use predictive maintenance or streamline your operations to save money and time. You should also define the potential impact of the data on your business and create a plan to use it once you have the information.
- Make A List Of Must-Have Capabilities: Once you define your use case and how it stands to do X, Y, and Z for your business, develop a list of requirements. Consider analytics-as-a-service platforms, which allow you to pay for access to insights. At the same time, vendors handle the specifics of the architecture.
- Do A Lot Of Research: Learn what other companies are doing to use Big Data. What tools do they use? What results do they see? Sign up for demos or free trials to see if a tool is right for you. You may be tempted to become excited by the promises made by different customer experience platforms or by the fact that IoT sensors and devices allow you to convert almost anything into data. Keep your eye on the problem when you are evaluating different solutions.
- Consult Experts: Although any organization could benefit from working with a specialist, mid-sized businesses sometimes have the internal talent required to tackle Big Data projects. An expert can help identify the most important use cases, align your data initiatives with the big-picture goals and select the tools to support the key objectives. The right consultant can work with your in-house IT team to fill gaps and maximize their abilities.
- Governance Is Critical: Businesses must now comply with CCPA requirements and GDPR or risk hefty fines, which could damage their reputation or force them to close. New state laws in the works could add to the compliance rules. Non-compliance fees can be thousands of dollars for each record. One small oversight can quickly wipe out your empire.
- Take Care Of The Storage Situation: Many small and mid-sized businesses need more space to house servers on-site and the staff to maintain them.
- We recommend that companies with little IT/cybersecurity expertise work with an expert help them evaluate storage solutions, their costs, capacities, and security concerns.
- Data Collection/Integration/Processing: What data do you need to analyze? What is the source? What are your plans for using the insights? Do you require real-time or batch processing? Working with an expert in this area will likely be the best option.
Big Data Solutions And How They Work
Here are the most popular Best Big data analytics solutions today:
SAS
SAS, the leader in business analytics services and software since 1976, says that a business's size no longer matters when getting market and business insights.
SAS turns your data into valuable insights to help you make better decisions and gain a new perspective on your company, whether it is a large, medium, or small organization.
SMBs (small and mid-sized businesses) face similar challenges to large enterprises. SAS's analytics, automated planning, and data mining are easy to use.
This allows businesses with limited resources to achieve more. These analytics can help businesses overcome obstacles to growth and competition. SAS's message for SMBs is straightforward: "Identify what works and fix what doesn't." SAS can help you make more informed decisions.
Contact them for pricing, details, and free software trials.
Google Analytics
Start collecting data with your website. You can start with an asset that you already own -- your website. Google Analytics is a free platform that allows small businesses to gather and analyze data about their website from multiple touchpoints.
Google Analytics allows you to extract data over time to reveal trends and provide other useful information to help you make informed decisions.
By tracking and analyzing visitors' behavior, you can better meet your online store or website goals. This includes determining where the traffic comes from, what audiences are doing, and how much time they spend on your site.
You can also analyze the traffic on social media and make changes to your campaigns in social media marketing based on what works and doesn't work. You can extract valuable information from mobile visitors to improve your mobile experience.
IBM Cognos Analytics
IBM Cognos Analytics is a powerful business analytics solution that makes predictive and advanced analytics available to small businesses.
This platform automates complex data analysis and mining systems, so you don't need any experience. This self-service solution provides a range of services for data access, refinement, and storage, giving you tools to present and prepare data in an easy and actionable manner to guide your decision.
Unlike other analytics solutions that focus on a specific area of business or project, IBM Cognos Analytics unifies your data analysis into one platform.
It can be used for any type of data analysis, including marketing, finance, HR, and other business areas. The "Natural language technology" helps you to identify problems, recognize patterns, and gain meaningful insight to answer key questions like what drives sales, deals that are likely to be closed, and how to keep employees happy.
IBM can provide pricing information.
Qualtrics
Research may be a good option if you need rich data sources. Qualtrics allows you to conduct various studies and surveys to gather quality insights to help you make data-driven business decisions.
Qualtrics also offers Qualtrics experience management (QualtricsXM), a set of four applications that allows you to manage and improve your business's experiences with all stakeholders, including customers, employees, and prospects.
Qualtrics XM allows you to measure, prioritize, and optimize your experiences across four fundamental experiences in business: employee, customer, brand, and product experiences.
Qualtrics also offers real-time insight, survey software, and programs for advertising testing, concept tests, market research, and concept testing.
Read More: Big Data Analytics Benefits - How To Analyse Big Data
Why You Should Hire A Big Data Developer
Why hire big data developers by 2023? Understanding why companies are focused on data will be helpful. Every day, consumers generate data up to 2,5 quintillion bits.
New technologies are also emerging, which makes it more difficult for organizations. Specialists, like engineers, can help businesses understand raw or unstructured information and transform it into useful chunks.
Here are some reasons to hire big data analysts in the future.
Stay Ahead Of The Curve
This number will double in the coming years as we live in an online-only world. The growing demand is a sign of one thing.
Businesses now see Data analytics as a necessity to gain a competitive advantage. Hiring data specialists early will also relieve the stress of rushing to fill a position on short notice. Hiring an engineer sooner rather than later makes more sense, as the pressure will be on your employees.
Resolve Issues As They Arise
Specialized talent and a problem-solving mindset can help you solve problems as they arise and excel where your competitors fail.
The complexity of big data tasks is increasing, and 77% report challenges with adopting this trend. Hire professionals who are willing to learn and improve as the technology changes.
Better Decision-Making Processes
In the modern age, running a business requires crucial daily decisions. Data science can reveal patterns and outcomes that will give you near-perfect accuracy.
An engineer can clear up any doubts when making these decisions.
Big Data Is Serious Business
Small and large businesses both have multiple points of data collection. It can range from complex inventory updates to simple information about customers when they check out.
Businesses face a challenge when they must store this data legally and securely and comply with different privacy and security laws, such as GDPR or APAC, in Europe and Asia. An experienced data engineer will ensure you can achieve these goals and drive your business forward.
Big Data Development: The Most Popular Technologies
The term "Big data development" encompasses a variety of technologies used to achieve different objectives.
If you are an IT-driven business, it is important to hire a team that has experience in the following areas:
Predictive Analysis
Predictive analytics is crucial when it comes time to forecast future events based on past data. This technology can be used by an engineer to predict future events, such as consumer trends or market trends within a certain period.
Predictive Analytics is usually used with other technologies, such as mathematical modelling, data mining, and machine learning.
SQL-Based Technologies
SQL is a programming tool that allows you to manage data stored in a warehouse or store. When deployed in web-based applications, this technology provides retrieval and accumulation methods.
SQL technology simplifies horizontal scaling across multiple devices and increases flexibility in dealing with different sets. Organizations looking for professionals in this field deal with terabytes and terabytes worth of data each day.
Artificial Intelligence
Artificial intelligence (AI) combines various computer science techniques to create smart machines with greater accuracy and speed than human minds.
AI enhances other technologies, such as deep learning and machine learning, to extract intelligence from raw data.
R Programming
R is the preferred language for statisticians and data miners who work on open-source projects. Free software makes this programming language popular with full-stack web developers.
Engineers use R to create unified building environments such as Visual Studio or Eclipse. They also use it to compute and visualize statistical data and build statistical software for data analytics in real-time.
Data Lakes
Data lakes are a central repository for storing all types of structured and unstructured data at a large scale. This technology allows companies to store data from various sources, such as log files, IoT, social media, clickstreams, and more.
Engineers can parse the data using machine learning or predictive analytics to identify real-time business opportunities, drive productivity, and engage customers.
Skills And Responsibilities For A Big Data Developer
Data Analysis
Hire big data architects who are experienced in both statistical and quantitative analysis. Data engineers typically use tools such as Hive for real-time analytics.
Hive is a tool that helps developers analyze large amounts of data in Hadoop HDFS. Visualizing data is another way big data professionals can analyze it. This means they need to be familiar with visualization tools such as QlikSense or QlikView.
Coding
Big data developers who can code should be able to demonstrate extensive knowledge in at least one programming language.
Look for developers proficient in Scala or Java, R, or Python. These computer languages may have different syntaxes, but the logic remains the same. A candidate familiar with one language will be able to quickly adapt to another and meet the demands of your company.
Data Expertise
Data expertise requires both machine learning and mining skills. Mining skills can optimize your company's data extraction, storage, and processing processes.
Rapid Miner KNIME and Apache Mahout are the most popular mining tools to complement expertise. Machine learning skills will also help you to personalize, classify and recommend business-growth systems.
Transforming Data
Business owners need to convert unstructured data to make sense of it. SQL, or Structured Query Language, is businesses' primary language to accomplish this goal.
The language can manage and transform structured information stored in multiple databases. SQL is also the backbone of this field, and having a talented person familiar with it will be a great asset to your business.
Warehouse Data
It can be difficult to keep up with structured data, especially when businesses produce and extract huge amounts of data daily.
However, businesses are increasingly turning to warehousing to complement their unstructured data with structured data. NoSQL is a powerful tool for storing and managing all types of data, including unstructured and semi-structured.
Special Skills And Expertise
Some skills are common to all candidates. Hiring people with special expertise and skills, like Apache Spark, Cloudera Cassandra, MongoDB, and HBase, is better.
This will give you a competitive advantage over your competitors. Cyber Infrastructure Inc. can help you hire big data developers with expertise in any technology stack to join your dedicated offshore team.
What To Look For In A Data Developer: More Than Just Skills And Expertise
The development field is highly tech-driven and requires high technical knowledge. Businesses and organizations must go beyond technical knowledge.
Here are some tips to help you Hire big data developers that will adapt to your needs and bring various technical skills to drive product innovation.
Find Out If You Have Problem-Solving Skills
Businesses should anticipate and solve problems as they occur before they become detrimental to their organization.
By leveraging different tools and techniques, big data experts with problem-solving skills can assist you in achieving this. A problem-solving mentality can also help you assess the possible consequences of a specific risk and create mitigation plans in case of the risk.
Hire Candidates With A Creative Mind
Innovative thinking is essential to improve business performance while minimizing risk and effort. Experts in big data with an innovative approach can evaluate the feasibility of multiple solutions before implementing them.
Your team will provide you with numerous suggestions to drive product innovation and business growth so that you can stay on top of the competition.
Education And Integration Of Global Development Processes
We live in a world of fast-paced technological advancement. Big data developers need to have a background in global development and education.
They should also be able to integrate processes that bridge the gap between education, health, and employment. This background will allow you to build a more sustainable team with lower attrition.
Leadership Skills
Like any other employee in your company, big data experts can be found anywhere. Talents with leadership skills will transform your business on many levels, including establishing value and engaging teams for better results.
A leader engineer will motivate junior developers to innovate and bring fresh ideas.
Conclusion
There's plenty of evidence to prove that Big Data analytics is no longer the exclusive domain of Big Tech or corporate America.
The tools have become more affordable and accessible, and this could be what helps level the playing field. Big Data analytics is becoming more important due to rising customer expectations, COVID supply chain issues, and other converging factors.
