According to McKinsey, Big Data can profoundly impact every sector of the global economy. Big data refers to structured or unstructured data. It is stored in the cloud or traditional databases. Businesses then analyze this data for uses ranging from getting customer and competitive insights to developing new products and services and optimizing operations and strategies. This is known as big data analytics.
Technology advances keep businesses competitive. Hence, there is always a demand for the latest technologies. The 5G technology is already helping provide more reach and faster services—blockchain in the finance sector, IoT in the medical field and more so on. Big Data is the fuel that makes the development and adoption of such technologies possible. That is why businesses are swarming to it.
How big is Big Data?
Big Data has spread across every field. It is being used by the smallest of businesses to the largest enterprises. Governments use big data for public administration and policymaking. The infographic explains how big data is the driving force for all firms and how immense the data pools can become. Take a big user of data such as Gmail as an example. Gmail processes 294 Billion emails a day compared to 300 Billion stars in the Milky Way galaxy. It would be impossible for Gmail to process, analyze, and support customer needs around its email flow without Big Data capabilities.
Big Data has spread across every field. It is being used by the smallest of businesses to the largest enterprises. Governments use big data for public administration and policymaking. The infographic explains how big data is the driving force for all firms and how immense the data pools can become. Take a big user of data such as Gmail as an example. Gmail processes 294 Billion emails a day compared to 300 Billion stars in the Milky Way galaxy. It would be impossible for Gmail to process, analyze, and support customer needs around its email flow without Big Data capabilities.
Impact of Big Data
In recent years, big data analytics have been evolving faster. Incorporated with new technologies, advanced analytics are more focused. Artificial intelligence, machine learning and quantum computing are being increasingly used for big data advanced analytics.
Big Data is speedy and profitable. It increases customer satisfaction by improving customer service. It helps in targeting relevant markets and improving efficiency. Cost reductions and data storage can be other advantages.
Cloud-based technologies help in ample data storage. Cloud is cheaper, so it is cost-effective. Cloud systems have analytics, so it further reduces costs. This helps in intelligent business decisions via data analysis.
Big Data systems need to be secured against data breaches. Data encryption technologies are evolving to deliver that. As systems become more secure, more companies are inclining towards Big Data adoption. With the increased adoption of big data, regulators are stepping in. Government policies are being formulated for collecting, developing, and using Big Data. Governments have realized that the social and economic values of Big Data can be immensely beneficial and are keen to adopt it themselves.
Business insights are essential for any scale business. With advancements in technology, big data is becoming accessible to any sized company. This interest from small and medium-sized enterprises has also boosted the demand for Big Data services.
Benefits of Big Data
One of the key findings in the survey by New Vantage Partners in 2020 found that “transformation and innovation are the primary drivers of investment into artificial intelligence (AI) and Big Data” helped shape technological advancements in 2021. Some of the benefits are discussed below.
Amassing a lot of data helps in making an informed decision. It helps in finding trends, spotting patterns, and revealing human behaviors. These are used to make pricing decisions, product placement decisions and assist in marketing campaigns. These fall under predictive analytics and behavioral analytics.
The collected data always needs to be up-to-date. This means that the data analysis must be current to help businesses change strategies in real-time. Current information is necessary for business survival. If information is obsolete, then the competitors can get ahead. Outdated data poses the risk of making wrong decisions. The strategy could be wrongly formulated. Product placement could be wrongly targeted. Multiple things could go wrong with little data and archaic data. This requirement for keeping large pools of data current and processing it in real-time has driven a lot of innovation in the big-data space in recent years, both by large big data providers and smaller startups.
Challenges of Big Data
Big Data is vast. The handling, storing, analyzing, and utilization is a big challenge. This can be due to multiple reasons, as discussed below.
Unknowledgeable Professionals
Big data requires skilled professionals for handling. The data needs to be interpreted, which requires analysts, scientists, and such. Even with the innovative tools, it still does not bridge this gap. There are not enough professionals available that can help. So, companies need solutions to address this.
No Proper Understanding of Big Data
Many companies which switch to big data technology fail to understand it. The team dealing with data might not know about storage and handling. They might not know how to process it and the importance of updated data. Sensitive data might be lost. There might not be backups of reports. So, when data is needed, it is not available.
Massive Data Storage Issues
Massive amounts of data storage have become a pressing matter. The quantity of knowledge is expanding exponentially. This data is from videos, audios, pdf, texts etc. For example, Walmart stores 2.5 Petabytes per hour which is the storage capacity of an adult human brain.
Tool Selection
Sometimes, businesses newly shifted to Big Data systems have trouble choosing the right tools. This will support the analysis and storage of the mined data. Lack of knowledge of requirements can lead companies to make wrong decisions.
Data Combining
Data sets get combined from several sources. These are social media platforms, rival business logs, emails, reports, news articles, pdf etc. Integrating data is necessary for analysis, business intelligence and making reports. Many companies struggle to find the right solutions to merge data from all these sources.
Data Security
Data security is usually last on the priority list. Inadequate cybersecurity can invite hackers to attack and steal data and incur heavy losses. With a global increase in security breaches and hacks, sometimes by organized groups and nefarious government-backed entities, data security has become an integral part of Big Data implementation.
Offshoring Big Data Services
Offshoring can help overcome many of the challenges associated with big data. It is cost-effective and can be time-saving for businesses.
Offshoring helps get access to large and concentrated pools of Big Data professionals. Big Data professionals might not be available in a client’s surrounding area or country. What is available might not be up to the standard of developmental goals. Off-shore Big Data services and solutions providers can take care of all the client’s needs without worrying about sourcing, managing, and retaining professionals.
As mentioned earlier, offshoring saves money. Data science teams are expensive if hired. Offshore data science teams are always available. It saves team building costs and hiring costs. A data scientist’s expense in a developed market can come to around $200,000/year. An offshore team can work at flat rates, and hourly wages can ensure 100% utilization.
Using an offshore data science team, a company can then concentrate on essential business activities with the load taken off. The in-house teams can utilize the insights and analytical data reports without worrying about Big Data resources availability, costs, and iteration. They can price products accordingly, target customers, and market products. The vendor is in charge of the administration of the offshore team. The client can focus on the in-house teams.
Offshore teams are flexible. You can hire a group according to requirements in a short time. If extra hands are needed, then the vendor will expand the team. Offshore teams’ maintenance is low, and they can reorganize around new needs quickly so that the business can make changes and keep profiting.
Most offshore teams work in a consultative way and help clients develop complete solutions, not just find them, workers. They can do the analyses of the data and generate reports and insights.
As they specialize in Big Data, most offshore teams will have strongly related cybersecurity capabilities. They can help clients implement and execute security measures and help with any related compliance and audits.
Mashkraft for Big Data Solutions
Mashkraft has extensive experience in servicing Big Data customers across the world. Our offshore team can provide turnkey Big Data services and solutions. We have successfully offered Big Data services to clients across Europe, North America, Asia, and Africa. Please reach out, and our experts will help you find the right solution that fits your budget and needs. You can find out more details about our services and successes at www.mashkraft.com.