The big data revolution is here to stay. Dubbed as the ‘new-gen currency’, the value of the big data industry is predicted to reach $77 billion by 2023. Big data is reshaping the digital transformation of organisations, and data professionals must align their careers with the trend to be ahead.
Today, companies across all industries are going all out to become data-driven for better efficiency and a competitive edge. Businesses are focusing on big data applications and building a roadmap for big data solution implementation. In this scenario, can you be left behind? Why not take the Big Data Engineer course and watch your career transform for the better?
- 1 Big Data for businesses
- 2 What is Big Data solution
- 3 When to consider a Big Data solution for your organization
- 4 Conclusion
Big Data for businesses
Big data refers to the high-speed disparate data sets that cannot be processed using traditional methods. From a business point of view, the main feature of big data is the value – its capability to fuel valuable insights. Thus, big data is helping organisations to adopt a. Big data is also being converted into precise, business-specific metrics and used for predicting future trends and outcomes. Healthcare, education, banking, government, retail, transport and logistics, agriculture, and smart cities, are some of the industries that deploy big data solutions.
Organisations must take advantage of Big Data for efficiencies and successful outcomes. Big Data solutions enable us to take a scientific approach to what customers want, what are the customer sentiments, how successful a course of action proves to be, how efficient a particular marketing initiative is, and so on. Big data solutions help to act on these insights to improve the customer experience and make data-driven decisions for a business edge.
What is Big Data solution
As organisations become data-driven, they are not always prepared to handle this disruptive shift. It takes a lot of consideration to develop a clear big data implementation strategy. These big data solutions are a challenge for some organisations, as they have to deal with a range of software products, deployment patterns and solutions for successful outcomes.
Big data solutions are essentially big data initiatives implemented by an organisation. There are several things to consider before implementing any big data solution. What data types may be used to bring the desired change in the organisation? Which data is unrelated to the goals? What data to use or discard? How to clean the data and make sure it is in a usable format? How will this data help to achieve the objectives? These constitute the big data solutions that organizations consider before transitioning to an organizational big data strategy.
When to consider a Big Data solution for your organization
To begin with, an organization must ask whether big data is the right solution to its business problem and whether it provides a business opportunity. It must examine what insights and business value are possible with big data technologies and whether insights are available from existing enterprise data. Is it possible to add on to the existing data warehouse? What would be the impact on the existing IT governance?
Are any specific skills required to understand and analyze the requirements to build and maintain the big data solution? Organizations must ascertain whether the right people are assigned to the projects. Specific skill sets are necessary to understand and maintain the big data solution, such as industry knowledge, domain expertise, and technical know-how of big data tools and technologies. Data scientists must be onboard armed with expertise in modeling, statistics, analytics, and math. Data scientists must understand the domain, look at the massive quantity of data and identify ways to generate meaningful insights.
An organization must also consider whether the big data solution is implemented incrementally. There must be a clear definition of the scope of the business problem and the expected business revenue gain.
In smaller business cases, the scope of the problem and projected benefits from the solution must be clearly outlined. The benefits will not accrue if the scope is too small or too large, as it will be challenging to get the funding, upgrade technologies and complete the project within the timeframe.
Most organizations choose to implement a big data solution incrementally as not every analytical or reporting requirement requires a big data solution. Projects that perform parallel processing on a large dataset may not require a big data solution.
A big data solution may be considered when there is complexity in the volume, variety, velocity, or veracity of the data.
An organization may want to consider a big data solution
When the volume of data has increased:
- The data is in petabytes and exabytes and might grow further in the near future.
- The data volume poses challenges for storage and processing using traditional methods, such as relational database engines.
- The data processing can be processed in parallel on available hardware.
When the variety of data has increased:
- The content and structure of the streaming data are not predictable.
- The data format varies widely, including structured, semi-structured, and unstructured data.
- New data types have emerged that were not previously mined for insights.
When the velocity of the data has increased or changed:
- It is changing rapidly and requires an urgent response.
- Traditional technologies and methods cannot any longer handle the data coming in real-time.
When the veracity of the data requires a revisit:
- The authenticity or accuracy of the data is unidentified.
- The data has ambiguous information.
- It is unknown whether the data is complete.
Most organizations are implementing big data solutions to increase revenue and accelerate digital transformation. By now, it is evident that big data laggards may be vulnerable, unable to hold out the growing competition.
On the other hand, the successful implementation of big data solutions requires a strategic approach aligning with business goals, resources, and the right people on board. The latter is key to ensuring that the shift to big data solutions is smooth and non-disruptive.
If you are a professional working in a data-driven environment, you must take the big data engineering course. Be a part of organizational expansions into big data and improve your career prospects with better positions and salaries.