Big Data includes not only data management, but also data analysis in order to look ahead and make decisions. Big Data is increasingly taking over the world. The tech giants such as Google, Amazon, Twitter and Facebook have well settled their data assets for their businesses and marketing competitions. Some sectors such as media, tourism and transport have reinvented by this phenomenon, and other industries are in their infancy.
The Big Data revolution
We are living in the golden age of Big Data, where our daily interactions with the digital world are aggregated into massive data sets, and we are improving the ways we deal with data in the hope that analysis of our aggregated world can turn out fresh and valid insights that benefit us. Over the years, Big Data has been transformed from optimizing existing data to amassing increasingly diverse and exogenous data sources to improve analytical relevance and accuracy.
The faster processing of these records improve certain processes considerably, including about dynamic pricing with continuous and demand-specific variability. Companies are now able to do business forecast with comprehensive data without help from outside, and can better prepare for the future of their business.
All businesses in this digital era need quick adaptability to the constantly changing environments. Big Data analytics applications have great potentials to satisfy these demands. For example, the recommendation systems used by Amazon and Netflix can give customers the advices from other consumers in real-time basis, and the mobile applications such as Uber and Waze aim to recommend the most appropriate action depending on the context.
How to make Big Data work for your business?
The implementation of business data collection and the choice of appropriate technology in preparation for the Big Data initiative are the only half of the battle. This is because when the decision for the right database software and the appropriate analysis tools are made and after these components are integrated into the IT infrastructure, you may start moving the company to the next level and develop a real strategy for success.
Effective project management processes are indispensable for the success of Big Data analytics initiatives. To create and implement effective Big Data analytics, a company can consider the following five strategies:
1.Identify the strategic data.
Although Big Data analytics is created by large data sets, it doesn’t mean that companies need to examine data from all sources or all information in a data source. Rather, they should strategically identify data that lead to valuable analytical insights. For examples,
- what information should be combined in order to find out the factors associated with key customers?
- or, what data is required to uncover hidden patterns in stock market transactions?
The focus on these questions in the planning stage is necessary to help a company achieve its business goals. In some cases, the company might actually need to analyze all the data; whereas in many other cases, a data subset is good enough for the basis of the investigation.
2. Determine effective business rules and manage their complexity.
Dealing with complexity is one of the most important aspects of most Big Data analytics initiatives. To obtain the right analysis results, the involvement of users is important. For many companies, the users would be the department which is responsible for a particular business process, and have to do with business data in their everyday work.
In this way, the technical staff can ensure in advance all the necessary business rules are identified. Once the rules have been documented, the technical staff may assess how complex must be the solution, and how much staffing hours are required to generate valuable insights from the input data.
3. Translate business rules across departments in relevant analysis.
Business rules are only the first step in developing effective Big Data analytics applications. The more considerate when the applications are developed at the first time, the less labouring efforts are needed to revise them in the future. Many projects require constant repetition loops due to lack of communication between the project team and the experts from the departments. Improving communication and collaboration can simplify the process in the analytical solution development.
4. Adjust the system permanently to new requirements.
In addition to the initial development work, successful Big Data analytics projects require continuous attention and updates. This includes regular maintenance or revision of database queries and knowledge of changing business requirements.
For example, if data volumes continue to grow, for better understanding of the analysis process, business users will inevitably ask additional questions. In nowadays dynamic business environment, the analysis team must keep up with these additional requirements, and the rising demands on the infrastructure that supports the Big Data Analytics applications. An analysis system will prove its value over time if it can be adapted to the changing requirements.
5. Always keep all your users in mind
Since the interest in self-service Business Intelligence (BI) is growing, you should not be surprised that the key consideration in Big Data analytics programs is shifted to the end users. Of course, a stable IT infrastructure is important for the storage and processing of large amounts of data in the forms of structured and unstructured. However, it is equally important to develop an easy-to-use system that takes into account the different needs from the users. For example, to satisfy the needs for different users, from executives through front-line staff, since these the different user groups may access and use the Big Data analytics applications in different ways.
Big Data analytics applications are increasingly being used in business processes. However, there are no shortcuts leading to successful Big Data analysis. Any companies need to follow best practices to bring their Big Data initiatives on the right track. The technical details of a Big Data are complex and must be deeply analyzed; but that is not enough; companies need to consider beyond the technical aspects and think about the business factors. As the time comes to close the gap between technology and business, companies are able to achieve greater values from their data.