In this blog, I shall cover information on big data, analytics, it’s definitions, types and the type of output it generates after processing. You all must be wondering why we need analysis on big data and how the outcome adds value. Well, big organizations utilise the analyzed data to make informed decisions while tackling the business challenges and landing into a zone where the contingency plans work well.
What is Big Data?
In simple terms, any data set which is large in size and complex in nature for the traditional applications to process is known as Big Data. So what is Big Data Analytics? It is a complex process which involves
Tools like Microsoft Excel and Spreadsheets were utilized by big organizations back in the good old days to work on data sets. However, it has become more of a necessity today to survive the cutting edge competition in the market. There are other benefits associated with the adoption of big data analytics. I am mentioning some below:
- Cost Reduction – Cloud-based solutions offering recluse to storage issues for the large and complex data set is backed by the smartness of the tools like Hadoop which helps the business teams to strategize the goals.
- Faster & Better decision making – Earlier the data was analyzed to predict the future market trends. Now, the speed and accuracy in these tools have resulted in the companies to analyze data in time thereby helping the organizations to make decisions.
- Research and Development – This data also helps big organizations identify the need to launch products that focus on customer expectations. With the availability of analyzed data, the product development can take place keeping in mind the market trends.
- Market Trends – You can reap the maximum benefits out of the big data analytics if you are good with reading the numbers and simultaneously aligning it with the changing market dynamics. These market trends can make or break the future of a company. Many e-commerce companies have utilized these trends and gained growth.
There are majorly three types of Big Data Analytics –
- Descriptive Analytics – “What has happened in the past” – is the trick to understand this type of analytics in detail. It is the simplest type as the complex data can be broken down into simpler units. The beauty of it lies in how it works. The real-time data set is then compared with the historical data to derive insights on how to approach the future. A good example here is of google analytics where the past trends are compared to check the performance of the marketing campaigns.
- Predictive Analytics – The term itself points towards the concept of “Forecasting”. The probability of what will happen in future can be determined here. These future outcomes were not anticipated by the Business Intelligence tools in the past and this is where Business Analytics came into the picture with the power of Machine Learning Programming and statistical algorithms.
- Prescriptive Analytics – Now that we have predicted the decisions to be made, the next step remains in how to avoid doing mistakes done in the past and implement strategies to maximize output in the future. The data for prescriptive analytics can be both internal (within the organization) and external (like social media data). Business rules are preferences, best practices, boundaries and other constraints. These techniques are not in active use in many organizations today but will eventually catch up. Organizations utilize this technique to manage and forecast inventory.
I am preparing my next piece on creating and conducting surveys. Till then, please comment on topics of your interest pertaining to Big data analytics.
