The importance of using big data in the business world has increased rapidly over the past few years and is now at the forefront of business thinking.
Even though businesses generally agree on the value of big data, a unified approach to big data projects is lacking. As a result, companies often implement these projects in haphazard ways and mistakes occur. For example, some of them mistakenly concentrate their effort and analysis on the technology instead of business goals, thus increasing the risk of failure.
The statistics paint the same picture. According to IT Manager Daily, almost 55% of big data projects fail, which means that some companies do not have effective approaches to solving the issues surrounding big data implementation. If you are about to start you own big data project and have some concerns about the implementation, you have come to a right place.
Below, we will identify five essential steps to completing big data projects successfully.
Big data project implementation plan
Step 1: definition of the business use case.
Step 2: thorough planning.
Step 3: definition of technical requirements
Step 4: assess the value for the business
Step 5: understand the impact on information architecture
Let’s review these step in detail to make sure that you implement big data project like an expert.
Step1: Definition of the business use case
In many cases, companies begin big data project with particular business drivers, for example, profit increase. However, as they continue the implementation, it becomes apparent that some of these drivers cannot provide as much benefit as expected, so they shift the focus to those that do. To make sure that the project goes as planned, you have to address the questions below.
- What is the primary goal of the project?
- What are the secondary goals of the project?
- What are the obstacles to reaching each goal?
- Who are the decision makers?
If you address these questions, you will have a clear picture of how to start the project, what business drivers are prioritized, and how viable it is to use them. Remember: the more specific your answers are, the more likely your project will be a success.
Step 2: Thorough planning
Poor planning is one of the main reasons why big data projects fail, writes Bernard Marr for Forbes. According to him, one thing failed projects have in common is 'they are all caused by a lack of adequate planning'. Therefore, poor planning can cause a wide range of issues that can seriously hinder implementation. In some cases, companies fail to plan sufficient resources for implementation while others face technical issues that were not predicted.
To avoid these issues, your planning must be superior and address all possible sources of problems.
Step 3: Definition of technical requirements
This step includes an effort to analyze all available data that will be used for the project. When you complete the analysis, you will be able to determine the quality of data and predict what kind of results it can deliver.
To make sure your analysis is appropriate, make sure you review the existing technical environment, determine the data sources, and decide how you will work with the data, says Erwin Thompson, an IT expert from Proessaywriting. There are many subtasks to complete during this process, such as an exclusion of irrelevant attributes of data sources, sketching the current architecture, conducting surveys to identify additional data surveys, and many more others.
Step 4: Assess the value for the business
This step requires you to conduct an analysis of the total of cost of ownership and includes time-to-business value. At this point, all decision makers should be involved in the process to determine whether the project can be delivered by the company as well as what additional resources might be necessary. In the end, you will determine the time when the solution will begin bringing value and how it will affect the existing resources.
Step 5: Understand the impact on information architecture
There are many ways in which big data projects can impact a company’s architecture and they generally use standardized software to deal with the volume of processed data. To make sure that the architecture can accommodate the data, determine it will be able to support some unpredictable workload. This is done by using cloud storage services that offer great processing capacity and dynamic storage and retrieval.
Many big data projects fail because of lack of planning and a unified approach to implementation. To avoid failure, make sure you conducted a thorough analysis of your options and possible issues and use the techniques described in this article to help you. As the result, your projects will be a success regardless of the volume of data and issues you encounter along the way.