Big data has evolved over the years to handle extremely large datasets. The experts describe big data projects in the three ways – Volume, velocity, and variety.

Businesses today, can receive huge datasets, known as big data, from traditional business systems, social networks, IoT devices, or any number of systems that are spread throughout an organization.

According to a recent survey presented by the Business Application Research Center, organizations that incorporate big data into their processes reported 8 percent higher revenues and 10 percent lower costs. Such financial results are the key reasons why companies drive to investigate big data and explore its competitive edge.

Once a company is ready for this technology, it should follow these five best practices that can help it manage big data successfully.

Focusing More on Business Value

Big data projects can be engaging for the IT department even while working with the most recent technology platform. Although, big data projects are complicated and potentially expensive which can turn into an IT vanity exercise. Firstly, for any project, organizations should focus on the business value. The projects should be aligned with a specific business goal. An ROI should be set. It should be kept in mind that big data is a business project, not an IT project.

Promotion of Data-Driven Culture

The human asset of an organization be it current employees and new hires should be trained on data value and its power to improve profitability. The companies should assess the skill set of their current decision-makers. Attention should be paid on how data is, or is not, used in their decision making. The results of companies' observations should be placed to ensure employees' preparedness to interpret and utilize the outcomes of their big data.

Accepting the Evolutionary Features of Big Data Projects

Following an agile approach to data, transformation can lead to the constant assessment of a company's progress and its readjustments as needed. The companies can course-correct throughout both their project and the life of the data. The companies can expect new insights and opportunities to exploit and explore data as it has become more tangible with time. The focus should always be on data quality. Every step in the process should be validated and reconciled.

Standardizing Best Practices

Maintaining standard approaches to ease skills gaps in an organization is a necessity. The volume of data has exploded over the past many years tightening the human workforce market for competent big data engineers. The companies would need to hire someone without specific knowledge of another field, to bridge the skill gap, and train him to translate the business value into the technology requirements. The two common standardization of best practices are – implementing a cloud-based solution and prioritizing automation at every step.

Enhancing Creativity

The best results can be derived from big data from combining disparate data sources in fresh or unexpected ways. For instance, combining weather data with supply chain interruptions or moisture sensor readings with crop failure information. As an organization has many interconnected activities, enhancing creativity by combining different sources of information in a way that represents the realities of the organization can be a standard practice to manage big data.