In this article, we will discuss about the big data, 3 V’s of big data, types of big data & big data strategy.
What is Big Data?
Big data are often defined because the high-volume,velocity and type of data which will be consisted of the demand which is cost-effective, different sorts of processing for the improved insight and analyzed the data.
Let’s discuss the characteristics of massive data.
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Characterisitics of massive data
3 ‘V’s of massive Data – Variety, Velocity, and Volume.
Variety of Big Data refers to structured, unstructured, and semistructured data that’s gathered from multiple sources.
Velocity essentially refers to the speed at which data is being created in real-time.
Volume provides the number of knowledge and therefore the type of data as we already know that Big Data indicates huge ‘volumes’ of information that’s being generated on a day to day from various sources like social media platforms, networks, human interactions, etc. Such an oversized amount of knowledge are stored in data warehouses.
Types of big data
● Structured data
● Unstructured data
● Semi structured data
Structured is one among the kinds of massive data and By structured data, the info are often processed, stored, and retrieved in a very fixed format. It refers to highly organized information which will be readily and seamlessly stored and accessed from a database by simple computer program algorithms. As an example, the worker table in a very company database are going to be structured because the employee details, their job positions, their salaries, etc., are present in an organized manner.
Unstructured data is data that either doesn’t have a predefined data model or isn’t organized in an exceedingly predefined manner. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important forms of big data.
Semi structured is that the third style of big data. Semi-structured data pertains to the information containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the information that although has not been classified under a selected repository (database), yet contains vital information or tags that segregate individual elements within the info. Thus we come to the tip of kinds of data.
Advantages of Big Data
• One of the largest advantages of massive Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to create better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
• Big Data analytics could help companies generate more sales leads which might naturally mean a lift in revenue.
• Businesses are using Big Data analytics tools to know how well their products/services do within the market and the way the shoppers are responding to them. Thus, the can understand better where to speculate their time and money.
What is the Big data strategy?
A big data strategy defines and lays out a comprehensive vision across the enterprise and sets a foundation for the organization to use data-related or data-dependent capabilities. A well-defined and comprehensive big data strategy makes the benefits or big data actionable for the organization.
Besides the gains of realizing a competitive advantage, enterprises require an unlimited data strategy because it transcends organizational boundaries. Without an unlimited data strategy, enterprises are visiting be forced to affect a diffusion of information related activities which is able to presumably be initiated by different business units.
Various departments are likely to start out out up their own analytics, business intelligence or data management programs, without taking into consideration the overall long-term strategic objectives.
A well-defined enterprise big data strategy should be actionable for the organizations. So on realize this, organizations can follow the following 5-step approach to formulate their big data strategy:
Define business objectives execute a current state assessment identify and prioritize use cases formulate an unlimited data roadmap embed through change management each of the steps to formulate an infinite data strategy is explained in further detail below: formulating an unlimited data strategy
STEP 1: Define business objectives so as to leverage big data in any organization, it’s first necessary to completely understand the corporate business objectives of the enterprise. Start with understanding how a company is successful, before exploring how big data technologies and solution might enhance the long term performance. the massive data strategy should align to the corporate business objectives and address key business problems, because the first purpose of massive data is to capture value by leveraging data.
STEP 2: Execute a current state assessment during this step, the primary focus is to assess the current business processes, data sources, data assets, technology assets, capabilities, and policies or the enterprise. The aim of this exercise is to help with gap analysis of existing state and thus the specified future state.
STEP 3: Identify and prioritize use cases in step 3, envision how predictive analytics, prescriptive analytics and ultimately cognitive analytics.
STEP 4: Formulate an infinite data roadmap the following step is probably the foremost intense and contentious phase and without a doubt will account for majority of the time in formulating data strategy. The sponsors and stakeholders will have a key role to play in prioritizing these initiatives. the highest results of this phase is also a roadmap to roll out the prioritized big data initiatives.
STEP 5: Embed through change management although technically not a vicinity of the big data strategy formulation, change management (involving the hearts and minds of people) will have a profound impact on the success or failure of an infinite data strategy. Change management should encompass organizational change, cultural change, technological change, and changes in business processes. Data governance, which deals with the final management of availability, usability, integrity, and security of data , becomes a crucial component of change management. Appropriate incentives and ongoing metrics should be key part of any change management program.